Juan Camilo Arias Robledo - WUR

Master Thesis Business, Management & Organization August 2019

An exploratory assessment of the existing frameworks and instruments used

to evaluate organizations in terms of learning and innovation

2019

From team to

organizational

ambidexterity

MSc Thesis

Business,

Management &

Organization

Juan Camilo Arias Robledo


From team to organizational ambidexterity: An exploratory assessment of the existing frameworks and

instruments used to evaluate organizations in terms of learning

and innovation

Juan Camilo Arias Robledo

August, 2019


Wageningen University

BMO – Business Management & Organization Group

From team to organizational ambidexterity:

An exploratory assessment of the existing frameworks and instruments

used to evaluate organizations in terms of learning and innovation

Author : Juan Camilo Arias Robledo

Registration nr. : 890810018020

Code : MST-80430

Supervisor : Renate Wesselink

Co-supervisor : Emiel Wubben

Supervisor Organization : Juan Pedro Fernández Sanin

Study : MSc Animal Sciences

Wageningen, August 2019


Copyright

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Abstract

The aim of this quantitative qualitative study is to identify the relationship between the

variables, responsible for organizational ambidexterity (AD). In the study the main focus is

on opportunity identification (OI) and organizational learning (OL). When researching the

relation between AD, OI and OL, the author believes organizations might be better able to

measure, prioritize and improve organizational AD. For the design, first a theoretical model

was created based on existing literature explaining AD, OL and OI. Secondly, an

encompassing survey was developed using 11 sets of standardized questions. 180

collaborators of an existing company completed the survey. That data was collected and

statistically analyzed using SPSS and AMOS.

In conclusion the author found that there is good correlation between the used

parameters of OI and OL. Hence, we can confirm that the methodology used was correct.

One conclusion drawn was that within companies, teams with empowered leadership, have

better OI and OL. However the tool is not valid to determine what the relationship is

between the OL, OI and AD.

For future research, it is recommended to reduce the amount of questions within a

survey, as the respondents lose focus after a certain amount of time. Moreover, more

research is needed to aim to define organizational AD directly, and not via other constructs

such as OL and OI.


Table of contents

Abstract VI

List of abbreviations III

List of tables IV

List of figures IV

1. Introduction 1

2. Theoretical framework 5

2.1. Ambidexterity 5

Exploration 6

Exploitation 7

2.2. Opportunity identification 8

2.3. Organizational learning 11

2.4. Levels of analysis 13

2.5. Other model variables 14

Team level predictors of innovation 15

Leadership 16

Environment 17

2.6. Connections between variables 18

2.7. Research questions 21

3. Methodology and research design 23

3.1. The object of research 23

Team Alpha 24

3.2. The survey 25

Demographics, leadership & team 30

Team level predictors of innovation 30

3.3. Leadership 33

3.4. Organizational learning 33

3.5. Opportunity identification 34

4. Results and analysis 35


4.1. Results and discussion 38

Cronbach's Alpha 38

4.2. Descriptive statistics and correlations 40

Organizational learning 40

Opportunity identification 41

Environment 42

5. Conclusion 45

6. Discussion 47

7. References 49


List of abbreviations

Abbreviation Explanation

4I Intuiting, interpreting, institutionalizing and integrating

AD Ambidexterity

BIE Business idea evaluation

BIG Business idea generation

OI Opportunity identification

OIC Opportunity identification competence

OL Organizational learning

SMAC Social, Mobile, Analytics, and Cloud

TLPOI Team-level predictors of innovation


List of tables

Table 2.1. The 4I model....................................................................................................... 12

Table 3.2. Model variables, subsections and constructs. ..................................................... 27

Table 4.1. Teams demographics and survey results, based on individual survey answers. 37

Table 4.2. Reliability analysis and Cronbach’s alpha coefficient. ...................................... 39

Table 4.3. Pearson correlations for sub-sections, constructs and frameworks. ................... 43

Table 4.4. Continuation Pearson correlations for sub-sections, constructs and frameworks.

.................................................................................................................................................. 44

List of figures

Figure 1.1. Theoretical framework. ....................................................................................... 3

Figure 2.1. Creativity-based model of entrepreneurial opportunity recognition. ................. 9

Figure 2.2. Flow between Team Level and Organizational Level ...................................... 14

Figure 2.3. Team-Level predictors of Innovation variables. ............................................... 15

Figure 2.4. Theorical framework. ....................................................................................... 22

Figure 2.5. Questionary flow and item distribution. ........................................................... 27


1. Introduction

As technology is speeding up its development, and the world becomes hyperconnected, the

capacity of an organization to learn becomes increasingly relevant. Those organizations that

perform well in predicting changes, and that innovate and implement regularly, may have better

chances to survive during chaos (Baggen, 2017)

Prior studies within this domain have concluded in order to be successful, organizations have

to understand themselves, their parts and interconnections, while developing strategies and

capabilities simultaneously (Duncan, 1976). Those organizations that want to remain relevant,

competitive and profitable, must learn to identify, evaluate and develop new skills and

capabilities consistently. This becomes challenging in current turbulent environment

“characterized by technological change and national and global competition” (Baggen, 2017,

p. 11).

To remain relevant, organizations must apply to the rules of competition, that fluctuate

through time. The old paradigm rules of competition that dictated success are being replaced

by new rules. Nowadays the focus is on continuous improvement and innovation. As Hodgetts

et al. (1994) also explain, this requires both exploration of new opportunities and the fast

exploitation of this opportunities. This trend is commonly known as ambidexterity, from now

on referred to as AD. Unlike management fads or purely quantitative techniques, ambidexterity

is not an addition nor supplement of the traditional management. Rather, it is an organizational

strategy that drives an ongoing, continuous process, one that requires radical changes in

organization design and day-to-day operations (Andriopoulos & Lewis, 2009; Ayd, 2017;

Saadat & Saadat, 2016).


While there are no universal criteria for identifying a total quality enterprise, 10 core values

are generally recognized (Hodgetts, Luthans, & Lee, 1994): customer-driven, leadership, full

participation, reward system, reduced cycle time, prevention not detection, management by

fact, long-range outlook, partnership development and public responsibility. These are

characteristics that successful organizations have in common; however, it is typical for

successful organizations, to divide the activities in teams, according to the company’s purpose

and milestones. Hence it is fair to say that the teams are causing, in a greater part, the

organizational success (Baggen, 2017; Bell, Brown, & Weiss, 2018; Hülsheger, Anderson, &

Salgado, 2009) and innovation (Edmondson & Harvey, 2018). However, teams are

heterogeneous and dynamic and are influenced by external and internal variables such as

participation safety, leadership, environment, motivation, etc. Moreover, various authors have

described interactions between the individual, team and organization levels (REFs). These

interactions allow for teams to transform knowledge into products and services (integration).

Also they assist organizations in re-evaluating processes or aspects already incorporated

(reflection).

Successful organizations need to learn, evaluate, incorporate and institutionalize new

knowledge. So, it is of upmost importance to identify, understand, measure and improve the

factors that influence these organizational capabilities such as, AD. Looking at the centrality of

the concept in this research, a more detailed notion is preferred. AD is the combination and

simultaneous execution of exploitation and exploration, explained as the simultaneous capacity

of detecting opportunities, together with an efficient process of integrating and,

institutionalizing said knowledge.

Many empirical and conceptual studies lack a clear procedure to evaluate organizational AD,

and it is not always evident which of the variables should be taken in to account. Hence, as the


evaluation is difficult, it is even more hard to improve weak points regarding AD. Thus, there

is still a lot that can to be learned from AD, both conceptually and empirically. Hence, this

research aims to identify the intricate relationships of the variables responsible for

organizational AD, which include opportunity identification (OI), and organizational learning

(OL). To shortly elaborate on the two concepts mentioned, OI can be defined as the ability of

individuals to think of new ideas for products, processes, practices or services that can lead to

new value-creation for the organization (Baggen, 2017). Furthermore, OL, can be divided in

the two most common categories; behavioral learning and cognitive learning (Lumpkin &

Lichtenstein, 2005). In the next chapter OL and OI will be explained in detail.

When researching the above, organizations might be better able to measure, prioritize and

improve weak points to develop organizational AD. The model (Figure Error! Reference

source not found.1.1) includes the three variables used in this report, and also illustrates the

connections between the frameworks.

The next chapter will provide more explanation on the variables and also secondary variables

such as leadership, environment and team-level predictor of innovation, will be discussed as

they can have an effect on the interaction between OI, OL and AD.

Figure 1.1. Theoretical framework. The dependent variable (Ambidexterity-AD) is

dependent on the independent variables (Organizational Learning-OL and Opportunity

Identification-OI)


2. Theoretical framework

In this chapter, the different concepts and notions will be explained based on current

literature and previous studies. This part explains the theoretical foundation of the research, and

puts it within the current scientific domain. Also it explains important concepts that need more

context and definition. First ambidexterity will be explained more into detail. Next, we will talk

about two other main concepts, namely OI and OL will be further explained. Later on, we will

discuss the levels of analysis that this research will use, and after we will talk about secondary

model variables and the connections between those. In the last section, we will provide the

research questions. We discuss these in the theoretical framework, as it is important to

understand all concepts before diving into the research questions.

