Understanding How to Enhance Business Creativity · Methods and Participants ... why creativity is...

Understanding How to Enhance Business Creativity

Robert Dew

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Thesis by publication in fulfilment of the requirements for the degree of

Doctor of Philosophy (PhD)

written and submitted by

Robert Dew

B.App.Sc. (Physics) QUT

MBA QUT

Creative Industries Faculty

Queensland University of Technology

Brisbane, Australia

June 2009

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I dedicate this thesis to the men whom I most admire and

whose influences on my life have helped me develop the

capacity to both commence and complete this journey:

Theo Fouras for inspiring me to be enthusiastic;

Garfield Prowse for leading me to start thinking for myself;

Greg Hearn for showing me a way forward;

Rick Schram for teaching me the importance of integrity;

And my father, Anthony Dew for showing me the value of

hard work and self belief

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Abstract


This PhD study examines some of what happens in an individual’s mind

regarding creativity during problem solving within an organisational context. It

presents innovations related to creative motivation, cognitive style and

framing effects that can be applied by managers to enhance individual

employee creativity within the organisation and thereby assist organisations

to become more innovative.

The project delivers an understanding of how to leverage natural changes in

creative motivation levels during problem solving. This pattern of response is

called Creative Resolve Response (CRR). The project also presents evidence

of how framing effects can be used to influence decisions involving creative

options in order to enhance the potential for managers get employees to

select creative options more often for implementation.

The study’s objectives are to understand:

• How creative motivation changes during problem solving

• How cognitive style moderates these creative motivation changes

• How framing effects apply to decisions involving creative options to solve

problems

• How cognitive style moderate these framing effects

The thesis presents the findings from three controlled experiments based

around self reports during contrived problem solving and decision making

situations. The first experiment suggests that creative motivation varies in a

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predictable and systematic way during problem solving as a function of the

problem solver’s perception of progress. The second experiment suggests that

there are specific framing effects related to decisions involving creativity. It

seems that simply describing an alternative as innovative may activate

perceptual biases that overcome risk based framing effects. The third

experiment suggests that cognitive style moderates decisions involving

creativity in complex ways. It seems that in some contexts, decision makers

will prefer a creative option, regardless of their cognitive style, if this option is

both outside the bounds of what is officially allowed and yet ultimately safe.

The thesis delivers innovation on three levels: theoretical, methodological and

empirical. The highlights of these findings are outlined below:

1. Theoretical innovation with the conceptualisation of Creative Resolve

Response based on an extension of Amabile’s research regarding

creative motivation.

2. Theoretical innovation linking creative motivation and Kirton’s research

on cognitive style.

3. Theoretical innovation linking both risk based and attribute framing

effects to cognitive style.

4. Methodological innovation for defining and testing preferences for

creative solution implementation in the form of operationalised

creativity decision alternatives.

5. Methodological innovation to identify extreme decision options by

applying Shafir’s findings regarding attribute framing effects in reverse

to create a test.

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6. Empirical innovation with statistically significant research findings which

indicate creative motivation varies in a systematic way.


identify innovation descriptor framing effects


expand understanding of Kirton’s cognitive style descriptors including

the importance of safe rule breaking.


validate how framing effects do apply to decisions involving

operationalised creativity.

Drawing on previous research related to creative motivation, cognitive style,

framing effects and supervisor interactions with employees, this study delivers

insights which can assist managers to increase the production and

implementation of creativity in organisations. Hopefully this will result in

organisations which are more innovative. Such organisations have the

potential to provide ongoing economic and social benefits.

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Keywords

Creativity

Motivation

Creative motivation

Creative production

Framing effects

Cognitive style

Operationalised creativity

Organisational creativity

Supervision

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List of Publications and Refereed Conference Papers

In fulfilment of QUT’s requirements for thesis by publication, material from this

study has been submitted for publication as detailed below. This author was

the sole author for the papers below as submitted:

Study 1: Dew R 2008 ‘Creative Resolve Response: How changes in creative

motivation relate to cognitive style’. Accepted for publication The

International Journal of Management Development. (Accepted February

2009)

Study 2: Dew R 2008 ‘Innovation and Creativity Framing Effects’. In review for

Creativity Research Journal.

Study 3: Dew R 2008 ‘Cognitive Style, Creativity and Framing Effects’.

Accepted for publication by Journal of Creative Behavior. (Accepted

November 2008)

Additional publication arising:

Dew R 2007 ‘Creative Resolve Response: How changes in creative motivation

relate to cognitive style’. In Proceedings ISPIM 2007 Innovation for Growth: The

Challenges for East and West.

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Table of Contents Abstract ....................................................................................................................................... 3 List of Publications and Refereed Conference Papers ...................................................... 7 Prologue .................................................................................................................................... 10

Research Questions ............................................................................................................ 13 Structure ................................................................................................................................ 14

Literature Review ..................................................................................................................... 16 Creativity Definitions ........................................................................................................... 16 Creativity Measurement .................................................................................................... 17 Deviance, Rule Breaking and Creativity in the Business Context .............................. 21 Operationalised Creativity ................................................................................................ 22 Creative Production ........................................................................................................... 23 Creative Motivation ............................................................................................................ 25 Organisational Effects on Creative Motivation ............................................................. 27 Mood Effects and Creativity ............................................................................................. 30 Cognitive Style ..................................................................................................................... 32 Framing Effects .................................................................................................................... 37 Creativity and Framing under Uncertainty .................................................................... 38 Creativity and Attribute Framing ..................................................................................... 41 Creativity and Goal Behaviour Framing ......................................................................... 44 Literature Review Summary ............................................................................................... 46

Areas for Investigation ............................................................................................................ 46 Rationale for Study 1: Variable Creative Motivation during Problem Solving ........ 47 Linking Creative Motivation and Self Determination Theory ...................................... 50 Linking Creative Motivation and Cognitive Style ......................................................... 51 Introducing Creative Resolve Response ........................................................................ 53 Table 1: CRR Motivation Modes ....................................................................................... 54 Creative Motivation with Increasing Success Certainty .............................................. 55 Creative Motivation with Increasing Failure Certainty ................................................ 56 Adaptor Creative Resolve Response .............................................................................. 59 Innovator Creative Resolve Response ............................................................................ 61 Rationale for Study 2: Framing Effects and Operationalised Creativity ................... 66

Chapter 1: Creative Resolve Response; How Changes in Creative Motivation Relate to Cognitive Style .................................................................................................................... 74

Introduction .......................................................................................................................... 75 The Creative Resolve Response (CRR) model ............................................................... 81 Adaptor CRR ........................................................................................................................ 82 Innovator CRR ...................................................................................................................... 85 Hypotheses ........................................................................................................................... 89 Methods and experimental design ................................................................................. 89 Results .................................................................................................................................... 96 Analysis .................................................................................................................................. 97 Discussion ........................................................................................................................... 100 Conclusion ......................................................................................................................... 108 References ........................................................................................................................ 110

Chapter 2: Innovation, Creativity and Framing Effects ................................................ 118 Abstract.............................................................................................................................. 118 Introduction ....................................................................................................................... 119 Defining and Measuring Creativity ............................................................................... 123 Framing Effects ................................................................................................................. 126 Hypotheses ........................................................................................................................ 132 Method............................................................................................................................... 136 Results ................................................................................................................................. 143 Discussion ........................................................................................................................... 154

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Conclusion ......................................................................................................................... 161 References ........................................................................................................................ 163

Chapter 3: Cognitive Style, Creativity and Framing Effects ........................................ 169 Abstract.............................................................................................................................. 169 Introduction ....................................................................................................................... 170 Cognitive Styles ................................................................................................................ 172 Creativity in Organisations ............................................................................................. 173 Framing Effects ................................................................................................................. 174 Hypotheses ........................................................................................................................ 176 Methods and Participants .............................................................................................. 179 Procedure and Instruments ............................................................................................ 181 Limitations of the Experimental Design ........................................................................ 184 Results: Risk Based and Attribute Framing for the Entire Sample ............................ 185 Results: Risk Based Framing and Cognitive Style ........................................................ 187 Results: Fluency/Flexibility Preferences and Cognitive Style .................................... 190 Results: Originality/Novelty Preferences and Cognitive Style ................................. 192 Results: Divergence Preferences and Cognitive Style .............................................. 195 Results: Rule Breaking Preferences and Cognitive Style........................................... 198 Results Summary ............................................................................................................... 201 Discussion ........................................................................................................................... 201 Conclusion ......................................................................................................................... 212 References ........................................................................................................................ 215

Conclusions ........................................................................................................................... 219 Study One Response to Objectives .............................................................................. 219 Study Two Response to Objectives ............................................................................... 227 Study Three Response to Objectives ............................................................................ 230 Caveats and Limitations ................................................................................................. 233 Enhancing Understanding of Creativity Management ............................................ 241

References ............................................................................................................................. 244

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Prologue

This research program arose out of consulting work for a large Australian

mining and construction company. During the several years that the author

was retained by that organisation there were several occasions where

training in creative problem solving tools was provided to various managers

within the client organisation. Whilst on the whole the training was well

received, the author was astounded when some of the trainees responded

with comments such as “Yes, that’s all fine but what is the minimum we have

to do with this stuff?” and “Will this be included in my KPI’s (meaning key

performance indicators used for employee performance appraisals)?” Whilst

at first these comments were dismaying because they suggested that

motivation to be creative was lacking within this organisation, ultimately the

challenge of managing employee creative motivation became engaging,

intriguing and more relevant.

Introduction

Creativity is important for businesses because it potentially improves problem

solving outcomes. Many organisations are interested in enhancing creativity.

Recently many researchers and practitioners alike have also suggested that

creativity and innovation are important for managerial effectiveness

(Basadur, 2004; Drucker, 2004; Cameron M Ford, 2002; S. S. Gryskiewicz, 2000b;

Reiter-Palmon & Illies, 2004). This contrasts with the historic paradigm that

creativity was irrational and therefore the antithesis of good management

(Lataif et al., 1992; Mintzberg & Sacks, 2004; Pech, 2001; Scratchley &

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Hakstian, 2000). Almost universally creativity and innovation are now

accepted as important for both managers and organisations.

Despite the acknowledgement of the importance of creativity in

organisations and the focus on increasing creativity to drive innovation, many

managers appear to struggle to improve their organisations’ creative output.

Leavy (2002) suggests that organisations have been ‘found out’ in the last 10

years regarding their ability to manage creativity. It is not immediately clear

why creativity is so hard to enhance in organisations and there are a range of

different points of view.

Berkshire (1995) identified how managers constrain creativity with controlling,

competitive, and critical behaviours, by implementing rationalisations, and

failing to escape routine thinking. Assink (Assink, 2006) suggests the problem

may be inherent in organisational designs where a range of factors reduce

successful firms’ innovation capabilities. Assink seems to suggest creativity can

be a victim of the organisational the success derived from a winning business

strategy, risk-reducing culture and reliance on historically useful mental

models. Elsbach and Hargadon (2006) assert that the interaction of the

organisation and management upon the employee can lead to overwork

and high pressure for performance. They show how these factors are

significantly damaging to professional creativity.

Välikangas and Jett (2006) assert that the leadership challenge for creativity

involves ‘learning to manage the independent thinkers’. These employees are

those who are determined to innovate on their own terms, refusing to accept

professionalism as a valid constraint on non-conformance.

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The difficulty for managers to accept the research findings above is that

enhancing creativity would seem to come at an unacceptable price: Few

managers are prepared to reduce their management control, shelve good

cost saving initiatives, decrease standardisation efficiencies, reduce their

monitoring of employees or decrease their expectations of quality,

performance, productivity and risk reduction to allow creative types to do

more of whatever they please.

Management is about planning, leading, organising and controlling. How do

you plan for emergence? Why would you lead others to undermine your

leadership? When should organisations increase complexity and risk? What

controls do not constrain?

The study is not about finding a compromise between these extremes. Instead

the vision that inspired this research is about how to synthesise the two

seemingly contradictory points of view. It is about how to create options that

align most managers’ philosophies about what it is they are supposed to do in

order for their businesses to perform with what is needed to enhance

employee creativity at a personal intervention level.

The study does not view this problem through the lens of the organisation.

Hamel (Hamel, 2000) proposed a radical restructure to do this and has gone

on to suggest that the next step is to change the process of management

innovation (Hamel, 2006). This study instead starts with a level of analysis

around the interactions between managers and employees because this

smaller problem is easier to start with. It suggests that interactions with

managers affect employees’ motivation to be creative when solving

problems.

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This conceptualised interaction is to some extent a simplified abstraction

because most managers are also employees that report to higher level

managers. These higher level managers are themselves managed by some

other even more powerful managers until Board level is reached. The

employee-manager creativity interaction happens between each level in the

organisation. A further simplification is also required.

There are many points in the problem solving process that employee

creativity motivation can be evaluated and influenced. In order to simplify

these investigations only two aspects of employee creativity enhancement

were considered: how to increase employees’ desire to amplify their creative

production and how to then influence them to choose more creative solution

options rather than less creative ones.

Research Questions

The background (outlined above) and the literature review (see below) lead

to the following research questions around the theme of what manager’s can

do to enhance employee creativity. The studies objectives are to understand:

• How does creative motivation change during problem solving with the

potential for creative production?

• How does cognitive style moderate these creative motivation changes?

• How do framing effects apply to decisions involving creative and non-

creative options to solve problems?

• How does cognitive style moderate these framing effects?

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Structure

This document starts with a review of the relevant literature relating to the

nature of creativity in business, creative motivation, framing effects and

cognitive style before presenting the areas for investigation of this research.

The research was arranged into three separate studies, each of which was

submitted individually for publication prior to this document being created.

References for each of the three studies are included at the end of each

paper as well as in a master list at the end of the thesis. Chapter 1 reports

findings that relate to the first two research questions above in terms of

natural fluctuations in creative motivation in individuals during specific group

problem solving sessions. Chapter 2 reports on findings that relate to the third

research question above. This research discusses general implications of

framing effects as they apply in preferences for creative options. Chapter 3

repeats the methodology used in the previous paper with a different focus:

the research examines how individuals with different cognitive styles respond

to framing effects applied to decisions involving creative options. This work

relates to research question 4 above. In the following paragraphs each paper

is now outlined in more detail.

The first paper in the study links individual creative motivation, problem solving

progress and cognitive style. This paper shows how individual problem solving

motivation varies during problem solving. Outcome certainty is proposed as a

proxy for problem solving progress since most of the time it is impossible to

know during problem solving efforts how much progress the problem solver

has actually (objectively) achieved. (Of course the problem solver may

subjectively perceive progress has been made). The results show that

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individual creative motivation initially increases peaking at around 20%

certainty of outcome. Creative motivation then reduces to a minimum at

around 70% certainty of outcome. At greater levels of certainty creative

motivation again increases with outcome certainty. Whilst the pattern of

varying motivation (called Creative Resolve Response) is consistent, the level

of motivation appears to be moderated by cognitive style as measured by

the Kirton Adaptation Inventory (KAI) (Kirton, 1976).

The second paper in the study examines how decisions involving creative and

non-creative options are influenced by framing effects. The paper shows that

risk based and attribute based framing effects apply to decisions involving

creativity. It also shows that merely describing an option as innovative

enhances individual decision making preference for that option in binary

choices. In some contexts this preference is more powerful than the original

risk based framing effects first presented by Tversky and Kahneman (Tversky &

Kahneman, 1981). The paper also codifies operational creativity (defined

below) into specific context options. The results show that operational

creativity is perceived as an extreme option in some contexts. Extreme

options contain both advantages and detriments that must be considered by

the decision maker in order to compare against the more moderate

alternative presented in the decision. Shafir (Shafir, 1993) showed that

extreme options are significantly less preferred when decisions are presented

as rejections, rather than as a choice between an extreme and a moderate

option. The second paper uses the finding of Shafir to develop a new

methodology which identifies when operational creativity options are indeed

extreme.

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The third paper investigates whether or not framing effects are dependent on

cognitive style. The results identify one specific context where participant

responses to risk based framing are significantly different for sub groups with

different cognitive styles. This suggests that framing effects may not be

universal as implied by previous research approaches. The third paper also

shows that in specific contexts preferences for operationalised creativity

options are moderated by cognitive style as measured by the KAI. Generally

the cognitive style sub group that preferred the operational creativity option

also perceived it as an extreme option. Importantly the results unexpectedly

suggest that cognitive style is not important in determining attitudes to rule

breaking in contexts described as safe. This finding contradicts rule breaking

preferences for different cognitive styles as originally described by Kirton

(Kirton, 1976). Overall the third paper does support the idea of different

cognitive style subgroups, but it shows that creativity and rule breaking

preferences are more complex than previously suggested by the body of

cognitive style research.

Literature Review

The starting point for these studies is to understand other researchers’ prior

contributions to understanding creativity, creative motivation, cognitive style

and framing effects.

Creativity Definitions

Amabile (1997) defines business creativity as the production of novel and

appropriate solutions to organisational problems. Amabile’s model is

compatible with other authors’ definitions. For example, Plsek (1997) and

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Gryskiewicz (2000) have similar conceptions of creativity but also go on to

define innovation as the implementation of creative solutions. Csikszentmihaly

(1996a) and Simonton (1993; 1999) assert that creativity is only meaningful as

an evaluation by third party beneficiaries of a problem solution. Interestingly

this recognition for creativity is still based on both the relative novelty and

appropriateness of the solution.

Creativity Measurement

According to Cropley (2000) there are more than 255 different tests for

measuring creativity. Measures of creativity that were simple to use, well

validated and relevant to business contexts were deemed most appropriate

for this study.

Commonly used creativity tests include Mednick’s Remote Association Test

(1962; 1967), Torrance Test of Creative Thinking (1962), Creativity Index

(Gough 1981) and Rainmaker Index (Stevens, Burley et al. 1998). One

common test rejected for use in this study was Amabile’s Consensual

Assessment Technique (1982). It was rejected because it is a time consuming

test that requires multiple raters, and this made it outside the resource scope

of what was possible. A test useful for this study is the Guilford Divergence Test

(Guilford J P 1967) which provides a reliable and basic starting point for

assessing creative outputs in business settings. The advantage of this test is

that it is very simple and yet powerful for assessing creativity. The test proposes

three measures of creativity: fluency, flexibility and originality.

Fluency is a measure of the number of options produced to solve a problem

and is essentially a measure of volume (E P Torrance & Haensly, 2003). Fluency

assumes that a more creative employee will be able to generate more

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potential ideas to solve a problem. However in practice fluency as a unique

measure of creativity is flawed because it is possible to create many ideas

that do not vary significantly and still achieve a high fluency score. For

example consider the problem of trying to buy a gift for a partner. This is a

situation where it is quite possible (and often valuable) to be creative. A fluent

problem solver might consider flowers as a good gift and then proceed to

consider a large number of different types of flowers that could constitute a

bouquet. In doing so their creativity is limited because other gifts (for example

a massage, chocolates or a sky diving lesson) are not considered. Clearly

whilst it is to some extent more creative to propose more options to solve a

problem other measures of creativity are also required.

A second measure of creativity is flexibility1

1 Current Torrance Tests omit flexibility as a possible score due to correlations with fluency. In the tests

conducted in this study shown later fluency and flexibility are combined. The separation shown here is

definitional not operational

which relates the number of

different categories or themes that generated ideas can be grouped into (E P

Torrance & Haensly, 2003). Flexibility is essentially a measure of spread of

ideas and assumes that a more creative problem solver will be able to

suggest a wider range of possible solutions to a problem. In practice flexibility

is somewhat harder to measure than fluency because in many instances it is

not clear how far apart two different ideas need to be in order to be

considered as being from different themes or categories. In our gift giving

example above is difficult to be conclusive as to whether or not a pot plant is

in the same category as a bouquet of flowers or in a new category. Despite

this practical concern it is apparent that a compared to a problem solver

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who only considered a variety of bouquets as a gift, one who also considered

a pot plant as well is in that instance more creative.

Fluency and flexibility are applicable business creativity management

because of these outputs ability to be measured directly by examining a

problem solver’s list of proposed ideas. The third measure of creativity used in

business (originality) is paradoxically more and less applicable to business

creativity management.

Originality is a measure of how rare an idea is (E P Torrance & Haensly, 2003)

and essentially is an indicator of unusualness or novelty. Such a quantity can

only be determined in comparison to responses generally proposed by a

normal population. An idea is rare to the 1% level if it is proposed by less than

1 in 100 normal problem solvers as a response to the problem at hand. This

requirement for comparison makes originality harder to measure in business

and therefore less useful than the other two creativity measures. Original ideas

in business are often considered to be risky due to their inherent liability of

newness (Stinchcombe, 1965). However original business ideas have the

potential to achieve the greatest impact on performance (Schumpeter,

1983), so originality as a creativity output measure cannot be ignored by

managers.

The Torrance Test of Creative Thinking (1962) measures very similar outputs to

Guilford ‘s Divergence Test (Guilford J P, 1967) except that it includes an

additional creativity characteristic called elaboration. Elaboration is the

ability of the creative problem solver to extend or modify an existing idea with

more detail (E P Torrance & Haensly, 2003). Elaboration presents similar

measurement difficulties to originality in that elaboration levels are typically

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determined in reference to a normal population. It should also be noted that

typically Torrance considered the elaboration measure in the context of

children that were modifying existing proto drawings in order to make them

“more complete”. The relative level of additional detail added was

considered proportional to elaboration creativity. Such evaluations were

fundamentally subjective.

Elaboration was not considered in this research as it is typically manifested in

business problem solving situations during attempts to implement creative

ideas (for more on this see Basadur, 1997). Problem solvers typically elaborate

their creative potential solutions in order to get them to fit better within

organisational constraints and/ or fit with other organisational stakeholders in

order to maximise implementation success chances. Since this study is limited

to the understanding of how to enhance the production of creative ideas

and then increase the preference to choose to implement these creative

ideas, it was decided that elaboration could be eliminated from

consideration. This is not to say that elaboration is not relevant in the business

context, nor that more elaborated creative options would not potentially be

more preferred for attempted implementation. Instead it has been left to later

research studies to properly deal with elaboration creativity within the

business context.

There is quite a substantial debate over the appropriateness of the underlying

measures of creativity, and whether or not divergent thinking is in fact a major

component of creativity. Milgram and Livine (in Kaufman and Baer 2005:

pages 187-190) provide a good summary of the concerns proposed by a

range of researchers. Milgram and Livine go on to describe the important

trend towards measuring creativity in context. However they also confirm the

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reliability, convergent validity and validity of ideational fluency-based

measures of creativity including both Guilford’s and Torrance’s tests (page

188).

Whilst the testing validity provides an adequate basis to consider fluency,

flexibility and originality as valid creativity measures, it was determined that

additional variables could also be considered that incorporate some aspect

of creativity’s domain relevance. Two other measures of creativity that the

study does include that relate to how creative options are perceived by

employees in response to the norms of their organisations, namely deviance

and rule breaking. These measures were chosen for their connection to the

concept of cognitive style (Kirton, 1972) as a predictor of creativity in an

organisational context, as well as their ease to be operationalised in the form

of experiment used.

Deviance, Rule Breaking and Creativity in the Business Context

The models of creativity consider creativity in abstract without regard to the

organisational context. Organisational factors are very important to creative

production (Andriopoulos, 2001; Ismail, 2005; Robben, 1998; Schepers & Berg,

2007; Tierney, Farmer, & Graen, 1999; Woodman, Sawyer, & Griffin, 1993).

Creativity measures consider creativity from the perspective of creative

production. These approaches fall short of including the organisation as

reference point for determining what it means to be creative (and Barlow,

2001; Basadur, 1994; for example see McLean). Thus it is also important to

understand “contextual creativity” – how employees perceive what it means

to be creative at work. Many authors (including Basadur, 2004; Berkshire, 1995;

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Boeddrich, 2004; S. Gryskiewicz & Taylor, 2003; Leavy, 2002; Mumford, 2000;

Proctor, 1999; Rieple, 2004; Scratchley & Hakstian, 2000) have asserted that

organisations either purposefully or inadvertently decrease creativity (Klein,

1990). This can be manifest in many ways for example via punitive personal

accountability, overzealous risk management, conservative capital allocation

procedures or inflexible corporate governance initiatives.

In organisations where purposeful creativity reduction occurs, groups and

individuals that are too creative when solving problems can be subject to

sanctions (T. M. Amabile, 1998; Kirton, 1984a; Pinchot & Callahan, 2000). In

organisations where inadvertent creativity reduction occurs, groups and

individuals are subject to increased oversight and decreased autonomy after

any failed innovation attempt (Kirton, 1978a).

Thus employees in many organisations automatically associate creativity with

deviance or rule breaking (Pascale & Sternin, 2005; Pech, 2001; Sternin &

Choo, 2000; Wells, Donnell, Thomas, Mills, & Miller, 2006). This can occur where

inadvertent creativity reduction is the norm and where purposeful creativity is

the norm. Generating more options, a wider range of options, novel or rare

options requires a preparedness on the part of the employee to overcome

organisation signals that promote efficiency and risk reduction (Dewett, 2004;

Shaw, O'Loughlin, & McFadzean, 2005; Sutton, 2001) during problem solving.

Operationalised Creativity

The five indicators of creativity – fluency, flexibility, originality, deviance and

rule breaking are all used in this study to operationalise creativity. Creativity is

operationalised when a manager or employee is presented with a choice of

options to solve a problem, at least one of which includes an alternative that

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exhibits greater relative fluency, flexibility, originality, deviance and/ or rule

breaking. The element of choice is important in a business context due to the

requirement for explicit or implicit approval for a creative output to be

granted before attempting to implement the creative alterative to solving the

problem. Note that the successful implementation of such an alternative

would represent an innovation as defined above.

Improving the potential for successful implementation of creative ideas in a

business context is beyond the scope of this study. What is relevant to this

research is how to influence the choice to try and implement a creative

option. In other words – how to increase preference for operationalised

creativity. As well, before such a choice can be made operationalised

creativity options must be produced.

Creative Production

Amabile’s three factor theory of individual creativity (1983; 1996; 1997; 1998)

combines creativity skills, domain-relevant knowledge and task motivation as

sole components intrinsic to creativity. Creativity skills build on an individual’s

natural ability to be fluent, flexible, or original with training in generic heuristics

or other techniques for enhancing creative problem solving skill. An example

of such a generic heuristic might be the SCAMPER technique as presented by

Michalko (Michalko 2000). The SCAMPER acronym stands for substitute,

combine, adapt, magnify or add, put to other uses, eliminate or reduce and

reverse or rearrange. Each of these actions represents a generic attention

redirection tool designed to help a problem solver prompt themselves for

more fluent, flexible or original solutions. It is apparent from the acronym’s

meaning that the attention redirection tools could be applied within any

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problem solving domain that the problem solver is familiar with. Of course an

individual may act creatively without the requirement to use a formally

acquired heuristic. Consider a creative artist for whom creative expression is

an informally acquired ability. In Amabile’s definition this talent also should be

included in the term ‘creativity skills’.

Amabile’s second factor in creative production is domain knowledge. This is

defined as specific technical expertise that relates to the problem at hand.

Amabile’s three factor model suggests that some minimum amount of

relevant domain knowledge is required in order to have any chance of

solving a problem. This is similar to what Csikszentmihalyi calls knowledge of

the “field of accomplishment” (Csikszentmihalyi, 1996b). Amabile’s model also

suggests that creativity increases proportionally with domain knowledge.

There is potential that research relating to priming (also called fixation or

thinking inertia) will ultimately show in some contexts domain knowledge can

prevent creative discoveries from being made. No such papers could be

identified. This is significant as results showing an inverse relationship between

domain knowledge and creativity would invalidate Amabile’s three factor

model in at least some situations.

In fact, Amabile’s three factor theory has been empirically supported by a

variety of researchers (Conti, Coon, & Amabile, 1996; Ruscio, Whitney, &

Amabile, 1998), though Taggar (2002) proposes further potential factors for

consideration. Since the product of the three components determines total

creative output (or partly determines creative output if Taggar’s model is

true), any change in one of the factors will proportionally change the

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problem solver’s results regardless of whether Taggar’s additional factors

apply.

Whilst there is significant research to suggest both creativity skills and domain-

relevant knowledge can be increased by training, this is relatively costly and

time consuming (for example see Wang; & Horng;, 2002) in the short term

compared to management interventions designed to influence creative

motivation. Thus I argue managers should be interested in understanding how

to affect an individual employee’s creative motivation.

Creative Motivation

Creative motivation is an important component of individual creativity. In

Amabile’s three factor model or creativity, creative motivation is the extent to

which a problem solver will choose to engage their existing creativity skills and

domain-relevant knowledge. Amabile (1997; 2005; 1983; 1990; 1996; 1997;

1998; 2002; 2004) outlines how intrinsic and extrinsic motivators combine to

determine creative motivation and shows that individuals are more creative

when motivated appropriately.

An individual’s motivation for any task at a particular instant is determined

from intrinsic motivators (like curiosity, interest and fatigue) and extrinsic

motivators (including rewards, recognition and resource abundance).

Generally creativity is motivated by intrinsic factors (T. M. Amabile, 1997;

Cooper, Clasen, Silva-Jalonen, & Butler, 1999; B A Hennessey & Amabile, 1998;

Katz, 2002).

A specific finding of Amabile’s work is that extrinsic motivators most

commonly operate to reduce creative motivation (Amabile 1997). This is the

case even when the extrinsic motivators are designed to reward creative

26

behaviour. There are extrinsic motivators which do not reduce creativity: this

special class of extrinsic motivators were designated by Amabile as synergistic

extrinsic motivators. They act to enhance creative motivation when intrinsic

motivation is already present (Polland 1994; Amabile 1997; Hennessey and

Amabile 1998; Ruscio, Whitney et al. 1998).

Two common examples of synergistic, extrinsic motivators are recognition and

resource support (Amabile, 1997). A creative employee who is intrinsically

motivated to solve a problem creatively is further motivated when recognised

for their creative work. Similarly a creative employee who is intrinsically

motivated to solve a problem creatively is further motivated when resources

are provided that ensure they can continue their creative work regardless of

results. Monitoring the creative employee’s progress is an extrinsic de-

motivator for creativity and would be expected to reduce creative output (T.

M. Amabile et al., 2002; Williams, 2004). Offering additional payment or

resources as a bonus for superior creative work are also extrinsic motivators

that dampen creativity even though designed to enhance it.

In general creativity seems to be a specific case of Deci and Ryan’s Self

Determination Theory (Deci & Ryan, 1985b, 2000a, 2000b). According to Self

Determination Theory intrinsic motivation is undermined by the presence of

tangible extrinsic motivators. This supports Amabile’s body of research cited

above which suggests intrinsic motivation is critical for creative motivation.

Despite the substantive evidence citing the importance of intrinsic motivation

and the detrimental effects of extrinsic motivation on creativity, there is a

contrary opinion asserted by Eisenberger (e.g. Eisenberger and Cameron,

1996; Eisenberger and Shanock, 2003). Eisenberger and Cameron (1996)

27

suggest that review of the research shows “reward for a high degree of

creative performance can be used to increase generalised creativity”

(p1162). This debate reached its height in the late 1990’s but is still ongoing. A

reasonable summary is that whilst the importance of intrinsic motivation for

creativity still has many advocates, it is now agreed by even these that under

some conditions rewards can increase creativity (e.g. Eisenberger and

Shanock, 2003; Amabile and Kramer, 2007).

Organisational Effects on Creative Motivation

Problem solving in an organisational context is by definition subject to extrinsic

motivators due to the overarching imperative to achieve organisational

objectives. Many of these factors are not synergistic and hence serve to

inhibit creativity even though they are designed to improve individual

motivation to perform. This suggests that creativity in organisational settings

will be naturally lower than other domains (i.e. those without overarching

objectives like profitability, return on investment and/ or market share).

Assink (Assink, 2006) identifies a range of factors that inhibit organisational

innovation capability. The most relevant of these to this study include the

reticence to innovate when an existing product is successful, problems with

managing the risks associated with innovation, excessive bureaucracy,

change resistance, an inability to unlearn, obsolete mental models, difficulty

in forecasting innovation returns, and compliance focussed organisation

culture. In fact, many authorities (including Basadur, 2004; Berkshire, 1995;

Boeddrich, 2004; S. Gryskiewicz & Taylor, 2003; Leavy, 2002; Mumford, 2000;

Proctor, 1999; Välikangas & Jett, 2006) have asserted that organisations either

purposefully or inadvertently decrease creativity (via punitive personal

28


procedures or inflexible corporate governance initiatives). For example

Elsbach & Hargadon (2006) argue that overwork and high pressure for

performance are significantly damaging to professional creativity and

advocate recuperation periods of so called “mindless” work to improve this.

Amabile et al (2002) found that creativity is reduced under the time pressure

experienced by many in organisations. Wells et al.’(2006) presented data

showing a significant correlation between creativity and deviance in

organisations. Dewett (2004) goes as far as suggesting that an employee’s

willingness to take risks is the key determinant of individual creativity.

