Willingness to Pay as a Range: Theoretical Foundations ...

Willingness to Pay as a Range:

Theoretical Foundations, Measurement,

and Implications for Marketing Mix Decisions

Inauguraldissertation

to attain the academic degree

doctor rerum politicarum

(Doktor der Wirtschaftswissenschaften)

at the

ESCP Europe Business School Berlin

by

Dipl.-Ing. Florian Dost

Berlin

2012

Doctoral examination committee

Head: Prof. Dr. Frank Jacob

Examiner: Prof. Dr. Robert Wilken

Examiner: Prof. Dr. Bernd Skiera

Day of disputation: 29.03.2012

Table of Contents

i

Table of Contents

Table of Contents ....................................................................................................................... i

List of Figures .......................................................................................................................... iii

List of Tables ............................................................................................................................. iv

List of Abbreviations ............................................................................................................... v

I. Preamble .............................................................................................................................. 1

1 Introduction ........................................................................................................................ 2

1.1 Willingness to Pay (WTP) as a Range ......................................................................................... 2

1.2 Thesis Objectives and Structure ................................................................................................... 5

2 A Framework for the Role of WTP as a Range in Marketing Mix Decisions ...................................................................................................................................... 7

3 Introduction to the Manuscripts ................................................................................ 9

II. Manuscripts...................................................................................................................... 11

4 Measuring Willingness to Pay as a Range, Revisited: When Should We Care? ........................................................................................................................................... 12

5 On the Edge of Buying: A Targeting Approach Based on Consumers’ Willingness-to-Pay Ranges ................................................................................................ 13

5.1 Introduction ...................................................................................................................................... 13

5.2 Theoretical Foundations .............................................................................................................. 15

5.2.1 Willingness to pay as a range ........................................................................................................ 15

5.2.2 WTP range–based targeting approach ...................................................................................... 16

5.3 Empirical Studies ............................................................................................................................. 18

5.3.1 Study 1: Price promotions in the FMCG category ................................................................. 18

5.3.2 Study 2: Different marketing mix activities in a high-involvement category ........... 20

5.3.3 Study 3: Price promotions in the FMCG category (competitive setting) ..................... 25

5.4 General Discussion .......................................................................................................................... 31

5.4.1 Key findings and implications ....................................................................................................... 31

5.4.2 Limitations and further research ................................................................................................ 32

5.5 Appendix A: Results of Study 3 .................................................................................................. 33

ii

6 Verhaltensorientierter Ansatz zur Erklärung von Preisreaktionen bei Commodities ........................................................................................................................... 34

III. Conclusion ........................................................................................................................ 35

7 Overview of Results ...................................................................................................... 36

8 Empirical Extension to Manuscript No. 3 ............................................................ 38

8.1 The Link of WTP Ranges and Cognitive Effort in Price-Related Choice ..................... 38

8.2 Study Design ...................................................................................................................................... 38

8.3 Procedure ........................................................................................................................................... 39

8.4 Results ................................................................................................................................................. 39

8.5 Discussion .......................................................................................................................................... 41

9 Secondary Analysis of the Interplay Among WTP, Range, and Uncertainty .............................................................................................................................. 43

9.1 Introduction ...................................................................................................................................... 43

9.2 Study Design ...................................................................................................................................... 44

9.3 Results ................................................................................................................................................. 45

9.4 Discussion .......................................................................................................................................... 47

10 Implications of the Findings ..................................................................................... 48

10.1 WTP-as-a-Range Model ........................................................................................................ 48

10.2 WTP Range Measurement ................................................................................................... 50

10.3 WTP Range Management..................................................................................................... 51

10.4 A Call for Dynamics in WTP as a Range Research ...................................................... 53

References ................................................................................................................................ 55

List of Figures

iii

List of Figures

Figure 1.1: WTP as a range (adapted from Wang, Venkatesh, and Chatterjee, 2007) .......... 3

Figure 2.1: Willingness to pay as a range in marketing mix decisions ....................................... 7

Figure 2.2: Overview of research questions ......................................................................................... 8

Figure 3.1: Overview on the manuscripts .............................................................................................. 9

Figure 5.1: Willingness to Pay as a Range ........................................................................................... 16