2.1. Ambidexterity

This first section will focus on further explaining AD, as it is the main concept within the

research. Hence, it is important to talk about the definition. AD is described as the combination

and simultaneous execution of exploitation and exploration. AD creates tension for the teams

and the organization, due to the competitive and constant demand for organizational resources,

support and organizational structures (Andriopoulos & Lewis, 2009). Managers, generally

engage more in exploitation than exploration activities (Keller & Weibler, 2015). Keller &

Weibler also concluded that “managers that are considered ambidextrous, balance exploration

and exploitation in close correspondence to experienced leadership attempts of their superiors,

which account for the influence power of transformational leadership on AD at the team level”

(Keller & Weibler, 2015, p. 3). To move from learning, towards using that what has been

learned, an integration must take place and “managers below senior management level are


required to link exploration and exploitation to facilitate the integration and coordination of

exploration and exploitation” (Keller & Weibler, 2015, p. 3).

As discussed before, the concept of OL is directly linked with the capability of AD and is

related to high organizational performance. (Kitapçi & Çelik, 2013; Ojha, Acharya, & Cooper,

2018). AD can be analyzed by many factors, such as internal characteristics of the organization

(Jansen, Tempelaar, van den Bosch, & Volberda, 2009), leadership (Keller & Weibler, 2015),

and top management team skills (Cao, Simsek, & Zhang, 2010). It must be considered that these

processes and factors depend on the specific environment of each organization, team and

individual.

Exploration

Individuals seek knowledge by making connections between stimuli or by generating new

insights. In such situations, there is a focus on exploring new ideas that may lead to innovation

and change. Studies have been suggesting that individuals rarely come up with intuitive ideas

and insights without external support. As indicated by several studies, leaders play an important

role in directly influencing creative individuals (e.g., Amabile, Conti, Coon, Lazenby, & Heron,

1996; Redmond, Mumford, & Teach, 1993 in Berson et al., 2006). For example, a

comprehensive study of creative performance found that leaders who were successful in

encouraging followers to use their intuition as a driver for OL were found to be supportive and

non-controlling. Leaders may play an important role in helping individuals realize what they

have learned. Furthermore, leaders manage the meaning and frame the experience for their

collaborators as a viable basis for action. In other words, leaders help individuals set the

learning in a domain or context where it is meaningful. After the knowledge is acquired, the

stage of integrating involves sharing the learning and achieving convergence through


conversation among members that lead to shared understandings (Crossan, Lane, & White,

1999). This leads to the following propositions made by Berson et al. (2006, P.568, 588):

• Proposition 1. Leaders facilitate exploration among followers by providing the

contextual support to develop their ideas.

• Proposition 2a. Leaders provide the common basis and shared understanding

needed to integrate learning at both the group and organization level.

• Proposition 2b. Leaders at different organizational levels guide the integration

of new and existing learning.

Exploitation

As stated before, AD is the simultaneous application of both exploration and exploitation

(focused here on OL), and manifests itself in routines, structures, and practices of the

organization (Berson, Nemanich, Waldman, Galvin, & Keller, 2006; Crossan et al., 1999).

Depending on the nature of communications within organizations, leaders at lower levels may

have some influence over institutionalized learning. In terms of the organizational level,

exploitation depends more upon explicit knowledge, while exploration depends more upon tacit

knowledge.

• Proposition 3. Leaders at different organizational levels guide the

institutionalization of new and existing learning. (Berson et al., 2006, p. 590)

So, leadership and the environment in which teams learn, might play an important role in

the development of OI capabilities, and thus might influence OL and organizational AD. This

is why we included the variables as secondary variables in the model, to understand in which

extent, this variables might act as success and fail factors for organizational AD.


In this section we further explained AD, in order to get a better understanding of the concept.

AD can be described as the combination and simultaneous execution of exploitation and

exploration. Moreover, the role of leaders and AD is further discussed. Leaders play an

important role in stimulating creativity amongst individuals. Moreover, research has shown that

leaders help individuals to put the whole process of learning in a domain or context where it is

meaningful.

2.2. Opportunity identification

In this section OI will be explained in-depth. OI can be defined as the ability of individuals

to think of new ideas for products, processes, practices or services that can lead to new value-

creation for the organization (Baggen, 2017). Moreover, OI has an important relation with the

level of entrepreneurship within an organization; you must first have entrepreneurial

opportunities to create entrepreneurship (Venkataraman, 2000, p. 220).

A quote from the OL literature (Garvin, 1993) suggests the close link between OI and OL:

“New ideas are essential if learning is to take place. Sometimes they are created de novo

through flashes of insight or creativity; at other times, they arrive from outside the organization,

or are communicated by knowledge insiders. Whatever their source, they are a trigger for

organizational improvement” (Garvin, 1993, p. 81).

In order to model OI we will use the model “stage of creativity” which provides the necessary

elements. The stages of the model include: 1- Preparation, 2- Incubation, 3- Insight, which

together form the discovery phase; 4- Evaluation and 5- Elaboration, which together create the

formation phase. (Figure 2.1) (Lumpkin & Lichtenstein, 2005).

The discovery phase (number 2 and 3), can be understand as the generation of opportunity

ideas: initial ideas or envisioned futures in the mind of an individual (Lumpkin &


Lichtenstein, 2005). It can be measured with the Business Idea Generation instrument (BIG)

(Baggen, 2017, p. 48). The nature of these ideas is closely related to the prior knowledge and

experience of an individual (Venkataraman, 2000). Furthermore, idea generation is recognized

as being a domain-specific form of creativity (Hülsheger et al., 2009; Offermann & Coats,

2018).

Creative individuals are able to link relevant information and are sensitive to valuable,

unique information. Creativity can help in coming up with a new opportunity, but creativity

might be hindered by basic knowledge structures that constrain creative imagination

(Offermann & Coats, 2018). The second phase of the creativity-based model of entrepreneurial

opportunity recognition, the formation phase (number 4 and 5), can be understand as the

business idea evaluation, and is the capacity to recognize valuable ideas on the market. Based

on experience, individuals develop frameworks which help them to interpret new and seemingly

Figure 2.1. Creativity-based model of entrepreneurial opportunity recognition. Adapted from

Lumpkin & Lichtenstein, (2005)


independent situations, and to “connect the dots” between them (Baron & Ensley, 2006, p.

1341). This implies that individuals develop cognitive frameworks for identifying business

opportunities. An individual with an idea compares this idea to the developed framework in

order to estimate its potential. This phenomenon can be measured with the Business Idea

Evaluation (BIE) (Baggen, 2017, p. 50)

OI allows new information to be reshaped into products, services, etc. This

institutionalization allows to effectively satisfy the demands of the stakeholders and

shareholders inside the chaotic environment of the teams and organizations (Cao et al., 2010).

All the processes are carried by the people that conform to the organization and work on three

levels: individual, team and organizational. They are responsible for the variation, selection and

retention of new processes or learnings that can occur over time (Berson et al., 2006).

Employees that are able to identify opportunities significantly contribute to realizing all

kinds of profitable business outcomes. As such, OI is of interest to employers as it is a key

driver of competitiveness. In existing firms, OI can be an individual as well as a team effort

(Baggen, 2017). This means that the OI of teams and leaders, is directly related with the OL

capacity. It can be inferred that, if the teams possess good OI, the OL will also be good; this is

the assumed connection between OL and OI.

In short, this section focused on further explaining OI. The relation between OI and OL is

basically that new ideas and the identification of opportunities are essential for learning. To

model OI in this research, we will use ‘stage of creativity’ which has a discovery phase and a

formation phase. Within organization, OI causes new info to be reshaped into products, services

and more. Moreover, when employees possess good OI, OL will be good too. The latter is the

assumed connection between OI and OL.


2.3. Organizational learning

To remain relevant, organizations must be able to develop learning capabilities (Berson et

al., 2006). These focus on strategic renewal, which requires organizations to invest and search

new ways to do things, and at the same time, exploit what they have learned (Chen, 2017; Keller

& Weibler, 2015). Furthermore, learning can be considered as a permanent activity and as the

general objective of an organization (Bocaneanu, 2007). Learning goes beyond the individual

level (Berson et al., 2006) because it implies that the collective learning process, highly

depending of the leadership and its environment (Paul & Texas, 2014), extends from the group

level to the whole organization (Garvin, 1993; Saadat & Saadat, 2016). For this matter, OL

becomes of high interest for companies striving for differentiate from others.