There seems to be a tendency in organisations to rank appropriateness of

solutions over novelty (T. M. Amabile, 1998; Kirton, 1984b, 1991; Matherly &

Goldsmith, 1985). Managers can potentially improve creative motivation in

their organisations with specific management interventions that increase the

potential for intrinsic motivation, utilise synergistic extrinsic motivators and/ or

insulate problem solvers from extrinsic motivation effects (Mumford, 2000;

Mumford & Others, 1997; Mumford, Scott, Gaddis, & Strange, 2002; Oldham &

Cummings, 1996)

Forbes and Domm (Forbes & Domm, 2004) agreed that “external” controls

designed to increase productivity could diminish involvement and creativity.

In this context “external” controls equate to supervision and management

push for completion. Despite this they showed how creativity and productivity

can increase under circumstances where there is high involvement: They

assert that some extrinsic rewards can enhance personal involvement, and

hence creativity.

29

Overall the body of research above suggests that creativity in organisational

settings will be naturally lower than other domains. Thus there is a significant

and relevant problem for managers related to managing creativity: what to

do given that the majority of management approaches just do not seem to

work for creativity because they are fundamentally and essentially extrinsic in

their motivation approach? How to motivate employees to complete tasks

with creativity when intrinsically motivated employees are not available?

Despite the current focus on the importance of organisational creativity and

innovation, many managers in organisational settings often appear unwilling

or unable to change the extrinsic motivators inherent in their organisations.

And as outlined above switching from extrinsic motivation modes of

traditional management to more intrinsically based motivation seems to be

the antithesis of good management for many. Management of employees is

just not perceived by managers to be compatible with increased autonomy

support in many cases.

These managers find it inappropriate to give up budgets, monitoring, key

performance indicators, commissions, bonuses, promotions, transfers,

demotions and reprimands. Replacing these things with recognition, pre

approved resources independent of performance and job redesign for

increased curiosity, interest and self expression just to improve creativity is not

acceptable. Even at a very simple level many managers believe that they

should direct employees to do a job, and that they are not supposed to

surrender to their employees the choice of which task or project might interest

them the most. Many of these managers interact with their employees on the

basis that it is not appropriate to enrich work in order to ensure that it is

enjoyed: if work is enjoyable that is a bonus not a requirement.

30

The potential for supportive supervisors and leaders to enhance creativity

during organisational problem solving has also been examined by a variety of

researchers (including T. M. Amabile et al., 2004; Baer, Oldham, & Cummings,

2003; Basadur, 2004; Boerner, Eisenbeiss, & Griesser, 2007; Clapham, 2000; de

Jong & Hartog, 2007; Egan, 2005a, 2005b; Forbes & Domm, 2004; S.

Gryskiewicz & Taylor, 2003; Mumford et al., 2002; Oldham & Cummings, 1996;

Reiter-Palmon & Illies, 2004; Sosik, Avolio, & Kahai, 1997; Välikangas & Jett,

2006).

The common finding in this body of research is that employee creativity

increases in an organisational context where supervisors are perceived to be

supportive towards creativity and to some extent are able to shield

employees somewhat from the overarching extrinsic achievement

imperatives demanded by the organisation. Viewed through the lens of

creative motivation, supportive supervisor behaviour can be conceptualised

a synergistic extrinsic motivator because being supportive to creativity may

include recognising creative individuals and providing resource support. This

suggests that increases in employee creativity may only occur when the

employee is already intrinsically motivated to be creative. Enhancing

creativity further and/ or managing non-intrinsically motivated employees

requires a consideration of some other factors that can affect creative

motivation including mood.

Mood Effects and Creativity

Much of the prior research that relates to mood and creativity is based on

how mood disorders are correlated with creativity (for a summary see

Rickards and Runco et al. 2008, see also Amabile and Barsade et al. 2005).

31

This work tends to consider creativity over an individual’s lifetime rather than

on a moment to moment basis. However Vosburg’s work considers the

potential for mood to effect creativity in the short term. Vosburg (1998)

provides evidence that consistency of problem solving approach is not

normal: mood can affect creativity in complex ways during individual

problem solving activities. Vosburg used contrived means to affect moods

and then measured the resulting difference in creative output.

Vosburg empirically validated that mood affects creativity in a more complex

manner that previously accepted. Specifically positive mood does not

unconditionally facilitate creative problem solving and negative mood does

not unconditionally hinder creative problem solving. Vosburg found that

under certain conditions negative mood can facilitate, and positive mood

can inhibit, creative problem solving. To some extent both employee mood

responses and/or their sensitivity to organisational controls is important to their

creativity.

Kaufmann and Vosberg (Kaufmann & Vosburg, 1997) also showed that mood

effects were correlated with creative production in an unexpected way: in

their study negative mood seem to enhance creativity and positive mood

seemed to diminish creativity. This suggests that negative moods are more

likely to activate or sensitise the individual to their natural intrinsic motivators.

Recent work by Friedman et al (Friedman, Forster, & Denzler, 2007) has

connected problem solving context with mood interactions. However

Kaufmann and Vosburg cite earlier research by other researchers (including

Isen, Means, Patrick, & Nowicki, 1982) that correlates creativity production

positively with mood and apparently conflicts with their own findings. They

32

suggest that feedback is a key factor in how mood effects moderate

creative production.

Feedback in the two studies cited above was inherent in the task. In another

study a form of feedback was provided by a supervisor rather than inherently

from the task. George and Zhou (George & Zhou, 2007) investigated creativity

and mood relationships in the context of supervisor supportive behaviour.

They found that positive and negative mood both facilitated creativity when

supervisors were perceived to be supportive.

A possible conclusion from the research findings relating mood and creativity

is that an individual’s creative motivation is not constant during a specific task

because an individual’s mood can change. It is also plausible that these

factors (both motivation and mood) may be moderated by an individual’s

cognitive style.

Cognitive Style

Kirton’s (Kirton, 1976) Adaption Innovation inventory (KAI) is a validated

measure of cognitive style relevant specifically to employees operating in

organisational contexts. KAI has been validated empirically by many

researchers (Fleenor & Taylor, 1994; Foxall & Hackett, 1992; Goldsmith &

Matherly, 1987a; Keller & Holland, 1978; Riley, 1993; Taylor, 1989). There are

now more than 350 peer reviewed studies that utilise KAI. As such it can be

considered a consistent, reliable and valid measure of cognitive style.

Despite evidence of KAI’s reliability, cognitive style is not a definitively

predictive construct. Cognitive style influences problem solving and decision

making, rather than determining it. In a review published in the Psychological

Bulletin, Kozhevnikov (Kozhevnikov, 2007) outlined these issues and the

33

conjecture over the correct dimensions of cognitive style in the following

statements:

…At the present time, many cognitive scientists would agree that

research on cognitive styles has reached an impasse. In their view,

although individual differences in cognitive functioning do exist, their

effects are often overwhelmed by other factors, such as general

abilities and cognitive constraints that all human minds have in

common. The paradox of the current situation is that interest in building

a coherent theory of cognitive styles remains at a low level among

researchers in the cognitive sciences; however, investigators in

numerous applied fields have found that cognitive style can be a

better predictor of an individual’s success in a particular situation than

general intelligence or situational factors. In the field of industrial and

organizational psychology, cognitive style is considered a fundamental

factor determining both individual and organizational behaviour (e.g.,

Streufert & Nogami, 1989; Sadler-Smith & Badger, 1998; Talbot, 1989)...

(Page 464).

For this thesis, KAI was used rather than other more general models of

cognitive style (e.g. Sternberg, 1990; Bruner, 1990) because of the specific

relevance of KAI to business creativity. That is KAI has face validity with

business contexts.

The KAI scale was synthesised from three independent problem solving

related scales for originality, efficiency and conformity preferences. The

originality scale measures preference for ideas that are unorthodox, novel or

unusual. The efficiency scale measures preference for detailed, orderly and

34

appropriate behaviour. The conformity scale measures acceptance of

prevailing rules, organisational paradigms and group norms. Kirton (1976)

constructed the scale by selecting different numbers of self report questions

from each of the subscales in order to sort the normal population into a

normal distribution. KAI score is determined from 32 question responses. It

ranges from 32-160. The overall population exhibits a mean KAI of 96 with a

standard deviation of 13, normally distributed. KAI is seems stable regardless

of age, career, industry or nationality. Ethnicity and gender seem to be

independent to KAI.

Individuals with KAI score greater than 96 are called Innovators by Kirton.

Individuals with lower KAI scores are called Adaptors. According to Kirton’s

descriptions, Innovators are motivated make large changes and break

prevailing rules, norms and paradigms. Adaptors apparently prefer making

incremental changes that remain within organisational expectations. This

results in Adaptors being more conforming. Kirton perhaps pejoratively

describes Innovators as “preferring to do things differently” and Adaptors as

“preferring to do things better” (Kirton, 1976).

Other research supports Kirton’s assertions that Innovators and Adaptors solve

problems differently. Hammerschmidt (Hammerschmidt, 1996b) showed that

cognitive style determined a role preference for either designing or

implementing solutions. Adaptors were less likely to propose radical solutions,

and they were more likely to completely implement known problem solutions.

In Hammerschmidt’s studies they studied rules, often in total silence and then

constructed detailed written plans. Innovators were observed in

Hammerschmidt’s experiments to disregard rules, move around more and

challenge constraints like time limits. Comments from the Innovators in

35

Hammerschmidt’s studies included “rules were made to be broken” and “he

who cheats first cheats best” (see Hammerschmidt, 1996a pages 68-69).

The reasons for the differences in problem solving behaviour between

Innovators and Adaptors may lie in the some other reported factors. For

example, Innovators tend to exhibit higher levels of self esteem (Goldsmith &

Matherly, 1987c; Houtz, Denmark, Rosenfield, & Tetenbaum, 1980; Keller &

Holland, 1978). In these studies self esteem was defined as an individual’s

sense of importance or self worth. Note that this is not necessarily only a

positive trait – inappropriately high levels of self worth can be exhibited as

arrogance or overconfidence.

Other research suggests that Innovators are more tolerant of ambiguity (Keller

& Holland, 1978) and optimistic (Wunderley, Reddy, & Dember, 1998) when

compared to Adaptors. Innovators are also more likely to solve problems with

an internal locus of control (Engle, Mah, & Sadri, 1997; Houtz et al., 1980; Keller

& Holland, 1978; Luck, 2004; Tetenbaum & Houtz, 1978). These characteristics

would suggest that Innovators are more likely to be motivated to produce

operationalised creativity options in response to the extrinsic motivators

prevalent in organisations. Within an organisation there is often a general

requirement to overcome management controls in order to be creative in

many problem solving and decision contexts. This can result in creativity being

perceived as non-conforming or deviant. Thus Innovators are often perceived

to be more creative (or at least willing to be more creative) within

organisations because of their preference for rule breaking.

However, these differences between Innovators and Adaptors do not

necessarily imply one is inherently more creative than the other. Kirton

36

asserted that neither cognitive style is inherently more capable of creativity

(Kirton, 1978b). Indeed whilst Amabile defined creativity skills to include

generic divergent thinking, other researchers (Basadur, 1997; Sand, 2003)

have asserted that generic convergent thinking processes are also required

for organisational creativity. How do we reconcile equality between

Innovators and Adaptors in terms of inherent creativity, with the differences

between the two styles just discussed, particularly as manifested in business

contexts.

The resolution of these potentially conflicting considerations perhaps comes

from Amabile’s three factor model: Adaptors may be equally creatively

skilled as Innovators, but more sensitive to organisational requirements for

conformity. That is, Adaptors may have equal potential to be creative in

organisations, but perhaps are affected more by the dampening of extrinsic

motivational factors inherent in organisations generally. It is possible that in

certain organisational contexts Adaptors could be more motivated to be

creative than Innovators – for example in certain research organisational

cultures where avoiding creativity might be perceived as non-conforming. In

such organisations Adaptors may actually end up being more creative than

Innovators, even though the organisational culture is a dominant extrinsic

motivating factor.

Differences in creative production due to sensitivity to motivational factors

may not be the only difference between Innovators and Adaptors. Optimism,

locus of control and tolerance for ambiguity could be expected to affect

preferences for operationalised creativity options. Fluent and flexible

alternatives have greater chances for success, but are less efficient than less

creative, simple, tried and true methods. Original alternatives may lead to

37

potentially superior results, but be perceived as more risky (and therefore

more ambiguous in terms of value) due to their novelty when compared to

non-creative proven options. Any of these operationalised creativity

alternatives may be perceived as relatively divergent or rule-breaking. In

summary, exploration of the difference between innovators and adaptors in

creativity is an important stimulus for this thesis. However, regardless of which

aspects of operationalised creativity are more relevant to an individual with a

given cognitive style, decisions involving operationalised creativity are also

potentially subject to framing effects.

Framing Effects

Framing effects relate to changes in preferences that occur due to the way

that a decision is presented. Framing may naturally apply when an individual

considers solving a problem creatively. Creativity could be framed in many

ways: divergence from past, rule breaking/disruption, additional work,

rareness/uniqueness, novelty, self expression, interest, enjoyment, humour,

change, threat, required, rational or emotional in character. Depending on

perspective, these frames can be perceived positively or negatively: for

example “rule breaking” may be a negative frame for an auditor or law

enforcement official but positive for a teenager or entrepreneur.

Levin et al (I. G. Levin, Schneider, & Gaeth, 1998) provide a useful typology

that describes three framing effects:

• Framing under uncertainty;

• Attribute framing;

• Goal behaviour framing;

38

The independence of these effects has subsequently been validated (Levin I

P, Gaeth G J, Schreiber J, & Lauriola M, 2007). Framing effects research

implies that these psychological perceptual biases are generic and

universally applicable. Thus we can consider creativity during problem solving

as affecting risk, or as a desirable/ undesirable attribute or as behaviour with

the potential to impact on goal achievement (i.e. how does being creative

potentially assist with solving the problem at hand). Each different type of

framing effect and its potential relationship to creativity is outlined below.

Creativity and Framing under Uncertainty

Levin et al. (1998) cite 29 studies regarding “risky choice framing” (framing

under uncertainty) and assert that this kind of framing is a “standard” framing

effect (p151). The most significant of these is Tversky and Kahneman (1981).

Tversky and Kahneman showed that individuals may exhibit a preference

reversal when equivalent choices are framed positively or negatively.

Essentially they showed that rationally equivalent choices were subject to

perceptual distortions based on how the choice was presented. When

subjects were presented with a choice that had a smaller sure gain or a

larger risky gain, they tended to favour the sure gain (even if the expected

returns adjusted for risk from the choices were equivalent). Significantly if the

same choice was presented in terms of its costs rather than gains, with a sure

smaller cost or a larger risky cost, subjects tended to choose the larger risky

cost. This led Tversky and Kahneman to determine a hierarchy of weightings

that related to choices involving the potential for gain and loss. Typically

subjects were most sensitive to loss, then sensitive to risk and least sensitive to

gains. A form of the classic risky choice is shown below:

39

Suppose that you are in charge of a government immunisation program to deal with an impending outbreak of a rare disease that is expected to kill 600 people. Two alternative programs have been proposed to combat the disease. Which program would you favour if costs for each program are the same?

A If Program A is adopted 200 people will be saved

B If Program B is adopted there is a 1/3 chance 600 people will be saved and a 2/3 probability that no-one will be saved

When this problem was presented as framed above the majority of people

choose option A. This is predictable for choices of this type because the

problem is framed in terms of gains (i.e. lives saved). When the problem is

reframed in the form below Tversky and Kahneman found a significant

reversal of preference:

Suppose that you are in charge of a government immunisation program to deal with an impending outbreak of a rare disease that is expected to kill 600 people. Two alternative programs have been proposed to combat the disease. Which program would you favour if costs for each program are the same?

A If Program A is adopted 400 people will die

B If Program B is adopted there is a 1/3 chance no one will die and a 2/3 probability that 600 people will die

Option B apparently becomes more preferable when the choice is framed in

terms of loss (i.e. deaths) rather than gains. Levin et al cite 22 other papers

that support Tversky and Kahneman’s findings (see I. G. Levin et al., 1998).

This choice of certainty versus risky gain may have an analogous application

to individual problem solvers: an individual may perceive that attempting to

be creative involves a risk of failure compared to a “non-creative” problem

solving approach. However attempting to be creative may offer the potential

of a higher utility solution.

Consider the problem of walking through a minefield at night without the

benefit of specialist mine detection equipment. Whilst this is not strictly a

40

business problem traversing a mine field can be representative of business

problems like identifying the best candidate for a job, choosing which new

product to develop or negotiating with a fickle customer. How does one walk

through a mine field in conditions like these? Consider this problem literally in

its specific context…

Analysis suggests that the shortest point through a mine field is a straight line.

However, a straight line through the mine field is certain to fail if the designer

of the mine field is competent and committed because such a designer

would want to harm enemies who were otherwise unaware of the presence

of mines. The most radical alternative to a straight line is a random path with

twists and turns. Such a path is highly likely to detonate a mine as the more

distance travelled within the field, the more likely that a mine will be

encountered and activated. “Carefully” walking through a mine field

(presumably some kind of tiptoe stepping) seems to be both a tautology and

an oxymoron and in either event unlikely to produce a successful problem

solving outcome.

Of course the insight solution is to ensure that one traverses the mine field last

(i.e. after watching someone else makes it through successfully). This solution is

ideal if it takes advantage of an externality like watching how the enemy

traverses the mine field first from a hidden position, or a wandering goat

somehow safely makes its way through. This insight solution typically requires

either a redefinition of the problem or some kind of lateral thinking

provocation to discover.

In a business context the decision about how to approach problems like the

mine field problem can be framed in different ways by managers. One

41

framing perspective might be that attempting lateral approaches does not

guarantee success and typically costs more to attempt. As a result problem

solvers may be pressured to “get on with it” rather than look for more radical

(creative) solutions. Creativity as framed in this context is an uncertain,

negative thing to be avoided.

However another manager may focus on the importance of attempting

lateral/ insight based approaches as a way of finding breakthrough solutions

with superior potential utility. Such a manager may encourage employees to

spend some of their time at work each week on problems that capture their

interest in order to increase the potential for highly creative, valuable

solutions. Creativity in this context is framed as an uncertain, positive thing to

be supported (this is similar to choosing to buy a lottery ticket: the cost is low

and the gains whilst unlikely are potentially very high). Thus regardless of the

environment or the problem solver’s cognitive style it is possible that being

creative while problem solving can be framed under uncertainty as either

something to avoid (negative) or something to cultivate (positive).

Creativity and Attribute Framing

Levin et al. (1998) cite many other studies that relate to framing phenomena

which disconfirm the preference reversals asserted by Tversky and Kahneman

(1981). Levin et al. (1998) propose that these disconfirming studies are

actually subject to either Attribute framing or Goal behaviour framing. This is

relevant because creativity can be framed as an attribute for a problem

solution.

Attribute framing relates to how different weighted aspects of a decision

choice may be prioritised. Shafir asked subjects to choose or reject one of

42

two options for a variety of problem contexts (including a child custody

battle, preferred holiday destination, university course enrolment and lottery

prizes). Each choice was described qualitatively in terms of several different

elements (for example holiday destinations were described by their weather,

beaches, hotel, water temperature, and nightlife). In all cases one option was

“enriched” in that all of its elements were relatively good or relatively poor

relative to the “impoverished” option (which was of average quality for all

elements). Shafir showed that there is a tendency for subjects to weight

desirable elements as more important when choosing and undesirable

elements more important when rejecting. This resulted in subjects tending to

choose enriched options over impoverished options when selecting. However

when rejecting subjects tended to reject enriched options over impoverished

options: thus the enriched option was both the most preferred and least

preferred option depending on whether the decision was framed as “select”

or “reject”.

Attribute framing effects apply to perceptions of products, decisions about

optional extras and consent for surgical procedures. For example 75% lean

meat is apparently better tasting and less greasy than 25% fat meat (Levin I P

& Gaeth G J, 1988); yoghurt that is 0% fat is apparently more attractive than

100% fat free yoghurt (Janiszewski C, Silk T, & Cooke A D J, 2003); internet

hosting packages are more highly valued when they include extra services

rather than being discounted in price (Stibel J, 2005); pizzas and cars tend to

be more expensive and feature laden when customers start with fully loaded

product bundles and delete options rather than building up from scratch

(Levin I P, Schreiber J, Lauriola M, & Gaeth G J, 2002; Park, Sung Youl, &

Deborah, 2000); and more patients consent to surgery when discussed in

43

terms of survival rather than mortality rates (Marteau T M, 1989; Wilson D K,

Kaplan R M, & Schneiderman L J, 1987).

This attribute framing phenomenon is important for creative motivation in

problem solving. Creative solutions to problems can be seen as positive

attributes or negative attributes. Consider the common problem that a city

commuter must solve in order to get to work: The introduction of a new public

transport system may be seen by some commuters as bad because it

invalidates past traditions (“I preferred the old days when there were trams

because they were cheap and safe”). However other travellers may evaluate

the new system as a progressive step forward (“I like the fact that the new

public transport is better for the environment”). In addition commuters will

frame the decision to use the new public transport as either an opportunity to

choose (“Now I have a new alternative to get to work”) or an opportunity to

reject (“Why would I give up my car to travel by public transport?”).

In the absence of concerted framing effects, individual problem solvers will

decide using one of four possible attribute frames: negative/ choose;

positive/ choose; negative/ reject; positive/ reject. The existing literature

would suggest that most commuters that use the positive/ choose frame

would favour the new transport system. Similarly commuters using the

negative/ reject frame would be expected not to favour the new system.

Interestingly in Australia the Public Transport Users Association’s website which

seems to want to enhance public transport usage presents public transport in

terms of its costs relative to a car (see Public Transport Users Association,

2007).

44

During problem solving an individual may stop to consider whether or not

creative solution is really worth it. Their decision is likely to be dependent of

their perception of creativity as a positive or a negative and whether or not it

is a given or an option. Thus creativity can be considered as an attribute of a

problem solution. However creativity is “unnatural” for some (in that it clashes

with preferences for conformity and efficiency). In these situations creativity

may be exhibited by conforming in a novel way. Thus the decision to be

creative can be conceived of as a behavioural change and therefore

subjected to a third kind of framing effect.

Creativity and Goal Behaviour Framing

Levin et al. (1998) cite other studies that result in different framing effects

when behavioural change is considered. Goal behaviour framing relates

decisions to change and take a new action in order to achieve a goal. For

example Meyerowitz and Chaiken (Meyerowitz & Chaiken, 1987) reported

that women were more likely to undertake self breast examinations when

they were told of the risks of not doing the self examination rather than when

they were advised of the benefit. In other words people tend to take more

notice of potentially negative consequences when choosing to modify their

behaviour.

It would be expected that Goal behaviour framing and attribute framing

would operate in similar manners because they both relate to decisions

involving potential gain. However, goal behaviour framing and attribute

framing effects are at first confounding because each influences toward

apparently paradoxical decision outcomes. Both are based on the idea of

getting something. Typically attribute framing relates to getting something

45

more in a product that costs more, whereas goal behaviour framing is based

on getting a benefit at the cost of doing something new. The apparent

paradox comes from the fact that attribute framing seems to propose

positives are more important when choosing attributes, whereas goal framing

seems to propose negatives are more important when choosing whether or

not change behaviour.

The key to this paradox is to include the reference points inherent in the two

scenarios: attribute framing requires a comparison of the attribute against a

money cost. Various researchers cited above for their framing effects

research have suggested that an attribute’s perceived value is typically more

concrete in a decision maker’s mind than money. Because of this in attribute

framing situations, the attribute’s positive aspect is most important. However

this is reversed in the case of a change in behaviour – where having to do

something different and new is seen as a significant cost, so the focus

becomes on whether or not it is worth making the effort.

So a choice between options with both attribute benefits and money costs is

typically framed positively with a focus on gain because the attributes are

more significant generally than the money. A choice between options with

both attribute benefits and behavioural change is typically framed negatively

with a focus on reducing the change cost because the change is more

significant generally than the benefits. This comes about because people are

more sensitive to loss than to gain (see Kahneman D, Knetsch J L, & Thaler R H,

1990): they cannot make an adequate comparison between attributes and

money, so the largest factor gets focus. They can adequately compare

change and benefits, so the focus switches to costs.

46

This argument suggests that goal behaviour framing and attribute framing are

similar effects with different reference points. This assertion is significant to this

study because how creativity framing effects are investigated determines

whether or not they will manifest as relatively positive or negative attributes or

goal behaviours. For example if a choice involving a creative output of a

problem solving effort is proposed, creativity becomes an attribute and

attribute framing applies. However as an input to problem solving the

decision as to try to be creative is a goal behaviour framing situation.

Goal behaviour framing was ultimately determined to be outside of the

scope of this study due to the difficulty with designing methods to test

hypotheses relating to these effects and the potential for equivalence with

attribute framing depending on reference points.

Literature Review Summary

The literature review above highlights some of the complexity and wide range

of studies relating to creativity: Creativity has been considered through the

lenses of different definitions, subjective and objective assessment,

measurement approaches, cognitive style, environmental effects, mood and

framing effects. This literature review is comprehensive, but necessarily limited

in its scope to prior research that is more directly relevant to the studies to be

developed in this thesis.

Areas for Investigation

The literature above provides the basis for determining new areas of

investigation which link creative motivation, cognitive style and framing

47

effects. A more general justification for combining these areas is implied by

Latham and Pinder (2005) in their extensive review of motivation theory. These

authors assert that motivation, needs, values, traits, cognition, environment

and affect mutually interact.

Based on needs, values, and the situational context, people set goals

and strategize ways to attain them. They develop assumptions of

themselves and of their identity. This too affects their choice of goals

and strategies. (p 498)

However, motivation studies commonly differentiate process questions (e.g.

goals, expectancies and choices) from content questions (e.g. needs and

drives) (Vecchio, Hearn and Southey, 1996). Given the focus of this thesis on

creative process and choice, Latham and Pinder articulate well why a

process approach to creative motivation has been chosen over other

possible motivational constructs (e.g. needs):

…Needs based theories explain why a person must act: they do not

explain why specific actions are chosen in specific situations to obtain

specific outcomes. (p488)

The next section introduces specific rationales for each of the three studies.

Rationale for Study 1: Variable Creative Motivation during Problem

Solving

Amabile’s body of research related to creative motivation suggests that an

individual’s creative motivation is the result of a combination of extrinsic and

intrinsic motivators. As explained in the literature review above extrinsic

motivators in general diminish creative motivation even if designed to

48

encourage creativity, unless they are synergistic. Synergistic extrinsic

motivators act to increase creativity, but only in the presence of intrinsic

motivation.

Neither Amabile’s research nor any other work on creative motivation has

specifically examined how creative motivation may change during problem

solving efforts within a business context. Whilst the extrinsic and intrinsic

motivators may not change significantly during a specific instance of business

problem solving, it seems likely that the problem solver’s perception of how

successfully they are progressing is likely to impact their motivation levels.

At the start of the problem solving effort the individual does not know for sure

(but hopes) that the problem can be solved. At some later stage after

investment in the task of problem solving the individual will have progressed

to either a conclusion or a preparedness to continue problem solving. One of

the two possible conclusion outcomes is if the problem is solved (success). The

other occurs if the problem solver determines that it is not worth continuing

their efforts (failure). These two outcomes represent a perceived level of

certainty on the part of the problem solver about the success or failure of their

efforts. As well, the problem solver may determine that there is more work to

do.

That is, when problem solvers pause to review their progress, they can make a

determination about their problem solving progress. Assuming that they do

decide they are neither successful nor failed in their efforts, they may

continue problem solving. There is no reason that the problem solver should

assume that no progress or indeed that positive progress has been made.

Problem solvers can variously perceive that a solution is closer or further away.

49

This perception affects their motivation (consciously and/ or unconsciously) to

be creative in their problem solving efforts. Thus during problem solving, the

individual’s perception of outcome certainty progresses from completely

uncertain (at the start of the task) to ultimate success or failure (at the

conclusion of the task). General motivational research supports this theory.

For example Atkinson’s (1974) expectancy x value theory asserts that the

potential for success or failure is a significant factor of total motivation.

Vroom’s (Vroom, 1964) expectancy theory also suggests that motivation will

vary during problem solving: Expectancy is defined by Vroom as an

individual’s belief that a level of effort will result in the potential for success.

Given that an individual is likely to monitor progress during problem solving, it

is expected that this feedback will affect their perception of expectancy. In

other words if problem solving is progressing at least satisfactorily expectancy

(and therefore motivation) should increase. As setbacks (temporary or

otherwise) are perceived by the problem solver expectancy should

decrease, causing overall motivation to decrease as well.

Whilst expectancy theory suggests overall motivation will change during

problem solving, it does not necessarily follow that creative motivation will

similarly vary. Variable creative motivation is however indirectly supported by

a variety of researchers investigations into mood and creativity (George &

Zhou, 2007; Kaufmann, 2003; Kaufmann & Vosburg, 1997; Vosburg, 1998),

which showed that mood affects creativity in complex ways. Whilst these

researchers did not investigate creative motivation and mood (rather

creative production), Amabile’s three factor theory of creativity would

suggest that mood interacts with creative production via motivation as it is

unlikely that mood will affect stable domain knowledge and creativity skill.

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Overall these findings suggest that creativity could vary during problem

solving due to variable mood effects or more general motivation expectancy

effects. How creative motivation may vary systematically is not explained at

all by any of the above research however. In order to conceptualise this, it is

necessary to reconsider extrinsic and intrinsic motivation effects.

In particular the paper by George and Zhou cite above investigated how

supervisor support affects mood and creativity. Supervisor support is a form of

synergistic extrinsic motivation. George and Zhou findings regarding the

relationship between extreme moods (good and bad) and creativity support

the concept of mood being related to creative motivation. If an extreme

mood is indicative of high involvement of the individual trying to solve the

problem at hand it is reasonable to conclude that the problem solver is

intrinsically motivated. Supervisor support would synergistically support the

individual’s intrinsic motivation to be creative and thereby result in increased

creativity.

Supervisor behaviour and creativity has been examined in a number of

studies (Oldham & Cummings, 1996; Redmond, Mumford, & Teach, 1993;

Tierney et al., 1999). The general findings of these studies and Amabile’s

findings regarding the importance of intrinsic motivators for creative

motivation support the potential for creative motivation to be a specific

example of Deci and Ryan’s (Deci & Ryan, 1985b, 2000a, 2000b) self

determination theory.

Linking Creative Motivation and Self Determination Theory

Deci and Ryan’s work asserts two important conclusions regarding motivation

that suggest how creative motivation can vary. Firstly they suggest that in the

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presence of tangible extrinsic motivators, intrinsic motivation diminishes.

Secondly they suggest that there is a hierarchy of motivation effects

processed by individuals (Vallerand, 2000). This hierarchy of motivational

effects may equally apply to creative motivation.

If this is true then it would suggest that once an individual satisfies extrinsic

motivational requirements, intrinsic motivators may become more important.

If so individual creative motivation would be expected to potentially increase

during successful problem solving efforts as organisational imperatives to

resolve the problem situation were perceived by the problem solver to be

managed. It is likely that this simple relationship is too simple however due to

the potential impacts on creative motivation from cognitive style.

Linking Creative Motivation and Cognitive Style

Christensen-Szalanski (1978) asserted that problem solvers are influenced by

characteristics of the decision task, the decision problem itself, the decision-

making environment, and the characteristics of the decision maker. Kwang et

al. (2005) and Van Den Broeck et al.(2003) show relationships between KAI

and values. The next section suggests how the idea of variable creative

motivation due to hierarchical motivation sensitivities may be moderated by

cognitive style2

2 As argued earlier, KAI was chosen over other general measures of cognitive style

because of its face validity for business contexts and its robust empirical base.

. Kirton’s (1996) definition of cognitive style was used in

preference to other definitions of cognitive style because it relates explicitly to

innovation in organisations and it has substantive empirical validation.

52

Martinsen (1994) investigated links between cognitive style and motivation for

insight problems. Such problems require significant creativity to solve. Whilst

Martinsen did not use KAI as his scale for cognitive style (instead using

Kauffman’s (1979) theory of Assimilative and Explorative cognitive styles which

is qualitatively similar to KAI but less empirically validated) his findings suggest

that cognitive style and probability of success combine to produce creative

motivation. He also suggested that there are situations where cognitive style

and success probability can combine to produce ‘over motivation’ for

creativity. This suggests that neither Adaptors nor Innovators will be necessarily

superior in producing creative outputs.

Martinsen’s over motivation is in partial contrast to Cummings’ (1997) findings

that more paradigm breaking ideas were produced in a business context

when problem solvers were KAI Innovators. This is supported by Casbolt (1984)

who found that Adaptors were in general less creative on two tasks than

Innovators. It appears that Adaptors exhibit different creative outputs to

Innovators – Adaptors are less likely to propose radical solutions even though

they may be just as creatively skilled as Innovators.