Figure 5.2: Differences in Choice Rate by Consumer Group ........................................................ 24

Figure 5.3: Retailer Gains in Choice Rate by Consumer Group ................................................... 28

Figure 7.1: Overview of findings in the manuscripts ...................................................................... 36

Figure 8.1: Results of extension study ................................................................................................. 40

List of Tables

iv

List of Tables

Table 5.1: Results of Study 1 .................................................................................................................... 20

Table 5.2: Results of Study 2 .................................................................................................................... 23

Table 5.3: Predictive Validity in Study 2 ............................................................................................. 25

Table 5.4: Comparison of Retail Targeting Approaches ................................................................ 30

Table 5.5: Choice Rate Means and Comparisons of Study 3 ........................................................ 33

Table 9.1: Secondary analysis regression results ............................................................................ 46

List of Abbreviations

v

List of Abbreviations

CP ceiling reservation price

ed. editor

eds. editors

et al. et alii (and others)

EUR Euro (currency)

e.g. exempli gratia (for example)

FMCG fast moving consumer good

FP floor reservation price

Hrsg. Herausgeber

i.e. id est (that means)

ICERANGE incentive-compatible elicitation of the range in a consumer’s reservation prices

IP indifference reservation price

BDM lottery mechanism by Becker, DeGroot, and Marschak (1964)

MANOVA multivariate analysis of variance

OLS ordinary least squares

pp. pages

PWOM positive word of mouth

sec. second

SCL shift-in-choice likelihood

SD standard deviation

SE standard error

vs. versus

Vol. volume

WTP willingness to pay

WOM word of mouth

z.B. zum Beispiel

1

I. Preamble

1 Introduction

2

1 Introduction

1.1 Willingness to Pay (WTP) as a Range

In the wake of global economic turmoil and increasing pressures from global competition,

marketers seek salvation in the individualization of their marketing mixes. The individual-

level customization of product, price, promotion, and (with the advent of customizable online

shopping portals) even place thus appears on the agendas of most marketing practitioners and

researchers. Yet these efforts to enhance individual value propositions must first ensure

knowledge about consumers’ valuations and choice behavior. For example, individual

willingness to pay (WTP), or a person’s reservation price, is a fundamental concept for

individual choice, in both marketing and other fields such as micro-economics. Researchers,

marketing managers, and policy makers all see WTP as a monetized individual value (or

utility) for a good or service.

Thus optimal individual pricing decisions and predictions of individual consumer choice often

rely on measured WTP values. Pricing decisions might apply to whole segments or

populations of people, based on demand functions. In theory, aggregated individual WTP

values form demand. To estimate unbiased aggregate-level demand functions, together with

unbiased individual-level valuations, researchers need a valid method to measure WTP (e.g.,

Cameron & James 1987; Gijsbrechts 1993; Jedidi, Jagpal, & Manchanda 2003; Miller,

Hofstetter, Krohmer, & Zhang 2010; Voelckner 2006). Once they know demand or WTP,

marketing managers, as well as policy makers, might want to determine how they can

influence WTP, generally to enhance revenues or adoption of a good or service (e.g. Ajzen &

Driver 1992; Homburg, Koschate, & Hoyer 2005; Kalra & Goodstein 1998; Prelec &

Simester 2001). Thus, we are interested in the “measurement” and the “management” of

WTP, and both aspects depend on a model that links WTP to individual choice behavior and

ultimately to aggregate-level choice behavior. Therefore, this thesis considers all three

aspects: model, measurement, and management.

To find a valid measurement method, recent research has proposed measuring WTP as a range

rather than a single point. Wang, Venkatesh, and Chatterjee (2007) argue that common

definitions of a reservation price, such as “the price at or below which a consumer will

demand one unit of the good” (Varian 1992, p. 152), “the price at which a consumer is

1 Introduction

3

indifferent between buying and not buying” (Moorthy, Ratchford, & Talukdar 1997, p. 265),

or “the minimum price at which a consumer would no longer purchase” (Hauser & Urban

1986, p. 449), are equivalent only if consumers make rational choices and are certain about

their preferences and product performance. However, value perceptions and actual behavior

are subject to limited rationality in individual behavior. Consumers suffer from bounded

rationality (Simon 1955) and construct their preferences during the course of their decision

making (Bettman, Luce, & Payne 1998). Thus choice is subject to uncertainty.