The process of learning, or incorporating new concepts can be applied to many segments

like process manufacturing, business intelligence, human resources, product innovation,

culture, leadership, etc. Pervaiz K. et al., (2009) define organizational learning (OL) as the

organizational capability to keep and improve the performance. The learning is based on

previous experiences and the ability of achieving productivity from vivid and implicit science,

to share science and to use science in the organization (Crossan et al., 1999). Furthermore it is

relevant to acknowledge that the processes that contribute to learning outcomes, are complex

and they occur on multiple levels of analysis (Berson et al., 2006; Crossan et al., 1999; Leufvén,

Vitrakoti, Bergström, Ashish, & Målqvist, 2015)

Understanding the process is relevant in order to assess the efficiency of the integration and

the reflection processes within the organization, which are the processes through which the

teams can institutionalize new knowledge and, the organizations are able to re-evaluate policies,

products or processes already incorporated in the company’s structure (Figure 2.2).


The 4I framework (see table 1) places a clear connection between OL, and AD (exploration

and exploitation) and makes it possible to measure OL.

Level Process Inputs / Outcomes

Individual Intuiting Experiences

Images

Metaphors

Group Interpreting Language

Cognitive map

Conversation/dialogue

Institutionalizing Shared understandings

Mutual adjustment

Interactive systems

Organization Integrating Routines

Diagnostic systems

Rules and procedures

With this framework we identify the flow of learning between levels and the tension between

exploration and exploitation processes as fundamental challenges of strategic renewal. There

are many factors that could influence these processes, some of which are part of the

institutionalized learning itself (e.g., reward systems, information systems, resource allocation

systems, strategic planning systems, and structure). However, in the 4I model we recognize that

the ideas that occur to individuals, ultimately are shared through an integrating process. It is the

individuals, the social processes and the group dynamics through which they interact, that

finally may facilitate or inhibit organizational learning. One interesting aspect, might be the

effects of different types of leadership.

Table 2.1. The 4I model, including flow between individual and organizational level. Adapted from ‘’An

organizational learning framework: From intuition to institution, by M.M. Crossan, 1999, Academy of

Management Review, 24(3), 522-537. Copyright (1999) by the Academy of Management Review.


This section discussed OL. Learning goes beyond the individual level and flows through

different levels, and thus it can be said there is a collective learning process within

organizations. The 4I model can assist in measuring OL, as it identifies the flow of learning

between the different levels.

2.4. Levels of analysis

When analyzing a company on AD there are numerous levels of analysis, from the internal

factors which include individual, team and organizational learning (Crossan et al., 1999;

Hülsheger et al., 2009), to external factors, such as political, economic, social and technological

factors (Campbell, 1998). In this research the focus will be on organizational internal factors.

The internal levels of analysis (individual, team and organizational level) tend to interact,

and it is precisely this interaction that is the pathway for organizations towards learning

(Crossan et al., 1999). In the current study, we will illustrate the flow between these levels.

Imagine a feed company who wants to develop a new product for pets; the teams are the ones

that are able to make the discussion about the characteristics, but the tools provided by the

organization, the space to develop and the support to carry those ideas into a real product, will

play a relevant roll on the speed and success of the creation of this product. Another example

is that, in the organizational level, the company promotes the participation of the teams in

competitions and seminars, and this, will influence the teams, to come up with new ideas, or

recognize opportunities in those spaces (Pervaiz K., Ann Y. E., & Mohamed Z., 2009).

The flow from the team level, towards the organizational level, is to be understood as

“integration” (Pertusa-Ortega & Molina-Azorín, 2018; Senge, 1990), and is the moment when

the ideas created by teams are integrated in to the organization’s policies or portfolio. The flow

from the organizational level, towards the team level, is going to be understand as “reflection”


(Widmer, Schippers, & West, 2009), and it is the process through the organizations un-learn or

evaluate current aspects of their processes and involve teams to improve said aspects. For

example, once evaluations are done, as result, communication towards teams should be

assertive explanatory. The teams should understand, as part of the organization, what aspects

are being neglected, such as discipline, communication, perseverance, client service, etc.

(Figure 2.2). So, as said above, individuals are not as good predictors of innovation, and teams

are the actual engine in terms of integrating ideas into the organization. Besides this, managers

are reluctant to address innovation matters towards individuals. These are the main reasons for

this report on organizational level and team level, and not in individual level.

This section discussed the level of analysis of the research. First of all, this research will focus

on internal factors of an organization. Secondly, it will focus on team and organizational level.

2.5. Other model variables

For the correct assessment of teams and organizations, the aspects that might influence AD

must be characterized and categorized. For this matter, we choose to divide this aspects into

three variables, that are summed below. The variables are internal and respond to the daily

Figure 2.2. Key concepts do describe flow between Team Level and Organizational Level


activities or the teams. This variables have been studied before, and successfully proved

relationships with OL and OI.

1—Team level predictors of innovation, which are going to be analyzed using the framework

described by Hülsheger et al., 2009. This framework responds to the necessity of Meta-analysis

at the team level analyses focused on innovation processes. It covers a wide range of theories

and compares measurement methods and levels. This might be connected with the team’s OI

capacity. Therefore is interesting to address its variability.

2— Leadership, based on Berson’s Leadership and OL, defining the type of leader (Berson

et al., 2006)

3—Environment, using Hengen’s disciplines adapted by Walker (2017). To avoid confusion,

environment here refers to the atmosphere in which teams develop daily tasks. Hence, it is not

about an ´external´ environment such political, or economic factors.

Below the three variables will be explained further in detail.

Team level predictors of innovation

Organizations have great interest in teams inside the organization. They want teams learn to

transform, adapt and improve different aspects of its daily business. The general perception is

that inside organizations, some teams perform better than others. The characteristics of these

better performing teams allow these teams to learn faster, work better together, and achieve

Figure 2.3. Team-Level predictors of Innovation variables. Hülsheger, et.al (2009)


relevant milestones for the organizations. So it is important to know what are those

characteristics that make these particular teams special. Knowing the characteristics is of

importance to the companies that want to remain relevant.

The framework on Team-Level Predictors of Innovation can help to identify what makes

teams different (Hülsheger et al., 2009). Two types of variables can be differentiated: team-

input variables and team-process variables. Team-process variables display substantial and

generalizable relationships with innovation. Team-input variables display only relatively small

relationships with innovation. Following this, teams can be evaluated with seven input

variables: goal interdependence, background diversity, job relevant diversity, task

interdependence, team longevity, team size and cohesion. Also teams can be evaluated with

five team-process variables: vision, support for innovation, internal and external

communication and task orientation, which is directly related with motivation (Figure 2.3).

Leadership

Leaders are expected to act as drivers for innovation, they can use many tools to refer to

issues. Moreover they have the ability to get involved emotionally with all the team’s members,

and can contribute by sharing their learning and align it with the goals of the organization. “The

ideas of individuals become meaningful, legitimate, and integrated in their own cognitive maps

when they make sense to others, especially to their leaders” (Berson et al., 2006). For these

reasons, a leader’s vision may be a source for building a shared language or mental model,

which can validate, legitimate and connect the ideas of individuals to the team level.

It is clear that there is not a “magic combination” of skills and traits that makes a leader a

great leader (Dansereau, Seitz, Chiu, Shaughnessy, & Yammarino, 2013; Keller & Weibler,

2015). Furthermore different characteristics matter in different circumstances (Ojha et al.,


2018). However, this does not imply that organizations are unable to improve leadership. In

this report, we focused on behavioral theories, which is about what a good leader does. We

choose this theory as the focus in general is on internal factors that drive organizations. Later

on in this document, the use of the three leadership types (from behavioral theory) will be

explained.

Environment

Many authors state that environment is key for the team performance (Chiva, Alegre, &

Lapiedra, 2007; Ojha et al., 2018; Walker, 2017). However, organizations may fail in assigning

the team’s innovation performance to the environment, or to its intrinsic traits. It is relevant for

organizations to identify, whether the resources and efforts should focus on improving the

team’s core characteristics, or only improve the team’s performance in its business

environment. The environment of the teams, can be analyzed using the five “component

technologies” of a learning organization: personal mastery, mental models, shared vision, team

learning, and systems thinking (Senge, 1990). Nonetheless, these disciplines might miss the

influence of leadership in the before mentioned environment. To solve this, we will make use

of one extra discipline (the fifth discipline) called “empowering leadership”, which “is focused

on using knowledgeable change agents with authority to implement managerial policies and

initiatives to activate the latent learner and promote the desired behaviors of LO in the

workplace” (Walker, 2017, p. 28).

In this section we discussed the different internal variables that might influence AD. First

there is team level predictors of innovation, which can help to identify what makes teams

different. Then there is leadership, in which the focus is on behavioral theory, which is about

what a good leader does. The last variable is the team’s environment.


2.6. Connections between variables

In this final section, we will discuss the connections between the variables. The fifth

discipline, as discussed before, became a guideline on how to structure organizational

management values and initiatives to encourage effective learning (Senge, 1990; Walker,

2017). However, more than just increasing learning, the process to self-inform about the

knowledge and awareness generated by the organization, would and should, inform the

practices of the organization itself. This in turn would make the process more efficient and

effective, as the organization is better able to respond to what it learned, both about itself and

about the outside environment (Garvin, 1993). Following these ideas, five disciplines can be

identified which can be used to assess a learning organization. These disciplines can be

understand as “component technologies” of a learning organization and are the following

(Senge, 1990):

• Personal Mastery: “a discipline of continually clarifying and deepening our

personal vision, of focusing our energies, of developing patience, and of seeing reality

objectively.”