Much of the past KAI research implies that because an individual’s cognitive

style is constant then their approach to problem solving can be expected to

be non-dynamic (though no specific research that examines this was found

during literature reviews). In practice we observe in colleagues and ourselves

varying levels of motivation during problem solving efforts, particularly in

organisational contexts. We also observe how some colleagues tend to focus

on the potential upside in a problem solving situation, whereas others seem to

be very sensitive to failure risks and consequences.

53

Introducing Creative Resolve Response

This suggests the potential of examining the relationship between creative

motivation and cognitive style. To this end, I define Creative Resolve

Response (CRR) as the pattern of variation that applies to individual creative

motivation during problem solving. It is hypothesised that CRR suggests how

creative output may change due to changes in creative motivation levels

that naturally occur during problem solving efforts.

CRR can also be conceptualised as the pattern of creative motivational

response obtained by combining various motivational modes. I develop this

idea further below and present it graphically in Table 1 below. The CRR

motivation modes I define in this table are primary, secondary, solution,

coping and emergency. Each of these is categorised in terms of creative

motivation.

I propose that in each of these mutually exclusive modes the problem solver

will be more sensitive to either extrinsic or intrinsic motivators. The CRR model

assumes that modes of motivation are variously activated during problem

solving (usually unconsciously) as a subject’s perception of certainty about

solving (or failing to solve) a problem changes.

A subject’s “primary motivation mode” may be indicative of their natural

response to organisational imperatives. CRR predicts that a problem solver is

expected to prioritise either intrinsic or extrinsic motivations initially when

54

solving a problem3

Table 1: CRR Motivation Modes

. This primary motivation mode represents an implicit

resolve about how an individual approaches and responds to the outside

world in terms of their creativity.

Mode [& Certainty] Type Sensitivity Typical Response Creative Motivation

Primary

[Uncertain]

SIM Intrinsic Break paradigm Very High

SEM Extrinsic Classify problem Very Low

Secondary

[Possible success]

SIM Extrinsic Develop innovation Moderate

SEM Intrinsic Increment changes Moderate

Solution

[Near Certain Success]

SIM Intrinsic New interests High

SEM Extrinsic Lock in gains Low

Coping

[Possible Failure]

SIM Extrinsic Forced compliance Moderate

SEM Intrinsic Forced changes Moderate

Emergency

[Near Certain Failure]

SIM Intrinsic Extreme solutions High

SEM Extrinsic Stop losses Low

In primary mode the problem solver responds more sensitively to some

motivator types, though both motivator types still affect them. Subjects with a

greater initial sensitivity to intrinsic motivators (defined herein as SIM’s) exhibit a

3 This is where CRR significantly differs from Deci and Ryan’s theory of self

determination, as their theory does not allow for the potential for different

prioritisation of intrinsic and extrinsic motivations across individuals.

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primary intrinsic motivation mode. Primary extrinsic motivation mode is

exhibited by subjects with greater initial sensitivity to extrinsic motivators

(defined herein as SEM’s). SIM’s are expected to be more motivated to be

creative when operating in primary motivation mode because of their

reduced sensitivity to extrinsic motivators.

Creative Motivation with Increasing Success Certainty

Figure 1 further elaborates the CRR model. It suggests that when primary

motivations are satisfied, the opposite type of motivation (extrinsic or intrinsic)

emerges as more important. The problem solver then acts in “secondary

motivation mode”. In this mode the problem solver becomes more sensitive

to motivators that are the opposite of their primary type: SIM’s become more

sensitive to extrinsic motivators and SEM’s become more sensitive to intrinsic

motivators. This prompts SIM’s creative motivation to decrease and SEM’s

creative motivation to increase. Both problem solver types advance to their

final motivation mode when they have satisfied both intrinsic and extrinsic

motivators relating to the problem at hand.

This occurs as problem solving continues to progress towards near certain

success. At this point both intrinsic and extrinsic motivations are essentially

managed, so the problem solver switches to “solution motivation mode”.

Solution mode is similar to primary mode for both SIM’s and SEM’s, only less

extreme in terms of overall motivator sensitivity. At high levels of success

certainty SIM’s declare the problem solved and move onto more interesting

issues to satisfy intrinsic motivators. In contrast, at the same levels of success

certainty SEM’s refuse to “push their luck further” and proceed to lock in the

gains achieved from prior problem solving efforts.

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Figure One: Creative Motivation and Increasing Success

Creative Motivation with Increasing Failure Certainty

Now consider a scenario where a subject’s problem solving efforts appear to

fail soon after commencement on a particular task. In this situation the

subject must respond to their failure and their implicit creative resolve may

falter. The problem solver changes from primary mode to “coping motivation

mode” to deal with the impact on their self image. In coping mode, primary

motivations are subjugated in order to achieve some kind of success: SEM’s

are forced to respond to their failure so far by making some changes. So even

though they don’t want to be more creative, they act more creatively and

appear more motivated to diverge. Whilst this has the potential to satisfy their

intrinsic motivations, it may not satisfy their primary (extrinsic) motivations.

SIM’s are similarly forced to respond to their failure so far: they exhibit more

compliant approaches to solving the problem because their convention

breaking approaches have backfired. Similar to SEM’s above, this has the

57

potential to satisfy the SIM’s extrinsic motivations, but it may not satisfy their

primary (intrinsic) motivations.

If the problem solver continues to perceive increasing failure certainty in

coping mode, they escalate to an “emergency motivation mode”. In this

state increased sensitivity to unsatisfied primary motivations becomes all

consuming. In attempting to deal with unsatisfied intrinsic motivations, SIM’s

act as if “there is nothing left to lose” and propose extreme creative solutions

(thus they exhibit high creative motivation). SEM’s focus becomes on dealing

with extrinsic failure motivation by trying to avoid or minimise consequences –

“stopping further loss”. SEM’s may seem to essentially give up on solving the

problem (thus they exhibit low creative motivation). The CRR pattern for both

SIM’s and SEM’s with increasing certainty of failure is shown in figure two below.

Figure Two: Creative Motivation and Increasing Failure

These two predictions about varying creative motivation combine to form the

complete CRR pattern of variable motivation.

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There is no scale for determining SIM’s and SEM’s, nor any test proposed by this

study. However, I propose that these notional conceptual problem solving

types can be theoretically related to Kirton’s KAI (1976). In general it can be

hypothesized that KAI scores can be related to factors that may affect

creative problem solving progress. For example, KAI scores have been found

to positively correlate with self esteem (Goldsmith & Matherly, 1987b, 1987c;

Houtz et al., 1980; Keller & Holland, 1978). According to Shukla & Sinha (1993)

self esteem is a pre requisite for creativity. This requirement is likely to be even

more relevant in a business context where extrinsic motivation factors tend to

inhibit creativity. Individuals with low self worth and sense of importance are

unlikely to be creative when the organisation climate restricts creativity and

rewards other behaviours. Equally, individuals with a high self worth and sense

of importance are likely to act in accordance with their intrinsic motivations,

including those that motivate creativity. So the correlation found between KAI

orientation and self esteem suggests that Innovators are more likely to be

motivated to be creative in business contexts. There are other findings to

support increased creative motivation from Kirton’s Innovators.

KAI scores have been found to positively correlate with tolerance for

ambiguity (Keller & Holland, 1978) and locus of control (Engle et al., 1997;

Houtz et al., 1980; Keller & Holland, 1978; Luck, 2004; Tetenbaum & Houtz,

1978). Wunderley L J et al. (1998) also found a correlation between optimism/

pessimism and Innovation/ Adaption respectively. Again these studies would

suggest that in business contexts, Innovators are more likely to be motivated

to be creative because of their increased tolerance for ambiguity, internal

locus of control and optimism.

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Some studies, (Foxall, 1986; Foxall & Bhate, 1999; Foxall & Hackett, 1994; Foxall

& Szmigin, 1999) have failed to find significant correlations between KAI and

functional management preferences. It could be that these results were

overwhelmed by the influences of other more significant factors like domain

specific knowledge or that both Adaptor and Innovator problem solvers can

produce equivalent results in solving the specific problems in the situations

studied. Equally it may also be that motivation effects such as those

postulated by the CRR model were significant. This possibility is now

elaborated.

Adaptor Creative Resolve Response

Adaptor problem solvers are characterised by preferences for efficiency and

conformity (1978a; Kirton, 1980; 1984a; 1988). Thus they focus on creative

solutions that offer appropriateness over novelty. They exhibit relatively lower

self esteem, pessimism and a tendency towards external locus of control.

Adaptors and SEM’s self evidently would seem to have the same primary

motivation mode: sensitivity to extrinsic motivators.

If the Adaptor perceives that early progress toward a solution occurs during

problem solving, it is asserted that their extrinsic motivation to achieve results is

diminished relative to their intrinsic motivation to develop novelty. To some

extent their early success overcomes their natural pessimism. It is also asserted

that the Adaptor’s extrinsic motivations to complete the problem solving task

and conserve scarce resources remain constant. Hence there is a net

incremental increase in creative motivation (a move toward response),

though this increase is expected to be slight.

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Assuming that the Adaptor perceives that their good progress continues and

a successful solution becomes more likely, it is asserted that their extrinsic

motivation to complete the problem solving exercise resumes dominance

and the Adaptor’s natural pessimism resumes. This can be conceptualised as

the Adaptor believing that continuing to “push their luck” is not worth the

“risk” of continuing to be creative when a successful result is so close. The net

effect is a decrease in the Adaptor’s creative motivation. In effect the

Adaptor wants to lock in the gains they now see as highly likely and do not

value any further investment in novelty.

Alternatively, if an early setback during the problem solving task is perceived

by the Adaptor, then their extrinsic motivators to conformity and diligence

become dominant. The Adaptor’s anxiety to resolve the situation increases.

This forces the Adaptor to try and develop more novel approaches to

manage the problem because of their increasing pessimism. Thus creativity

increases as a move away response like a kind of last resort. So creativity

increases slightly as a result of pessimism! If setbacks are perceived to

continue, the Adaptor’s extrinsic motivation considerations related to

resource scarcity and their external locus of control influence them to

terminate problem solving efforts. The Adaptor’s pessimism becomes a self

fulfilling prophecy requiring that the Adaptor to prevent further failure. In

effect they reduce their creativity to prevent further losses by implementing a

“stop loss” position.

Thus the pattern of Adaptor creative motivation has parallels in theory with SEM

CRR described above. The Adaptor’s creative motivation at any moment

during problem solving is determined by the interplay of extrinsic and intrinsic

motivators that change as the certainty of outcome becomes more or less

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likely. The success or failure of the outcome is not important, only the change

in perceived certainty. For the Adaptor creative motivation is expected to be

at its lowest during both maximum outcome uncertainty and maximum

outcome certainty. The Adaptor’s creative motivation (whilst still relatively low

compared to the Innovator) is expected to be at a maximum when outcome

uncertainty is neither zero nor maximum. Innovators exhibit a characteristically

different response even though they can be subject to the same extrinsic

motivating factors in similar problem solving situations.

Innovator Creative Resolve Response

Innovator problem solvers are characterised by preferences for originality

(novelty) and non-conformity. They exhibit relatively higher self esteem,

optimism and a tendency towards internal locus of control. Thus Innovators

focus on creative solutions that offer novelty over appropriateness. Innovators

and SIM’s self evidently would seem to have the same primary motivation

mode: sensitivity to intrinsic motivators.

If early progress toward a solution is perceived by the Innovator, it is asserted

that their intrinsic motivation to be original is somewhat sated by their early

success and creative motivation decreases slightly. The extrinsic motivation to

achieve results becomes a focus especially as the success in organisational

context often brings resources to continue being creative. Recall that

Amabile (1997) identified continuing resource supply and recognition for

creative efforts as synergistic extrinsic motivators. Successful problem solvers in

organisations are often told to “keep doing what you are doing”, thus gaining

creative rights and escaping the accountability oversight which acts as an

extrinsic de-motivator for creativity.

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In this case, the potential for increased resources, recognition, increased

creative rights and decreased monitoring motivate the Innovator to conform

by completing the problem solving task. The easiest way for the Innovator to

do this is to use their (so far) successful creative approach to the problem and

proceed without additional novelty. Hence there is a net incremental

decrease in creative motivation (a move toward response), and this

decrease may be large if the Innovator is sufficiently forward thinking.

Assuming that the Innovator perceives that their good progress continues and

a successful solution becomes even more likely, then their extrinsic motivation

to complete the problem solving exercise wanes. The Innovator is not

characterised by diligence or efficiency and optimistically assumes that the

problem is essentially solved. The Innovator rationalises that as success is

already certain, at least some of the impending rewards of success can be

“spent now”. Thus the Innovator’s intrinsic motivation for originality again

dominates and creative motivation increases even though it is no longer

required to solve to problem. In effect the Innovator “gets carried away

experimenting” with some more interesting and original non-conforming

solutions. The Innovator’s high self worth and sense of importance validate this

freedom to experiment after having “effectively” solved the problem even

though the result may not be completely achieved yet.

Alternatively, if an early setback during the problem solving task is perceived

by the Innovator, then their extrinsic motivators to conformity and diligence

become dominant. In extreme cases the Innovator is subjected to oversight

and controls that reduce their autonomy and available resources to be

creative. Even the threat of such interventions will have the potential to de-

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motivate the Innovator from continuing to be creative, as asserted by Kubes

(1992).

Thus after a perceived initial partial setback the Innovator experiences real or

imagined external pressure to stop experimenting and make measurable

progress towards a solution. This perceived pressure forces the Innovator to

conform to the dominant paradigm for managing the problem and their

creativity decreases to allow the Innovator to retain some of their autonomy.

In a sense the Innovator is forced to “get with the program”.

If setbacks are perceived to continue after this conformance, then the

Innovator’s intrinsic motivation for originality and their internal locus of control

influence them to resume novel problem solving efforts. Their internal locus of

control and optimism causes them to assume (rightly or wrongly) that they will

be able to solve the problem eventually. The Innovator in extreme cases may

go into an “emergency mode” to retain their creative autonomy. In effect

they perceive that they have nothing to lose and everything to gain by trying

everything they can think of to solve the problem. The result is that their

motivation to be creative increases by a large amount.

Thus the pattern of Innovator creative motivation is similar to that proposed as

SIM’s CRR detailed above. Additionally Innovator creative motivation is

determined by the same interplay of extrinsic and intrinsic motivators as the

Adaptor, but the Innovator’s CRR is expected to be the mirror image of the

Adaptor’s. The Innovator’s creative motivation is at its highest during both

maximum outcome uncertainty and maximum outcome certainty. The

Innovator’s creative motivation is at its lowest when outcome uncertainty is

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neither zero nor maximum. SIM’s and SEM’s from the CRR model seem to extend

understanding of Kirton’s Innovator and Adaptor cognitive styles4

KAI theory suggests that the Adaptors will have a lower creative motivation

initially than an Innovator for any problem due to differences in preferences.

CRR suggests the same prediction, but goes further suggesting that an

individual’s creative motivation varies during a specific problem solving task

based on the perception of progress (or lack thereof) where progress relates

to increasing outcome certainty. Figure 3 above combines all of these

theorised changes in creative motivation during problem solving into a single

diagram.

.

4 It is important to note that neither CRR nor KAI purport to measure creative ability, merely the

preferred style or pattern of creativity. Using KAI four groups of problem solvers can be

conceptualised: relatively creative Innovators, relatively non-creative Innovators, relatively

creative Adaptors and relatively non-creative Adaptors. These extremes relate to creative

output, not to creative motivation. Analogous groups can be conceptualised for CRR.

65

Expected Outcome

Creative Motivation

InnovatorAdaptor

100% Bad100% Good 50% Bad50% Good Uncertain

Freedom to Create

Consolidate Gains

Disrupt for Advantage

Nothing to Lose

Constrained by Firm

Lock in Gain

Consider small changes

Analyse to simplify

Forced to change

Stop further loss

Figure Three: Creative Resolve Response and KAI

In summary, an individual’s preference for the Innovator or Adaptor cognitive

style is considered to be robust and consistent over time, in contrast with task

motivation which can change significantly and frequently. CRR suggests that

primary motivation mode is constant over time but includes variations in

creative motivation based on task progress. It seems reasonable therefore to

conclude that there is some meta model capable of relating cognitive style,

creative motivation and task progress. CRR is such a model. The first of three

studies in this research examined whether or not the proposed pattern of

motivation was able to be validated. The next sections discuss the rationale

for Study 2 and Study 3.

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Rationale for Study 2: Framing Effects and Operationalised

Creativity

As the literature review demonstrated, the operation of framing effects in

organisations is well documented. However, the body of research related to

framing effects has not examined whether or not specific framing effects

apply differently to decisions involving creativity. Given the link between

motivation and creativity, also discussed in the literature review, it seems

reasonable to consider that framing effects may be influential in creative

contexts.

For example, one way that framing effects and creativity might interact is

that each of the various operationalised creativity options could invoke

framing effects. This would mean that framing may naturally apply when an

individual considers solving a problem creatively even if the decision is not

presented in a way that normally invokes framing effects. Consider a problem

solver who has developed two potential solution options to resolve a business

issue. Assume that one option is perceived as relatively non-creative and the

alternative includes some kind of operationalised creativity. The problem

solver must compare the two options and decide which to try and

implement.

If the operationalised creativity option in question includes fluency, the

problem solver may perceive impacts on risk considerations. Thus risk based

framing effects may automatically affect the decision being made even

though it was presented in a form that would not normally be associated with

risk based framing. Alternatively the operationalised creativity option

considered in the decision may include originality or novelty characteristics.

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Such characteristics clearly have attribute biases (potentially positive and

negative) that advertisers seek to exploit with slogans like “new and

improved” and “established for more than 100 years”. Again attribute based

framing may automatically apply to decisions involving operationalised

creativity options. Even if operationalised creativity does not automatically

invoke framing effects, it may moderate how they apply to decisions.

The literature relating to framing effects implies that they are universally

applicable across two dimensions. The first universal applicability of framing

effects relates to domain: framing effects are conceptualised within the

literature as being non-specific to any set of domains. That is framing effects

apply in the same way in contexts ranging from investment decisions to

health choices to purchase evaluations to determinations about the best

partner (see the examples in Levin et al. (1998) for the range of different

domains that framing effects have been tested in). It may be that framing

effects apply differently to decisions involving operationalised creativity

because motivation applies differently to creativity. The effects of

operationalised creativity options cited in the previous paragraph could also

apply to decisions that were presented with risk or attribute framing effects

included. In these situations operationalised creativity could enhance or

diminish the primary framing effect presented in the decision regardless of

whether it was risk or attribute based.

In summary, Study 2 therefore investigates some of the above questions. It

considers whether or not framing effects are universally applicable to

decisions involving operationalised creativity. The research includes an

investigation into the potential for operationalised creativity options to invoke

or distort risk based and attribute framing. Study 3 relates to how framing

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effects may apply differently for individuals when making decisions involving

operationalised creativity options.

Rationale for Study 3: Framing Effects and Cognitive Style:

Framing effects are usually conceptualised within the literature as being

uncorrelated with personality, cognitive style, age, ethnicity or gender.

However, it is conceivable through simple thought experiments to hypothesise

how personality, for example, could moderate framing effects. Extroverted

and introverted individuals are affected likely to by goal behaviour framing

effects differently when making choices about the value of active listening in

a sales training environment. More technical views of personality imply

framing effects variability to a greater extent.

Jung’s Judging types and Perceiving types (Jung, 1926) exhibit different

preferences for decisiveness (Judging types tend to feel anxiety if they

hesitate and risk losing an opportunity, whereas Perceiving types feel anxiety

if they act prematurely and lock themselves in to an otherwise avoidable

failure). Perhaps the difference in orientation to action is a manifestation of

different sensitivity to risk based framing effects. Jung’s Thinking and Feeling

types (Jung, 1926) are by definition likely to respond to attribute framing

effects differently. Consider two managers of different personality types in this

dimension trying to decide about an initiative that involves increasing

profitability by retrenching staff. The Thinking type manager would be

expected to be far more influenced by profitability concerns because that

personality type is characterised as making decisions by utilising objective,

impersonal criteria. The Feeling type manager would be expected to take

greater consideration of the effects of retrenching workers because that

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personality type is characterised as making decisions based on how the

decisions affect others or using more subjective criteria. Whilst personality,

age, ethnicity and gender may all moderate framing effects, of particular

relevance to this research is how cognitive style might moderate decisions

involving operationalised creativity and framing effects.

One area of cognitive style that has not been investigated is how Adaptors

and Innovators respond to framing effects. Kirton’s investigations into

cognitive style specifically relate to individual problem solving and decision

making approaches within an organisational context. Clearly organisations

can create very different frames within which to view operationalised

creativity. Choosing to try and implement the more creative option within a

business context does not guarantee success and typically costs more to

attempt than a more traditional approach. As a result problem solvers may

be pressured to “get on with it” rather than opt for more radical (creative)

solutions. Thus problem solvers in business contexts can be constrained by

extrinsic performance requirements to minimise costs and maximise certainty,

which in turn reduce creativity.

For many business problems, compliance with the paradigm that proposed

the problem is easier if the individual stops wasting time and effort to

implement insight solutions and instead gets on with resolving the problem in

the simplest, most direct way. Operationalised creativity in this context is an

uncertain, negative thing to be avoided. Individual problem solvers that

comply with this paradigm exhibit low originally, high conformance and high

perceived efficiency in the choices they make about which options to

implement. These characteristics are associated with low KAI scores – i.e.

Adaptors.

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However some problem solvers in organisation contexts are affirmed by

choosing to break both the formal and informal business rules and either insist

or persist in looking for insight solutions to typical business problems. In other

organisations employees are supported when they spend time looking for

and choosing to implement operationalised creativity solutions. Such problem

solvers exhibit higher originality, lower conformance and lower perceived

efficiency in their decisions relating to which potential solution options to

implement. Creativity in this context is an uncertain, positive thing to be

supported (this is similar to choosing to buy a lottery ticket: the cost is low and

the gains whilst unlikely are potentially very high). Such characteristics are

associated with high KAI scores - Innovators.

Thus regardless of the environment or the problem solver’s cognitive style it is

possible that being creative while problem solving can be framed under

uncertainty as either something to avoid (negative) or something to cultivate

(positive). It seems reasonable to hypothesize that in situations where

creativity is framed under uncertainty, Innovators will be more predisposed to

be creative than Adaptors and thus framing effects will apply differently to

individuals with different cognitive styles. This is a similar hypothesis to the

expected non-universality of framing effects across domains: operationalised

creativity and/ or cognitive style may automatically invoke and/ or moderate

different risk based framing effects depending on whether or decisions are

presented with or without inherent framing effects. The same considerations

could apply to decisions involving (or with the potential to involve) attribute

framing effects.

As presented previously in the example relating to choices about public

transport, creative solutions to problems can be seen as positive attributes or

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negative attributes. Innovators are likely to consider creativity as a positive

attribute due to their preference for originality and non-conformance.

Adaptors are likely to consider creativity as a negative, especially when they

perceive that being creative reduces efficiency. Thus it seems likely that

cognitive style may play a significant part in the attribute framing of creativity.

In summary, from the previous sections it is apparent that Innovators and

Adaptors may respond significantly differently to different framing effects.

Compared to Adaptors, Innovators may be less concerned with risk, tend to

value desirable attributes more and respond to beneficial outcomes

communications more readily in some contexts (where originality and non-

conformance are involved) and less in others (where conformity and

efficiency are involved). Decisions involving creativity may be automatically

framed differently by individuals with different cognitive styles even when

framing effects might not normally be present. Finally for decisions involving

both operationalised creativity and framing effects, individual cognitive style

may moderate the framing effects present. Study 3 seeks to improve the

understanding of how cognitive style, operationalised creative and framing

effects interact in individual decision making.

By way of conclusion Figure Four overviews how the three studies combine in

pursuit of a better understanding of how to enhance business creativity.

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Figure Four: Theoretical Overview

Whilst business creativity is not necessarily produced as a linear process, the

following description steps through the models linearly for ease of

comprehension. When there is an opportunity for creativity in business, a

problem solver’s intrinsic motivation and extrinsic motivation combine to

produce creative motivation. Intrinsic motivators are creativity enhancing

and extrinsic motivators are generally creativity diminishing, except for

synergistic extrinsic motivators (which in the presence of intrinsic motivation

are additionally creativity enhancing). The amount of creativity is proportional

to the creative motivation, relevant domain knowledge and creativity skill of

the problem solver. The problem solver’s efforts manifest to produce creative

outputs. These outputs can be measured in terms of the degree of fluency,

flexibility, divergence and rule breaking. Conveniently these measures can be

conceptualised as non-mutually exclusive types of creativity. The amount of

each type produced is moderated by the problem solver’s cognitive style.

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Cognitive style also impacts on the decision that the problem solver makes

about which potential problem solving solutions are preferred for

implementation. This decision is further moderated by framing effects related

to both the way that the decision is presented and the kinds of creativity

involved.

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Chapter 1: Creative Resolve Response; How Changes in

Creative Motivation Relate to Cognitive Style

Purpose – This paper introduces a new phenomena related to creative

motivation called Creative Resolve Response (CRR). CRR predicts how

creative motivation will vary during problem solving.

Design/methodology/approach –66 MBA students were asked to respond at

random intervals during different class problem solving activities. Participants

were asked to rate on two preset scales their perceived certainty of solving

the problem successfully and creativity level required. Mean creativity

required responses were calculated for subgroups with different cognitive

style ranges at each outcome certainty level. T-tests were used to determine

significant differences between various means.

Findings – The results suggest that creative motivation will vary systematically

as a problem solver’s perception of problem solving progress increases in a

wax-wane-wax pattern.

Research limitations/implications – Post hoc analysis suggested that

potentially confounding effects related to problem heterogeneity, learning

effects, environment, group interaction and interviewer response bias were

not significant. However the relatively small sample size and limited scope of

the problem activities suggests that further research is required to establish the

extent that the findings can be generalised.

Practical implications – CRR promises a new form of extrinsic control for

managers to enhance creativity via extrinsic motivation. The author makes

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suggestions on how managers may enhance creativity by influencing

employees to reconsider their perceived level of problem solving progress.

Originality/value – This paper links expectancy theory, cognitive style and

creative motivation and provides an alternative approach to directly trying to

motivate employees to be more creative.

Keywords Creativity, Motivation, Cognitive style, Management, Group work

Paper Type Research paper

Introduction

Enhancing organisational creativity to foster greater innovation is a challenge

for contemporary managers. This study investigates how creative motivation

waxes and wanes during problem solving tasks as a function of perceived

outcome certainty. Understanding how creative motivation varies during

problem solving is useful for managing creativity. If creative motivation varies

systematically and predictably during problem solving, then managers can

affect creative motivation (and therefore creativity) by influencing

employees’ perceptions of their problem solving progress. This indirect

approach would help to overcome the limitations of traditional management

interventions which fail to enhance creative motivation because they act as

extrinsic motivators.

Generally, creativity during organisational problem solving is enhanced by

intrinsic motivational factors, though some extrinsic factors (so called

synergistic extrinsic motivators) may also assist (Amabile, 1997a; Hennessey &

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Amabile, 1998; Polland, 1994; Ruscio et al., 1998). Synergistic extrinsic factors

include recognition and continued resources to support creative endeavours.

These factors are generally only effective in enhancing creativity in individuals

where intrinsic creative motivation is also prevalent. Other extrinsic factors (i.e.

those that are not synergistic, including money rewards) tend to inhibit

creativity by decreasing creative motivation. How an individual responds to

extrinsic factors may be explained by their cognitive style.

Kirton (1976) classifies the range of individual problem solver cognitive

styles in an organisational context in the Kirton Adaption–Innovation Inventory

(KAI). The KAI scale provides a reliable and consistent measure of cognitive

style that has been empirically validated by many subsequent researchers

(including Fleenor and Taylor, 1994; Foxall and Hackett, 1992; Goldsmith and

Matherly, 1987a; Keller and Holland, 1978; Riley, 1993; Taylor, 1989). KAI is

comprised of three independent problem solving constructs: originality is the

preference for generating many novel, unusual or unorthodox ideas;

efficiency is the preference for detailed, appropriate and orderly behaviour;

and conformity is the tendency to conform to prevailing rules or group norms.

The scale allows an individual to be classified as an Innovator or Adaptor from

their responses to 32 questions. A KAI survey produces a KAI score ranging

from 32–160 with a mean of 96 and standard deviation of 13. A KAI score

increases with an Innovation preference and decreases with an Adaption

preference.

Despite the body of evidence that KAI is stable over time, there are

concerns about the validity of cognitive style as a predictive construct and

about which dimensions of cognitive should be preferred. Kozhevnikov

explains this as follows:

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…At the present time, many cognitive scientists would agree

that research on cognitive styles has reached an impasse. In

their view, although individual differences in cognitive

functioning do exist, their effects are often overwhelmed by

other factors, such as general abilities and cognitive constraints

that all human minds have in common. The paradox of the

current situation is that interest in building a coherent theory of

cognitive styles remains at a low level among researchers in the

cognitive sciences; however, investigators in numerous applied

fields have found that cognitive style can be a better predictor

of an individual’s success in a particular situation than general

intelligence or situational factors. In the field of industrial and

organizational psychology, cognitive style is considered a

fundamental factor determining both individual and

organizational behaviour (e.g. Streufert & Nogami, 1989; Sadler-

Smith & Badger, 1998; Talbot, 1989) and a critical variable in

personnel selection, internal communications, career guidance,

counselling, and conflict management (Hayes & Allinson, 1994).

In the field of education, researchers have argued that

cognitive styles have predictive power for academic

achievement beyond general abilities (e.g. Sternberg & Zhang,

2001). (Kozhevnikov, 2007 p.464)

…In summary, the most significant contribution of applied

studies was the expansion of the cognitive style concept to

include constructs that operate in relation to complex cognitive

activities. As a consequence, one distinguishing characteristic of

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these studies is the use of self-report questionnaires as a method

of style assessment, reflecting a new tendency in cognitive style

research to study conscious preferences in organizing and

processing information. Another significant contribution of these

studies is the examination of external factors that affect the

formation of an individual’s style. The studies converged on the

conclusion that cognitive styles, although relatively stable, are

malleable, can be adapted to changing environmental and

situational demands, and can be modified by life experiences.

The main problem with these studies is the same as I discussed

earlier—the explosion of style dimensions: The number of styles

was defined by the number of applied fields in which styles were

studied. As a consequence, the cognitive style construct

multiplied to include decision-making styles, learning styles, and

personal styles, without clear definitions of what they were or

how they differed from the “basic” cognitive styles identified

previously. The set of theoretical questions regarding the

mechanisms of cognitive styles, their origins, and their relation to

other psychological constructs remained open. (page 470)

Much of the past Kirton Adaption–Innovation (KAI) research implies that

because an individual’s cognitive style is constant then their approach to

problem solving can be expected to be consistent. However Vosburg (1998)

and Kaufmann (2003) provide evidence that consistency in an individual’s

problem solving approach is not normal. For example, mood can affect

creativity in complex ways. One reason that creative motivation (and

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therefore creative output) is likely to change during a problem solving task is

that the problem solver perceives that the likelihood of problem solving

success or failure changes during the task. At the start of the problem solving

effort, the individual does not know for sure (but hopes) that the problem can

be solved. At some later stage, after there has been an investment of time

and effort in the task of problem solving, the individual will have progressed to

either a resolution or a preparedness to continue problem solving. It seems

reasonable to expect that such progress evaluations also affect creative

motivation.

Martinsen (1994) investigated links between cognitive style and problem

solving motivation. Martinsen’s findings suggest that cognitive style and

probability of success combine to produce motivation. Whilst Martinsen did

not use KAI as his scale for cognitive style (instead using Kauffman’s (1979)

theory of Assimilative and Explorative cognitive styles), his work is relevant to

KAI-based studies because Kauffman’s and Kirton’s definitions of cognitive

style are very similar.

Martinsen (1994) also suggested that there are situations where cognitive

style and success probability can combine to produce ‘over motivation’ for

creativity. This over motivation results in a reduction of creative output and

suggests that in this situation neither cognitive style will be superior in

producing creative outputs. This is in partial contrast to Cummings’ (1997)

findings that more paradigm-breaking ideas were produced in a business

context when problem solvers were KAI Innovators. Adaptors exhibit different

creative outputs to Innovators. For example, they are less likely to propose

radical solutions, even though they may be as creative as Innovators.

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Other researchers have investigated other aspects of cognitive style that

relate indirectly to creativity. For example, KAI scores have been found to

positively correlate with self esteem (Goldsmith and Matherly, 1987b; Houtz et

al., 1980; Keller and Holland, 1978). In these studies, self esteem referred to the

individual’s sense of importance or self worth. According to Shukla and Sinha

(Shukla and Sinha, 1993), self esteem is a prerequisite for creativity. This

requirement is likely to be even more relevant in a business context where

extrinsic motivation factors tend to inhibit creativity. Individuals with low self

worth and sense of importance are unlikely to be creative when the

organisation climate restricts creativity and rewards other behaviours.