To account for bounded rationality, preference uncertainty, and product performance

uncertainty, WTP should be conceptualized and measured as a range of prices.1 Wang et al.

(2007) propose ICERANGE, a method focused on the floor, indifference, and ceiling

reservation prices. Each reservation price corresponds to one of the WTP definitions and is

linked to choice probabilities of 1, .5, and 0, respectively. The difference between the ceiling

and floor reservation price is the WTP range. A representation of Wang and colleagues’

conceptualization of a WTP range (hereafter, simply “range”) and the respective choice

probabilities appear in Figure 1.1.

Figure 1.1: WTP as a range (adapted from Wang, Venkatesh, and Chatterjee, 2007)

1 WTP as a range is a unique concept compared with other purchase behavior–related concepts that feature price ranges, such as the range-frequency theory for reference prices (Parducci 1965). The WTP range features reservation prices; range-frequency theory is about reference prices. A (point-based) reservation price constitutes the upper boundary of reference price ranges. For an individual consumer, WTP range and reference price ranges refer to different price levels. See Chapter 3 for a detailed comparison.

1 Introduction

4

However, literature about WTP as a range and its uses is scarce, and knowledge remains

incomplete. Three major areas of inquiry correspond to the model, measurement, and

management categories. Each area exhibits research gaps at several levels, from aggregate

demand down to individual consumers’ internal decision making.

First, Wang et al. (2007) assume that WTP as a range is a novel conceptualization of

individual WTP, but they do not explain how their conceptualization influences individual

buying decisions or, ultimately, the marketing mix and pricing decisions. More generally,

they do not discuss potential changes to the model that might induce changes in pricing

decisions, too. Nor has any research determined how range-based WTP estimates relate to

traditional point-based estimates at the individual consumer level. This part of the model is

important to evaluate what past research has explained using point-based methods. Consumer

uncertainty appears to be the sole driver of WTP ranges, but empirical results are inconclusive

about the uncertainty–range link (Wang, Venkatesh, & Chatterjee 2007). Thus far, alternative

antecedents, or different modes of consumer decision making, have been neglected.

Second, the measurement benefits of range-based methods are unclear when considering the

relationship between point- and range-based methods. To put it simply: Why should

marketers care about WTP as a range from an empirical perspective? And when should they

apply range-based elicitation methods? The existing methodology, such as ICERANGE

procedure (Wang et al., 2007) may be complex for many respondents, because it implicitly

assumes that respondents can state their reservation prices for any purchase probability within

their individual WTP range. Strictly speaking, this assumption contradicts the general finding

of consumers’ preference uncertainty.

Third, adding the dimension of a WTP range should broaden the possibilities by which

marketing mix activities influence consumer behavior and ultimately profit. Because WTP

ranges have never been considered in previous studies of WTP antecedents or marketing mix–

related choice behavior, extant results may have been misinterpreted in light of the new WTP

conceptualization.

In summary, the conceptualization of WTP as a range generates various questions about the

measurement and management of WTP ranges, as well as the relevant theoretical foundations,

as manifested in the model that explains the link among antecedents, WTP, choice behavior,

and optimal marketing mix decisions. Therefore, all three aspects—model, measurement, and

management—constitute part of my inquiries.

1 Introduction

5

1.2 Thesis Objectives and Structure

In a series of manuscripts, I propose a modified model to substantiate the theoretical

foundations of the WTP-as-a-range concept and consider its relations to extant point-based

WTP concepts. The modified model and resulting consequences for the link between WTP

and choice will be tested empirically. To improve the practical applicability of the concept

and the related measurement methods, a modified variant of a range-based WTP method will

be developed and empirically compared and examined. A comparison with traditional (point-

based) methods should reveal the conditions in which the new range-based methods are more

useful or even superior in the context of individual-level and aggregate demand–level pricing

decisions. Finally, because conceptualizing WTP as a range extends the toolset for managerial

actions targeted at individual consumer profitability, the concept shall be applied further to

marketing mix decision problems including empirical validation of the findings. Taken

together, these contributions offer important recommendations for the measurement and

management of WTP as a range, as well as a sound theoretical foundation for the model.