• Mental Models: “mental models are deeply ingrained assumptions,

generalizations, or even pictures of images that influence how we understand the world and

how we take action.”

• Shared Vision: “a practice of unearthing shared pictures of the future that foster

genuine commitment and enrollment rather than compliance.”

• Team Learning: “the capacity of members of a team to suspend assumptions and

enter into genuine thinking together.”

• Systems Thinking: “seeing inter-relationships that underlie complex situations


and interactions.” (Senge, 1990)

Using this approach, we will characterize the perceived environment, from the teams and the

leaders. Understanding the differences of the surroundings, protocols and rituals that are

intrinsic part of the teams, we will be able to assign the respective variation to the environment

interactions and not of the teams its selves.

The leadership has proven to be relevant for the OI and for the development of a LO (Avolio

et al., 2004). Leaders facilitate exploration among followers by providing the contextual support

to develop their ideas (Berson et al., 2006). For the learning organization, the innovation process

is part of the capabilities tool-box; the teams, as a learning organisms motivated by leaders,

engage in innovation, and have intrinsic characteristics, numerous variables that perform as

predictors of innovation performance (Baggen, 2017; Edmondson & Harvey, 2018). For the

correct assessment of a learning organization and its relationship with the AD, the proper

identification of the profile of the teams which engage in innovation becomes relevant, due to

the complexity of its interactions; if organizations can isolate the different aspects that influence

innovation, they have a clear approach to prioritize and make precise changes and

improvements.

Hülsheger et al. (2009) describes an approach to characterize teams, the different variables

that influence innovation process were divided in two categories; input –and process variables.

For the input variables, ‘team size’, ‘job-relevant diversity’ and ‘goal interdependence’ showed

a positive significant correlation with innovation performance, while ‘team longevity’, ‘task

interdependence’ and ‘background diversity’ displayed a small negative, yet, not significant,

relationship with innovation (Hülsheger et al., 2009). Their study also showed that, in general,

team process variables are strongly linked to overall measures of innovation, being ‘vision’,


‘external communication’, and ‘cohesion’ as the most relevant among all the variables

((Hülsheger et al., 2009)).

According to Senge’s theory, an organization which can facilitate these core concepts can

establish a cycle of perpetual learning and self-improvement, which will in turn generate a

sustainable competitive advantage for the organization. However, Walker, in his study, after a

critical review of the fifth discipline, decided to include a sixth discipline: “empowering

leadership”, which “is focused on using knowledgeable change agents with authority to

implement managerial policies and initiatives to activate the latent learner and promote the

desired behaviors of LO in the workplace” (Walker, 2017, p. 28).

To illustrate the connection between OI and OL Lumpkin & Lichtenstein (2005) made the

following propositions:

• The more organizational learning practices are enacted by both, entrepreneurs

and entrepreneurial firms, the higher the likelihood that new opportunities will be

recognized.

As mentioned, one of the factors affecting AD is leadership (Berson et al., 2006). Leaders

play a central role in the OL process in multiple ways. First, by providing the contextual support

in the organization (Berson et al., 2006; Keller & Weibler, 2015), leaders obtain the needed

resources for learning to occur through exploration and exploitation (Avolio et al., 2004).

Second, leaders are critical to the integration of learning across group and organizational levels.

Leaders enable and enhance this integration by providing a foundation of shared understandings

of needs and purpose at different levels of the organization (Keller & Weibler, 2015). These

processes can be evaluated from the optics of the 4I learning processes of entrepreneurial


intuition and interpretation (Baggen, 2017; Berson et al., 2006), and therefore, the choice is

made to focus on leadership as another one of the factors linking between teams and

organization.

2.7. Research questions

The aim of this study is to identify the intricate relationship between the variables

responsible for organizational AD, including OI and OL. The research can be seen as a

quantitative study design, which is used for a thorough, holistic and in-depth exploration in the

specific situation, phenomenon, group or community (Baxter & Jack, 2008).

The research is divided into three steps. The first step is building a theoretical or conceptual

model, based on the literature on AD, OL and OI and their interactions and flow among the

different levels, such as individual, team and organization. The second step includes the

development of a survey, which is build based on validated methods from primary and

secondary literature and its recommendations. The third and final step covers empirical research

on survey delivery and data collection.

AD is composed of exploration and exploitation and is dependent of OL. OL, in turn, will

be measured through the concept of 4I (Intuiting, Interpreting, Institutionalizing, Integrating).

The last variable is OI, which will be analyzed from the concepts of self-perceived opportunity

identification competence, Business Idea Generation (BIG) and Business Idea Evaluation

(BIE). Additionally, the secondary model variables will also be introduced. Environment,

Team-Level Predictors of innovation and Leadership might modulate the flow relationship

between the model variables.


Figure 2.4. Theorical framework. The dependent variable (Ambidexterity) is dependent on the independent

variables (organizational Learning and Opportunity identification), influenced by the moderator variables

(Environment, Team-level predictors of innovation and Leadership)

Now that the figure is explained, the main research question is as following:

What is the relationship between the variables responsible for organizational AD?

For this research question, focus will be on OI and OL, as these are main concepts expected

responsible for org AD. However, to measure OI and OL, other variables (such as the 4I model)

are necessary to explain OI and OL in turn.

To answer this question, we need to evaluate the connections between the variables, for this,

propositions also need to be addressed, and will be answered later in this report:

I. What is the relationship between the constructs and items of OI and OL frameworks?

II. How does OL influence organizational AD?


3. Methodology and research design

In this section we will introduce the design of this research, the methodology implemented,

and the motivation behind all the different decisions. The methodological part will be divided

in 4 main sections, which will respectively describe the object of research, the survey, and

define in-depth the concept of leadership, organizational learning and opportunity

identification. The survey contains theoretical categories, meaning that each framework,

consists of several constructs, and each of these constructs comprehends several subjects or

sub-sections. These subsections are derived from prior theories and inductively developed

theory. This report aims to connect strategies, and understand the data collected in the context

of Colombian agricultural and animal industry.

3.1. The object of research

To be able to answer the above research questions, it was proposed to work together with an

commercial organization. Because of the background of the main author, it was more

convenient to try to involve an organization active in lifestock production. The reason to use

only one organization is based on exclusiveness issues and confidentiality. This is illustrated

by the observation that none of the organizations contacted wanted to work along, other

organizations, some of these even requested to have the competence information, without

delivering their own.

The chosen organization is in the animal feed and technology sector, a Colombian enterprise

characterized and recognized for its innovation. This Colombian feed manufactured, from now

on referred as the Company, is composed by more than 600 collaborators, from 9 nationalities;

31% of the Company is female, and 46% is composed by young talent between 20 and 35 years


old. At the beginning, there was a proposal to work with 7 teams, however, after some

interviews with the CEO and the top managers, the number of teams raised to 36, with the pre-

condition that the managers will take full responsibility on the successful completion of the

survey. The filed “name” would be included, just for follow-up purposes, and once the

responses were recorded, the names are deleted from the data base, this to have anonymous

answers, because individual level, is not in the scope of this report.

The Company is characterized and recognized for its innovation (Cano, 2015; Colombia-

inn, 2017). It is based in Medellín, the capital of Antioquia – Colombia. Its main activity is

focused on food and innovation towards animal and human nutrition. The organization aims to

replicate successful behaviors and characteristics inside the Company; however, it lacks

information or tools to identify these and thus, improve the existing ones or learn new

capabilities. By characterizing its teams, based of literature and accepted variables as predictors

of innovation or learning capacities, the Company will be able to make precise improvements

on the teams and the organization, to develop further its organizational AD.

Team Alpha

In a meeting held in the Netherlands, the Company’s CEO said that the organization is

growing in numerous aspects: For example, Team Alpha is perceived as one of the Company’s

most important Business teams, due to the intricate relationship within the rest of the business

teams. This team is currently focused on developing everything related to the internet of animals

(IoA), extract-transform-load (ETL), and data services (DS). It is composed by a team of young

and motivated professionals. The CEO stated about Team Alpha “ This team has proved to go

beyond expectations, show fast learning, and delivers relevant projects that are placing the

Company on the spotlight”. However, effectively replicate Team Alpha’s success into other


parts of Company P, has proven to be difficult. Looking at the existing literature, Team Alpha

could be explained as “a division of technological and digital nature, which has as purpose to

enable technologies to be used in the protein production, transforming technology in tools and

knowledge to help the producer”, (“Premex, nutrition for the agricultural industry,” 2018). The

corporate structure is defined as a network structure, less hierarchical, more centralized and

flexible than most managerial structures, being the managers (leaders) who coordinate and

control relationships, both internal and external (Johnson, Whittington, Scholes, Angwin, &

Regnér, 2018). The chosen teams are playing a relevant roll on the strategy towards 2020. For

these reasons becomes of high interest to characterize them, identify defining aspects such as

environment, team profile and leadership, to facilitate the replica of Team A’s successful

behavior into the other teams. To wrap up Team A will work as the control group due to its

characteristics, from which we have reason to assume that Team A is ambidextrous.