Alternatively, individuals with a high self worth and sense of importance are

likely to act in accordance with their intrinsic motivations, including those that

motivate creativity. So the correlations found between KAI orientation and

self esteem suggest that Innovators are more likely to be motivated to be

creative in business contexts, supporting Cummings’ (Cummings, 1997)

findings described above.

There are other findings to support increased creative motivation from

Kirton’s Innovators. KAI scores have been found to positively correlate with

tolerance for ambiguity (Keller and Holland, 1978) and locus of control (Engle

et al., 1997; Houtz et al., 1980; Keller and Holland, 1978; Luck, 2004; Tetenbaum

and Houtz, 1978). Wunderley et al., (Wunderley et al., 1998) also found a

correlation between optimism/pessimism and Innovation/Adaption

respectively. These studies are support for the proposition that in business

contexts, Innovators are more likely to be motivated to be creative because

of their increased tolerance for ambiguity, their internal locus of control, and

tendency for optimism. In contrast to the above studies’ confirmation of the

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correlations between KAI score and personal characteristics, other studies

(Foxall and Bhate, 1999; Foxall and Hackett, 1994; Foxall and Szmigin, 1999)

have failed to find significant correlations between KAI and functional

management preferences. This lack of correlation is initially confounding

when cognitive style could reasonably be expected to have a significant

influence in these studies. It could be that these results were overwhelmed by

the influences of other more significant factors such as domain specific

knowledge or other circumstances whereby the specific problem enabled

both the Adaptor and Innovator to produce equivalent problem solving

results.

The Creative Resolve Response (CRR) model

This paper proposes an alternative explanation to these conflicting findings

called Creative Resolve Response (CRR). In this model, Adaptors and

Innovators change their creative output during problem solving due to

changing levels of creative motivation. At least to some extent this allows

both styles of problem solver to tackle the same kinds of problems, though

they would be expected to approach the problems in systematically different

ways. Such a conclusion is supported by Vosburg (Vosburg, 1998).

Vosburg empirically validated that mood affects creativity in a more

complex manner that previously accepted. This finding is important to the

CRR model as mood effects may be correlated to motivation changes during

problem solving. Specifically, positive mood does not unconditionally

facilitate creative problem solving and negative mood does not

unconditionally hinder creative problem solving. Vosburg found that under

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certain conditions negative mood can facilitate, and positive mood can

inhibit creative problem solving. Subsequently, Amabile (Amabile et al., 2005)

has asserted that positive mood assists with creativity in business contexts,

despite the apparent conflicts with Vosburg (Vosburg, 1998) and also

Kaufmann and Vosburg (Kaufmann and Vosburg, 1997). The difference

between Vosburg’s and Amabile et al.’s findings appears to be related to the

level of analysis: Vosburg was testing specific (in specific problem solving

contexts), whereas Amabile’s teams were looking for longer timescale

correlations. The CRR model is more comparable to Vosburg’s work due to

the shorter reference timescale involved (i.e. CRR describes creative

motivation within a single problem solving context rather than over a longer

period in an organisational setting).

Specifically the CRR model is based on the assumption that high self

esteem (defined as high self worth and sense of importance), and internal

locus of control and optimism enable more sensitivity to intrinsic motivation (as

is the case for Innovators). The corollary is that low self esteem, external locus

of control and pessimism would suggest more sensitivity to extrinsic motivation

(as is the case for Adaptors). The model incorporates expectations of how

Adaptors’ and Innovators’ sensitivity to intrinsic and extrinsic motivational

factors can change with their perception of outcome certainty, which then

subsequently changes their overall creative motivation.

Adaptor CRR

Adaptor problem solvers are characterised by preferences for efficiency and

conformity. They exhibit relatively lower self esteem, pessimism and a

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tendency towards external locus of control. Thus Adaptors focus on creative

solutions that offer appropriateness over novelty.

In order to understand the Adaptor’s CRR, consider the following

hypothetical problem solving attempt. Upon commencement of a problem

solving task in which the Adaptor holds an unbiased view, the individual can

be assumed to be completely uncertain about the potential for a good or

bad result. Whilst this view may change quickly, both the CRR model and KAI

theory suggest that the Adaptor will have lower creative motivation initially

than an Innovator for solving the problem. In Figure 1, this corresponds to the

points for each line at 0% expected outcome certainty.

If the Adaptor perceives that early progress towards a solution occurs, it is

asserted that their extrinsic motivation to achieve results is diminished relative

to their intrinsic motivation to develop novelty (which remains low relative to

the Innovator). To some extent their early success overcomes their natural

pessimism. It is also asserted that the Adaptor’s extrinsic motivations to

complete the problem solving task and conserve scarce resources remain

constant. Hence there is a net incremental increase in creative motivation (a

move towards potential gains response), though this increase is expected to

be slight. In Figure 1, this corresponds to the point for the solid line at 50%

expected outcome certainty on the left side of the graph.

Assuming that the Adaptor perceives that their good progress continues

and a successful solution becomes more likely, it is asserted that their extrinsic

motivation to complete the problem solving exercise resumes dominance

and the Adaptor’s natural pessimism resumes. This can be conceptualised as

the Adaptor believing that continuing to ‘push their luck’ is not worth the ‘risk’

of continuing to be creative when a successful result is so close. The net effect

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is a decrease in the Adaptor’s creative motivation. In effect the Adaptor

wants to lock in the gains they now see as highly likely and do not value any

further investment in novelty. In Figure 1, this corresponds to the point for the

solid line at 100% expected outcome certainty on the left side of the graph.

Alternatively, if the Adaptor perceives an early setback during the

problem solving task, then their extrinsic motivators towards conformity and

diligence become dominant. The Adaptor’s anxiety to resolve the situation

increases. This forces the Adaptor to try and develop more novel approaches

to manage the problem because of their increasing pessimism. Thus creativity

increases as a move away response – a kind of last resort. So creativity

temporarily increases as a result of pessimism. In Figure 1, this corresponds to

the point for the solid line at 50% expected outcome certainty on the right

side of the graph.

If setbacks are perceived to continue, the Adaptor’s extrinsic motivation

considerations, related to resource scarcity and their external locus of control,

influence them to terminate problem solving efforts. The Adaptor’s pessimism

becomes a self-fulfilling prophecy, requiring the Adaptor to prevent further

failure. In effect they reduce their creativity to prevent further losses by

implementing a ‘stop loss’ position. In Figure 1, this corresponds to the point

for the solid line at 100% outcome certainty on the right side of the graph.

Thus the pattern of Adaptor creative motivation (their CRR) is determined

by the interplay of extrinsic and intrinsic motivators that change as the

certainty of an outcome becomes more likely. The success or failure of the

outcome is not important, only the change in perceived certainty. For the

Adaptor, creative motivation is at its lowest during both maximum outcome

uncertainty and certainty. The Adaptor’s creative motivation (whilst still

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relatively low compared to the Innovator) is at a maximum when outcome

uncertainty is neither zero nor maximum. Innovators exhibit a characteristically

different response, even though they are subject to the same extrinsic

motivating factors.

Innovator CRR

Innovator problem solvers are characterised by preferences for originality

(novelty) and non-conformity. They exhibit relatively higher self esteem,

optimism and a tendency towards internal locus of control. Thus Innovators

focus on creative solutions that offer novelty over appropriateness.

In order to understand an Innovator’s CRR, consider the same

hypothetical problem solving attempt as described above for the Adaptor.

As with the Adaptor, the Innovator is assumed to commence the problem

solving task with an unbiased view of the potential outcome and is therefore

completely uncertain about the potential for a good or bad result. In this

case, the CRR model and KAI theory both suggest that the Innovator will have

a higher creative motivation than an Adaptor for solving the problem.

If early progress towards a solution is perceived by the Innovator, it is

asserted that their intrinsic motivation to be original is somewhat sated by their

early success and creative motivation slightly decreases. The extrinsic

motivation to achieve results becomes a focus, especially as success in an

organisational context often brings additional resources to enable the

problem solver to continue being creative on other projects. Recall that

Amabile (Amabile, 1997a) identified continuing resource supply and

recognition for creative efforts as synergistic extrinsic motivators. Successful

problem solvers in organisations are often told to ‘keep doing what you are

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doing’, thus gaining creative rights and escaping the accountability oversight

which acts as an extrinsic de-motivator for creativity.

In this case, the potential for increased resources, recognition, increased

creative rights and decreased monitoring motivate the Innovator to conform

by completing the problem solving task. The easiest way for the Innovator to

do this is to use their (so far) successful creative approach to the problem and

proceed without additional novelty. Hence there is a net incremental

decrease in creative motivation (a move towards potential gains response)

and this decrease may be large if the Innovator is sufficiently forward thinking.

In Figure 1, this corresponds to the point for the dotted line at 50% expected

outcome certainty on the left side of the graph.

Assuming that the Innovator perceives that their good progress continues

and a successful solution becomes even more likely, then their extrinsic

motivation to complete the problem solving exercise wanes. The Innovator is

not characterised by diligence or efficiency, and optimistically assumes that

the problem is essentially solved. The Innovator rationalises that as success is

already certain, at least some of the impending rewards of success can be

‘spent now’. Thus the Innovator’s intrinsic motivation for originality again

dominates and creative motivation increases even though it is no longer

required to solve to problem. In effect the Innovator ‘gets carried away

experimenting’ with some more interesting and original non-conforming

solutions. The Innovator’s high self worth and sense of importance validate this

freedom to experiment after having ‘effectively’ solved the problem, even

though the result may not be completely achieved yet. In Figure 1, this

corresponds to the point for the dotted line at 100% expected outcome

certainty on the left side of the graph.

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Alternatively, if the Innovator perceives an early setback during the

problem solving task, then their extrinsic motivators towards conformity and

diligence become dominant. In extreme cases, the Innovator is subjected to

oversight and controls that reduce their autonomy and available resources to

be creative. Even the threat of such interventions will have the potential to

de-motivate the Innovator from continuing to be creative as is asserted by

Kubes (Kubes, 1992).

Thus after a perceived initial partial setback, the Innovator experiences real or

imagined external pressure to stop experimenting and to make measurable

progress towards a solution. This perceived pressure forces the Innovator to

conform to the dominant paradigm for managing the problem and their

creativity decreases to allow the Innovator to retain some of their autonomy

at this point in the task. In a sense the Innovator is forced to ‘get with the

program’. In Figure 1, this corresponds to the point for the dotted line at 50%

expected outcome certainty on the right side of the graph.

Figure 1 Expected Creative Resolve Response

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If setbacks are perceived to continue after this conformity, then the

Innovator’s intrinsic motivation for originality and their internal locus of control

influence them to resume novel problem solving efforts. Their internal locus of

control and optimism causes them to assume (rightly or wrongly) that they will

be able to solve the problem eventually. The Innovator in extreme cases may

go into an ‘emergency mode’ to retain their creative autonomy. In effect,

they perceive that they have nothing to lose and everything to gain by trying

everything they can think of to solve the problem. The result is that their

motivation to be creative increases by a large amount. In Figure 1, this

corresponds to the point for the dotted line at 100% outcome certainty on the

right side of the graph.

Thus the pattern of Innovator creative motivation (their CRR) is determined

by the same interplay of extrinsic and intrinsic motivators as the Adaptor, but

the Innovator’s CRR is the mirror image of the Adaptor’s. The Innovator’s

creative motivation is at its highest during both maximum outcome

uncertainty and certainty. The Innovator’s creative motivation is at its lowest

Expected Outcome

Creative Motivation

InnovatorAdaptor

100% Bad100% Good 50% Bad50% Good Uncertain

Freedom to Create

Consolidate Gains

Disrupt for Advantage

Nothing to Lose

Constrained by Firm

Lock in Gain

Consider small changes

Analyse to simplify

Forced to change

Stop further loss

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when outcome uncertainty is neither zero nor maximum. The expected

Innovator and Adaptor CRR patterns are shown in Figure 1.

Hypotheses

The formulation of hypotheses below is based on the expected CRR pattern

graphed in Figure 1:

1. Innovator creative motivation varies with outcome certainty: (a) creative

motivation max at max certainty; (b) creative motivation high at zero

certainty; (c) creative motivation lowest at moderate certainty; and (d)

variation should be more significant with increasing KAI score.

2. Adaptor creative motivation varies with outcome certainty: (a) creative

motivation min at max certainty; (b) creative motivation low at zero

certainty; (c) creative motivation highest at moderate certainty; (d)

variation should be more significant with decreasing KAI score.

3. Innovators and Adaptors similarly motivated at moderate outcome

certainty.

Methods and experimental design

Participants

The data for this study were drawn from in-class problem solving exercises

designed to give postgraduate students opportunities to attempt problem

solving. The participants in this study were 51 respondents out of 66 individuals

enrolled in a Master of Business Administration unit called Creative Problem

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Solving. The international nature of the MBA program ensured that

participants were from a wide range of nationalities, though most of the

participants were Australian. Both genders and a range of ages were present

in the sample, though neither variable was reported. More than two thirds of

the participants were enrolled part time (typically working full time as

professionals or managers) and the rest were full time students. Participants’

prior MBA education ranged from nil (i.e. this was their first class) to almost

complete (i.e. several participants had to complete only this class to

graduate).

Entry requirements to the MBA ensured that all participants had at least

two years of work experience. Some students were able to enrol in the

program without an undergraduate degree provided they had extensive

work experience. Thus the sample included current and aspiring real world

managers with at least an undergraduate degree and two years of

professional work experience or more than five years of managerial

experience.

Participants were told that the broad purpose of the exercise was to

attempt to use tools and frameworks presented in the lecture and that the

survey data collected would be used for both teaching and research

purposes. Students were told that their participation would have no effect on

student grades for the class and that participation was voluntary. They were

also told that their individual responses would remain confidential and the

data would only be viewed in aggregate. Because the central outcome

variable of interest in the research program was creative motivation, all

potential class subjects available were offered the opportunity to participate.

The majority of participants treated these projects as class tutorial exercises.

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The problems used for the exercises were designed to provide learning

opportunities for specific creative problem solving skills and covered a wide

range of tasks. Some tasks were similar to those found in business domains,

and others were quite different to business problems. Only one problem was

attempted within each weekly class, and all participants undertook their

problem solving efforts at the same time and in the same general location.

Participants were given 30–60 minutes to complete problems in small self-

selected groups of four to six members. Some groups were able to solve some

exercises to their satisfaction earlier than others, and within the time limit.

Some groups failed to complete some exercises to their satisfaction within the

maximum time available in the class. The four problem solving exercises were:

• Launcher (use a small model catapult to launch a bullet 1m into a target

bucket);

• Mutual funds (critically evaluate an advertorial relating to investment

funds);

• Asit (improve an online program designed to teach creative problem

solving); and

• Wiki (resolve a corporate extortion attempt via a real-time, interactive and

online text-based platform).

Thus all the participants focused on open-ended problem solving tasks

that were specified by the lecturer. Most individuals participated in the study

throughout the entire short semester (six weeks). Because student attendance

was not constant, individual participation ranged from four responses to a

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single problem solving exercise, to up to five responses for all four problem

solving exercises.

At the beginning of the study, all participants completed an online KAI

survey that established their cognitive style. These measures were used as

controls in the quantitative analyses. Twenty-nine Innovators and 22 Adaptors

responded. The mean KAI score was 100.2 with a standard deviation of 12,

compared to a normal population of mean KAI 96 with a standard deviation

of 13. Whilst this sample mean is higher than the upper 95% confidence limit of

99.6, the nature of the analysis does not require the sample to be necessarily

representative of the general population, as comparisons between different

cognitive styles within the sample will be made.

Procedure and Instruments

At different intervals during the problem solving tasks, subjects were asked to

individually complete responses to two questions: ‘how creative do you think

that you need to be to solve this problem’ and ‘how certain are you that you

will be successful’. Both questions required subjects to circle the most

appropriate response on a Likert scale. The creativity Likert scale had 7

discrete points ranging from ‘nil’ to ‘max’ (nil, v. low, low, moderate, high, v.

high, max). The certainty Likert scale ranged from ‘uncertain’ to ‘certain’ (0%,

20%, 40%, 60%, 80%, 100%).

Amabile (Amabile et al., 2005) provides significant precedence and

validation for using self-reported scales for creativity assessment in

organisation creativity research, and also validation for the creation of new

simple self-reported scales. The Likert scales used in this study were chosen to

minimise the loss of significant data due to noise, and because an initial pilot

study suggested that more detailed scale increased the cognitive load too

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much for subjects. During and after the initial pilot study, subjects reported

that they did not understand the survey and were unable to record survey

responses without losing their momentum in solving the problem.

Likert scales with 6–7 point scales are common in psychological research,

even though more graduated scales could offer finer detail. An example is

Kirton’s (Kirton, 1976) test, which uses only a 5 point scale (v. hard, hard,

moderate, easy, v. easy). In fact all of the examples cited in Levin (Levin et

al., 1998) require a forced choice from two alternatives.

In order to maintain good response rates, the researcher visited each

group during the problem solving exercises and verbally requested that the

next survey point be completed. There was no way to ensure that participants

attended to each problem solving exercise, or that participants who were

strongly involved in the exercise would stop work to respond to the survey. As

a result, final response rates were 351 out of a possible 580 for Innovator

responses (60.5%), and 280 out of a possible 440 for Adaptor responses

(63.6%). Data or results were not discussed with participants until their

participation was completed. No participant’s data was excluded from the

analysis.

Limitations of the experimental design

Potentially confounding variables in this experimental design were those

related to the problem heterogeneity, learning effects, class environment,

group interaction and interviewer response bias. The four problem solving

exercises proposed were from very different knowledge domains. This

presents potential problems for comparison of subject responses to different

problems. This difficulty was managed by considering outcome certainty as

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the independent variable – in essence the problem being solved was

irrelevant as long as outcome certainty could be consistently reported. From

this perspective, the heterogeneous nature of the problems provides support

for the generalisation of the results to other unrelated problem contexts. Post

hoc analysis of the frequency of specific outcome certainty responses

showed that different problems had different ranges and peak outcome

certainty responses, confirming the heterogeneity of the exercises.

Interestingly, post hoc analysis of the mean outcome certainty of responses

over time also showed a general trend to slightly increase outcome certainty

as well.

Increasing outcome certainty suggests that learning effects may be

present. (Learning was the original objective of the class exercises.) Within a

specific problem exercise, learning is not a confounding variable. Indeed, the

corollary of the CRR model is to understand how creative motivation changes

as learning about how to solve a particular problem increases. Learning

effects, however, are potentially confounding if they apply between different

problem exercises. Given the heterogeneous nature of the problem exercises

and the week time delay between each one, these confounding inter-

problem learning effects were expected to be low. This is supported by the

lack of significant differences in mean outcome certainty for the first survey

responses to each problem.

The class environment promoted demonstrating successful creative

problem solving. As a result this would be expected to place additional

extrinsic motivation in the form of both control (i.e. implied expectation) and

intangible reward (i.e. recognition) for creativity. Different participants would

be expected to respond in differing degrees of sensitivity to these extrinsic

95

motivators, resulting in a variance in overall reported motivation. However,

the degree of this variance would be expected to average out over the

sample and be effectively constant for a particular problem solving exercise,

and therefore not confound the analysis.

There is generally a problem with individual administration within a group

task. However, in this study group, effects acted again as a form of (mainly)

extrinsic motivation, so the same considerations for a class environment would

be expected to apply. Since group problem solving is now commonplace in

business contexts, using group-based problem solving was considered more

representative and able to be generalised. Interestingly, individual reporting

showed very different perceptions of outcome certainty during the task within

the same small group of participants. This suggests that an individual response

was appropriate to measure.

The final potentially confounding variable was interviewer response bias. It

would be expected that students in a creative problem solving class would

over report the need to be creative as a response to the lecturer. Again these

effects act as an extrinsic motivator, and provided that the effect is constant

across a specific problem and individual participant sensitivity is variable,

analysis can proceed using mean responses. Similar to the group work

consideration, many business problems are attempted in the context where a

supervisor is hoping for successful results, which is similar (from a motivational

perspective) to the student–lecturer relationship.

In summary, the main concerns for the study design were the presence of

additional extrinsic motivators (including some synergistic motivators) that

would be expected to affect overall creative motivation. Since CRR relates to

the variability of creative motivation, these confounding effects can be

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ignored as they were constant. In addition several of the potentially

confounding effects were analogous to real world situations (i.e. group work

and supervision) and therefore the results are potentially more able to be

generalised to the real world.

Results

Data collection efforts yielded two quantitative data sets: creative motivation

and outcome certainty responses for each problem, and KAI scores for the

participants. To examine the relationship between creative motivation and

outcome certainty, the creative motivation responses were coded cardinally:

score 0 for ‘nil’ creativity, through to score 7 for ‘maximum’ creativity.

Participant responses were aggregated by the KAI score into either Innovator

(KAI>96), Adaptor (KAI<=96), Extreme Innovator (top quartile of all KAI scores)

or Extreme Adaptor (bottom quartile of all KAI scores) sub groups. For each

sub group, mean and standard deviation required creativity score was

calculated for each outcome certainty measure, regardless of problem

exercise. Comparisons between different mean required creativity scores

within subgroups and between subgroups was completed using two-tailed t-

tests to establish confidence intervals for differences.

Figure 2 shows how the mean creative motivation score varies against

outcome certainty. The solid lines with the largest amplitude of creative

motivation represent extreme Innovators (at the top) and extreme Adaptors

(at the bottom). The dashed lines with less amplitude represent all Innovator

and all Adaptors (again top and bottom respectively).

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Figure 2 Results Graph – Mean Required Creativity vs. Outcome Certainty.

Qualitative inspection of the graph reveals that mean creative motivation

response does appear to vary for both cognitive styles. In addition, Innovators

report higher mean creativity scores than Adaptors for all outcome certainty

levels. These effects appear stronger for extreme Innovators and Adaptors.

Analysis

Two-tailed t-tests of significance were conducted for each outcome certainty

value in order to determine if there was a significant difference between

mean responses for all Innovators and all Adaptors. Additionally, two-tailed t-

tests were conducted for each outcome certainty to determine if there was a

significant difference between mean responses for extreme Innovators and

Adaptors. The results of the analysis are shown in the Table 1, and suggest that

for all outcome certainties (except 40% and 100%), the mean creativity score

for all Innovators is significantly higher than mean creativity score for all

Adaptors.

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Table 1 All Innovators vs. All Adaptors – Mean Creativity Responses

Outcome Certainty 0% 20% 40% 60% 80% 100%

Innovator Mean Creativity 4.83

n=6

4.68

n=50

4.130

n=69

4.15

n=104

4.15

n=86

4.33

n=36

Adaptor Mean Creativity 3.57

n=7

4.07

n=26

3.90

n=53

3.82

n=92

3.82

n=84

4.06

n=18

Significance level: difference

between means

10% 5% Not

(30%)

5% 5% Not

(>30%)

The results in Table 2 show that for all outcome certainties (except 100%),

mean creativity score for extreme Innovators is significantly higher than mean

creativity score for extreme Adaptors5

.

Table 2 Extreme Innovators v. Extreme Adaptors – Mean Creativity Responses

Outcome Certainty 0% 20% 40% 60% 80% 100%

Extreme I Mean Creativity 4.50

n=2

5.31

n=16

4.50

n=16

4.10

n=30

4.15

n=27

5.00

n=20

Extreme A Mean Creativity

3.00

n=4

4.09

n=11

3.79

n=29

3.56

n=32

3.67

n=30

4.40

n=10

Significance level: difference

between means

5% 5% 10% 5% 6% Not

(20%)

5 The need for extreme caution in interpreting p<.10% results is noted.

99

In addition, the level of significant difference between the means for each

outcome certainty can be compared by examining the bottom row of both

tables. It is evident that the level of significant difference between means for

each outcome certainty is generally equal or greater for the extreme

subgroups than the entire group (e.g. the significant difference between

mean creativity at outcome certainty of 40% is not significant for all Innovators

and Adaptors, but is significant to the 10% level for extreme Innovators and

Adaptors).

Innovator Response Analysis

Two-tailed t-tests of significance were conducted for sequential pairs of

selected outcome certainty values in order to determine if there was a

significant difference between mean responses for all Innovators over

different ranges of outcome certainty. The outcome certainty pairs were

selected to maximise potential differences based on the shape of the Figure 2

graph. A similar analysis was conducted for extreme Innovators. The results of

both analyses are shown in Table 3. The results show mean creative response

varies significantly across all selected outcome certainty ranges for extreme

Innovators, and across only the 20–60% range for all Innovators.

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Table 3 Innovator Creativity Variation

Outcome Certainty Comparison 0%–20% 20%–60% 60%–100%

All Innovators significance level:

difference between means

Not

(>30%) 1%

Not

(>30%)

Extreme Innovators significance

level: difference between means

Not

(20%) 1% 1%

Adaptor Response Analysis

Similar t-tests of significance were conducted for all Adaptors’ and extreme

Adaptors’ mean creativity responses over the same outcome certainty

ranges. The results of the analysis are shown in Table 4. The results show mean

creative response varies only significantly across two of the three selected

outcome certainty ranges for extreme Adaptors.

Table 4 Adaptor Creativity Variation

Outcome Certainty Comparison 0%–20% 20%–60% 60%–100%

All Adaptors significance level:

difference between means

Not

(>30%)

Not

(30%)

Not

(>30%)

Extreme Adaptors significance

level: difference between means 5%

Not

(30%) 5%

Discussion

Some important aspects of CRR are supported by this study to statistically

significant levels. The results suggest that mean creative motivation does vary

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systematically during problem solving, based on perception of outcome

certainty and depending on a subject’s cognitive style. Though the overall

variation is relatively small, it is significant. Whilst this supports the general

hypothesis of CRR, the specific pattern of response predicted was not

observed:

H1 (a) Innovator creative motivation is max at max certainty was

supported;

H1 (b) Innovator creative motivation high at zero certainty was not

supported;

H1 (c) Innovator creative motivation lowest at moderate certainty was

supported;

H1 (d) Variation more significant with increasing KAI score was supported.

H2 (a) Innovator creative motivation is min at max certainty was not

supported;

H2 (b) Innovator creative motivation low at zero certainty was supported;

H2 (c) Innovator creative motivation highest at moderate certainty was

not supported;

H2 (d) Variation more significant with decreasing KAI score was

supported.

H3 Innovators and Adaptors being similarly motivated at moderate

outcome certainty, was not supported.

The results tend to support the findings of Kaufmann and Vosburg

(Kaufmann and Vosburg, 1997) and Vosburg (Vosburg, 1998), though the

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theoretical basis for the variation is different: CRR is founded on changing

sensitivity to intrinsic and extrinsic motivators rather than mood effects.

This study shows that creative motivation appears to follow a wax–wane–

wax pattern, regardless of cognitive style. A possible explanation for Innovator

CRR and Adaptor CRR varying ‘in phase’ is that both respond more sensitively

to extrinsic motivation (which in general lowers creative motivation) at 0%

outcome certainty. Once the problem at hand is better understood (20%

outcome certainty), extrinsic motivations are somewhat satisfied. Problem

solver motivation sensitivity then switches to responding to intrinsic motivations

(which in general increase creative motivation and therefore creativity). This

switch to intrinsic motivation could be understood as one or a combination of

the following:

• Increased involvement in the problem due to time invested (i.e. there is an

escalation of commitment);

• Reduced concerns over failure consequences as problem understanding

increases (i.e. there is relief that uncertainty is reduced because the

problem has been identified); and

• Increased interest in the problem solving exercise due to a better

understanding of what is involved (i.e. there are more positive

expectations about how to resolve the problem).

Once peak motivation has been reached (at 20% outcome certainty),

increasing certainty further results in a switch back to trying to progress with

the problem solving task in order to satisfy extrinsic motivators. This results in a

reduction in creativity as the problem solver concentrates on developing and

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evaluating possible solutions implied by the previous problem definition, rather

than creating new solutions or looking for additional possible problem

definitions.

Finally at minimum creative motivation (at 60% outcome certainty), the

problem solver begins to believe that the problem solving task will be

ultimately successful. This starts to satisfy all extrinsic motivators and leaves

only intrinsic motivation remaining. Largely released from the concern of

whether or not the result will be successful, the problem solver starts to explore

additional creative options that are more interesting: the final creative

finishing touches perhaps or other related but non-critical aspects of the

problem’s context. The result is increased creative motivation and creativity.

Thus this study suggests that extrinsic motivation is initially prioritised during

problem solving over intrinsic motivation. CRR fits with the body of work

completed by Deci and Ryan regarding Self Determination Theory and some

aspects of Cognitive Evaluation Theory (see Gagné and Deci, 2005 for a

summary). Broadly, Deci and Ryan’s body of research shows that tangible

extrinsic motivators reduce intrinsic motivation. This fits neatly with Amabile’s

(Amabile, 1996; Amabile, 1997a; Amabile, 1997b; Amabile, 1998; Amabile et

al. ,2002; Amabile et al., 2004) research which shows that non-synergistic

extrinsic motivators reduce creativity, and intrinsic motivation enhances

creativity. CRR extends the current understanding of responses to intrinsic and

extrinsic motivation by showing how problem solvers’ sensitivity to each type

of motivation can change during problem solving, resulting in a low–high–

low–high pattern of response, as perceived outcome certainty increases from

0–100%.

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The above description of CRR, however, is conceptually simplified. In

practice, subjects in the same small group did not report similar measures of

outcome certainty. In addition, while the overall trend was increasing mean

outcome certainty over time, an individual’s specific outcome certainty

response was not predictable at any point. Some subjects’ outcome certainty

decreased over time, even though the average for their small group was

increasing. Finally, CRR only shows average creative motivation responses,

not specific individual responses, and it is valid only for single-sitting problem

solving tasks. Problem solving tasks undertaken over several different sessions

may exhibit very different CRR patterns due to a range of changing factors6

In general, the CRR phenomenon is stronger for more extreme cognitive

styles (both Innovator and Adaptor), suggesting that the sensitivity to intrinsic

and extrinsic motivators does increase for subjects with cognitive styles further

away from the average. The overall difference in creative motivation

between Innovators and Adaptors suggests that Adaptors in general are

more sensitive to extrinsic motivation, which tends to reduce creative

motivation and creativity. This would suggest that Adaptors should exhibit

lower levels of creativity than Innovators in organisational environments where

significant non-synergistic extrinsic motivation is common.

.

Assink (Assink, 2006) identifies a range of factors that inhibit organisational

innovation capability. In fact, many authorities (Berkshire, 1995; Basadur, 2004;

Boeddrich, 2004; Gryskiewicz and Taylor, 2003; Leavy, 2002; Mumford, 2000;

Proctor, 1999; Välikangas and Jett, 2006) have asserted that organisations

6 In particular the context here – a university classroom – is a limiting factor in how far

we can generalise these findings.

105

either purposefully or inadvertently decrease creativity (via punitive personal


procedures, or inflexible corporate governance initiatives). For example,

Elsbach and Hargadon (Elsbach and Hargadon, 2006) argue that overwork

and high pressure for performance are significantly damaging to professional

creativity and advocate periods of so-called ‘mindless’ work for recuperation.

Amabile (Amabile et al., 2002) found that creativity is reduced under the time

pressure experienced by many individuals in organisations.

Most extrinsic motivational factors are not synergistic (as defined by

Amabile, 1997a) and hence serve to inhibit creativity even though they are

generally expected to improve individual motivation to perform. There seems

to be a tendency in organisations to rank appropriateness of solutions over

novelty (see Amabile, 1998; Kirton, 1984, 1991; Matherly and Goldsmith, 1985).

This study’s finding that Adaptors appear to be more sensitive to extrinsic

motivation regardless of outcome certainty, suggests that organisational

extrinsic motivators are more likely to affect Adaptors than Innovators. This is

supported by Casbolt (Casbolt, 1984) who found that Adaptors were in

general less creative on two tasks than Innovators. Wells’ (Wells et al., 2006)

study also supports this by showing a small but significant correlation between

creativity and deviance in organisations. Dewett (Dewett, 2004) goes as far as

suggesting that an employee’s willingness to take risks is the key determinant

of individual creativity.

The potential for supportive supervisors and leaders to enhance creativity

during organisational problem solving has also been examined by a variety of

researchers (including Amabile et al., 2004; Gryskiewicz and Taylor, 2003;

Välikangas and Jett, 2006; de Jong and Hartog, 2007; Boerner et al., 2007;

106

Egan, 2005a; Egan, 2005b; Reiter-Palmon and Illies, 2004; Basadur, 2004;

Mumford et al., 2002; Clapham, 2000; Sosik, 1997; Baer et al., 2003; Oldham

and Cummings, 1996; Forbes and Domm, 2004). Of these studies, Forbes and

Domm (Forbes and Domm, 2004) specifically examined the perceived trade

off between creativity and productivity.

Forbes and Domm (Forbes and Domm, 2004) agreed that ‘external’

controls designed to increase productivity could diminish involvement and

creativity. In this context ‘external’ controls equate to a supervisor’s or

management’s push for completion. Despite this, they show how creativity

and productivity can increase under circumstances where there is high

involvement. They assert that some extrinsic rewards can enhance personal

involvement, and hence creativity.