In pursuing these aims, this thesis starts with a perspective on the nature of rationality and

uncertainty in choice that is similar to the assumptions delineated by Wang and colleagues:

Consumers have a bounded rationality and construct their preferences in the decision-making

process (Wang, Venkatesh, & Chatterjee 2007; see also: Bettman, Luce, & Payne 1998). Still,

consumers can engage in rational processing, at a restricted and uncertainty-prone level. This

perspective is useful for two reasons. First, readers already familiar with WTP as a range will

have an easy access to the research in this thesis. Second, by slowly broadening the view to

related research dealing with behavioral choice models, which uses reference prices and

decision heuristics, this thesis aims to establish findings for future consolidations of

descriptive research on behavioral choice and normative (bounded) rational choice.

Therefore I present a framework that centers on the WTP-as-a-range concept and that is

embedded in marketing mix decision making in Chapter 2. After I introduce and position the

three focal manuscripts in Chapter 3, the second part of this thesis is dedicated to those three

manuscripts, which constitute Chapters 4–6.2 Each manuscript constitutes a distinct, self-

contained piece of research. Finally, the last part of this thesis is dedicated to a synthesis of

the various findings according to its overarching framework, as well as an extension of their

2 The manuscripts have been adapted to match the overall structure of this thesis, so the enumerations of the headlines, layout of text and tables, and citation styles may differ from those in the original publications.

1 Introduction

6

distinct findings. Chapter 8 empirically substantiates the conceptual results related to

cognitive efforts in WTP ranges from the third manuscript (Chapter 6); Chapter 9 integrates

the data sets of all manuscripts using secondary and meta-analyses, which review links

between uncertainty and WTP ranges that were inconclusively demonstrated by Wang and

colleagues (2007). Chapter 10 provides a synthesis of the major results and proposes avenues

for further research.

2 A Framework for the Role of WTP as a Range in Marketing Mix Decisions

7


The optimal allocation of scarce resources to the marketing mix requires particular attention

of marketing academia. For example, the current Marketing Science Institute (MSI) research

priorities ask: “What are effective pricing strategies, tactics, and practices for complex

products in a multi-media, multi-channel environment that allow for increasingly customized

pricing decisions? How should firms determine the absolute level of marketing spending and

how should spending be allocated at the strategic level—that is, across products, customer

groups, and geographies?” (MSI 2011, p. 9). These questions provide the boundaries for the

research framework of this thesis, depicted in Figure 2.1.

Figure 2.1: Willingness to pay as a range in marketing mix decisions

First, an optimal marketing mix decision requires a model that links marketing variables to

marketers’ goals (e.g. Little 2004). This model constitutes the central pillar of the framework.

At the highest level, the variables refer to aggregate consumer behavior, such as market


8

reaction or demand. Relevant at the individual level is observable, individual-level consumer

behavior, such as individual choice behavior. Individual behavior then is linked to

unobservable variables at the organism level, such as willingness to pay, or in this case WTP

as a range. The WTP ranges can be driven by other unobservable variables that are subjacent

in the organism, such as preferences or uncertainty (Wang, Venkatesh, and Chatterjee, 2007),

or they may be the result of a heuristic that combines past experiences with conjectures

derived from observed information, such as current prices (e.g. Wathieu & Bertini 2007; Park,

McLachlan, & Love 2011).

Second, a marketing manager needs valid information about the key variables in the model.

Consequently, valid and reliable measurement methods must retrieve the variable states at the

desired level of information. Measurement constitutes the second pillar of the framework.

Third, an optimal marketing mix not only adapts to the current state but also seeks to alter it.

The marketer uses elements of the marketing mix to manage and manipulate the value states

in the model, such as by setting an optimal price, setting the right amount of advertising

spending for the right communication channel to increase WTP levels or manipulate levels of

uncertainty, or targeting the right group of consumers. Management is thus the third pillar of

the framework.

The research questions identified in Chapter 1.1 and their links to the framework are depicted

in Figure 2.2.