3.2. The survey

The survey was developed with help of the human resources department using Qualtrics®

software, and it contains 11 sets of standardized questions which will be called items, following

a randomized scheme (Error! Reference source not found.Figure 2.5. Questionary flow and

item distribution) in order to collect individual data about the variables of interest. The first

questions should be of easy response to gain confidence and empathy with the respondents. A

difference in language might influence the quality of the answers (Baggen, 2017). Spanish,

which is the main language of the Company, is not the mother tongue of many of the employees,

for this reason the survey was developed both in English and Spanish, so the employee was

able to choose its preferred language. Due to the extensive length of the survey, 5 pauses were

located every certain number of questions at the end of each randomized block. The employee


then is able to stop the survey and pick it up another time. This will guarantee that the

respondent will receive the questions in different order, without affecting the related subjects,

so the mental fatigue won’t affect the same questions for every response. The questions were

reviewed and approved by the managers and the HR department.

The survey was administrated to the Company’s employees, with the collaboration of the

communications department, via institutional email and published in the Company’s internal

web page. The stimulus for all the respondents was the same, to get a similar cognitive and

social reaction. Next to the survey it was important to do more research. Hence, secondary

sources were researched and interviews were taken with the top level managers. These

interviews were done to get more in-depth information about the Company and its goals.

Furthermore, the interviews were held to give the Company information about the following

steps of the research and to inform them about the procedure. The managers had the task to

socialize with their team briefly about the relevance of the survey, then the survey was sent

through email. For two weeks, the weekly news flash contained a reminder. And at the last

reminder is an email from the manager reminding the team to complete the surveys. Qualtrics

was connected to a dashboard, that showed in real time the employees that answered the survey

and in which percentage were going. The HR department handed in the team employees list of

names and business units. This list would later be used to check the full competition of the

survey.

Both figure 2.5 and table 2 were created to clarify the survey. Figure 2.5 (see below) explains

how the survey is set up. From left to right, you can read the different stages that the

collaborators go through when making the survey. Based on figure 6, table 2 was created. Table

2 explains the literature review and the authors of the concepts that are being used, to address

the variables that are being measured.


The following sections will describe the segments of the survey (Figure 2.5. Questionary

flow and item distribution), the measurements and the relationships with the frameworks

described above. The figure shows how the collaborators received the questions (items). Each

framework contains several constructs, and each construct contains several items. These

constructs are presented randomly for each collaborator.

Fig

ure

2.5

. Q

ues

tio

nar

y f

low

an

d i

tem

dis

trib

uti

on

.


Table 3.2. Model variables, subsections and constructs.

Framework

(Variable)

Constructs Sub-sections Items Type of

variable

Author /

Reference

Team-Level

Predictors of

Innovation

Input variables 1 Background

diversity

2, 3, 5, 6, 7 General

demographics

Hülsheger et

al., 2009

2 Aim

interdependence

26 - 30 5-item Likert

scale

3 Job-relevant

diversity

4

4 Task

interdependence

21 - 25

5 Team size 13

6 Team longevity 8 9-item Likert

scale

7 9 6-item Likert

scale

8 Cohesion 86, 87, 88 5-item Likert

scale

Team Process

variables

9 External

communication

89, 90, 91 5-item Likert

scale

10 Internal

communication

14 - 20

11 Participation

safety

36 - 40

12 Support for

innovation

92, 93, 94

13 Task orientation 95, 96 5-item Likert

scale

14 97 5-item multiple

choice

15 Vision 31, 32, 33 Open question

16 34, 35 5-item Likert

scale

Organizational

Learning (OL)

4I 17 Intuiting 41, 42, 45,

54, 55, 56, 59

5-item Likert

scale

Baggen,

2017.,

Crossan et al.,

1999 18 Interpreting 44, 48, 49,

58, 62, 63

19 Integrating 46, 47, 51,

60, 61, 65

20 Institutionalizing 43, 50, 52,

53, 57, 64,

66, 67

Leadership 21 Type of leadership 68 - 85 5-item Likert

scale

Judge &

Piccol, 2004

Opportunity

identification

(OI)

22 Self-perceived Opportunity

Identification competence

98 - 101 5-item Likert

scale

Baggen, 2017.

23 Business Idea Generation (BIG) 102 Open ended

question

Business Idea

Evaluation

(BIE)

24 Experienced

entrepreneur

103 - 107 5-item rank

question

Baggen, 2017

25 Novice

entrepreneur

108 - 112

Environment 26 Mental models &

personal mastery

113, 114, 115 5-item rank

question

Walker, 2017.


Organizational

learning

environment

27 Shared Vision 116, 117

28 Systems Thinking

& Team Learning

118, 119

6 disciplines

variables

29 Participating

group size

121 0-100 Rating

scale question

Senge, 2009,

Walker, 2017.

Desirability of

natural light

122

Flexibility 123

Ownership 124

Tactile quality 125

Frequency of Use 126

Visual Vibrancy 127

Access to

technology

128

Area Size 129

Visual Privacy 130

Acoustics 131

Public Access 132


Demographics, leadership & team

The questions contained in this block, are mainly demographics including age and gender.

The questions for Longevity in the Company (Q.8) and in the current team (Q.9), will define

the level of expertise, also contains the classification of leader (Q.10), to differentiate leaders

and collaborators answers from each other. The employees were asked to indicate the current

team or unit they belonged, indifferent if they had to work with many others, they ave to indicate

the main team (Q.5 & Q.6).

Team level predictors of innovation

This framework, adapted from Hülsheger et al. (2009), is divided in two main sections, Input

variables and Team-process variables. The measurement method is assessed via self-report. We

expect higher correlations between team process variables with self-ratings of innovation

compared with independent ratings or objective indicators of innovation, if the same individuals

report on team processes as well as on their team’s or their own innovative performance,

correlations are likely to be higher, because not only is the same measurement method used

(surveys) but also the same information source (team members) (Hülsheger et al., 2009). As

listed on the


Table 3.2, the Input variables described by Hülsheger are: Job-relevant diversity, Background

diversity, Task interdependence, explained as de ease of a team to complete tasks, without

depending on other teams. In other hand, Goal interdependence, another input variable, is

described as the amount of contrast between teams goals, without losing vision over the

organization`s purpose. Team size is also part of Team-level predictors of innovation, inside

Input variables construct, and is as simple of the amount of collaborators inside a team. And

finally, Team longevity, is the time average or experience that team members have inside said

team, can also be called “teams age”. Team process variables are Vision, referring to team’s

and organization’s purpose. Here, collaborators should indicate which one are the respective

goals and purposes. Low values in this item will indicate lack of communication from the

leaders and might also be a sign of lack of compromise from the employees. Consequently,

Participative safety, Support for innovation and Task orientation, are all deeply connected to

team’s leadership. Because is the leader who will facilitate the active participation of the

collaborators and give them confidence to make mistakes. At the same time, is the leader who

has certainty of organizational purpose. Thus, it is the leader who should orientate tasks towards

the accomplishments of said goals.

Cohesion, Internal communication and External communication, the last items of TLPOI

framework, will evaluate the capacity of teams to have discussion about non-job-related topics.

It is believed that most of the ideas, come from hall conversations inside the organizations. A

proper cohesion, boosted by internal communication will bring innovative discussions, and

once these ideas are fostered inside the teams, can be shared with other teams and leaders, to

bring them to life and generate innovation for the organization. All of these constructs have

several questions, and were evaluated with Likert scales and open questions for Background

diversity and Job-relevant diversity.


3.2.2.1. Background diversity. Described as the diversity of the origin of the team

members. This value is measured by the distance between the city in which the team members

grew, interpreted as, the larger the distance, the less aspects-in-common shared by the team

members. Background diversity should remain in low levels (Frances J & Luis L, 1996),

because differences in power-distance, language, rituals, etc., can hinder team innovation

performance (Hülsheger et al., 2009). The location of birth was recorded in open question (Q.3),

then the cities were homogenized, and the geographical coordinates are assigned. To measure

team-city of birth diversity, the average distance was calculated using the Haversine Equation

(Equation 1), where φ is latitude, λ is longitude, R is earth's radius (mean radius = 6,371km)

a = sin²(Δφ/2) + cos φ1 ⋅ cos φ2 ⋅ sin²(Δλ/2)

3.2.2.2. Job-relevant diversity. This sub-section of TLPOI, can be understand as the

heterogeneity of technical, social and scientific abilities inside a team. The heterogeneity should

not be low enough to lack objective discussion, and should not be high enough to slow internal

processes. The job relevant diversity was recorded using open question (Q.4) where the

respondent indicates its profession or expertise area. This categories where homogenized using

the International Standard Classification of Occupations (“International Standard Classification

of Occupations (ISCO),” 2014). These values where then used as distances for Job-relevant

diversity. We assumed that the bigger the distance between numbers, the bigger the differences

between respondent background. Averages for teams were then calculated.