Implications of CRR

CRR provides one such potential ‘external control’ for managing creativity

not proposed by Forbes and Domm (Forbes and Domm, 2004): influencing

employee perception of outcome certainty. Mean creative motivation of

individuals in a small problem solving group appears to vary during a specific

problem solving task in a pattern of low–high–low–high, in line with increasing

perception of outcome certainty. For both Innovators and Adaptors, creative

motivation appears to be high at 20% and 100% outcome certainty, and low

at 0% and approximately 60% outcome certainty. Thus managers that can

influence employees’ perception of outcome certainty (towards 20% and/or

100%) are expected to increase creative motivation, and therefore creativity.

Specifically, (useful) peak mean creative motivation seems to occur when

a problem solving task is perceived to be approximately 20% complete. Once

a problem solving task is perceived by employees to have progressed

107

beyond this point, creative motivation and creativity appear to decrease

until the project is perceived by the problem solvers to reach substantial

completion. A manager may be able to increase creative motivation and

therefore creativity, by influencing employees to reconsider their perception

of problem solving progress. Where a manager can provide validation that

the problem should be reconsidered from another definition or create doubt

as to the current problem solving strategy, employees may be prepared to

revisit the problem from a different starting point. This reframing approach

would also be expected to enhance productivity unless the resulting

employee perception is that the problem solving task has become hopeless.

Influencing employees’ outcome certainty perception acts as an external

management control. Managers using this approach should expect creativity

changes to be greatest for employees with extreme cognitive styles.

Based on the findings of this study, managers should also expect Adaptors

to exhibit relatively lower motivation at all outcome certainty levels than

Innovators, particularly in organisations with strong extrinsic controls. This

suggests that managers should select Innovators over Adaptors to complete

tasks where higher creative motivation and creativity is preferred. Managers

could also apply CRR to enhance collaboration during problem solving.

Conflict between problem solvers with very different styles (as shown by

Hammerschmidt, 1996) could be potentially managed by ensuring problem

solvers maintain different perceptions of the likely certainty of outcome for

the problem solving task. To some extent this provides an alternative

approach to that suggested by Mumford (Mumford et al., 2001), involving

shared mental models where problem solving group members agree on how

108

to approach a problem solving task. This is particularly important for problem

solving groups comprised of individuals with diverse cognitive styles.

This study suggests that such a group’s creative motivation levels will

remain in conflict if all group members agree on outcome certainty for the

duration of the problem solving task. In this situation, Adaptors will always feel

that less creativity is required than Innovators. However, if a manager can

enable individuals to undertake problem solving together, whilst retaining

different perceptions of outcome certainty, then creative motivation levels

within the group would more closely match and conflict should reduce. This

application of CRR seems at odds with the dominant paradigms in managing

problem solving groups, which relate to increasing collaboration via a

common view of the problem and how to approach it. A practical resolution

to this problem could be to structure the group so that individuals work

independently and concurrently on the same problem. Research on this

nominal group technique suggests other creativity benefits unrelated to

motivation

In practice it was observed that problem solvers rarely had their

perception of outcome certainty directly affected by the group process,

even when leaders emerged within the small problem solving groups. Further

research into the application of CRR for management and leadership of

creativity is therefore required.

Conclusion

This paper introduces the Creative Resolve Response: a model for how

Innovators’ and Adaptors’ creative motivation can be expected to vary

during a problem solving task. The study provides support to the theory that

109

individuals with different problem solving styles vary in their sensitivity to

intrinsic and extrinsic motivators. Adaptors appear to be more sensitive than

Innovators to extrinsic motivation. In addition, sensitivity to extrinsic motivation

is prioritised at very low and moderate levels of outcome certainty. Between

these levels of outcome certainty, intrinsic motivation appears to be

prioritised. Intrinsic motivation also appears to be prioritised at high levels of

outcome certainty. This CRR pattern appears to be more significant for

individuals with extreme cognitive styles.

CRR is a potentially important phenomenon because it promises a new

form of extrinsic control for enhancing creative motivation and creativity: the

influence of outcome certainty. Managers that can influence employee

outcome certainty are expected to be able to better manage creative

motivation. Managers that can support problem solving groups to sustain

different individual perceptions of outcome certainty (relative to cognitive

style) are expected to be able to improve group collaboration by matching

individual creative motivation levels more closely.

Further research is required to examine how problem solver motivation

varies in response to attempts to influence outcome expectation certainty. It

may be that attempting to influence outcome certainty changes the CRR

pattern of response, which would reduce the management utility of this

study’s findings.

110

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Chapter 2: Innovation, Creativity and Framing Effects

Abstract

Framing effects are usually associated with perceptual

distortions that affect decision making, especially decisions

involving risk related behaviours. This paper empirically

demonstrates framing effects associated with innovation

decisions in a sample of 146 postgraduates and business

managers. In addition, we observed a reversal of preferences

for decisions involving embedded creative characteristics

(fluency/flexibility, originality/novelty, deviance and/or

divergence) when these decisions were reframed from choices

to rejections. These results suggest that innovation and creativity

framing effects may be useful as extrinsic motivational

management tools for unlocking intrinsic motivators to creativity.

The findings are highly relevant to managers wishing to enhance

employee creativity because prior research has suggested that

extrinsic motivators dampen creative motivation.

Keywords: Framing Effects, Innovation, Creativity, Motivation

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Introduction

Creativity and innovation are considered important for managerial

effectiveness, according to both practitioners (Drucker, 2004; Reiter-Palmon &

Illies, 2004), and academic researchers (e.g., (Basadur, 2004; Cameron M

Ford, 2002; S. S. Gryskiewicz, 2000b)). Indeed, Woodman, Sawyer, and Griffith

(1993) suggest creativity is foundational to organisational effectiveness.

However, managing for increased creativity seems to be somewhat at odds

with other management approaches, for example, those which emphasize

increased efficiency and rational problem solving. Sadler-Smith (2004) argues

for augmenting rational decision making with “gut feel”. Mintzberg and Sacks

(2004) criticise MBA education as damaging to management creativity,

despite the fact that these programs are designed to improve management

skills. Välikangas and Jett (2006) assert that the leadership challenge involves

“learning to manage the independent thinkers” (p. 44) who refuse the

constraints of professionalism and instead innovate on their own terms.

Similarly, Leavy (2002) suggests that organisations have been “found out” in

the last 10 years regarding their ability to manage creativity. Despite the

current focus in organizations on the importance of creativity and innovation,

many managers often appear unwilling or unable to enhance creativity.

Berkshire (1995) identified a range of managerial behaviours that can hinder

creativity, including controlling, competitive, and critical behaviours,

rationalisation, and routine thinking.

Motivating employees to be creative is therefore perhaps the main

challenge that managers face in increasing creativity in the organisation.

However, managers may also be part of the problem. According to Ford and

Gioia (C M Ford & Gioia, 2000)

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“…instead of generating potentially creative alternatives,

managers usually adopt well understood, previously successful

options. This empirical evidence suggests that managers rarely

concern themselves with creativity during their day-to-day

decision making activities. These findings also suggest that this

domain is quite different from those commonly investigated by

creativity researchers where creativity is a primary concern

guiding actors’ choices (e.g., science, the arts, R&D, etc.).

Thus, despite the substantial dividends one might expect from

creative managerial action, the expedient decision processes

typical of this domain tend to preclude creative choices.” (p.

709)

Thus both management and employee motivation to select creative

options seems to be an important part of enhancing creativity in the

organisation.

Motivation can be intrinsic, extrinsic or a combination of the two types.

Extrinsic motivators are those that are initiated by someone else. For example,

a project deadline, sales commission or recognition for quality work are all

examples of typical organisational extrinsic motivators that could apply to

employees. Intrinsic motivators are those that are internal to the individual (for

example, being curious about how some equipment works). Amabile (T. M.

Amabile, 1997; 1998) showed that most management interventions are based

on extrinsic motivators and these generally dampen creativity. This work was

supported by other studies (T. M. Amabile, Hennessey, & Grossman, 1986; B. A.

Hennessey, 1989; B A Hennessey & Amabile, 1998; Kashdan & Fincham, 2002;

Kruglanski, Friedman, & Zeevi, 1971) that in combination suggest that

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creativity requires self determination in accord with Deci and Ryan’s Self

Determination Theory (Deci & Ryan, 1985a). One of the key assertions of Self

Determination Theory is that intrinsic motivation diminishes with increased

extrinsic motivation.

The nature of creative motivation therefore proposes a significant

challenge to managers, namely, how to create intrinsic employee motivation

for creativity when by definition management interventions act as extrinsic

motivators. One potential approach might leverage Amabile’s (1997)

identification of “synergistic extrinsic” motivators (like recognition) that can

generally augment existing intrinsic employee motivation. Amabile’s

synergistic extrinsic motivators act to increase intrinsic motivation already

present. Examples include recognition and resource support. Synergistic

motivators are limited in their effectiveness however, because they rely on

existing intrinsic motivation. The body of research on creative motivation has

not addressed how to resolve this creative motivation management problem.

Instead the research has tended to focus on environmental factors (see

Simonton, 2003) that affect creativity via intrinsic motivation and cognitive

style. For example Jaskyte and Kisieliene showed that employee intrinsic

motivation and cognitive style can support creativity within an organisational

culture that values diversity. Tierney(Tierney et al., 1999) found that leader-

employee interactions, the intrinsic motivations of both, and cognitive styles

of both affect creativity of employees. Other studies also relate to supervisor

affects on employee creativity (DiLiello & Houghton, 2006; George & Jing,

2007; Lonergan, Scott, & Mumford, 2004; Oldham & Cummings, 1996;

Redmond et al., 1993; Tierney & Farmer, 2004; Williams, 2004). The actual

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mechanisms by which supervisors and environments may affect creativity are

not specified in the research above.

It seems worthwhile therefore to identify the specific supervisor

interventions that enhance employee creativity either directly or indirectly by

affecting creative motivation. Such interventions could conceivably happen

prior to the production of potentially creative ideas or at the point where a

decision about whether to opt for a more creative or less creative solution to

solve a problem.

This paper therefore investigates how decisions involving creativity and

innovation are subject to framing effects (first introduced by Tversky &

Kahneman, 1981). Framing effects are typically imposed by whoever presents

or restates a decision and so are potentially useful as an extrinsic

management intervention. For example in one study the price of a pizza

ordered ended up significantly less when customers had to add ingredients to

an empty pizza base compared to removing ingredients from a fully loaded

pizza (I. P. Levin, Schreiber, Lauriola, & Gaeth, 2002). The price difference was

a direct result of customers removing fewer ingredients from fully loaded

pizza, so that it ended up retaining more toppings than one built from

nothing. This study is one example that shows that the way a decision is

presented can affect decision maker preferences.

Importantly, framing effects create perceptual distortions in the mind of

the decision maker to affect their preferences. Individuals that are subject to

framing effects are typically unaware of any external influence on their

decision. Thus the perceptual distortions created by framing effects act

inherently as intrinsic motivators, even though they are externally imposed.

This suggests that framing effects might be used by managers to unlock

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employee intrinsic creative motivation, even though they are essentially an

extrinsically imposed effect.

This paper therefore introduces framing effects as a possible extrinsic

motivational approach for enhancing creativity. The research tests how

framing effects can influence the preference for relatively more creative

options over less creative options. Despite the fact that framing effects have

been examined in multiple domains, no research could be found that has

empirically investigated how any of the three main types of framing effects

relate to innovation and creativity. The study aims to discover contexts

involving innovation and creativity that activate framing effects and how

these might be used to influence employees to prefer and select creative

decision options. The next section explains the basis for how creativity was

operationalised into testable decisions.

Defining and Measuring Creativity

Amabile (1997) defines business creativity as the production of novel and

appropriate solutions to organizational problems. This agrees with other

authors’ definitions of creativity and innovation, including Plsek (1997) and

Gryskiewicz (2000a). These authors define innovation as applied (successfully

implemented) creativity with three components: novelty, user utility and

problem solver utility. To achieve the potential for innovation, an individual

must first develop creative outputs (some of which may subsequently be

successfully implemented to become innovations). Just what constitutes a

creative output and how to measure this is not clear from the current

literature.

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Creativity tests essentially attempt to measure creative outputs. Although

many tests measuring creativity are based on subjective or self assessment

(Cropley, 2000) some commonly used objective tests include Mednick’s

Remote Association Test (1962; 1967), the Torrance Test of Creative Thinking

(1962), the Creativity Index (Gough, 1981) and the Rainmaker Index (Stevens

G A, Burley J, & Devine R, 1998). Of particular interest to this research is the

objective Guilford Divergence Test (Guilford J P, 1967) which provides a

reliable and basic starting point for assessing creative outputs in business

settings. The test proposes three measures of creativity: fluency, flexibility and

originality. Fluency is a measure of the number of options produced to solve a

problem (essentially a measure of volume). Flexibility is the number of distinct

themes that group the options proposed (essentially a measure of spread).

Originality is a measure of the rareness of the proposed options (essentially a

measure of unusualness or novelty). Thus the Guilford Divergence Test

provides one way to conceptualise what managers may call “operational

creativity”.

I define operational creativity (for this study) as a specific alternative

decision option that offers relatively increased creativity compared to a more

typical course of action. This can apply to making a decision or solving a

problem. Operational creativity options provide increased fluency, flexibility,

and/ or originality compared to the available alternative. For example, if an

employee is late for a sales meeting, various alternative route options are

available, each of which operationalises creativity. One route may be

considered because it offers many possible streets that could be taken

through a market district to get to the meeting (operational creativity:

fluency). Another approach to this problem might be to develop routes that

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utilise different modes of transport other than car travel in order to avoid

traffic. This could include cycling through a park or catching a river ferry.

These alternative routes increase the spread of routes available for

consideration (operational creativity: flexibility). Finally, a quite rare approach

might be for an executive to take to the footpath on roller blades in order to

avoid traffic snarls (operational creativity: originality).

It is not the utility of these various options that is important from a creative

perspective. Instead, what is important is the fact that decision alternatives

are available in order to solve a problem. Additionally, it is not the

development of these creative options that is being considered in this study –

the creative options already exist and the employee is required to make a

decision regarding the relatively less creative (normal) option or the relatively

creative option.

Managers can increase both the preference and implementation for

operational creativity by increasing creative motivation in their organisations.

Three options are apparent from Amabile’s research cited above: specific

management interventions that increase the potential for intrinsic motivation,

insulating problem solvers from extrinsic motivation effects inherent in the

organisational environment, and/or utilising synergistic extrinsic motivators

(specific extrinsic motivators – for example, recognition – that enhance

creative motivation when intrinsic motivation is present).

Unfortunately, these options are not viable in many organisational

situations as some jobs just need to be done despite the lack of intrinsic

motivation involved. Performance imperatives in organisations are an

inescapable part of the business world. Incentives for superior performance

and sanctions for inferior performance act as non-synergistic extrinsic

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motivators and reduce employee creative motivation. Being creative in some

organisational contexts may be regarded as non-conforming or deviant due

to the requirement to overcome (extrinsic) controls in order to be creative.

One approach to overcome these problems that is discussed below could be

for managers to use the various framing effects (I. G. Levin et al., 1998) to

unlock relevant intrinsic motivators. How the use of framing effects could

increase innovative and creative outputs has not been examined by previous

research.

Framing Effects

Framing effects cause changes in option preferences because of the

way a decision is presented. These effects activate perceptual biases or

distortions that cause decision makers to make different choices because

they weight aspects of the decision alternatives differently due when framing

effects are involved. As a result framing effects can cause decision makers to

be inconsistent in their preferences in a predictable way. Levin et al. (1998)

provide a useful typology that describes three types of framing effects: risk

based, attribute based, and goal behaviour. The independence of these

effects has subsequently been validated (Levin I P et al., 2007). These three

types of framing effects, and how they might affect creativity and/ or

innovation decisions, are explained further below.

Risk based Framing

Risk based framing effects were first reported by Tversky and Kahneman

(1981). Essentially, they showed that choices equivalent in their rational merit

were subject to perceptual distortions based on how the choice was

presented. Participants were presented with a simple decision involving two

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options, both of which had equivalent expected returns after adjusting for risk.

Importantly, the decision could be framed in terms of gains or losses. For

example, an investment decision could be presented in terms of the potential

investment gains or in terms of potential investment losses. When posed with a

choice between a small, almost certain gain; and a risky, larger gain,

participants tended to favour the more certain option. When the decision

was reframed as a choice between a small, almost certain loss, or a larger,

risky loss, participants tended to favour the less certain option. This led Tversky

and Kahneman to determine a hierarchy of weightings that related to

choices involving the potential for gain and loss. Typically participants are

more sensitive to potential losses than potential gains. They are also more

sensitive to risk (variability of outcomes) than to the absolute magnitude of

gains or losses. These findings have been replicated extensively in other

research (for a review see I. G. Levin et al., 1998).

Risk based framing is potentially important to employees’ decisions

involving innovation and/or creativity because such decisions may be

perceived to involve a degree of risk. An innovative plan could be perceived

as a risky option with potential gains because of its breakthrough approach.

A creative option may offer potential gains otherwise unavailable using a

standard solution. However, both creative and innovative alternatives could

equally be perceived as unfavourably risky because they include untried and

unusual aspects. Risk based framing is only one of the framing effects

identified that may apply to innovation and creativity.

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Attribute Framing

Attribute framing relates to how different weighted aspects of a choice

may be prioritised. Unlike risk based framing effects, attribute framing effects

apply to decisions where the outcomes are certain – in choosing a particular

option it is certain that all the attributes (positive and negative) associated

with that option are obtained. Shafir (1993) showed that when participants

were asked to choose from two options that involved a bundle of features

(attributes) that were both positive and negative, the positive features were

apparently more important than the negative features. When participants

were asked to reject an option, negative attributes became more important.

Shafir used this to show how attribute framing could be used to reverse the

preferences in a variety of domains.

Attribute framing effects apply to many situations including perceptions of

products, decisions about optional extras, and consent for surgical

procedures. For example, 75 per cent of lean meat is apparently better

tasting and less greasy than 25 per cent of fat meat (Levin I P & Gaeth G J,

1988); yoghurt that is zero per cent fat is apparently more attractive than 100

per cent fat free yoghurt (Janiszewski C et al., 2003); pizzas and cars tend to

be more expensive and feature laden when customers start with product

bundles and delete options, rather than building up their order from scratch

(Levin I P et al., 2002; Park et al., 2000); and more patients consent to surgery

when its discussed in terms of survival rather than mortality rates (Marteau T M,

1989; Wilson D K et al., 1987). Attributes seem to be pervasive concepts in

decision making. A special case of attribute framing relates to the descriptor

“free” in the case of volume offers for fast moving goods. According to

Gendall, Hoek, Pope and Young (Gendall, Hoek, Pope, & Young, 2006):

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“…if using a volume promotion for fast moving consumer goods,

“buy x get one free” is likely to be more effective than “y for the

price of x”….”

If innovation and creativity are perceived as risk free qualities of the

presented options, then attribute framing effects may apply to such

decisions. We can imagine that a coffee proof laptop is “innovative”

compared to a regular laptop. A humorous TV advertisement is “creative”

compared to a regular one. Including an audio customer testimony in a

presentation to a Board of Directors to enhance its validity may be creative

and/ or innovative. In these contexts, innovation and creativity become

attributes, even though they are derived from other features (in these

examples, coffee resistance, humour and validity respectively). This suggests

that attribute framing effects could apply to decisions involving innovative

and creative options. In addition describing something as “innovative” may

elicit a disproportional positive or negative response similar to the positive

response elicited by the descriptor “free” as presented by the findings of by

Gendall et al. above.

Note that attribute framing effects potentially apply only when the

innovative or creative aspect already exists, that is, where creativity has

already been operationalised. In situations where the innovative or creative

aspect has yet to be developed, goal behaviour framing applies.

Goal Behaviour Framing

Goal behaviour framing relates decisions to change and the taking of

new actions in order to achieve a goal. For example, Meyerowitz and

Chaiken (1987) reported that women were more likely to undertake self

examinations of their breasts when they were told of the risks of not doing the

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self examination, rather than when they were advised of the benefit. This

suggests that people tend to take more notice of potentially negative

consequences when choosing to modify their behaviour. This is the opposite

to attribute framing, where positive attributes were more important than

negative ones when making a decision. When attribute framing applies,

people tend to frame decisions positively if choosing. When goal behaviour

framing applies, people tend to frame decisions negatively.

Attribute and Goal Behaviour Framing Conflict

Goal behaviour framing and attribute framing are at first confounding

because they are so similar, and yet result in apparently contradictory

outcomes. Both are based on the idea of attaining something. Typically,

attribute framing involves a decision comparing tangible product features

and money costs, whereas goal behaviour framing relates an intangible

future personal benefit to having to change a habit. The apparent

contradiction comes from the fact that attribute framing seems to propose

positive attributes are more important when choosing product features,

whereas goal framing seems to propose that negative attributes are more

important when choosing whether or not change behaviour.

The key to understanding the two framing effects is to consider the

reference points inherent in the two scenarios: attribute framing requires a

comparison of the attribute (a tangible product feature) against a money

cost. Various researchers cited above have suggested that an attribute’s

perceived value is typically more concrete in a decision maker’s mind than

money (Kahneman D et al., 1990; I. G. Levin et al., 1998; I. P. Levin & Gaeth,

1988; I. P. Levin et al., 2002; Shafir, 1993). Because of this (in attribute framing

situations), the attribute’s positive aspect is most important. However, this is

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reversed in the case of a change in behaviour – having to do something

different and new is seen as a significant cost, so the focus becomes on

whether or not it is worth making the effort.

An alternative explanation for the difference in attribute and goal

behaviour framing effects may simply be related to timing. Attribute framing

effects apply to choices where the gain (the attribute) will immediately be

realised. Goal behaviour framing effects have potential gains that are likely to

be manifested in the future.

People are more sensitive to loss than to gain (see Kahneman D et al.,

1990). Therefore, a choice between options with both attribute benefits and

money costs is typically framed positively with a focus on gain because the

attributes are more significant and immediate. A choice between options

with both intangible benefits and behavioural change is typically framed

negatively with a focus on reducing the change cost, because the decision

maker resists the change more than they value the intangible benefits which

will take time to materialise.

This argument suggests that goal behaviour framing and attribute framing

are similar effects with different reference points (tangibility and/or timing).

This assertion is significant to this study because how framing effects related to

creativity and innovation are investigated determines which framing effects

are likely to apply. For example, if a choice involving an innovative or creative

output of a problem solving effort is proposed, the innovative or creative

aspect becomes an attribute and attribute framing applies. However, as an

input to problem solving, an individual’s decision as to whether to try to be

innovative or creative is a goal behaviour framing situation.

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Hypotheses

The overarching research question posed relates to identifying whether or

not innovation and operational creativity options elicit inherent decision

biases either way. Depending on the decision framing applied, innovation

and operational creativity options could invoke positive bias, negative bias,

or both positive and negative biases for decisions involving attribute framing

effects. Similarly innovation and operational creativity options could be

perceived to increase or decrease outcome certainty for decisions involving

risk based framing effects.

For example, relatively innovative options may be generally preferred over

non-innovative options regardless of whether the decision maker is choosing

or rejecting alternatives. Based on Shafir’s (1993) attribute framing effects

research cited above this would suggest that innovation in itself is perceived

simply as a positive attribute. Equally innovative options may be generally not

preferred over relatively non-innovative options in decisions framed as

choices and rejections. This would suggest that innovation is perceived simply

as a negative attribute. The key to this analysis is that if innovation is

perceived simply as either offering only benefits or offering only limitations,

then preferences for or against innovation should not change when choices

are reframed as rejections.

This need not be the case, however. An innovative option may be

preferred over a relatively non-innovative option only when the decision

maker is choosing between alternatives: when rejecting alternatives decision

makers may to tend reject relatively innovative options. In terms of Shafir’s

(1993) study cited above, this would make an innovative option “extreme”

(that is, the innovative option is perceived to be a bundle of both positive

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and negative attributes). The above discussion contains an inherent

assumption about the way innovation is perceived that may not be

immediately apparent: it is assumed that innovation does not affect

perceptions of risk.

It may be that describing an option as creative or innovative affects the

decision maker’s perception of outcome certainty. Tversky and Kahneman

(1981) showed that in situations where equivalent options are presented as

risky potential gains, the more certain gain option is preferred. When these

decisions are reframed to present equivalent options as risky potential losses,

the less certain loss is preferred. If innovation is perceived to affect certainty, it

will likely increase risk because it has an inherent “liability of newness”. This

suggests that in decisions explicitly involving risk, relatively innovative options

will only be preferred when the alternatives are framed as potential losses.

When the same decisions are reframed as potential gains, the less innovative

option will be preferred because it will be perceived as more certain. The

following hypotheses summarise the above discourse to predict perceptual

biases could apply to innovation:

Hypothesis 1. Describing an option as innovative invokes an inherent

positive bias when choosing, which causes the option to be more

preferred than it otherwise would have been.


negative bias when rejecting, which causes the option to be less

preferred than it otherwise would have been.

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uncertainty bias when choosing or rejecting, which causes the option

to be perceived as less certain

Hypothesis 3a. Describing an option as innovative will cause it to be

more preferred in decisions involving risk framing where options are

presented in terms of their potential losses

Hypothesis 3b. Describing an option as innovative will cause it to be

less preferred in decisions involving risk framing where options are

presented in terms of their potential gains

The above hypotheses are designed to test how inherent perceptual

biases can be invoked using “innovative” as a descriptor. However

operational aspects of creativity may also be naturally associated with

framing effects without any additional descriptors. For example, fluency and

flexibility increase options, and are therefore likely to be subject to attribute

framing. These operational creativity aspects could be associated with either

positive attributes or negative attributes, depending on the wording of the

question presented. For example, more volume could be considered as

offering more solutions (a positive attribute), however, it could also be

perceived as more work (a negative attribute). This dual nature of

fluency/flexibility suggests that these aspects are likely to be perceived both

positively and negatively, depending on the framing of the decision to be

made. Similarly, other operational creativity aspects would also be expected

to be considered as extreme.

Originality, novelty, deviance and divergence all represent change from

the status quo that can be considered as a positive or negative attribute.

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Hence they are likely to be subject to attribute framing effects and

considered as extreme options when compared to non-original, traditional,

conforming and convergent options.

Hypothesis 4. Options with operationalised creativity elements will be

perceived to have positive attributes when choosing.

Hypothesis 4a. Options with enhanced fluency/flexibility will be

preferred when choosing over options with relatively less

fluency/flexibility.

Hypothesis 4b. Options with enhanced originality/novelty will be


originality/novelty.

Hypothesis 4c. Options with enhanced divergence/deviancy will be


divergence/deviancy.

Hypothesis 5. Options with operationalised creativity elements will be

perceived to have negative attributes when rejecting.

H5a Options with relatively less fluency/flexibility will be preferred when

rejecting over options with enhanced fluency/flexibility.

H5b Options with relatively less originality/novelty will be preferred

when rejecting over options with enhanced originality/novelty.

H5c Options with relatively less divergence/deviancy will be preferred

when rejecting over options with enhanced divergence/deviancy.

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Method

Participants

The participants in this study were 107 postgraduate students (of whom 68

were currently also employed full time or part time) and 39 professional

employees. The postgraduate students were studying business either within a

business or creative industries faculty. The international nature of these

business programs ensured that participants were from a wide range of

nationalities, though most of the participants were Australian. Both genders

(81 males, 65 females) and a range of ages (18–55 years old) were present in

the sample. More than two thirds of the student participants were enrolled

part time (typically working full time as a professional or a manager) and the

rest were full time students. Participants prior MBA education ranged from nil

(this was their first class) to almost complete (several participants had to

complete only this class to graduate). Entry requirements ensured that all

participants had at least two years of work experience. Some students were

able to enrol in the program without an undergraduate degree provided that

they had extensive work experience. Thus the sample included current and

aspiring real world professionals.

The business employees were managers from a wide range of industries

including construction, mining, law, government, insurance, logistics,

consulting, energy, information technology and banking. Again, both

genders and a wide range of ages were represented, though the participants

were almost entirely Australian. This group was chosen from my past and

current consulting clients. Thus overall, despite the convenient nature of the

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combined sample, it can be considered as maximally variant as defined by

Patton (1990) and therefore representative of employees in general.

Participants were told that the broad purpose of the exercise was to

investigate creative motivation. Students were told that their participation

would have no effect on student grades for the class, and that participation

was voluntary. All participants were told that their individual responses would

remain confidential and that the data would only be viewed in aggregate.

Procedure

Participants were asked to complete a questionnaire comprising 25

questions. There were two versions of the questionnaire (Set One and Set

Two). Both versions had 18 similar questions. Set Two had seven questions

where decisions presented as choices in Set One were reframed as rejections.

Various comparisons of aggregate responses to pairs of questions from Set

One were made. Additional comparisons of aggregate responses to Set One

choice questions reframed in Set Two rejection decisions were also examined.

The questions were all presented as simple, forced choice, binary decisions.

All of the examples (more than 100) cited in Levin et al. (1998) require a

forced choice between two alternatives. Some questions were very similar to

those used in Tversky and Kahneman’s original design (1981) – for example,

see question 8 below. Others were based on Shafir’s (Shafir, 1993) attribute

framing research, modified to provide responses relevant to operational

creativity and innovation (see questions 9 and 10 below). Shafir (1993)

provides significant precedence and validation for using similar questions

within the same survey to test framing effects. The cognitive load of the task

was such that only three participants reported noticing a similarity between

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questions – for example, between questions 11 and 22 (shown further below).

Here is an excerpt from Set 1 including questions 8–10:

8 Suppose that you are in charge of a government

immunisation program to deal with an impending outbreak of a

rare disease: Lymphatic Anaemic Fever (LAF). This disease is

expected to kill 600 people. Two alternative programs have

been proposed to combat the disease. Which program would

you favour if costs for each program are the same?

A If Program A is adopted, 200 people will be saved.

B If Program B is adopted, there is a 1/3 chance 600 people

will be saved and a 2/3 probability that no one will be saved.

9 Suppose you are driving a friend to an important medical

examination and they advise you that they got the time wrong

and need to hurry. Which of the following routes would you

reject?

A The route you would normally travel.

B A potentially faster route with a short off-road section that

will be uncomfortably bumpy.

10 Imagine you have to make a decision about two similar

candidates for a job. Which of the following would you choose?

A The candidate who prioritises innovation.

B The candidate who prioritises compliance.

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Question 8 is an example of a risk based framing decision (framed in terms

of potential gains) repeated from Tversky and Kahneman’s original studies.

Responses to this question provided a basis for confirming that the sample

was representative. However, the majority of questions were new and

designed specifically for this study.

Some participant responses enabled preferences for innovation and

creativity to be evaluated. Question 9 is an example of how originality was

operationalised. To solve the problem of being late, Option B is a relatively

original approach compared to Option A. Question 10 is a simple attribute

framing decision for testing innovation preferences in one context.

In addition to examining responses to Question 10, participant preferences

for “innovative” as a descriptor could be determined comparing the results of

questions 11 and 22 below.

11 Suppose your company can invest in one of two new

products to be developed, based on your recommendation.

Which product would you favour if investment costs are the

same for each?

A Product A: 33 per cent chance of making $60 Million

returns.

B Product B: 80 per cent chance of making $25 Million

returns.



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same for each?

A Product A is a very innovative product: 33 per cent

chance of making $60 Million returns.

B Product B is a non-innovative product line extension: 80

per cent chance of making $25 Million returns.

In question 11, Product B is typically preferred by more respondents

because it is less risky, even though it produces potentially lower returns. It is

evident that question 22 is exactly the same as question 11, except that the

least preferred option (Product A) was additionally described as “innovative”,

and the most preferred option (Product B) was described as “non-

innovative”. Thus any significant change in the preferred options between the

two questions would suggest whether or not “innovative” as a descriptor was

perceived more positively or negatively in a risk based framing context. A

change in preference could be due to “innovative” being associated with

simple attributes (positive or negative), extreme attributes (both positive and

negative) and/or risk.

Tversky and Kahneman’s (1981) study provides a test for whether or not an

option is risky: If an option is equally preferred regardless of whether the

decision is framed in terms of gains or losses, then it is not perceived to

increase risk. But if the preference for an option changes depending on

whether the choice is presented in terms of potential losses or gains, then the

option is perceived to include risk. The option is perceived as more risky than

its alternative if it is preferred in decisions framed in terms of potential loss and

not preferred in decisions framed in terms of potential gain. The reverse of this

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also applies: the option is perceived as less risky than its alternative if it is

preferred in decisions framed in terms of potential gain and not preferred in

decisions framed in terms of potential loss.

Questions 11 and 22 above are “positively” framed because they present

the decision in terms of risky, potential gains. Questions 17 and 24 below show

the corresponding “negatively” framed options.



Which product would you favour if investment returns are the

same for each?


loss.

B Product B: 80 per cent chance of making $25 Million loss.




same for each?

A Product A is a non-innovative product line extension: 20

per cent chance of making $100 Million loss.

B Product B is a very innovative product: 80 per cent

chance of making $100 Million loss.

So if A (the innovative option) is preferred in Question 22 and B (again the

innovative option) is preferred Question 24, this would suggest two

conclusions. Firstly, that just describing an option as innovative made it

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somehow more preferred in this context. Secondly, innovation would be

perceived as not increasing or decreasing risk in this context. Overall

innovation in this context could be considered as a net positive attribute that

did not implicitly invoke risk based framing effects.

Shafir’s work provides a test for whether or not an option is extreme: if an

option is preferred regardless of whether the decision is framed as either a

choice or a rejection, then it simply contains positive attributes. If an option is

not preferred regardless of whether the decision is framed as either a choice

or a rejection, then it simply contains negative attributes. But if the preference

for an option changes depending on whether the decision is presented as a

choice or rejection, then the option is extreme (that is it contains both positive

and negative attributes).

In order to determine whether or not innovation and/or operational

creativity elements were extreme, two sets of questions were created. The

difference between Set 1 and Set 2 questions was that 17 of the Set 2

questions were presented as a rejection of one option, rather than a choice

of one option.

For example, consider Question 22 in Set 1 and Set 2. In Set 1 the decision is

framed as a choice between A and B. In Set 2 the decision is framed as a

rejection of A or B. In each set, A is the innovative option. If A was chosen in

Question 22 Set 1 and yet rejected in the similar Question 22 Set 2, this would

suggest that the innovative option was considered extreme (that is,

contained both positive and negative attributes). If option A was preferred in

both Sets then innovation would be a simple, positive attribute. If option A

was not preferred in both Sets then innovation would be considered to be a

simple, negative attribute.

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Table 1 summarises all the tests undertaken in this study:

TABLE 1 [Framing Effects Tested in this Study]

Perceptual

Bias Framing

Effect Decision

Type Innovation Operational

Creativity Expected

Result

Positive attribute All Choose

Tested

Tested Prefer

Negative attribute All Choose

Tested

Tested Reject

Extreme attribute Attribute Choose

Tested

Tested Prefer

Extreme attribute Attribute Reject

Tested

Tested Reject

Risky option Positive risk Choose

Tested

Not Tested Reject

Risky option Negative

risk Choose

Tested

Not Tested Prefer

Results

Data collection efforts yielded two quantitative data sets – selected

options (A or B) for each survey question in each of the two survey sets. To

determine if various innovation and creativity framing effects existed, various

pairs of similar questions (intra set and inter set) were compared. Chi-squared

analyses were used to determine if the distribution of responses from each

pair of comparable questions was significantly different.

Limitations of the Experimental Design

Potentially confounding variables in this experimental design were those

related to question interpretation heterogeneity, learning effects, class

environment, and interviewer response bias. The various questions proposed

were from very different knowledge domains (including investment, health

effects, recruitment decisions, career decisions and travel decisions).

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Comparisons between responses were limited to questions in the same

knowledge domain to avoid confounding results due to domain differences.

From this perspective, the heterogeneous nature of the problems provides

support for the generalisation of the results to other unrelated problem

contexts.

As noted above at least three participants realised the similarity between

the questions that I intentionally paired within the survey. This suggests that

learning effects may be present. Unfortunately, previous research has not

been reported in significant detail to determine if learning effects were

prevalent. Learning in this context is a confounding variable because the

desire to make choices consistently could overwhelm or distort responses

designed to highlight framing effects. Learning effects are also potentially

confounding if they apply between otherwise unrelated questions within the

survey. Given the cognitive load of the surveys, overall learning effects are

expected to be low. Ex post discussions with respondents provided anecdotal

support for this assertion. This is further supported by the agreement of the

results from this study with previously accepted research. Specifically, the

participants’ responses to unmodified framing questions (copied from prior

studies) were equivalent to those previously reported.

This leaves only effects due to the survey environments as potentially

distorting. Research respondents were able to undertake surveys in a range of

environments: at work unsupervised, at work with my supervision, as the first

exercise in a class related to creative problem solving that I taught, and in

classes given by other lecturers unrelated to creative problem solving. Some

class environments would be expected to promote innovation and creativity

as positive attributes. This would manifest as additional extrinsic motivation in

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the form of both control (that is, implied expectation) and intangible reward

(that is, recognition) in favour of creativity and innovation. If such an effect

had occurred, then the aggregate responses for questions from the

managers/professionals in the sample would appear to be significantly

different to the aggregate responses from the students. Chi-squared analyses

of responses from all Set 1 respondents (based on 39 managers and 63

students) did not reveal any significant differences for any of the questions.

The lack of significant differences seems to confirm that class specific

environmental effects are minimal and able to be ignored.

The final potentially confounding variable was interviewer response bias. It

would be expected that students in a creative problem solving class would

over report the need to be creative or innovative to the lecturer. If this was

true then aggregate responses from students in the creative problem solving

class in which I was the lecturer should be significantly different to the

aggregate responses of students from other classes. (The other classes were

related to leadership and marketing communications.) A second set of chi-

squared analyses of responses from all Set 1 student respondents (based on

22 lectured students and 41 other students) similarly failed to reveal any

significant differences for any of the questions. The lack of significant

differences seems to confirm that interviewer effects are minimal and able to

be ignored.

In summary, the main concerns for the study design were the presence of

potentially confounding variables that would be expected to distort

responses. Careful analysis of the responses suggests that none of these

confounding variables exhibited significantly measurable effects in this study.

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Risk Based and Attribute Framing Results

Certain questions in the surveys essentially repeated prior studies’

investigations of risk based framing effects and attribute framing effects.

Figures 1–3 show that the sample responded similarly to risk based framing

effects as those participants tested by Tversky and Kahneman (1981). That is,

participants significantly (p<0.01, χ²=47.3, χ²=25.0, χ²=6.9 for Figures 1,2 and 3

respectively) preferred smaller, less risky, positive outcomes over larger, more

risky, positive outcomes for three different domains.

FIGURE 1

Expected Risk Based Framing Effects [Gambling]

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FIGURE 2

Expected Risk Based Framing Effects [Saving/ Losing Lives]

FIGURE 3

Expected Risk Based Framing Effects [Company Investments]

Similarly three of the attribute framing results are in accord with those

reported in Shafir’s (1993) prior studies. A significant reversal of preference was

evident for decisions framed as a rejection of the least attractive option,

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rather than selection of the most attractive option. Shafir showed that when

selecting, participants tended to focus on positive attributes, and when

rejecting they tended to focus on negative attributes. Figures 4–6 show a

confirmation of these findings. All three reversals of preference are significant

(p<0.01 χ²=11.3, p<0.1 χ²=3.5, p<0.1 χ²=3.6, respectively).

FIGURE 4

Expected Attribute Framing Effects [Gambling Loss]

FIGURE 5

Expected Attribute Framing Effects [Gambling Win]

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FIGURE 6

Expected Attribute Framing Effects [Company Investment]

Note that even though figures 4–6 relate to questions involving risk, the

framing effect being measured is attribute framing. This is because the

second question in each figure is framed as a rejection rather than a choice.

The agreement of these results with Shafir’s (1993) study, whilst not

surprising, is important because it further establishes the representative nature

of the sample to support claims that other findings can be generalised.

Participants exhibited a significant reversal of preference (combined

p<0.01, d.f. = 5 χ²=23.9 for all three tests; and p<0.01 χ²=12.6, p <0.05 χ²=4.6,

p<0.05 χ²=5.0 for tests in Figures 7,8 and 9 respectively) when some of the

choices presented above were modified to include descriptors relating to

innovation (see figures 7–9). Figures 7–9 show comparisons for various

decisions involving innovation as a descriptor and risk based framing and

provide support for hypothesis 1.

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FIGURE 7

Innovation Preferences and Framing Effects [Company Investments Gain]

FIGURE 8

Innovation Preferences and Framing Effects [Company Investments Loss]

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FIGURE 9

Innovation Preferences and Framing Effects [Saving Lives]

The consistent preference for the innovative option in both positive and

negative risk framed decisions in figures 7–9 suggests that innovation is not

perceived to increase risk. Participants also preferred innovative employees

over compliant employees whether choosing or rejecting. Figure 10 also

shows how the preference for innovative employees was significantly weaker

when rejecting (p<0.05 χ²=4.6). This suggests that innovation is perceived as a

net positive, extreme attribute. This provides support for both hypothesis 1 and

hypothesis 2.

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FIGURE 10

Innovation Preferences and Framing Effects [Employee Choice]

Operational Creativity Preferences and Framing Results

Four aspects of operational creativity were tested for possible

attribute framing effects: fluency/flexibility, divergence/deviance,

originality/novelty, and rule breaking. Figure 11 shows a significant

(p<0.05 χ²=4.4) reduction of preference when a decision regarding a

more creatively fluent employee is reframed in terms of a choice to

reject. This suggests that creative fluency (at least in employees) is a

preferred quality when choosing and is subject to attribute framing

effects. The results shown in figure 11 provide support for hypothesis 4a

and hypothesis 5a.

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FIGURE 11

Fluency Preference and Attribute Framing Effects [Choosing an Employee]

Figure 12 shows a similar significant reduction of preference under attribute

framing conditions for decisions involving creative divergence: choosing an

employer (p<0.01 χ²=8.4). This suggests that creative divergence (in some

situations) is a preferred quality when choosing and is subject to attribute

framing effects. This provides support for hypothesis 4c and for hypothesis 5c.

FIGURE 12 Divergence Preference and Attribute Framing Effects [Choosing a Company]

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Figure 13 shows a similar significant (p<0.01 χ²=19.3) reversal of preference

under attribute framing conditions for a decision involving a novel route. This

suggests that creative novelty (in some situations) is a preferred quality and is

subject to attribute framing effects. This provides support for hypothesis 4b

and 5c. In combination these results suggest that the various operational

creativity elements are all considered net positive attributes and are subject

to framing effects in some decision contexts. All of the options involving

operational creativity were to some extent extreme. This provides support for

hypothesis 4 and hypothesis 5.

FIGURE 13

Originality Preference and Attribute Framing Effects [Choosing a Route]

Discussion

The study’s results suggest that there are significant and important inherent

perceptual biases that apply to decisions involving innovation as a descriptor

and aspects of operational creativity. Innovation as a descriptor does seem

to be associated with inherent attribute framing effects. In general, describing

an option as innovative significantly increases the preference for this option

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when choosing for decisions involving both risk based and attribute framing

effects. This supports Hypothesis 1. The results show that describing an option

as innovative significantly decreases the preference for this option when

decisions are framed as rejections rather than choices. This provides some

support for Hypothesis 2 and suggests that to some extent describing an

option as innovative activates positive and negative perceptual biases. The

lack of complete reversal of preferences when rejecting options described as

innovative (there was a reduction in preference only, however this was

significant) suggests that innovative as a descriptor is somewhat extreme. That

is innovative is, in general, perceived as mostly positive with some lesser

negatives.

Overall, the results suggest that risky options will be perceived as more

attractive if described as innovative, regardless of positive or negative

decision framing. This rejects Hypothesis 3 that the term “innovative” is

perceived to increase risk because if it did an option described as innovative

would be expected to be significantly less preferred in positively framed

decisions. Specifically, participants exhibited preferences for options

described as innovative that was even more powerful than the risk based

framing effects discovered by Tversky and Kahneman (1981) in the simple

binary decisions studied.

Overall, participants perceived “innovative” as a net positive attribute in

several different decision domains. Their preferences also suggested that

describing an option as innovative did not affect the perception of the

option’s inherent risk. It appears that simply describing a risky option as

innovative makes the option appear so attractive that this overcomes

concerns regarding the risks (to some significant extent) due to attribute

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framing effects. This could suggest that managers can influence employees

to be more creative by describing certain options as innovative (even though

creativity may be perceived by employees as risky). However, caution is

advised.

Managers attempting to leverage this innovative descriptor perceptual

bias, need to be mindful that the decisions tested presented innovative

options as an objective aspect they could choose immediately, rather than

as a potential gain (some time in the future) to be achieved from a change in

behaviour. Employees deciding whether or not to pursue an approach that

could yield an innovation are likely to be subject to goal behaviour framing

rather than attribute framing. Thus they may still decide not to undertake the

behaviours required to produce innovations. Attribute framing effects are

best used as a way of influencing choices after the innovation has been

developed. Describing something as innovative does not seem (after the

thing has been created) to increase the perception of risk. However,

innovative options are apparently perceived as somewhat extreme. This

suggests that decisions involving innovation are more likely to be pro

innovation when presented as a choice rather than as a rejection. This is

similar to the findings relating to operational creativity framing effects.

Attribute framing effects inherently applied to decisions involving

operational creativity expressed as fluency/flexibility. Participants significantly

preferred employees that worked on many possible solutions rather than a

few probable solutions (approximately 2:1) when choosing. When rejecting

there was only a slight preference for the more fluent employee, with the

results almost equally split between employees that worked on many possible

solutions and those that worked on only a few probable solutions. This

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supports both Hypothesis 4a and partially supports Hypothesis 4b: creative

fluency/flexibility is perceived as a positive, somewhat extreme attribute (that

is, fluency/flexibility is perceived positively overall, but with some inherent

negative attributes that are highlighted in decisions framed as rejections).

Perhaps a less focussed employee might be perceived to take longer to

complete tasks. The results suggest that participants were not very sensitive to

such negative attributes, and that fluency/flexibility was perceived mostly as

a positive attribute.

A similar result was found for divergence in two tests: a more divergent

company employer and a more innovative (less compliant) employee were

preferred (approximately 3:1 and 4:1 respectively) over less divergent options

when choosing. When rejecting the company employers, respondents

exhibited only a slight preference for the more divergent option. When

rejecting employees, the less compliant employee was still preferred (though

this dropped from around 4:1 to around 2:1). These results support Hypothesis

4c, and somewhat support Hypothesis 5c: creative divergence is perceived

as a positive, somewhat extreme attribute similar to creative fluency above.

Operational creativity expressed as originality was perceived as more

extreme.

A significant reversal of preference was found for the relatively original

idea of travelling off-road to a medical appointment to get there on time.

When framed as a choice preference, 3:1 were in favour of the more original

option, but framed as a rejection, the preference was 2:1 against the more

original option. This supports Hypothesis 4b and 5b, and suggests that

originality is perceived as a bundle of positive and negative attributes

(making it an extreme option). It may be that risk based framing effects also

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apply to originality, though this cannot be concluded from the available

data. Of course, the result may measure more about the participants’

perceptions of travelling off-road than choosing an original route – so further

work is required to establish how far this conclusion can be generalised.

Despite the former caveat it does appear that in general attribute framing

effects are inherently invoked by aspects of operational creativity, though

they apply differently to different operational creativity aspects. In general,

participants preferred operational creativity aspects for some decisions when

choosing, and this preference generally lessened when rejecting. It seems

likely that most operational creativity aspects are not associated strongly with

risk, but they are considered at least somewhat extreme.

There was one decision where the preference for originality increased

when the decision was reframed from choosing to rejecting. This suggests

responses to decisions involving operational creativity are context specific,

and whilst fluency/flexibility and divergence/deviance operational creativity

aspects are mainly perceived positively, originality/novelty may be

associated with positive and negative aspects including risk.

Unfortunately, the results do not support a conclusion regarding whether or

not risk based framing effects apply to operational creativity aspects

because it was impossible to conceptualise and pose decisions involving

creativity under uncertain loss. It seems reasonable, however, to conclude

that the dominant framing effects associated with operational creativity are

attribute (not risk based) framing effects due to the fact that they were

generally perceived as preferable. These findings lead to some interesting

implications for managers who wish to utilise framing effects to increase (or

decrease) creativity and innovation within their organisations. However, due

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to the context specific nature of operational creativity framing effects, it is

likely that these elements would be difficult for managers to utilise, even

without the complications associated with goal behaviour framing effects. It is

expected that framing effects are far easier to leverage with innovation

descriptors rather than with the operational creativity inherent in options.

Given the large number of tests where significant differences in

preferences were found it would seem that this study does offer valid findings.

However the large number of tests conducted increases the potential to find

significant results merely due to random effects. This cumulative Type I error

can be managed by determining a significance level based on the Sidak-

Bonferroni treatment see Shaffer (1995). Applying this calculation for the 9

significant items found, only results with p<0.002 should be considered as non

random; the other results above should be interpreted with caution. However

this correction approach might be too conservative because it increases the

potential for Type II errors even as it reduces the risk of Type I errors. There is

quite a reasonable chance that at least one test result could appear to be

significant to p<0.05 out of 25 tests when in fact no significance really existed.

However there is a much smaller chance that 9 in 19 tests would appear

significant just due to random effects. The chance for this can be calculated

as less than 0.0003 (for the derivation and calculations see Dew, 2008). This

suggests that the high proportional of apparently significant results in this study

should be considered as significant, because the potential for cumulative

Type 1 family wise error is vanishingly small.

The second potential concern regarding these tests is whether or not they

should be grouped together. This is not normally the case in prior studies of

framing effects due to the context specific nature of the decisions and

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framing effects involved. In order to validate the use of independent chi

squared analysis of questions, various groups of related questions were

constructed and Cornbach's Alpha scores were calculated for each. The

results of this analysis are shown below in Figure 14.

FIGURE 14

Validation of Independence of Questions

Group Questions KR20 n

Fluency/ Flexibility 1,6,18 0.200 100

Originality/ Novelty 12, 25 0.362 101

Divergence 3,7,9,10,20 0.306 101

Rule Breaking 4, 23 0.427 102

Risk based framing 2,5,8,11,14,17,19,22,24 0.244 97

Personal money 2,5 0.303 101

People's lives 8,14,19 0.488 100

Product Investment 11,17,22,24 0.224 97

All 1-25 0.422 95

All of these results are below the 0.7 value normally used to validate scale

consistency. This suggests that the tests are not consistent and therefore are

not testing the same things, so grouping chi squared results is not necessary or

appropriate.

This finding is somewhat confounding at first, because questions in the

same group should theoretically be measuring the same thing. If this was the

case it would be expected that their Cornbach's Alpha scores would indicate

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consistency. The reason for this discrepancy relate to the other contextual

aspects involved in the various questions. In particular (as with other framing

effects studies cited above) context seems to be a critical moderating

variable in how framing effects and preferences for creativity are evaluated.

Conclusion

Creative motivation seems to be affected by framing in complex ways.

Framing specific options with some operational creativity aspects may make

these alternatives generally more attractive in decisions framed as choices.

Describing an option as innovative will to some extent overcome risk concerns

and in any event, increases the preference for the option. However, whether

or not the perceptual distortions due to these framing effects are enough to

encourage behavioural change has not been tested. Thus managers who

wish to increase creativity and creative motivation in general should consider

describing certain decision alternatives as innovative in order to make them

appear more attractive and less risky. They should also try to frame decisions

involving innovation descriptors and operational creativity aspects as choices

rather than rejections. These influencing effects are expected to be weaker

under goal behaviour framing conditions (that is, when the innovative or

creative aspect of an option is less certain, less tangible and less immediate).

This is an important finding because framing effects are by their nature

external, and hence can act as surrogates for extrinsic motivation. Recall that

Amabile’s body of work (as outlined above) suggests that extrinsic motivators

almost universally dampen creativity, even when designed to enhance. These

findings support research that suggests certain kinds of supervisor approaches

enhance employee creativity (for example see George & Jing, 2007). It may

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be that these supervisors actually instinctively used innovation descriptors and

operation creativity based framing effects in a way that enhanced employee

creative motivation. Further work is required to understand how relevant these

framing effects are to encourage innovation and creativity in terms of

behavioural change. In any event, it seems that framing effects can provide

a way to externally leverage inherent perceptual biases. By using framing

effects managers can activate intrinsic motivators for decisions involving

extant creativity and innovation, and perhaps enhance potential creativity

and innovation motivation during ideation.

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References

Amabile, T. M. (1997). Entrepreneurial creativity through motivational synergy.

Journal of Creative Behavior, 31(1), 18-26.

Amabile, T. M. (1997). Motivating Creativity in Organizations: On Doing What

You Love and Loving What You Do. California Management Review,

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Chapter 3: Cognitive Style, Creativity and Framing Effects

Abstract

This study investigates how individuals with different cognitive styles

respond to choices involving framing effects. The results suggest that

cognitive style as defined by Kirton (1976) is far more complex than

previous studies indicate. Kirton characterises “Innovators” as rule

breakers and “Adaptors” as conformists. The most important finding of

this study is that in some decision contexts, Innovators and Adaptors

exhibit similar preferences for rule breaking. In other situations, Adaptors

actually prefer non-conformity in comparison to Innovators. The study

analysed responses from 146 university students and professional

managers to 25 binary choices involving investment decisions, job

choices and travel routes. The questions were constructed to reveal

significant reversals of preference related to risk and attribute based

framing effects. Additionally, some questions were constructed to

reveal preferences for certain operational aspects of creativity. Overall,

the results suggest that framing effects may provide an important tool

for unlocking individual creativity in organisations, as long as cognitive

style and context are carefully taken into account.

Keywords: Framing Effects, Creativity, Motivation, Cognitive Style

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Introduction

Employee creativity is essential for organisational effectiveness (Woodman,

Sawyer & Griffith, 1993). However, organisations seem to be constructed by

default to prevent creativity. Assink (2006) explains how factors including a

successful business strategy, risk-reducing culture, and reliance on previously

successful mental models can reduce firms’ innovation capabilities. Elsbach

and Hargadon (2006) argue that overwork and high pressure for performance

are significantly damaging to professional creativity, and advocate

recuperation periods of so called “mindless” work to improve this

predicament. These examples are symptomatic of a general management

paradox: the management approach required to foster organisational

creativity appears to be the antithesis of good management.

This apparent paradox may explain why organisational creativity is such a

challenge, according to Välikangas and Jett (2006), and why Leavy (2002) is

critical of organisational attempts to manage creativity over the last decade.

Ultimately, creativity within an organisational context is affected by individual

decisions about whether or not to choose “creative” options over more

“traditional” options. Whilst there is a growing body of research that relates to

how the organisational environment affects these kinds of choices, there is no

work examining how the framing of choices and an individual’s cognitive

style affect decisions involving creativity.

This study investigates whether or not framing effects apply differently to

individuals with different cognitive styles when making decisions, including

those that involve relatively creative and normal alternatives. The study uses

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Kirton’s (1976) classification for individual problem solving preferences to

measure cognitive style. This classification – the Kirton Adaption–Innovation

Inventory (KAI) – is specifically relevant to decision making in organisational

contexts because according to Kirton’s research, this variation in individuals’

cognitive styles elicits different responses to organisational cues relating to

creativity.

This paper investigates how framing effects and cognitive style affect

employee decisions involving creative alternatives. Amabile’s research (1998)

shows that extrinsic motivators (even rewards) tend to dampen creativity.

Most employee management controls are by definition extrinsically

motivating (and so damaging to creativity). Framing effects are interesting

because despite being externally imposed, they cause internal perceptual

distortions. Thus, framing effects may be useful for managers to solve the

problem of how to utilise extrinsic controls without dampening creativity.

It is expected that individuals’ cognitive styles vary the responses to framing

effects, because of different sensitivity to some aspects of a decision. For

example, Kirton suggests that Adaptors are more inclined to make decisions

that conform to the status quo rather than break rules, as Innovators prefer to

do. Thus, a decision framed in a way that highlights how an option conforms

to or disrupts the status quo would be expected to result in different

preferences for Adaptors and Innovators in general.

This paper investigates key features of cognitive style, creativity in

organisations and framing effects. Experimental results are presented that

compare decisions made by participants with different cognitive styles.

Analysis of these results provides a deeper understanding of how cognitive

style affects decisions involving framing effects and creativity. Finally, some

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implications for managers are presented that suggest how to frame

employee decisions in order to enhance organisational creativity.

Cognitive Styles

Kirton’s KAI (1976) is a measure of cognitive style that describes individual

problem solving preferences in an organisational context. KAI has been

empirically validated by many researchers (Keller & Holland, 1978; Goldsmith

& Matherly, 1987; Taylor, 1989; Foxall & Hackett, 1992; Riley, 1993; Fleenor &

Taylor, 1994) and is considered a reliable and consistent measure of cognitive

style. A KAI score for cognitive style ranges from 32–160 and can be

determined from 32 question responses. The overall normal population

exhibits a mean KAI of 96 with a standard deviation of 13.

The KAI scale was synthesised from three independent scales relating to

problem solving: originality (relating to preferences for unusual, unorthodox or

novel ideas); efficiency (relating to orderly, appropriate and detailed

behaviour); and conformity (acceptance of group norms, paradigms and

prevailing rules). Innovators report KAI scores greater than 96. Adaptors

typically report lower scores. According to Kirton’s descriptions, Innovators are

motivated to break rules and make large changes, whereas Adaptors prefer

to conform to current rules and make incremental changes. Kirton (1976)

pejoratively describes Adaptors as “preferring to do things better”, and

Innovators as “preferring to do things differently”.

Adaptors solve problems differently to Innovators – they are less likely to

propose radical solutions, and they are more likely to completely implement

known problem solutions. Hammerschmidt (1996) showed that cognitive style

determined a role preference for either designing or implementing solutions.

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The reasons for this may lie in the other aspects of cognitive style reported by

other researchers.

For example, self esteem positively correlates to KAI scores (Keller & Holland,

1978; Houtz, Denmark, Rosenfield & Tetenbaum, 1980; Goldsmith & Matherly,

1987). In these studies, self esteem (an individual’s sense of importance or self

worth) was generally reported as higher for Innovators. Similarly, Innovators

are significantly more tolerant to ambiguity (Keller & Holland, 1978) as a

group, and are more likely to exhibit an internal locus of control (Keller &

Holland, 1978; Tetenbaum & Houtz, 1978; Houtz et al., 1980; Engle, Mah &

Sadri, 1997; Luck, 2004). Wunderley, Reddy and Dember (1998) also found

Innovators are more optimistic. These characteristics are potentially important

for exhibiting creativity in organisations.

Within an organization, there is often a general requirement to overcome

management controls in order to be creative in many problem solving and

decision contexts. This can result in creativity being perceived as non-

conforming or deviant. Thus, Innovators are often perceived to be more

creative (or at least willing to be more creative) within organisations, despite

Kirton’s (1978) assertion that neither cognitive style is inherently more capable

of creativity. Measurements of creativity are required to understand these

conflicting assertions.

Creativity in Organisations

Creativity in business can be measured by creativity tests and used as a proxy

for quantifying creativity. Cropley (2000) identified at least 255 different tests

for measuring creativity, but argued that subjective creativity tests were not

reliable predictors of creative outputs. Commonly used objective tests include

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the Torrance Test of Creative Thinking (Torrance, 1962), Mednick’s Remote

Association Test (Mednick, 1962; Mednick & Mednick, 1967), the Creativity

Index (Gough, 1981) and the Rainmaker Index (Stevens, Burley & Divine,

1998).

Guilford’s (1967) Divergence Test (GDT) is useful in business settings for

measuring creative outputs because it is simple and reliable. GDT measures

creativity in three ways: fluency, flexibility and originality. Fluency is the GDT

measure of creative volume – having more options to solve a problem equals

more creative fluency. Flexibility is a measure of spread – having more distinct

categories of potential solutions means more creative flexibility. Fluency and

flexibility are essentially counts of the options proposed and of their different

categories, respectively. Originality is a measure of novelty or unusualness. It is

determined by how rare the options proposed are in comparison to the

normal population’s responses. GDT provides definitions for operationalising

creativity. A creative option may offer more fluency, flexibility or originality

than a more traditional (i.e. relatively non-creative) option. As discussed

above, decisions involving both creative and non-creative alternatives will be

subject to framing effects.

Framing Effects

Levin, Schneider and Gaeth (1998) classified three types of framing effects:

risk based, attribute, and goal behaviour. Research has subsequently

validated the independence of these effects (Levin, Gaeth & Schreiber,

2007). Risk based framing and attribute framing are applicable to this study.

Goal behaviour framing was not applicable to the methodology of this study.

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Tversky and Kahneman (1981) discovered risk based framing effects and

showed how perceptual distortions reduced rationality in decision making.

Subjects tended to choose sure gains over larger risky gains. However, if a

similar decision was framed in terms of its potential losses, subjects preferred

risky larger losses over unavoidable smaller losses. Tversky and Kahneman

discovered that for decisions involving potential gains and/or losses, how the

decision is presented can significantly influence the decision maker’s

preference.

It is apparent that risk based framing would be expected to be correlated to,

or moderated by cognitive style. Innovators would be expected to be more

prepared to take risks than Adaptors due to their preference for rule breaking

and tolerance for ambiguity. To the extent that creative options can be

perceived as risky or variable decisions within organisations, framing effects

should apply to decisions involving creative and non-creative alternatives.

Risk based framing is not the only framing effect to have been investigated

previously. Another kind of framing effect, “attribute framing”, may also be

correlated to or moderated by cognitive style. Attribute framing effects relate

to how different elements in a decision may be weighted due to the way that

the decision is presented. Shafir (1993) presented subjects with choices that

involved both positive and negative attributes. Unlike the risk based framing

decisions investigated by Tversky and Kahneman, Shafir’s alternatives did not

include quantifiable risks.

Shafir contrived one option to be enriched with more positive and more

negative features combined, when compared with a more moderate

alternative. He then asked participants to either choose or reject one of the

options (the enriched option or the moderate option). When decisions were

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presented a choice, more subjects tended to prefer the enriched option.

However, when the same decision was reframed as a rejection of one option,

more subjects tended to prefer the moderate option.

Shafir attributed this reversal of preference to positive and negative framing.

When dealing with decisions framed as a choice, participants tended to

consider only the positive aspects of the alternatives, and thus preferred the

enriched option. (The enriched option had greater positive attributes than the

moderate option.) When the same decision was presented in a way that one

alternative had to be rejected, participants tended to focus on the negative

aspects of the alternatives. This meant that the enriched option was

perceived as worse than the more moderate option. Thus, depending on how

a decision was framed (as a choice or as a rejection), people were more or

less likely to prefer the enriched option over the moderate option. Shafir

called this enriched option the “extreme” option.

Attribute framing would be expected to apply differently to subjects

depending on their cognitive styles, especially if the enriched attributes

included ambiguous, non-conforming or non-efficient aspects. Often creative

alternatives are perceived as having more extreme positive and negative

attributes than more traditional solution alternatives. This suggests that

attribute framing effects are likely to apply to decisions involving creative and

non-creative alternatives.

Hypotheses

Framing effects have been examined in a variety of domains (e.g. consumer

behaviour, gambling situations, and health related contexts) and have been

assumed to apply universally. However, no empirical research could be found

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that investigated how cognitive style correlates to, or moderates framing

effects, even though decisions in an organisational context and decisions

involving operationalised creativity are affected by cognitive style. Thus, the

basic research question is how risk based framing effects, attribute framing

effects, and cognitive style interact with decisions including those that involve

operationalised creativity options.

In general, Innovators are expected to be less sensitive to risk on average due

to their relatively higher tolerance for ambiguity, optimism, self esteem, and

their preference for rule breaking. This would suggest that Adaptors are more

likely to have their decision preferences influenced by risk based framing

effects.

H1Innovators will tend to be less affected by risk based framing compared

to Adaptors

Adaptors and Innovators are likely to perceive operational creativity aspects

differently as well. Operationalised creativity alternatives are expected to be

more preferred than normal options by Innovators (compared to Adaptors)

regardless of the type of operationalised creativity involved in the decision.

Operationalised creativity options inherently increase ambiguity by

generating more options to solve the problem at hand. Operationalised

creativity presents non-conforming approaches when compared to reuse of

tried and true solutions. Finally, operationalised creativity is typically less

efficient when it results in more work and increasing complexity.

Preference for operationalised creativity can depend on what inherent

aspects are being considered. For example, when comparing a less creative

option to a more fluent option, a decision maker may compare positive or

negative aspects of the two alternatives in making a decision. The fluent

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option includes more potential for success perhaps because it contains more

volume, but is also more work. If a decision maker focuses on success

potential, the fluency is preferred, but if they focus simply on the task at hand,

then fluency is rejected. Options that contain both positives and negatives

are called extreme options.

Innovators are expected to be more sensitive to the positives and less

sensitive to the negatives associated with operationalised creativity options.

This suggests that Innovators will not perceive operationalised creative options

as extreme options as defined above. Adaptors are expected to be more

sensitive to the perceived negatives of operationalised creativity options and

therefore perceive these alternatives (compared to more normal

approaches) as extreme.

Rule breaking is associated with creativity in organisational contexts (as a

special case of deviance or divergence). By its nature, rule breaking is risky

and therefore should be associated with risk based framing effects. Given

Kirton’s description of Innovators and Adaptors, rule breaking would be

expected to be positively attributed by Innovators relative to Adaptors.