Equation 1. Haversine formula to for distances between two longitudes and two latitudes.


3.3. Leadership

To determine the type of perceived leadership, we are going to differentiate between three

leadership styles, which have been mentioned before in chapter 2.5.2. Democratic leadership,

Laissez-faire leadership and Authoritarian leadership. These styles were chosen based on the

previous Company’s experience and each style is related to a group of 6 questions for a total of

18 questions. The respondent will choose one option between a 5-level Likert of agreement or

disagreement scale. The group with the highest score will be the most dominant perceived

leadership style, then 5 categories are assigned based on average scoring: 26-30, very high

range; 21-25, high range; 16-20, moderate range; 11-15, low range; 6-10, very low range.

3.4. Organizational learning

The 41's are related in feed-forward and feedback processes across the levels (Crossan et al.,

1999). Hence, for measuring the flow from individual level towards organizational level, the

survey included 27 items based on the 4I framework for organizational learning (Crossan et al.,

1999). The questions were modified to fit the Company’s language and culture and also to fit

the definitions given in the welcome page of the survey. For example ‘managers’ changed to

‘leaders’ and ‘employees’ changed to ‘collaborators’. The items were divided as indicated on

the Table 3.2. Model variables, subsections and constructs. The questions are oriented to pro-learning

culture and anti-learning culture and measured in a 5-level Likert scale of frequency (1- Always,

-2 Most of the time, 3- Half of the times, 4- Sometimes and 5- never). To illustrate this, the

items 41-53 are pro-learning behavior and the items 54-67 are anti-learning culture. Item 53

provides an example of a pro-learning culture: ‘Managers think about their interest in and

capacity for learning new things (their learning quotient), and the learning quotient of their

employees’. Item 67 provides an example of an anti-learning culture: ‘Managers think that


they personally know all they need to know and that their employees do not have the capacity

to learn much than they know’. These two items relate to the same aspect of the 4I framework,

Integrating. However, low-values answers will be evaluated differently, the first item, 53, in a

positive way, and the second item, 67, in a negative way.

3.5. Opportunity identification

The survey contains a performance test referred to as the opportunity identification

competence assessment test (OICAT) (Baggen, 2017, pp. 47–55). This test consists of two

tasks: Business Idea Generation (BIG) and Business idea Evaluation (BIE). In the first task, the

respondents were requested to come up with business ideas or projects related with

sustainability. These ideas were later evaluated by the Team A’s senior entrepreneurs using

three factors to score the competence of creative individuals: fluency, elaboration, and

flexibility (Carroll & Guilford, 1968) and then the results returned to the researchers. A

description of this method was shared with 3 collaborators, who scored these ideas

anonymously. The second task consisted of applying the frameworks about novice and

experienced entrepreneurs (Baron & Ensley, 2006) in which the respondents were requested to

rank the given arguments according to their importance when determining the potential success

of business ideas.


4. Results and analysis

As a principle of qualitative research, the data analysis was conducted simultaneously with

the data collection (Coffey & Atkinson, 1996). This allowed us to correct progressively adjust

the survey to the user experience, fix problems and react rapidly, reducing the amount of

desertion of the survey.

Each construct was calculated according to the methodologies described by the authors of

the frameworks (Table 3.2) and team averages were obtained for each construct. From 315

collaborators that were indicated to solve the survey, 226 started it (71.76%) and from these,

180 collaborators completed it (57.14% of the total of collaborators). As criteria for the

selection of the teams, the percentage of completeness of the surveys (100%) and the number

of completed surveys per team (more then 3) were used. Hence in the end, 17 teams remained

with a total of 180 completed surveys. From all the respondents, 43.4% were between 24 and

34 years, representing a big majority of young people, however, only 4.4% corresponded to the

ages of 18-24 years. For more information on the demography of the collaborators, please

reference to

. The amount of constructs and items is extensive compared with most of the references

(Baggen, 2017; Berson et al., 2006; Hülsheger et al., 2009; Lis, Tomanek, & Gulak-lipka, 2018;

Manuscript & Study, 2013; Raisch & Birkinshaw, 2008; Yang, 2016).


Characteristics Frequency Percentage

Background diversity: Age

Between 18 and 24 years 10 4%

Between 25 - 34 years 98 43%



Others ages 14 6%

Background diversity:

Gender

Men 120 53%

Women 106 46%

Team longevity:

Time inside the Company



Less than one year 51 20%

More than 5 years 49 19%

More than 10 years 31 12%

Others 28 11%

Job-relevant diversity:

Professional/technical

background

Animal Science 39 17%

Logistics 25 11%

Administration 18 7%

Chemical engineering 12 5%

Chemistry 11 4%

Industrial engineering 11 4%

Veterinary Medicine 11 4%

Commercial 9 4%

Others 91 40%

Background diversity:

City of birth

Medellín - Colombia 124 54%

Bogotá D.C. - Colombia 19 8%

Ciudad de Guatemala - Guatemala 8 3%

Itagüí - Colombia 7 3%

Armenia - Colombia 4 1%

Others - Colombia 64 28%

Self-perceived Leadership

No, I do not see myself as a leader inside my

team 143 63%

Yes, I see myself as a leader inside my team 83 36%

Total amount of collaborators in the target group 315

Total surveys answered: Surveys with at least three constructs answered (15%) 226

Response ratio: Collaborators who completed at least three constructs (15%) 72%

Total questions in the survey 132

Percentage of questions

answered for each survey

100% completed 180 79,60%

95% completed 2 0,90%

Table 4.1. Teams demographics and survey results, based on individual survey answers.


The amount on teams was reduced drastically to avoid missing values. As expected, team

Alpha was included inside the 17 selected teams.

4.1. Results and discussion

Several ways of statistical analysis were used to create results, with the majority of analyses

being unsuccessful to prove the validity of the construct. Confirmatory Factor Analysis (CFA)

was performed in order to test construct validity. CFA in AMOS with all the constructs

explained above, produced poor fit of the model. The p-value obtained for CFI (likelihood ratio

chi-square goodness of fit statistic) was 0.551. Literature reports that these values should be

higher than 0.6 to be acceptable (Arbuckle, 2005; Blunch, 2017). Consequently root mean

square error of approximation (RMSEA) was about 0.089, when values must be lower than 0.05

in most cases, although some reports work with values under 0.08. (Körner, Wirtz, Bengel, &

Göritz, 2015; Taghipour & Dejban, 2013)

Cronbach's Alpha

As the CFA was unsuccessful, an exploratory factor analysis (EFA) was conducted in SPSS

to assess the validity of all items regarding opportunity identification, organizational learning,

Team-level predictors of innovation, Leadership and Environment. Reliability analysis was

performed for each construct separately, evaluating each item for each construct. When




Others 38 16,80%

Total teams 36

Chosen teams: Teams with at least 3 surveys answered with 100% of the items

answered 17


performing reliability analysis in SPSS, Cronbach’s alpha coefficient showed survey’s

reliability with an Alpha of 0.738 and Cronbach's Alpha Based on Standardized Items showed

0.774 for 35 survey items. When corrected for those components with “Cronbach's Alpha if

Item Deleted” higher than the actual alpha, the Cronbach's Alpha Based on Standardized Items

rose to 0.87. Therefore, these items were discarded and excluded from further analysis. Type

of Leadership variables has zero variance, 100% of the respondents categorized its leaders as

Democratic leaders type (Judge & Piccol, 2004).

A second round of reliability analysis pointed out that the remaining factors do not increase

Cronbach’s alpha when deleted, therefore, 25 items remained. Reliability analysis was executed

based on Cronbach’s alpha α for each construct separately. A third round of EFA was executed,

which showed that all remaining variables loaded well on the intended factor, except for all

leadership factors, because all the collaborators got the same result: Democratic leadership, &

Laissez-faire, primary and secondary types of leadership. For this reason, Leadership variables

are not evaluated any further.

Table 4.2. Reliability analysis and Cronbach’s alpha coefficient.

Variable Construct Sub-section Valid cases

%

Cronbach's

Alpha

Rejected

items

Remaining

items

Corrected

Cronbach's

Alpha

Team-Level

Predictors of

Innovation

Input

variables

Background diversity 100,0% 0,173 2 0 0,215

Aim interdependence 94,7% 4,730 1 4 0,618

Job-relevant diversity 89,5% 0,170 2 3 0,205

Task interdependence 94,7% 0,440 0 5 0,456

Team size - - - 0 -

Team longevity - - - 0 -

Cohesion 92,1% 0,201 1 2 0,514

Team

Process

variables

External

communication 92,1% 0,765 1 2 0,828

Internal

communication 94,7% 0,894 2 1 0,921

Participation safety 94,7% 0,727 0 5 0,766

Support for

innovation 92,1% 0,605 1 4 0,670

Task orientation 92,1% 0,296 0 3 0,298


Vision - - - - -

Organizational

Learning (OL) 4I

Pro-learning 100,0% 0,826 0 13 0,843

Anti-learning 100,0% 0,819 0 13 0,830

Organizational

learning 100,0% 0,883 0 4 0,913

* Items with Cronbach's Alpha lower than 0.3, show poor reliability.