H2 Innovators will more strongly prefer operationalised creativity

alternatives

H2a Innovators will prefer relatively more fluent/flexible options

H2b Innovators will prefer relatively more original/novel options

H2c Innovators will prefer relatively more divergent/deviant options

H2d Innovators will prefer relatively more rule breaking options

H3 Only Adaptors will perceive operationalised creativity alternatives

as extreme

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H3a Only Adaptors will perceive relatively more fluent/flexible options

as extreme

H3b Only Adaptors will perceive relatively more original/novel options

as extreme

H3c Only Adaptors will perceive relatively more divergent/deviant

options as extreme

H3d Only Adaptors will perceive relatively more rule breaking options

as extreme

Methods and Participants

This study was based on subjects’ responses to one of two questionnaires,

each with 25 binary decisions. Some questions were based on Shafir’s (1993)

attribute framing research, and the rest were the same or similar to questions

presented by Tversky and Kahneman (1981). The study participants included

39 managers and 107 postgraduate students. Whilst the study was a

convenience sample, it is also considered maximally variant, and therefore

representative of aspiring and current managers as outlined below.

The postgraduate students were enrolled in creative industries or business

faculties, and studying business. A wide range of nationalities was

represented due to the international enrolment in these programs, with

Australian students being predominant. Male and female subjects’ ages

ranged from 18–55, though neither variable was recorded. Most students

(more than two thirds) worked as full time managers and were enrolled part

time. The rest were enrolled as full time students. For some of the MBA

students, this was their first class, and for others, this was their final class prior to

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graduation. For admission into the course, students needed a minimum of two

years’ prior work experience. Some students had more extensive work

experience and were accepted into their course programs without an

undergraduate degree.

The business managers from a range of industries were represented in the

manager sample. These included banking, construction, consulting, energy,

government, information technology, insurance, law, and logistics. Whilst this

group was entirely Australian, a variety of ages and both genders were

represented.

All participants were informed that the study was related to investigating

creative motivation. Students were informed that their participation was

voluntary and their involvement (or choice not to participate) would not have

any bearing on their grades. All participants were informed that the data

would only be presented in aggregate, with individual answers kept

confidential.

At the beginning of the study, all participants completed an online KAI survey

that established their cognitive style. The KAI scores were used to group

Innovators’ and Adaptors’ responses together respectively for the

quantitative analyses. There were 95 Innovators and 51 Adaptors who

responded. The mean KAI score was 102.3 with a standard deviation of 14.5.

(A normal population has a mean KAI score of 96 with a standard deviation

of 13.) This sample mean is higher than the upper 95% confidence limit of 99.6

(for a representative sample); however, this does not confound the analysis.

Chi squared analysis enables comparisons between subgroups of different

sizes. As a result, any sample used does not need to be representative of the

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general population when comparisons between different subgroups (in this

case cognitive styles) are to be made.

Procedure and Instruments

The experimental questions were designed to measure participants’

responses to decisions involving risk based and attribute framing effects. The

questions also enabled comparisons of subjects’ preferences for

operationalised creativity alternatives over more normal options. All of the

questions were presented as a forced choice between two alternatives

similar to the many examples cited in (Levin et al., 1998).

Some questions related to risk based framing were adapted from Tversky and

Kahneman’s (1981) study. For example, see question 8 and 11 below:

8 Suppose that you are in charge of a government

immunisation program to deal with an impending outbreak of a

rare disease: Lymphatic Anaemic Fever (LAF). This disease is

expected to kill 600 people. Two alternative programs have

been proposed to combat the disease. Which program would

you favour if costs for each program are the same?

A If Program A is adopted, 200 people will be saved.

B If Program B is adopted, there is a 1/3 chance 600 people

will be saved and a 2/3 probability that no one will be

saved.



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same for each?


returns.

B Product B: 80 per cent chance of making $25 Million

returns.

Both of the above questions are framed in terms of potential benefits (lives

saved) or gains (money returns). Because of this, most participants were

expected to choose the more certain of the alternatives presented (option A

in question 8 and option B in question 11). Participants could additionally be

expected to choose the more risky alternative when similar decisions were

presented in terms of potential detriments and losses rather than gains. These

expected responses provided a method to validate that the overall sample

was representative of the normal population.

The main hypothesis testing was completed by comparing the responses of

Innovators to those of Adaptors for each question. Innovator (KAI>96) and

Adaptor (KAI<=96) sub groups were determined for each question and

analysed using chi squared methodology. This enabled any significant

differences in risk framing effects due to cognitive style to be determined in

order to determine H1’s validity. Different binary decisions were presented for

testing H2 and H3 using the same chi squared analysis of Innovator and

Adaptor sub group preferences. For example, question 9 below shows a

decision contrived to determine preferences for originality/novelty.

9 Suppose you are driving a friend to an important medical

examination and they advise you that they got the time wrong

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and need to hurry. Which of the following routes would you

choose?

A The route you would normally travel.

B A potentially faster route with a short off-road section that

will be uncomfortably bumpy.

In the question above, option B represents an operationalised creativity

alternative to taking a normal route. This alternative presents some potential

benefits (i.e. it could be faster), but it is also strange (non-conforming)

because it is off-road and at least partly detrimental due to being bumpy. If

H2 holds, Innovators would be expected to be less negative than Adaptors

about the off-road non-conformance aspect of option B. This would suggest

that Innovator preferences for option B should be significantly greater than

Adaptor preferences.

Note that this combination of benefits and detriments also suggests that

option B could be perceived as extreme. This can be tested using a method

derived from Shafir’s (1993) study. Extreme options tend to be preferred when

presented within decisions framed as a choice like the Question 9 example

above. They also tend to be rejected within decisions framed as a rejection.

Question 9 above could be reworded so that the decision maker was asked

which option they would reject. In this case, it would be expected that

preferences for option B would significantly reduce and potentially reverse

due to attribute framing effects. Asking decision makers which option they

reject focuses them on the negative attributes of both decision alternatives. In

this case, option A has no negatives, so it is potentially superior to option B

(which is bumpy and non-conforming). Again Adaptors would be expected

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to show this change in preference more significantly than Innovators,

because the non-conformance is expected to be perceived as a potential

positive by Innovators.

The comparison proposed above required two sets of questions to be

created. Set 1 questions were mostly framed as choices. In Set 2, 17 of the 25

questions from Set 1 were reframed as rejections in order to identify

operationalised creativity options that were perceived as extreme.

Limitations of the Experimental Design

Question interpretation differences, class environment, learning effects and

interviewer response bias all had the potential to bias the results in this

experimental design. Question responses that related to different fields of

expertise (including career decisions, health issues, investments, recruitment

choices and travel decisions) were not compared. The heterogeneous nature

of the decisions tested subsequently provides support for these results to be

generalised to other domains.

Confounding biases from effects due to the class environment were believed

to be minimal. Ex post comparison of manager responses to student

responses showed no significant differences between the two subgroups’

preferences for any of the decision options. Additional ex post comparison of

student responses from different classes (those where I was the lecturer and

other classes that had a different lecturer) again showed no significantly

different responses to questions. Both comparisons were analysed used chi-

squared tests. This analysis confirms that environmental effects are likely to be

negligible for this experiment. It also suggests that interviewer response bias

was also low enough to be discounted.

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A similar result was expected for learning effects biases. Unfortunately,

previous research does not seem to have taken account of potential learning

effects within question sets. It may be that subjects prefer to choose

consistently across a questionnaire rather than consider each question in

isolation. This desire could distort (enhance or diminish) responses in a way

that might falsely indicate highlight framing effects or cognitive style

preferences. Given the complexity of the survey questions, the fact that only

three subjects recalled a similarity between any of the questions during ex

post discussions, and given the agreement between this study’s results and

previous research, the cognitive load of the task appears to be high enough

to mitigate any significant learning effects.

In summary, the main confounding variables identified in the study design

were able to be eliminated by either ex post analysis of responses from

different subgroups or by comparison with data from previous research.

Results: Risk Based and Attribute Framing for the Entire Sample

Some questions in the questionnaires were adapted from prior risk based

framing effects studies. The responses to these questions for the entire sample

are similar to Tversky and Kahneman’s (1981) findings. Participants significantly

preferred smaller, more certain and positive outcomes over equivalent,

larger, less certain and positive outcomes in three different decision domains

(p<0.01). This preference for certainty was reversed when subjects were asked

to choose between certain negative outcomes and equivalent alternatives

that offered a chance for a larger loss or detriment, and a chance for no

negative result at all. Decisions included choices between different vaccines

used to save lives, decisions regarding company investments, and gambling

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alternatives for disposable income. Overall, the results suggest that the

subjects in the sample respond similarly to subjects involved in prior studies

related to risk based framing effects.

FIGURE 1. Expected risk based framing effects [gambling]

FIGURE 2. Expected risk based framing effects [saving/losing lives]

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FIGURE 3. Expected risk based framing effects [company investments]

The remainder of the results below relate to comparisons of responses for

Innovator and Adaptor sub groups. Significant differences in preferences and

sensitivity to some framing effects were revealed.

Results: Risk Based Framing and Cognitive Style

Of the 25 questions presented in Set 1 and Set 2, eight in each set were

contrived to include risk based framing effects. The decision contexts

included gambling decisions to potentially lose or gain $800 to $3000,

investment decisions in the range $25 million to $100 million, and vaccine

choices to try and save up to 600 people infected with exotic diseases. Some

questions were framed in terms of potential gains or lives to be saved, while

others were framed in terms of potential losses or deaths. The majority of the

responses (seven in each set) showed no significant preference differences

between Innovators and Adaptors. However, one similar decision in each set

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did show a significant difference between Innovators and Adaptors as shown

in figure 4. This result was the only one which offered evidence in support of

H1.

FIGURE 4. Risk based framing and KAI [personal loss]

In Set 1, Innovator respondents were significantly more likely than Adaptors to

choose the option with a chance of no loss (but also the chance of an

ultimately larger loss) over a certain loss (n=101, p<0.05). In Set 2, Innovator

respondents were significantly more likely than Adaptors to reject the option

with a chance of no loss, though the result was not as strong (n=44, p<0.1).

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This suggests that for this decision context (involving certain or potential

personal loss), Innovators and Adaptors respond differently to risk based

framing effects. However, for other decision contexts (involving certain or

potential, personal or company gains; and life and death vaccination

choices), there was no significant differences between Adaptors and

Innovators.

Note that there was no significant difference between Set 1 and Set 2

responses for Adaptors (n=36), but there was a significant difference between

sets for Innovators (n=66, p<0.01) as shown in figure 5. This suggests that

Innovators perceive the option of taking a risk of losing more money in order

to potentially avoid all losses as an extreme option. Adaptors apparently

perceive this option as simply preferred, regardless of how the decision is

framed. Both of these findings are unrelated to the various hypotheses

presented above.

FIGURE 5. Risk based framing and KAI [personal loss]

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Results: Fluency/Flexibility Preferences and Cognitive Style

Two tests of preference for fluency/flexibility were conducted. In the first test,

respondents were presented with an alternative to their normal travel route in

order to potentially avoid being late. The alternative was described as

offering more options to change route in the future which inherently

increased both its flexibility and complexity. Thus, this decision represented an

instance to determine preferences for fluency and flexibility due to the

increased options inherent in the alternative. Thus, this situation was framed in

terms of avoiding potential loss via either a creative or non-creative option.

The second test of preference for fluency/flexibility presented a similar

decision framed in terms of potential gain. In this circumstance, the same

options (a normal route or a more flexible, complicated and alternative

route) were presented as alternatives when potentially arriving hours early for

an overseas flight. In this situation, there was some potential gain related to

being able to spend time in some intrinsically rewarding way, rather than

waiting at the airport. Thus, this decision represented an instance to

determine preference for fluency and flexibility in a situation of potential gain.

However, the decision is less clear cut than the previous example because it

could be expected that some respondents might be happy to arrive early at

an airport before an international flight in order to do some shopping, or try

and be allocated a better seat position on the plane. Figure 6 shows the

responses to both questions.

In both examples above there is a significant difference between Adaptors

and Innovators preferences (p<=0.05 for potential loss and p<=0.10 gain

situations respectively). It appears that in a situation of potential loss (being

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late), Innovators are more likely to choose the fluent/flexible alternative. It also

appears that in the same situation Adaptors are more likely to choose the

normal option. However, in a similar situation of potential gain (being early),

Adaptors more strongly prefer the flexible/fluent alternative, even though

both cognitive style sub groups for the most part would prefer to arrive early

at the airport. This provides mixed signals regarding a decision to support or

reject H2 and H2a.

FIGURE 6 Operational creativity framing and KAI [fluency/flexibility]

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In order to test whether or not the fluent/flexible option was considered an

extreme option (i.e. that it contained both advantages and detriments),

responses to the above questions were compared to similar Set 2 questions

that were reframed as rejections rather than choices. No significant

differences were found between responses for either question when choosing

and rejecting by either the Innovator sub group, Adaptor subgroup or the

entire sample. This suggests that the fluent/flexible option is not considered

extreme by any participant group and refutes both H3 and H3a.

Results: Originality/Novelty Preferences and Cognitive Style

Two tests of preference for originality/novelty were conducted. In the first test,

respondents were presented with a choice between jobs that offered usual

or unusual work. It is clear that this decision offers obviously a less novel and a

more novel choice. Figure 7 shows that as expected Innovators exhibit a

significant preference for novelty (p<=0.05) compared to Adaptors. This

provides support for both H2 and H2b. It also shows that a large proportion of

subjects overall wanted interesting jobs, regardless of their KAI.

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FIGURE 7. Operational creativity framing and KAI [novelty]

Please note that the results shown in figure 7 identify the responses as coming

from “Q21 Set 3”. Where Set 1 and Set 2 questions were exactly the same, the

responses were combined to increase the number of responses able to be

analysed and named Set 3. Any reference to Set 3 throughout the results

section means combined responses from Set 1 and Set 2 questions that were

worded in the same way. In this example, the similarity of Set 1 and Set 2

questions meant that a test for unusual work as an extreme option was not

able to be tested. This means that H3b was not able to be tested with these

questions.

In the second test for originality/novelty, participants were presented with two

route alternatives from which to choose. The decision context related to

being asked by a client to arrive early if possible for a meeting. In this case,

the operationalised creativity option was a choice to stop driving 200m from

the meeting point and travel this remaining distance by foot. The option was

presented as being potentially attractive by virtue of its shorter overall

distance than the normal route option.

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The second decision example is less clearly about novelty or originality as

stated. It may be that running/walking the last 200m is an original and/or

novel approach for many who can conceptualise themselves in this situation.

Alternatively, this option could simply be considered divergent from the

normal approach, rather than novel or original (in which case, it would still be

useful for testing operationalised creativity preferences). Finally, it may be that

what is being tested for at least some respondents is their desire to enjoy a

walk, or leverage their client’s preference to meet early, having

conceptualised the context as a potential negotiation situation. Despite these

concerns about how the alternatives in the second test were conceptualised

by participants, the results show significant preference differences between

Innovators and Adaptors (p<=0.05). See figure 8.

FIGURE 8. Operational creativity framing and KAI [originality/novelty]

The most interesting aspect of this result is that compared with the Innovators,

the Adaptors seem to prefer the most creative option. This provides evidence

to refute both H2 and H2b. Set 1 preferences shown in figure 8 were also

compared to Set 2 responses, where the same question was reframed as a

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rejection of one option. In reference to the fluency/flexibility tests above,

these enabled extreme options to be identified.

There was no significant difference between Innovators’ preferred responses

when choosing or rejecting. However, there was a significant difference

between Adaptors’ preferences when choosing, compared with their

responses when rejecting (though not a full reversal of preference). This

suggests that Adaptors viewed the creative option as somewhat extreme

and provides support for both H3 and H3b. This is shown in figure 9.

FIGURE 9. Adaptors consider originality/novelty option as extreme

Results: Divergence Preferences and Cognitive Style

Two tests of preference for divergence were conducted. In the first test,

respondents were presented with a choice between job offers from two

companies; one of which emphasised exploring tangents and the other

remaining focussed. It is clear that this decision offers a less novel and a more

novel choice. Figure 10 shows Innovators exhibit a significant preference for

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divergence (p<=0.01) compared to Adaptors, as expected. This provides

support for both H2 and H2c.

FIGURE 10. Operational creativity framing and KAI [divergence]

Whilst Adaptors showed no significant change in Set 2 preferences for Q3

options, there was a significant reversal of preference (p<=0.01) between

Innovators’ responses when choosing or rejecting a relatively divergent

option. This suggests Innovators perceive divergence as an extreme option in

this context. These results provide evidence to refute both H3 and H3c. See

Figure 11.

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FIGURE 11. Innovators consider divergent option as extreme

The second test of preferences relating to divergent options was presented as

a decision between a normal route and a different route when late for a

medical examination. The results are shown in figure 12.

FIGURE 12. Operational creativity framing and KAI [divergence]

In the above example, Adaptors are significantly (p<=0.05) more likely to

diverge than Innovators if late. This provides evidence to refute both H2 and

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H2c. In this example, the similarity of Set 1 and Set 2 questions meant that a

test for a different route as an extreme option was not able to be tested.

Results: Rule Breaking Preferences and Cognitive Style

Operational creativity can also be represented as “rule breaking” behaviour

because many organisations categorise non-conforming behaviour as

creative and against the rules. Kirton (1976) uses the rule breaking preference

as the key descriptor for describing the differences between Adaptors and

Innovators. It would be expected, therefore, that Innovators should be more

prepared to break rules when compared with Adaptors. Innovators would

also be expected to perceive rule breaking options as less extreme because

of their inherent preferences for non-conformity.

Two tests for rule breaking preferences were presented in the Set 1

questionnaire. The first test related to trying to avoid missing an overseas flight.

The operationalised creative rule breaking option in this case was to choose

to do a safe but illegal u-turn in order to avoid a road accident. The second

test presented in the Set 1 questionnaire was very similar. Again the decision

related to trying to avoid missing an international flight, but this question was

presented as a rejection rather than a choice. One further difference

between the two questions was how the potential delay was communicated.

In the first question, the delay was based on a radio report, but in the second

question, the delay was based on seeing road works and determining their

potential to make the participant miss their flight.

The results (shown in figures 13–14) do not show significant differences

between Innovators’ and Adaptors’ preferences for rule breaking in the

context presented. This is not what is expected from Kirton’s descriptions of

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how Innovators and Adaptors are expected to make decisions. This also

provides evidence to refute both H2 and H2d.

FIGURE 13. Similar rule breaking preferences Innovators and Adaptors [Q4]

FIGURE 14. Similar rule breaking preferences Innovators and Adaptors [Q23]

Despite these similarities in responses from different cognitive style subgroups,

rule breaking does exhibit significant (p<=0.05, p<=0.01) preference reversals

due to attribute framing effects. Set 2 questions 4 and 23 were similar to Set 1

questions 4 and 23, except that choices were reframed as rejections and vice

versa.

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The results below show that both Adaptors and Innovators perceive rule

breaking as a somewhat extreme option (p<=0.01 for Innovators and p<=0.05

for Adaptors). See figures 15–16 for responses to Q23. Similar results were

observed for Q4 (not shown). In combination, these results provide evidence

to refute H3 and H3d because of the similarity between Innovator and

Adaptor responses.

FIGURE 15. Innovators perceive rule breaking as extreme [Q23]

FIGURE 16. Adaptors perceive rule breaking as extreme [Q23]

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Results Summary

The following table details how the results compared with the hypotheses

previously listed:

TABLE 1. Results Overall

No. Detail Results

H1 Innovators will tend to be less affected by risk based framing compared to Adaptors

2 instances support H1 14 instances reject H1

H2 Innovators will more strongly prefer operationalised creativity alternatives

3 instances support H2 5 instances reject H2

H2a Innovators will prefer relatively more fluent/flexible options

1 instance supports H2a 1 instance rejects H2a

H2b Innovators will prefer relatively more original/novel options

1 instance supports H2b 1 instance rejects H2b

H2c Innovators will prefer relatively more divergent/deviant options

1 instance supports H2c 1 instance rejects H2c

H2d Innovators will prefer relatively more rule breaking options

2 instances reject H2d

H3 Only Adaptors will perceive operationalised creativity alternatives as extreme

5 instances in 7 reject H3

H3a Only Adaptors will perceive relatively more fluent/flexible options as extreme

2 instances reject either KAI subgroup perceiving as extreme

H3b Only Adaptors will perceive relatively more original/novel options as extreme

2 instances support only Adaptors perceiving as extreme

H3c Only Adaptors will perceive relatively more divergent/deviant options as extreme

1 instance rejects – Innovators only perceived option as extreme

H3d Only Adaptors will perceive relatively more rule breaking options as extreme

2 instances reject – Both KAI subgroup perceiving as extreme

Discussion

The overall results suggest that there are significant framing effects that apply

to decisions involving operational creativity. In some cases, these are

affected by an individual’s cognitive style. The most interesting findings of this

study are that in some contexts Adaptors significantly prefer operationalised

creativity options more than Innovators do, and that KAI sub groups’

preferences for rule breaking are similar in contexts described as safe.

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Risk based framing effects seemed to apply similarly to Innovators and

Adaptors in general, and as a result, H1 was not supported overall. For

fourteen of the sixteen decisions posed that involved risk based framing, there

was no significant difference between Innovator and Adaptor responses. This

suggests that Adaptors’ low tolerance for ambiguity is not activated as much

as might be expected in decisions involving risk based framing. One common

aspect of these decisions was that all the risks were quantified, which may

have had a reduced the apparent ambiguity involved.

What is interesting is that in both decisions where a significant difference was

found for risk based framing, the decisions involved the potential for personal

money loss. One decision was framed as a choice of whether to accept a

larger risky loss or a smaller certain loss. The other decision offered the same

options, but was presented as a rejection decision. In these decisions, both

Adaptors’ and Innovators’ preferences were as predicted by prior research

on risk based and attribute framing effects: respondents preferred the risky

loss even though it was bigger, when presented with a choice, regardless of

cognitive style. When rejecting the Adaptors still strongly rejected the certain

loss, and Innovators actually reversed their preference (slightly tending to

reject the larger risky loss). Combining the two results produced a highly

significant result (p<0.01, χ² =19.45). This suggests that Adaptors may be more

sensitive to risk based framing than Innovators when potential personal losses

are involved.

In other tests, significant differences between Innovators and Adaptors were

observed, which varied and were highly dependent on context. The

contradictory nature of these results did not assist in proving H2 overall or any

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of the related sub hypotheses involved different operationalised creativity

(fluency/flexibility; originality/novelty; divergence; rule breaking).

For example, in testing H2a it was found that Innovators significantly preferred

fluency/flexibility in one context (being late), but Adaptors significantly

preferred fluency/flexibility in a different context (being early). In the case of

H2b (which related to originality/novelty), a high proportion of Adaptors

preferred the original option (more unusual work), though significantly less

than Innovators preferred this option. But Adaptors were significantly more

likely to choose an original route (shorter, but with a 200m walk) in order to

respond to a client’s wishes for them to arrive earlier. One reason for this may

be in this case the original option is perceived by the Adaptor as being both

efficient and conforming (because it is shorter and meets the client’s

request).

H2c related to preferences for divergence. Compared with Adaptors,

Innovators preferred working for companies that focussed on exploring

tangents as opposed to remaining focussed. However (unexpectedly)

Innovators did not automatically prefer different options, with only 31%

choosing an alternative described only as “different” compared with one

that was “normal”. Adaptors were almost equally divided over whether or not

the different option was superior to the normal option in this case. The

contradictory nature between these statistically significant results again made

it difficult to confirm or refute H2c.

Overall the only conclusion proposed regarding operational creativity

preferences is that Innovators’ and Adaptors’ preference for operational

creativity can be significantly different, but changes in the decision context

will affect these preferences in ways that are hard to predict. In general the

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contradictory findings regarding operational creativity preferences were

mirrored with regard to tests for determining whether or not fluency/flexibility,

originality/novelty, and/ or divergence were perceived as extreme attributes.

Significant differences in preferences between cognitive style sub groups

when choosing and rejecting particular operational creativity elements were

contradictory so no support for H3 is asserted.

A different conclusion can be drawn from the testing of safe rule breaking

preferences. In the contexts tested, safe rule breaking was perceived as both

a positive attribute and an extreme option, regardless of cognitive style in

both tests. The evidence suggests refuting H2d and H3d. This implies that

managers might influence employees to choose more creative options by

framing creative behaviour as safe rule breaking. They should frame this

decision as a choice of what to do, rather than a rejection of what not to do

in order to leverage attribute effects. This is because both Adaptors and

Innovators perceive rule breaking as extreme.

A key part of framing this message may be that the rule breaking was safe to

do, which may have reduced subjects’ sensitivity to any of the perceived

negatives of rule breaking. This may also have been why preferences for rule

breaking options were not significantly different for Innovator and Adaptor

sub groups. This finding is intriguing because it seems to contradict Kirton’s

fundamental descriptions of Innovators and Adaptors. It would be expected

that Innovators would be more comfortable with rule breaking and Adaptors

should prefer conformance. Perhaps Innovator and Adaptor differences in

preferences for conformity only apply in contexts where non-conformity is

perceived as unsafe.

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Given the large number of tests where significant differences in preferences

were found it would seem that this study does offer valid findings. However

the large number of tests conducted increases the potential to find significant

results merely due to random effects. This cumulative Type I error can be

managed by determining a significance level based on the Sidak-Bonferroni

treatment see Shaffer (1995). Applying this calculation for the 13 significant

items found, only results with p<0.003846 should be considered as non

random; the other results above should be interpreted with caution. However

this correction approach might be too conservative. There is quite a

reasonable chance that at least one test result could appear to be significant

to p<0.05 out of 25 tests when in fact no significance really existed. However

there is a much smaller chance that say 10 tests would appear significant. The

chance for this can be calculated, and is derived in the equations below.

Start by considering a study involving a number of tests, all of which in

actuality are devoid of correlation. For these tests any correlation found is

falsely significant and constitutes a Type 1 statistical error. Equation [1] details

the probability of completing the tests and not getting any falsely significant

result

P(fsr=0|n@p) = (1-p)n

[1]

Where P(fsr=0|n@p) is the probability of zero falsely significant results for n

tests at a given significance level of p (typically p=0.05 or p=0.01). Now the

chance of at least 1 falsely significant result is shown in [2] below. This is simply

the chance that something other than zero false significant results occurs:

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P(fsr>=1|n@p) = 1- P(fsr>0|n@p)

P(fsr>0|n@p) = 1- P(fsr=0|n@p)

P(fsr>=1|n@p) = 1-(1-p)n

Calculating with values p=0.05, n = 25 gives:

[2]

P(fsr>=1|[email protected]) = 1-0.95

P(fsr>=1|[email protected]) = 0.723

25

Now the chance of 2 or more falsely significant results in n tests is given by

determining the probability of at least one falsely significant result in n tests

and multiplying this by the probability of at least a single false test for the

remaining tests after the first. The probability of this second factor is by

definition P(fsr>=1|n-1@p). This enables the derivation of P(fsr>=2|n@p) as

follows:

P(fsr>=2|n@p) = P(fsr>=1|n@p) x P(fsr>=1|n-1@p)

[3]

Equation [3] above can be generalised to apply to determine the probability

of finding m falsely significant results in n tests for a given significance level of

p or P(fsr>=m|n@p):

P(fsr>=m|n@p) = P(fsr>=1|n@p) x P(fsr>=1|n-1@p)x….P(fsr>=1|n-

m+1@p)

P(fsr>=m|n@p) = [1-(1-p)n] x [1-(1-p)n-1] x ….[1-(1-p)n-m+1

Table 2 below shows the calculated values of P(fsr>=m|[email protected])

]

[4]

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TABLE 2. Probability of Falsely Significant Results in 25 Tests for p=0.05

P(fsr>=1|[email protected]) 0.723













In this study 9 significant items were found in 25 tests used to test H1 and H2. In

addition 9 extra tests were completed to test H3, of which 4 appeared to be

significant. To determine if these results could be falsely significant due to

cumulative type 1 error, probabilities of falsely significant results were

calculated using equation [4]. The calculation was performed in order most

significant result to least significant result. This analysis was completed for both

the 9 apparently significant items in 25 tests (see Table 3) and the total of 13

apparently significant items in 34 tests overall (see Table 4).

TABLE 3. Probability of 9 Falsely Significant Results in 25 Tests for Given p

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TABLE 4. Probability of 14 Falsely Significant Results in 34 Tests for Given p













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This suggests that the likelihood of achieving this number of false positives is

vanishingly small, and hence the results can be considered as significant

despite the potential for a cumulative type I family wise error.

The second potential concern regarding these tests is whether or not they

should be grouped together. This is not normally the case in prior studies of

framing effects due to the context specific nature of the decisions and

framing effects involved. In order to validate the use of independent chi

squared analysis of questions, various groups of related questions were

constructed and Cornbach's Alpha scores were calculated for each. The

results of this analysis are shown below in Table 5.

TABLE 5. Validation of Independence of Questions

Group Questions KAI KR20 n

Fluency/ Flexibility 1,6,18 Adaptors 0.207 35

Fluency/ Flexibility 1,6,19 Innovators 0.236 65

Fluency/ Flexibility 1,6,18 All 0.200 100

Originality/

Novelty 12, 25 Adaptors 0.427 36

Originality/

Novelty 12, 25 Innovators 0.333 65

Originality/

Novelty 12, 25 All 0.362 101

Divergence 3,7,9,10,20 Adaptors 0.319 36

Divergence 3,7,9,10,20 Innovators 0.191 65

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Divergence 3,7,9,10,20 All 0.306 101

Rule Breaking 4, 23 Adaptors 0.602 36

Rule Breaking 4, 23 Innovators 0.316 66

Rule Breaking 4, 23 All 0.427 102

Risk based

framing 2,5,8,11,14,17,19,22,24 Adaptors 0.119 31

Risk based

framing 2,5,8,11,14,17,19,22,24 Innovators 0.306 66

Risk based

framing 2,5,8,11,14,17,19,22,24 All 0.244 97

Personal money

2,5 All 0.303 101

People's lives

8,14,19 All 0.488 100

Product

Investment 11,17,22,24 All 0.224 97

All

1-25 All 0.422 95

All

1-25 Adaptors 0.141 30

All

1-25 Innovators 0.505 65

All of these results are below the 0.7 value normally used to validate scale

consistency. This suggests that the tests are not consistent and therefore are

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not testing the same things, so grouping chi squared results is not necessary or

appropriate.

This finding is somewhat confounding at first, because questions in the same

group should theoretically be measuring the same thing. If this was the case it

would be expected that their Cornbach's Alpha scores would indicate

consistency. The reason for this discrepancy relate to the other contextual

aspects involved in the various questions. Consider the two questions below

designed to measure preferences for fluency/ flexibility:

1 Imagine you are driving and realise you are probably going to

be late for a job interview. Which of the following routes would

you choose?

A The route you would normally travel

B A flexible but more complicated route with several options to

change direction again later

6 Imagine you have to make a decision about two similar

employees for a promotion. Which of the following routes would

you choose?

A The employee who works on many possible solutions

B The employee who works on a few probable solutions

Both of these questions may indeed measure preferences to fluency/

flexibility, but the different contexts that the questions are related to cause

different decision making weightings to also be activated. This suggests that

context is a key third variable in considering preferences for decisions

involving creativity (which is in hind sight is probably to be expected).

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Conclusion

The hypotheses in this study were generally not supported even though they

were derived from expectations about preferences for creativity from Kirton’s

descriptions of Innovators and Adaptors. The surprising nature of these results

makes this paper more interesting.

This study suggests that Adaptors are more favourable than Innovators to

taking creative options in some contexts. It appears that Adaptors will express

preference for creative options (even if perceived as extreme) when these

seem to be more efficient and/or more compliant. This suggests that

Adaptors might be more motivated to be creative than Innovators in

situations where creativity is obviously useful and expected. It also suggests

that Kirton’s original descriptors of the two main cognitive styles are

somewhat simplistic when it comes to predicting both rule breaking

behaviour and creativity preference.

Operationalised creativity preference appears to be affected by framing

effects and context in complex ways. Framing operational creativity decision

alternatives may result in positive or negative associations that are

dependent on the subject’s cognitive style and the domain considered. In

general, this suggests that an understanding of cognitive style is likely to

complicate managers’ attempts to frame decisions about creativity overall.

There are three potential generalisations for practice, however, that emerge

from this paper.

The first is that risk based framing effects might seem to apply equally to

Innovators and Adaptors, except under conditions of personal loss. Prior to

understanding the results obtained in this study, it would be perhaps intuited

by managers that if a company were to operate innovatively then Adaptors

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should be barred from holding positions where they make decisions about

whether or not implement operationalised creativity options. For example it

wouldn’t seem to be effective to put an Adaptor in charge of investment

decisions that related to approvals for disruptive strategic innovations due to

the Adaptor’s expected preference for incremental change. However the

results obtained from this study suggest there is no reason to limit (to

Innovators) positions of authority that would deal with making company

investment decisions about operationalised creativity. Innovators and

Adaptors are equally likely to be averse to risk from a framing effects point of

view.

Secondly, attribute framing effects seem to apply to many decisions

involving operationalised creativity. Even though the result was not always

significant, every question in this study about creativity returned less

preference for the creative option when reframed from a choice to a

rejection. Therefore, managers that have the capacity to frame employee

decisions in terms of choosing the best option, rather than rejecting the worst

option are likely to be able to get more operationalised creative alternatives

chosen. Whilst this attribute framing effect seems to be different depending

on context and cognitive style, there was no net reduction of preference

observed in this study by presenting decisions involving creativity as choices

rather than rejections. It is quite possible to conceptualise in some quality

assured organisational environments how the idea of rejecting non-

conforming products could translate to automatically framing other decisions

(like those involving operationalised creativity) as rejections resulting in

reduced creativity implementation.

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Finally, despite the expectation that cognitive style differences imply different

rule breaking preferences, framing an option as minor, but also as safe rule

breaking, appears generally to make it seem more attractive to both KAI sub

groups. Thus, managers who wish to increase operationalised creativity in their

organisations should consider presenting certain decision alternatives as “rule

breaking” and “safe”. This should make these options appear both less risky

and more attractive to all employees, regardless of their cognitive style.

These generalisations should be interpreted with a note of caution however:

overall the most important aspect of how framing effects interact with

cognitive style seems to relate to context. The fact that the decisions

proposed in many cases were outside of the normal organisational domain

suggests that further research into classifying contexts and examining how

they impact on framing effects would be worthwhile.

This study does seem to provide some suggestion that framing effects are by

nature external and so may act as substitutes for the management control

provided through extrinsic motivation. The advantage of framing effects is

that they do not seem to dampen creativity, as is the case with extrinsic

motivators. Used carefully, attribute framing effects may unlock intrinsic

motivators to choose more creative options by leveraging inherent individual

perceptual distortions. In particular, framing creative options as both deviant

and safe may provide managers with the extra influence they need to help

their employees make more creative choices.

215

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Conclusions

This thesis delivers innovation on three levels informed by an understanding of

the linkages between creativity, motivation, cognitive style and framing

effects. The following sections summarise proposed applications for the

study’s findings and discuss constraints and limitations.

Study One Response to Objectives

The first investigation in this report provides evidence to suggest that the

Creative Resolve Response pattern of creative motivation might exist. There is

significant evidence to suggest that individual creative motivation does vary

during problem solving as a function of perceived outcome success certainty.

The findings are however different from those hypothesised in that the

variation for Innovators and Adaptors is in phase rather than the mirror image

predicted. The study suggests that creative motivation peaks during problem

solving at approximately 20% perceived outcome certainty (labelled here as

the creative motivation zenith). It also provides evidence for minimum

creative problem solving motivation at 60% perceived outcome certainty

(labelled here as the creative motivation nadir). Importantly the zenith and

nadir points for both Innovator and Adaptor sub groups appear at the same

levels of perceived outcome certainty. Additionally Adaptors on average

report significantly lower levels of creative motivation across the entire range

of perceived outcome certainty from 0% to 100%.

In order to assist managers to apply this finding it is asserted that perceived

outcome certainty may be a proxy for an individual’s opinion regarding

problem solving progress. Consider an individual’s perception of outcome

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certainty at the instant that they recognise there is a problem to be solved: at

that first moment they have no real idea whether or not the problem can be

solved and thus are completely uncertain as to what their problem solving

outcome might be. Thus they are at a 0% outcome certainty level and they

are by definition also 0% complete in solving the problem. This perception of

outcome certainty may rapidly change as the individual investigates how to

deal with the problem. In many circumstances the problem solver may

choose to abandon the problem if adequate initial increase in outcome

certainty is not perceived. This requirement for adequate increased outcome

certainty is based on two general motivation theories (Atkinson, 1974; Vroom,

1964).

Assuming that the individual commences problem solving then as they make

progress with resolving the situation their perception of the potential for

success increases in lock step with their progress. At the point where they are

completely certain that the problem is solved, by definition the problem

solving progress is 100% complete. This suggests that the creative motivation

zenith and nadir points of creative motivation identified in CRR may have

some specific qualitative meaning in terms of problem solving progress.

The zenith of creative motivation may be related to problem definition.

Problem solvers typically attack problems by trying to categorise or reconcile

problems in order to use previously successful problem solving strategies.

Belief that a problem has been properly or usefully defined is likely to result in

a reduction of anxiety regarding the potential to satisfy extrinsic motivators.

Thus 20% outcome certainty (the zenith point of creative motivation) is

asserted to qualitatively relate to the point where the individual problem

solver is satisfied that they have defined the problem. It seems reasonable to

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conclude that maximum creativity could naturally occur when the problem

solver switches from working out what the problem is to creating ideas to

manage the problem. This point may also represent peak intrinsic interest in

the problem as curiosity relating to how to understand the problem situation

peaks. Once the problem has been defined to the satisfaction of the problem

solver ideation is expected to begin.

Thus it is also asserted that the section of the CRR pattern of response

between peak and minimum creative motivation may correspond to idea

generation and/ or trial and error processes to attempt to solve the problem.

As more and more ideas are created and evaluated the problem solver has

the potential to make progress towards an acceptable solution. During these

efforts the problem solver may gradually exhaust a range of ideas with the

potential to solve the problem, or they may make a discovery to have a

sudden insight about how to deal with their problem. In any event, at some

point they become satisfied that they have optimised their problem solving

efforts and begin to implement their preferred option or options to resolve the

problem. At this point their perception of outcome certainty is that the

problem is practically complete because a solution strategy has been

formulated.

It is during this ideation and implementation phase that the problem solver

may increase their sensitivity to extrinsic motivators that encourage the

problem solver to resolve the problem. Two mechanisms are proposed as the

potential cause of this increase insensitivity: time pressure and completion

motivation. In organisational problem solving contexts, problem solvers are

typically faced with either an explicit deadline for completion or an implicit

understanding that “time is money”. Thus the more time that is spent on a

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problem the more sensitive the individual becomes to the extrinsic need to

solve the problem. In some cases the level of urgency to solve the problem

may increase, but this need not be the case for increased sensitivity to a real

or imaginary deadline to occur on the part of the problem solver. Amabile (T.

M. Amabile et al., 2002) shows that in general time pressures reduce creative

motivation. Creative motivation is also expected to be effected by

expectancy.

Expectancy theories of motivation (Atkinson, 1974; Vroom, 1964) suggest that

motivation is proportional to problem solving progress. Atkinson’s theory

asserts that motivation is proportional to potential for success. Vroom’s theory

includes expectancy that effort will result in success as a factor of motivation.

Both Vroom’s and Atkinson’s theories define this component of motivation as

an extrinsic factor contributing to overall motivation. As outcome certainty

increases the extrinsic component common to Vroom’s and Atkinson’s

motivational theories also increases (by definition). Increasing extrinsic

motivation results in reduced creative motivation (Amabile, 1997; T. M.

Amabile, 1996; T. M. Amabile, 1998). This waning of creative motivation in turn

reduces the problem solver’s creative output in accordance with Amabile’s

(T. M. Amabile, 1983) three factor model of creative production.

At some point it can be assumed that the problem solver ideally achieves

“practical completion”. At this point that problem solver has essentially

satisfied all extrinsic motivation requirements to solve the problem:

expectancy becomes near certainty and time pressures are subsequently

eliminated. It would be expected that at this point the problem solver

switches to satisfying their intrinsic motivations and subsequently creative

motivation increases after reaching a minimum level.

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Minimal creative motivation occurs at between 60% and 70% perceived

outcome certainty. This nadir in the CRR pattern is asserted to correspond with

the problem solver perceiving that they have achieved practical completion

in solving the problem. After practical completion is achieved the CRR

pattern suggests that problem solvers become more motivated to be

creative because the time and risk pressures associated with solving the

problem are relieved. These interpretations of the mechanism of CRR prompt

some practical application advice for managers that want to enhance

creativity in their organisations.

To some extent the findings related to CRR support previous research relating

to the requirement to reduce extrinsic motivators (like time and success

pressure) in order to enhance motivation. In addition CRR also suggests that

creativity is enhanced if Innovators are involved in problem solving more than

if Adaptors are involved in problem solving due to Innovators’ higher average

levels of creative motivation. However the most important potential

application of CRR is whether or not managers should try to get problem

solvers to stay in the problem definition stage of problem solving as long as

possible. Managers might do this by influencing problem solvers to believe

that their perceptions of outcome certainty (prior to practical completion)

are overly optimistic. This could be expected to be effective at increasing

creative motivation if it results in a reduction of individual outcome certainty

perception back towards 20%. The method of presenting this information is

also important: If the manager does not also provide support for problem

solvers in terms of corresponding reduced urgency and increased potential

for success, then they will exacerbate extrinsic motivators and increase the

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stress of the problem solvers. Thus the message from managers to employees

may need to include three key credible points:

1. There is more to this problem than meets the eye at first glance, so it

may be worthwhile to revisit some earlier parts of the problem solving

process.

2. Considering a wider range of problem definitions and more ideas will

likely increase the potential for problem solving success.

3. There is plenty of time to revisit other possible problem definitions

without risk of over running completion deadlines, so why not spend

the time reconsidering how to define the problem differently.

There is a second consideration relating to the application of CRR. Managing

a diverse problem solving team comprised of both Innovators and Adaptors is

likely to be hard to coordinate. Hammerschmidt (Hammerschmidt, 1996b)

shows how individuals with different cognitive styles exhibit very different

approaches to problem solving. Mumford and Feldman (Mumford, Feldman,

Hein, & Nagao, 2001) assert that such diversity should be managed by

creating a so called “shared mental model”. A shared mental model reduces

conflict by getting all problem solving team members to agree to common

approach to understand and deal with the problem at hand. At first

consideration establishing a shared mental model seems to be rational for the

management of problem solving groups. CRR suggests a different and

counter intuitive option.

A manager who wants a diverse team of problem solvers to operate together

may inherently or explicitly try to match the various individuals' levels of

creative motivation. CRR suggests that if individuals with differing cognitive

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styles are working on a problem together with similar perceptions of outcome

certainty, then they will certainty have different levels of creative motivation.

This can cause dysfunctional group conflict. Managers may be able to

intervene to reduce this conflict by promoting an approach that supports

“diverse mental models”.

Essentially a manager could try to influence Adaptors in the problem solving

team to reduce their perception of outcome certainty without amplifying

time or risk concerns in order to increase their creative motivation via the CRR

pattern of response. Additionally the manager might also try and influence

the Innovators in the problem solving team to increase their perception of

outcome certainty or to increase their concerns about time pressures and

completion risk so that the Innovator’s creative motivation reduces in

accordance with their CRR pattern. By reducing the Innovators’ creative

motivation and increasing the Adaptors’ creative motivation the manager

might decrease the intra group conflict due to different individual conclusions

about the level of creativity required.

The diverse mental model concept derived from the findings of CRR is about

assisting members of a problem solving team to retain different perceptions of

outcome certainty (paradoxically to match creative motivation levels). CRR

findings can also be used to decrease creative motivation.

The corollary of the above discourse is that managers who wish to reduce the

creativity during problem solving efforts may consider a range of options:

1. Increase time, performance risk or other extrinsic motivational pressures

2. Select problem solving teams biased to include more Adaptors

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3. Influence problem solvers to increase their perceptions of outcome

certainty towards 70%

CRR suggests some management activities that assist the systemisation of the

so called “fuzzy front end” of innovation as suggested by Boeddrich

(Boeddrich, 2004) and others (Koen et al.). CRR provides support for belief

that creativity production during organisational problem solving is the result of

the interplay between expectancy, urgency, cognitive style and perceived

outcome certainty. The complexity of this relationship and the relative rarity of

high expectancy, low urgency, high KAI, low outcome certainty problem

solving situations may also explain to aspects of so called “Hot groups”.

Hot groups have been examined by Bradford (Bradford, 1997) and

Rappaport (Rappaport, 1996) and are defined as teams that are extremely

creative because they exhibit an extreme level of member involvement and

commitment as ‘hot groups’. Members of hot groups apparently exhibit an

enthusiasm for their work that makes them apparently behave like people in

love. Little is understood about the mechanism for establishing hot groups,

though prescriptions about the organisational conditions required for them to

emerge has been outlined. Bradford and Rappaport assert that in general

hot groups occur in response to a non-urgent crisis; where there is significant

shared belief within the hot group about the importance of their actions in

tackling the problem solving task; and where organisational barriers to

creative production (like bureaucracy) are few. Viewed through the lenses of

creative motivation and CRR hot groups may arise in a particular set of

circumstances that match the conditions for creative motivation zenith: From

a CRR perspective expectancy is high (shared belief), urgency is low (non-

urgent crises) and outcome certainty is low (crises). From a creative

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motivation perspective intrinsic motivation is high (falling in love with the work)

and extrinsic motivators are suspended or reduced for the hot group (low

bureaucracy).

In summary, whilst the link to hot group formation is tenuous, the other findings

from Study One regarding Creative Resolve Response are important in the

context of the research objectives above. CRR does suggest that individual

creative motivation might vary in a systematic way as a function of outcome

certainty, though further research is recommended to confirm the extent that

this finding can be generalised. Whilst the pattern of response is similar for

both Innovators and Adaptors, Innovators on average report higher levels of

creative motivation at all levels of outcome certainty. These findings suggest

a range of possible new interventions for managers to enhance or reduce the

creativity of employees they supervise. The findings also suggest the potential

for diverse mental models as a method of team problem solving

coordination. The next section relates the application of findings from Study

Two.

Study Two Response to Objectives

Whilst Study One essentially investigated how to enhance the production of

creative ideas, Study Two was about how to enhance the potential for these

ideas to be chosen for implementation within the organisational context.

Study Two was conceived in order to identify relationships between framing

effects and decisions about whether or not to implement potential creative

solutions to problems (defined above as operationalised creativity).

The most important finding in Study Two was that describing an alternative as

“innovative” seemed to make it more attractive. This preference distortion

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appeared to apply across a range of different decision domains and in some

cases was so powerful as to counteract the effects of risk based framing

previously presented by Tversky and Kahneman (Tversky & Kahneman, 1981).

Managers wishing to enhance the potential for creative options to be

selected for implementation merely need to explicitly describe such options

as innovative and they may appear significantly more attractive to decision

makers. This preference shift seems to occur even if the options in question

are perceived as risky and potential losses and gains are quantified.

A second finding from Study Two is that in general operationalised creativity

options seem to be preferred when choosing but also seem to be perceived

as extreme (that is they contain both advantages and disadvantages). Shafir

(Shafir, 1993) showed that extreme options are preferred more often in

decisions presented as choices rather than rejections due to attribute framing

effects. Managers wishing to enhance the potential operationalised creativity

options to be implemented could try to retain control of the framing of

implementation decisions. Specifically they could try to ensure that such

decisions are presented as choices (e.g. “Which of these is best to do?”)

rather than rejections (e.g. “Which of these should we rule out?”). This finding

seems to hold regardless of the particular instance of operationalised

creativity under consideration in these studies (fluency, flexibility, originality,

novelty, divergence, deviance or rule breaking) in the limited range of

contexts tested. Further work is recommended to determine the extent that

this finding could be generalised.

In the event that a manager cannot control how an implementation decision

is framed, then it seems to be rational to deemphasize the operationalised

creativity aspects for decisions presented as rejections if creativity

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implementation is to be enhanced. This is because some operationalised

creativity aspects were apparently not preferred or at least less preferred

when decision makers were presented with choices framed in terms of

rejection. Similarly if managers wish to reduce the potential for the

implementation of creative options, then decisions could be framed as

rejections and operationalised creativity aspects of options could be made

explicit (though this effect did not hold in all contexts tested and further work

needs to be done to test the extent that this finding can be generalised).

Study Two suggests an important area for future research regarding creativity

production. It seems that framing effects are external interventions that

unlock an individual’s own perceptual distortions. Viewed through the lens of

creative motivation, framing effects seem to act like extrinsic motivators

except that they unlock or activate individual intrinsic motivations in the form

of perceptual distortions that affect preferences. Framing effects are not like

synergistic extrinsic motivators in that they do not seem to require intrinsic

motivation to be present in order to be effective, though further investigation

needs to be completed to confirm this. Framing effects seem to be a rare

form of extrinsic motivation that has an intrinsic effect. This suggests tentative

support for the idea that framing effects could be used earlier in the problem

solving process by managers to enhance creative production. This conclusion

is partially supported by other research that highlights the importance of the

supervisor-employee relationship to employee creativity (T. M. Amabile, 1998;

DiLiello & Houghton, 2006; George & Zhou, 2007; Oldham & Cummings, 1996;

Redmond et al., 1993; Tierney et al., 1999; Wang & Casimir, 2007). The ability

to effectively use framing effects to enhance employee creativity would

seem to be idiosyncratic to a relationship with a supervisor. Further research is

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required in order to investigate how framing effects could be explicitly used in

this regard.

In summary attribute framing effects seem to apply to decisions involving

creativity in complex ways. Despite this complexity, there are some apparent

consistencies that apply. The research findings suggest that managers might

improve the chances that operationalised creativity options will be selected

for implementation by:

1. Qualitatively describing these options as innovative, even when the

risks and potential gains and losses have been otherwise quantified.

2. Retaining control of how the implementation decision is framed in

order to ensure the selection decision is presented as a choice, rather

than submitting to a selection decision being presented as a rejection.

3. Making operationalised creativity aspects explicit in decisions framed

as choices, because generally creative options were either more

preferred or equally preferred to relatively less creative options due to

positive attributes.

4. Deemphasizing operationalised creativity aspects explicit in decisions

framed as rejections due to the perception that creative choices have

some negative attributes that are highlighted in rejecting decision

frames.

The next section relates the application of findings from Study Three.

Study Three Response to Objectives

Study Three extends the application of framing effects to decisions involving

operationalised creativity to consider the moderating effects of individual

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cognitive style. The first finding from Study Three is that it seems reasonable to

consider that risk based and attribute framing effects apply in general to all

individuals regardless of their cognitive style. This is important from an

organisational design perspective for firms that wish to enhance creativity.

Whilst the findings of Study One suggest that Innovators are more motivated

to be creative than Adaptors, Study Three does not suggest that Adaptors are

any more sensitive to risk based framing effects. To the extent that

operationalised creativity is perceived as risky inside the organisational

context, there seems to be no need to be concerned about the cognitive

style of implementation decision makers. Study Three suggests that Adaptors

in a position to authorise or veto operationalised creativity options may not be

significantly more or less affected by risk based framing.

The results of Study Three further suggest that in different contexts Adaptors

may actually tend to prefer some kinds operationalised creativity more than

Innovators. This suggests that it may be important from an organisation design

perspective to promote both Innovators and Adaptors to positions of

authority within the organisation in order to ensure that operationalised

creativity options have the greatest potential for selection and

implementation. The fact that different KAI subgroups exhibited different

preference tendencies for operationalised creativity in different contexts

makes it too complex to predict Adaptor or Innovator decision outcomes on

any case by case basis.

Overall it is possible to combine some findings of Study One and Study Three

as they relate to enhancing creativity within the organisation. It is possible that

by choosing individuals for roles based on their cognitive style creativity within

the organisation can increase. Compared to Innovators, Adaptors seem likely

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to be less motivated to develop operationalised creativity options. However

neither group was consistently more or less likely to select such options for

implementations over less creative alternatives. Managers that wish to exploit

this finding could consider reducing the proportion of Adaptors involved in

design functions, while possibly ensuring that they are adequately

represented in approval roles. These findings may not be the most important

from Study Three, however.

Both KAI sub groups seemed to exhibit significant preferences for safe rule

breaking over non-creative, compliant alternatives. This result suggests two

potentially important implications for managers that wish to enhance

creativity in their organisations. Firstly it suggests that Kirton’s (Kirton, 1976)

foundational description of Adaptors and Innovators in terms of their attitudes

to compliance and rule breaking might be too simplistic. Cognitive style

seems to be more complex than Kirton’s descriptions imply or perhaps human

cognition is too complex to be neatly described by cognitive style alone.

Perhaps even more importantly there is a clear application for potentially

enhancing operationalised creativity implementation inside the organisation:

Managers could present the selection of operationalised creativity as both

safe and deviant (i.e. rule breaking).

The extrinsic motivational nature of the typical organisation probably requires

managers to protect would be creative implementers from a negative

organisational response. Pichot and Callahan (Pinchot & Callahan, 2000)

present an archetypal case study of how organisations may inherently retard

creativity implementation. Two practitioner based articles support this

empirical finding (Pascale & Sternin, 2005; Sternin & Choo, 2000). Sternin and

Choo advocate permitting rule breakers to operate in defiance of

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organisational policy and processes provided that they are aligned with

organisational goals. Pascale and Sternin suggest that change agents within

an organisation may often remain unidentified. It is possible that this secrecy is

required to protect them from a negative organisational response to

instituting creative changes.

In summary Study Three suggests that cognitive style may be more

complicated than presented by Kirton, and in particular cognitive style seems

to interact differently with creative motivation production when compared to

how it moderates decisions involving operationalised creativity. In contrast to

consistently lower Adaptor creative motivation during problem solving

exhibited in Study One, in some contexts in Study Three Adaptors seemed to

prefer operationalised creativity more than Innovators. Study Three also

suggests that managers who wish to enhance the implementation of

operationalised creativity options in their organisations could frame these

options as safe rule breaking.

Caveats and Limitations

The extent to which the findings of the three studies above can be

generalised is limited. This section presents cautionary elements regarding the

conclusions above that relate to context, environment, definitions, individual

factors, scope, durability, application and alternatives.

The fundamental limitation of the study is that the effects observed were

confined to a limited range of contexts. In Study One for example the results

were aggregated across five different problem solving situations only. Whilst

the diverse nature of these experiments supports generalisation, there is no

way from this research to identify whether or not these results are exceptional

234

because of the problems involved. In Study Two and Three the question sets

were not able to cover the entire range of decisions faced within the

organisation. These concerns are further exacerbated by the fact that all the

data was collected in the laboratory and not in real world organisational

contexts. The potential for the effects to be increased, decreased,

moderated or overwhelmed by unidentified organisational factors not

present suggests some caution in applying the management suggestions

above.

The findings also require acceptance of the definitions used in the research. In

Study One, creative motivation was represented as a Likert scale. It is possible

that respondents attributed very different meaning to what was meant by

terms like “very high creativity” for example. A similar caveat could apply to

the idea that outcome certainty perception is an adequate proxy measure

for problem solving progress. Specifically 20% outcome certainty may not

relate reliably at all to problem definition; 70% outcome certainty does not

have to represent practical completion. The various questions in Study Two

and Study Three create instances of operationalised creativity that are also

open to interpretation. For example, an individual may perceive that

stopping short while driving to a destination in order to travel the last section

on foot and potentially arrive earlier is not original, novel, or divergent. Some

individuals may not even perceive this alternative as creative at all.

Many of these concerns relate to issues regarding individual meaning making.

These studies measure tendencies not cause and effect. It is not possible to

conclude for example that every decision maker will be more likely to choose

an innovative option over a non-innovative option. Different individuals will

process this decision idiosyncratically to arrive at their own personal

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conclusion. This reduces the findings in this research to “playing the odds” in

order to improve long run aggregate outcomes rather than “moving the

levers” for instant cause and effect. For operationalised creativity decisions

framing effects appear to be moderated by contextual considerations that

were not tested in these studies. Cognitive style further exacerbates these

limitations.

All of the effects in Study One and Study Three that involve cognitive style

were observed or more likely to be more pronounced for more extreme KAI

scores. The fact that more extreme Innovators and more extreme Adaptors

are the minority is not helpful for managing individuals with more moderate

cognitive styles. Future research could consider other possible moderating

variables not investigated in these studies including age, gender, and

ethnicity.

Another issue for future research concerns the assumption that the CRR

pattern of response is constant. It may be that attempting to influence an

individual’s outcome certainty perception also distorts their CRR pattern of

response. Further work is required to determine what happens to creative

motivations when employees are influenced in this way. Even if the effect of

influencing outcome certainty is predictable, the size of the effect may be

too small to be useful. In fact other approaches to enhancing employee

creativity like supervisor support may be more effective than attempts to

influence outcome certainty.

The utility of the framing effects findings from Study Two and Study Three may

also be limited because no investigation into goal behaviour framing effects

was undertaken. Even operationalised creativity decisions may be framed by

236

employees as behavioural choices rather than attribute based decisions

when translated into real world contexts. This could limit the extent to which

the findings in Study Two and Study Three can be generalised.

Even if the findings are able to be translated to real organisation situations

and contexts, the effects may not be durable. Specifically the descriptor

“innovative” may become so common over time that it loses its preference

distorting impacts. This effectiveness reduction could happen at individual,

organisational or even societal levels. Outside of organisations individuals are

assaulted by a myriad of different advertisements and other marketing

initiatives that attempt to utilise framing effects to influence purchase

decisions. Over time it is likely that resistance to these framing effects can be

cultivated. For example Sheehan asserts that Generation Y are subject to

around 22,000 advertisements each day and as a result have developed an

exceptional level of scepticism (Sheehan, 2005). However a more concerning

issue regarding framing effects relates to managers’ ability to retain control of

the framing of decisions related to operationalised creativity.

In many organisations procedures are institutionalised in forms that

automatically cause framing effects. Consider for example a quality

assurance audit that is designed to identify non-conforming components via

a visual inspection. This process is inherently framed as a decision whether or

not to reject the component in question. This decision framing has high utility

because there will be a tendency on the part of the decision maker to

identify and reject components that may be 90% ok, but are otherwise

defective. Applying the same approach to a management hiring decision

does not necessarily offer the same utility.

237

In comparison to the component quality assurance inspection example listed

above, consider a manager following a quality assured recruitment

procedure that operates in a similar way. In this situation is possible that a

manager may have to decide between two applicants – one who meets all

of the hiring criteria and has a history of mediocre past job performance,

versus another more extreme applicant that has excellent past job

performance but is somehow substandard on a few of the job performance

criteria. If this hiring decision is framed as a rejection (in a similar way to the

component inspection decision above) then there is a greater likelihood that

the excellent past performing candidate is likely to be rejected due to

attribute framing effects. This same hiring procedure framed in terms of

choosing the best applicant for the job would be highly likely to have a

different outcome.

Finally a methodological issue also requires discussion. Study Two and Study

Three have a potential research design flaw that became apparent after

they were concluded: Cumulative Type I error also called Familywise error

(see Shaffer 1995). Given the large number of tests where significant

differences in preferences were found it would seem that the two studies do

offer valid findings. However the large number of tests conducted increases

the potential to find significant results merely due to random effects. This

cumulative Type I error can be managed by determining a significance level

based on the Sidak-Bonferroni treatment (Shaffer 1995). Applying this

calculation for the 13 significant items found, only results with p<0.003846

should be considered as non random; the other results above should be

interpreted with caution. However this correction approach might be too

conservative and increases the potential to make a Type II error (Schaffer,

238

1995). There is quite a reasonable chance that at least one test result could

appear to be significant to p<0.05 out of 25 tests when in fact no significance

really existed. However there is a much smaller chance that 10 tests would

appear significant. The chance for this can be calculated, and is derived in

the equations below.

Start by considering a study involving a number of tests, all of which in

actuality are devoid of correlation. For these tests any correlation found is

falsely significant and constitutes a Type 1 statistical error. Equation [1] details

the probability of completing the tests and not getting any falsely significant

result:

P(fsr=0|n@p) = (1-p)n

Where P(fsr=0|n@p) is the probability of zero falsely significant results for n

tests at a given significance level of p (typically p=0.05 or p=0.01). Now the

chance of at least 1 falsely significant result is shown in [2] below. This is simply

the chance that something other than zero false significant results occurs:

[1]

P(fsr>=1|n@p) = 1- P(fsr>0|n@p)

P(fsr>0|n@p) = 1- P(fsr=0|n@p)

P(fsr>=1|n@p) = 1-(1-p)n

Calculating with values p=0.05, n = 25 gives:

[2]

P(fsr>=1|[email protected]) = 1-0.95

P(fsr>=1|[email protected]) = 0.723

25

Now the chance of 2 or more falsely significant results in n tests is given by

determining the probability of at least one falsely significant result in n tests

and multiplying this by the probability of at least a single false test for the

239

remaining tests after the first. The probability of this second factor is by

definition P(fsr>=1|n-1@p). This enables the derivation of P(fsr>=2|n@p) as

follows:

P(fsr>=2|n@p) = P(fsr>=1|n@p) x P(fsr>=1|n-1@p) [3]

Equation [3] above can be generalised to apply to determine the probability

of finding m falsely significant results in n tests for a given significance level of

p or P(fsr>=m|n@p):

P(fsr>=m|n@p)=P(fsr>=1|n@p)xP(fsr>=1|n-1@p)x….P(fsr>=1|n-

m+1@p)

P(fsr>=m|n@p)=[1-(1-p)n]x[1-(1-p)n-1]x….[1-(1-p)n-m+1

The table below shows the calculated values of P(fsr>=m|[email protected])

] [4]

Item Probability











In Study Three, 9 significant items were found in 25 tests used to test

hypotheses H1 and H2 detailed in that paper. In addition 9 extra tests were

completed to test H3 from that paper, of which 4 appeared to be significant.

To determine if these results could be falsely significant due to cumulative

type 1 error, probabilities of falsely significant results were calculated using

240

equation [4]. The calculation was performed in order most significant result to

least significant result. This analysis was completed for the 9 apparently

significant items in 25 tests (see the table below).

Item Probability










The analysis was also completed for the 13 apparently significant items in 34

tests overall (see the second table following).

Item Probability














This suggests that the likelihood of achieving this number of false positives is

vanishingly small, and hence the results can be considered as significant

241

despite the potential for a cumulative type I family wise error. Similar

calculations for Study Two were completed and combined overall with Study

Three. Study Two was based on 19 new tests of which 9 appeared to be

significant. The probability of this happening as a random event assuming null

hypotheses is approximately 0.0002. Overall combining Study Two and Study

Three, the probability of randomly achieving 22 falsely significant results in 53

tests is approximately 0.0003.

Enhancing Understanding of Creativity Management

Notwithstanding the range of limitations identified in the three studies, this

research does develop the creativity management research frontier. Xu and

Rickards suggest that the future of creativity research will relate to three

principles: universality, development and environment (Xu & Rickards, 2007).

Their model of creative management asserts the importance of creative

managers as critical to innovative companies. The findings of this study satisfy

all three principles. By definition Creative Resolve Response and Framing

Effects (as applied to creativity) seem to be universally applicable. Applying

these findings usefully as a manager may require development on the part of

the manager and also may facilitate development in the potentially creative

employee. Finally environmental factors seem to be critical in terms of the

overarching extrinsic motivation within the organisational context.

Some further areas for research that these studies suggest include:

► Understanding how creative resolve response changes when

perception of outcome certainty is influenced.

242

► Identifying systematic contextual groupings that predict

operationalised creativity preferences for Adaptors and Innovators.

► Determining other variables (e.g. age, ethnicity or gender) that also

moderate creative motivation, sensitivity to framing effects and

preferences operationalised creativity.

In general this research introduces management options to potentially

enhance creativity by unlocking intrinsic motivators. Creative Resolve

Response suggests that influencing employees indirectly by getting them to

reconsider the extent of their outcome certainty (and hence reduce their

belief in their own problem solving process) has the potential to increase

creativity production through greater creative motivation. Creative Resolve

Response is supported by research related to individual creativity factors

(Munoz-Doyague, Gonzalez-Alvarez, & Nieto, 2008) which shows creativity is

related not only to personality, expertise and intrinsic motivation but also to

cognitive style. Further support is also offered by Sternberg’s investment theory

of creativity (Sternberg, 2006) which splits the expertise factor above into

knowledge and intellectual skills; and adds environment as a new

component.

Additionally the findings related to framing effects in this project provide

further potential tools for managers to influence employee’s choices

regarding whether or not to implement operationalised creativity options.

Framing effects appear to unlock individual intrinsic motivations in the form of

perceptual distortions. Whilst this study limits the investigation of framing

effects to how they may influence operationalised creativity choices, there is

significant potential for applying framing effects earlier in the problem solving

243

process to enhance creative production. Kasof et al (Kasof, Chen, Himsel, &

Greenberger, 2007) propose “self-determined extrinsic motivation arising from

one’s personally held core values” can enhance creativity, supporting the

idea that some extrinsic motivators can increase creativity motivation.

Managers may also benefit from an increased understanding of the new

complexities discovered relating to cognitive style.

Finally this project expands the understanding of cognitive style to

unexpectedly identify decision contexts where Adaptors seem to exhibit

significantly higher preferences for operationalised creativity than Innovators.

Adaptors seemed to exhibit no significant differences to Innovators in their

preferences for safe rule breaking. This suggests that Adaptors may be more

creative than previously implied.

Combined, these conclusions suggest that there are more options for

creativity management than simply trying to reduce extrinsic motivational

effects inherent in the organisation.

244

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