4.2. Descriptive statistics and correlations

To analyze connections between variables, Pearson correlations were performed for all the

constructs and sub-sections (Table 4.3). For the correlations, “**” means significance at the

0.01 level (2-talied), and “*” means significant correlations at the 0.05 level (2-tailed). All the

frameworks showed correlation within constructs and items.

Organizational learning

Organizational learning showed interesting correlations, for example, the framework possess

antilearning items, and these showed negative correlation, for example, in terms of anti-learning

behavior, Institutionalizing (-0.883**) and interpreting (-0.893**), indicating the negative

effects that can cause the lack of policies or assignation, once ideas are proposed. Connecting

Organizational learning with opportunity identification showed correlation with the business

idea evaluation – construct, for the sub-section Experienced entrepreneur. This is aligned with

the belief that experienced entrepreneurs will be more efficient taking new knowledge, and

transforming it to tangible products or services (Hébert & Link, 2006), compared with the

novice entrepreneur (0.154), which, might have fresh ideas and the advantage of technology

(Beckman, Eisenhardt, Kotha, Meyer, & Rajagopalan, 2012), but lack the capacity to connect

the dots and run a successful innovation efficiently.


In terms of team-level predictors of innovation, strong correlations with Support for

innovation (0.740**), Task orientation (0.746**), Participation safety (0.526*) and External

communication (0.560*). This brings light to the relationship between teams, and how this can

promote innovation within an organization. Leader, are the ones who support and orientate the

daily tasks, in order to accomplish organizational purposes. If leaders communicate clearly the

goals, and provide the proper support for the ideas, within a safe and respectful environment,

teams will be more efficient to incorporate new knowledge (organizational learning).

Environment showed no correlations, this might obey to the difference of scoring, and

because many of the team members work either from home, or are in the field 50% of the time.

Opportunity identification

Opportunity identification behaved differently that the other constructs and frameworks.

Opportunity identification had a complex evaluation. First, ideas had to be generated. Then

these ideas had to be evaluated by experienced members of team Alpha. The ideas are classified

in categories, and these categories are part of the index. This framework is able to evaluate the

capacity of a team to produce new ideas. In total, 615 ideas were created. From these ideas,

46% complied with the requirements of clarity, alignment with the organization purpose and

the subject, which was sustainability. These ideas were classified in a total of 26 categories, i.e.

water management, recycling, clean energy, circular economy, etc. Business idea generation

showed no correlation with any other item, however was highly significant within its own

construct (0.966**) indicating consistency. Self-perceived opportunity identification correlated

with institutionalization, only indicating that successful innovation, will promote more

innovation due to self-confidence (Dutta & Crossan, 2005).


By other hand, Business idea evaluation, showed important contrast in terms of experienced

and novice entrepreneurs. Novice entrepreneurs performed better when requested to generate

ideas compared with experienced entrepreneurs (0.539* vs 0.121).

This means that opportunity identification connects with organizational learning trough

conversations between teams, powered by proper guidance of leaders, and organizational clarity

in terms of purpose and goals. Experienced entrepreneurs will be able to develop the ideas

generated by the novice entrepreneurs.

Environment

The environment takes relevance when innovation is a daily task. Correlations between

environment and organizational learning were found in teams who expressed were found

between those teams that expressed to recognize a greater system (system thinking), but not an

obligation to it (0.650**). Also teams that scored high in personal mastery, showing self-

management, also showed high scored in the organizational learning construct

“institutionalizing”, indicating that teams which work in an environment that promotes learning

and reflection, rather than just production, will perform accordingly, when requested to

incorporate new learnings, needed maybe to create a new business unit, or launch a new product

to the market (Ayd, 2017; Yang, 2016). There is a clear connection between Organizational

learning and the environment in which teams learn and produce new ideas.


1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 1

2 .876** 1 2

3 .908** .725** 1 3

4 .884** .741** .752** 1 4

5 .884** .735** .673** .722** 1 5

6 Intuiting + .826** .840** .742** .659** .709** 1 6

7 Interpreting + .688** .45 .902** .540* .422 .645** 1 7

8 Integrating + .763** .605* .755** .770** .580* .706** .688** 1 8

9 Institutionalizing + .646** .504* .475 .42 .838** .633** .379 .388 1 9

10 Intuiting - -.374 -.576* -.221 -.377 -.288 -.041 .142 -.057 .026 1 10

11 Interpreting - -.893** -.859** -.775** -.784** -.791** -.610** -.425 -.575* -.437 .669** 1 11

12 Integrating - -.294 -.293 -.102 -.463 -.302 -.029 .13 .209 -.107 .495* .401 1 12

13 Institutionalizing - -.883** -.759** -.684** -.797** -.921** -.628** -.371 -.604* -.560* .455 .890** .384 1 13

14 .355 .388 .104 .243 .568* .432 -.011 .16 .510* -.063 -.233 -.149 -.500* 1 14

15 BIG - Fluency .016 .037 .055 -.009 -.037 .172 .132 .136 -.088 .194 .072 .209 -.009 .113 15

16 BIG - Flexibility .044 .112 .051 .004 -.004 .192 .089 .057 -.039 .087 .018 .08 -.024 .086 16

17 BIG-Elaboration -.056 .026 -.056 -.072 -.08 .135 -.04 .001 -.19 .154 .051 .115 -.015 .118 17

18 BIE Experienced Entrepreneur .620** .729** .554* .492* .474 .635** .389 .335 .464 -.384 -.591* -.288 -.391 .075 18

19 BIE Novice Entrepreneur .154 .208 .191 .158 .016 -.072 .1 .082 -.117 -.488* -.257 -.123 -.105 -.131 19

20 .620** .760** .595* .543* .377 .775** .508* .559* .268 -.229 -.503* -.055 -.383 .233 20

21 Team size .448 .294 .438 .465 .376 .276 .399 .276 .495* -.129 -.332 -.327 -.217 -.088 21

22 Team longevity -.455 -.647** -.342 -.472 -.286 -.339 -.106 -.217 -.064 .682** .564* .417 .389 -.128 22

23 Job diversity .198 .373 .313 .047 -.028 .348 .371 .063 .013 -.149 -.113 .019 .05 -.082 23

24 Background diversity .033 .21 -.073 .111 -.016 .066 -.227 -.1 -.307 -.285 -.178 -.311 -.199 -.082 24

25 Task interdependency -.169 -.086 -.198 -.017 -.223 -.098 -.222 .229 -.354 .005 .089 .343 .087 -.009 25

26 Aim interdepend. .349 .145 .326 .425 .322 .471 .419 .475 .38 .441 -.068 .008 -.218 .111 26

27 Cohesion .241 .172 .252 .25 .177 .164 .207 .417 .008 -.068 -.227 .195 -.258 -.123 27

28 Support for innovation .740** .642** .612** .672** .719** .739** .462 .709** .523* -.077 -.604* -.046 -.720** .681** 28

29 Participation safety .526* .562* .38 .536* .469 .636** .237 .431 .331 -.081 -.45 -.224 -.477 .606** 29

30 Vision .246 .511* .098 .257 .156 .451 -.091 .276 .067 -.266 -.338 -.013 -.191 .224 30

31 Task orientation .746** .643** .735** .713** .565* .733** .694** .743** .421 -.08 -.523* -.062 -.555* .335 31

32 Internal communications .089 .215 .105 .061 -.025 .245 .109 .25 -.004 -.035 -.062 .255 .036 .228 32

33 External comunication .560* .537* .509* .498* .463 .752** .447 .624** .351 .138 -.41 .105 -.457 .276 33

34 .425 .241 .403 .176 .569* .187 .253 .27 .441 -.162 -.473 .102 -.548* .059 34

35 .081 -.052 .183 .136 -.019 -.218 .109 .251 -.221 -.233 -.225 .142 -.124 -.499* 35

36 .411 .650** .22 .469 .292 .540* .046 .239 .226 -.381 -.396 -.386 -.283 .186 36

37 -.162 -.173 -.063 -.147 -.219 -.264 -.064 -.403 -.318 -.081 .039 -.334 .105 -.179 37

38 .454 .234 .513* .466 .344 .352 .508* .33 .328 .088 -.332 -.253 -.289 .157 38

39 .48 .514* .478 .245 .43 .541* .414 .416 .44 -.136 -.397 .202 -.34 .497* 39 Leadership - Secondary style

En

vir

on

men

t

Personal mastery

Team learning

Sistem thinking

Shared vision

Empowerment of leadership

Op

p.

Iden

tifi

cati

on Self perceived opportunity

BIG

BIE

Team-level predictors of innovation

Tea

m-l

ev

el

pred

icto

rs

of

inn

ov

ati

on

Inp

ut

Team

Pro

cess

Pearson correlations


Org

an

izati

on

al

learn

ing

- 4

I

Intuiting

Interpreting

Integrating

Institutionalizing

Pro

-lea

rnin

gA

nti

-lea

rnin

g

Organizational learningO

rgan

izati

on

al

learn

ing

- 4

I

Pro

-lea

rnin

gA

nti

-lea

rnin

gB

IGB

IE

Institutionalizing

Integrating

Interpreting

Intuiting

Tea

m-l

ev

el

pred

icto

rs

of

inn

ov

ati

on


Self perceived opportunity

Team learning

Personal mastery

Leadership - Secondary style

Op

p.

Iden

tifi

cati

on

En

vir

on

men

tPearson correlations

Inp

ut

Team

Pro

cess


Shared vision

Sistem thinking

Table 4.3. Pearson correlations for sub-sections, constructs and frameworks.


15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1 1

2 2

3 3

4 4

5 5

6 Intuiting + 6

7 Interpreting + 7

8 Integrating + 8

9 Institutionalizing + 9

10 Intuiting - 10

11 Interpreting - 11

12 Integrating - 12

13 Institutionalizing - 13

14 14

15 BIG - Fluency 1 15

16 BIG - Flexibility .966** 1 16

17 BIG-Elaboration .899** .891** 1 17

18BIE Experienced

Entrepreneur.121 .217 .06 1 18

19 BIE Novice Entrepreneur .539* .586* .396 .414 1 19

20 .001 .059 -.019 .524* -.01 1 20

21 Team size -.169 -.086 -.265 .396 .069 .301 1 21

22 Team longevity -.25 -.365 -.336 -.587* -.572* -.483* -.386 1 22

23 Job diversity .135 .225 -.035 .468 .286 .647** .051 -.247 1 23

24 Background diversity -.136 -.097 .019 -.077 -.189 .024 -.48 .012 .027 1 24

25 Task interdependency .167 .048 .179 -.098 .034 -.118 -.547* .087 -.245 .086 1 25

26 Aim interdepend. .034 .028 -.029 .006 -.399 .429 .558* .072 .016 -.217 -.348 1 26

27 Cohesion .129 .1 .096 -.15 .105 .327 .228 -.158 .105 .015 -.306 .449 1 27

28 Support for innovation .045 -.016 .073 .323 -.146 .543* .173 -.265 -.095 -.153 .189 .383 .046 1 28

29 Participation safety .181 .167 .309 .416 -.019 .582* .189 -.446 -.014 -.171 .099 .34 .024 .823** 1 29

30 Vision -.156 -.146 .039 .204 -.292 .549* .002 -.305 -.036 .239 .267 .098 .109 .438 .587* 30

31 Task orientation .048 .038 -.018 .397 .054 .816** .459 -.433 .38 -.222 -.198 .584* .474 .695** .615** 31

32 Internal communication -.082 -.057 .026 .123 -.113 .465 .037 -.275 .105 -.345 .363 .094 -.216 .490* .486* 32

33 External comunication .063 .042 .088 .373 -.358 .565* .07 -.1 .103 .086 .335 .442 -.121 .726** .522* 33

34 -.258 -.305 -.308 .063 -.126 -.055 .061 .134 -.087 .089 -.139 -.05 .271 .178 -.222 34

35 -.017 -.046 -.091 -.062 .245 -.007 .155 -.156 .045 -.129 .222 -.04 .393 -.131 -.323 35

36 .289 .444 .363 .651** .357 .558* .189 -.687** .325 .169 .026 .091 -.04 .273 .529* 36

37 -.309 -.216 -.192 -.09 -.076 -.016 .009 .059 .184 .16 -.437 .042 -.084 -.217 -.106 37

38 .197 .171 .227 .23 .088 .057 .401 -.184 -.308 -.272 -.083 .229 -.165 .471 .512* 38

39 .106 .052 .065 .543* .197 .297 -.037 -.219 .092 -.29 .257 -.204 -.332 .653** .535* 39 Leadership - Secondary style

En

vir

on

men

t

Personal mastery

Team learning

Sistem thinking

Shared vision


Op

p. Id

enti

fica

tion Self perceived opportunity

BIG

BIE


Tea

m-l

ev

el

pred

icto

rs

of

inn

ov

ati

on

Inp

ut

Team

Pro

cess



Org

an

izati

on

al

learn

ing

- 4

I

Intuiting

Interpreting

Integrating

Institutionalizing

Pro

-lea

rnin

gA

nti

-lea

rnin

g

Leadership - Secondary style

En

vir

on

men

t

Personal mastery

Team learning

Sistem thinking

Shared vision


Op

p. Id

enti

fica

tion Self perceived opportunity

BIG

BIE


Tea

m-l

ev

el

pred

icto

rs

of

inn

ov

ati

on

Inp

ut

Team

Pro

cess


Organizational learningO

rgan

izati

on

al

learn

ing

- 4

IIntuiting

Interpreting

Integrating

Institutionalizing

Pro

-lea

rnin

gA

nti

-lea

rnin

g

30 31 32 33 34 35 36 37 38 39

1

.242 1

.419 .334 1

.428 .478 .495* 1

-.14 .131 -.352 .13 1

-.194 .119 -.041 .008 .449 1

.464 .331 .29 .307 -.414 -.264 1

-.296 -.1 .047 -.188 -.207 -.129 -.129 1

.142 .266 .098 .229 -.126 -.249 .216 -.164 1

.247 .322 .452 .408 .116 -.297 .224 -.268 .393 1

Table 4.4. Continuation Pearson correlations for sub-sections, constructs and frameworks.

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).


5. Conclusion

For the conclusion, first the sub questions will be answered, followed by the main research

question.

I. What is the relationship between variables of Opportunity identification and

Organizational learning?

For the teams evaluated, the variables that together comprehend opportunity

identification and Organizational learning showed strong relationships, especially

those in which teams that possess experienced entrepreneurs, and in which the

environment empowers leadership. Variables such as Business idea evaluation –

Experienced entrepreneur, have a clear connection with Intuiting (0.729**),

Interpreting (0.554*) and Integrating (0.492*). All subsections of organizational

learning, connecting the 4I framework section of exploitation. So there is an strong

possibility that teams, which possess experienced entrepreneurs will perform well

in exploitation.

Based on this research it can be concluded that teams which performed well in

organizational learning capabilities, together with the proper environment and

orientation, will perform in terms of exploration (He, 2012). higher than the mean,

which also showed better levels for opportunity identification proved to score

higher in most of the Team-level predictors of innovation, such as Support for

innovation (0.740**), Participation safety (0.526*) and Task orientation

(0.746**). This shows that the construct for this Team-level predictors of

innovation, is capable to potentiate, in some way Organizational learning and

opportunity identification.


II. How does Opportunity identification influence organizational AD?

The frameworks and its subsections used in this report were unsuccessful to

correlate or explain AD in any level. This indicates that the way of evaluating AD

might not be adequate, or some other explanatory variables should be included.

Main research question: What is the relationship between the variables responsible for

organizational AD?

The results on this report are not strong enough to answer the main reseach question,

because it lacks AD parameters based on perceived team AD. The team chosen as

ambidextrous, showed no direct indications of ambidexterity, and thus the modeling failed to

show significative results. However, this tool shows to be powerful to evaluate capacities

such as opportunity identification and Organizational learning individually.


6. Discussion

This report, which was design as a quantitative and qualitative research, attempted to design

controls in advance, to deal with both anticipated and unanticipated threats to validity. The

results and conclusions based on constructs here discussed, are not transposable or applicable

to all the organizations in the same segment, however, aims to present a clear methodology for

organizations to assess its opportunity identification competence, leadership, environment,

innovation and finally evaluate, in what extent can be considered a learning organization and

thus, an ambidextrous organization.

The teams included team Alpha, and most of the original targeted teams, however, clear

differences were not evident. So perform ambidexterity based on perception might not be

objective enough.

Frameworks based on online surveys are good tools to evaluate organizational learning and

opportunity identification within organizations, showing key points to improve. Each

framework is divided by categories, and if analyzed separately by the managers, improvements

can be achieved within teams, improving general organizational learning and opportunity

identification.

Organizational learning and opportunity identification have clear connections using Team-

level predictors of innovation and Environment assessment frameworks, this results are

consistent with the findings of previous findings of Widmer, Schippers, & West, (2009).

However, in the current report is not able to evaluate Leadership’s connection with

Organizational learning and Opportunity identification, however, might be interesting to test it

in other countries or fields, where Democratic leadership is not suited for the work performed,

i.e. the production section of the organization, where academic level is lower and innovation is


often not required. This shows that the construct for this framework is capable to predict, in

some way Organizational learning.


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Juan Camilo Arias Robledo - WUR

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