Figure 2.2: Overview of research questions

3 Introduction to the Manuscripts

9

3 Introduction to the Manuscripts To cover this broad area of inquiry, each research project in this thesis focuses on a different

pillar, such as model, measurement, or management, or a different level of information, or

both. The three main manuscripts constitute distinct contributions to research; they also are

closely related though and cover important research gaps in the overarching framework.

Furthermore, each manuscript covers a different level of aggregation, to present an array of

novel findings. Figure 3.1 provide an overview of their position within the framework.

Figure 3.1: Overview on the manuscripts

The first manuscript, “Willingness to Pay as a Range, Revisited: When Should We Care?”

deals with the central construct of WTP as a range. It covers valid “measurements” of the

range, while also reviewing the “model” at the levels of individual and aggregate choice

behavior, in the context of the “management” of optimal pricing decisions. I theoretically

propose and empirically show that traditional point-based methods reveal the midpoint of

WTP ranges. Furthermore, a method to measure WTP as a range that is simpler and less

restricted than ICERANGE, but still achieves comparable performance, is introduced in

Chapter 4. A Monte Carlo simulation reveals that except in very artificial conditions, point-

3 Introduction to the Manuscripts

10

based methods fail to reproduce the revenue maximizing prices identified by range-based

methods, even on an aggregate consumer choice level.

In “On the Edge of Buying: A Targeting Approach Based on Consumers’ Willingness-to-Pay

Ranges,” I propose an in-store targeting approach based on WTP ranges. It covers the higher

aggregation levels of the framework, consumer choice behavior and aggregate choice, and it

focuses on the management dimension for the marketing mix. By adopting a retailers’

perspective, Chapter 5 shows analytically and empirically that only the so called “uncertain”

consumers, whose range includes the current price, are affected by marketing mix activities

and therefore should be targeted. Specifically, this uncertain segment indicates significantly

different choice behavior due to marketing mix manipulations and subsequent changes in

price, WTP, or WTP ranges.

Finally, the third manuscript, entitled “Verhaltensorientierter Ansatz zur Erklärung von

Preisreaktionen bei Commodities und Empfehlungen für die Preissetzung auf Commodity-

Märkten,”3 theoretically conceptualizes potential behavioral links between WTP as a range

and reference price reaction models, using the two mental decision modes of dual process

theory (e.g., Epstein 1991; Godek & Murray 2008; Sloman 1996). The manuscript thus covers

the organism level for WTP range formation and choice behavior.

To complement these three manuscripts, the concluding chapters also offer two extensions:

(1) empirical evidence for the link between WTP ranges and a dual process of decision

making, as conceptualized in Chapter 6, and (2) an empirical assessment of the link between

WTP ranges and uncertainty as an antecedent.

3 This manuscript is the only part of the thesis written in German.

II. Manuscripts

4 Measuring Willingness to Pay as a Range, Revisited: When Should We Care?

12

4 Measuring Willingness to Pay as a Range, Revisited: When Should We Care?

Manuscript No. 1 This manuscript is forthcoming as: Dost, Florian & Wilken, Robert (2012). Measuring

Willingness to Pay as a Range, Revisited: When Should We Care?. International Journal of

Research in Marketing, forthcoming in Vol. 29 (2).

DOI: http://dx.doi.org/10.1016/j.ijresmar.2011.09.003

5 On the Edge of Buying: A Targeting Approach Based on Consumers’ Willingness-to-Pay Ranges

13


Manuscript No. 2 Authors: Florian Dost and Robert Wilken.

Publication status: Under review in Journal of Retailing.

5.1 Introduction

Shopper marketing attracts ever increasing attention, largely because more than 50% of

consumption choices occur while the consumer shops in the store (Inman, Winer, & Ferraro

2009). Investments in marketing mix activities at the point of purchase (e.g., sales

promotions, advertising) thus have grown more than 20% per year (e.g., Shankar et al. 2011).

Many of these activities remain unprofitable though; Ailawadi et al. (2006) report failure rates

of 50% across all promotions. To improve the effectiveness of shopper marketing activities,

retailers try to customize their activities. Rather than targeting the mass market in an

undifferentiated way, they focus on consumer groups or even individual consumers who

appear likely change their choice behavior, for example in response to a price discount for a

specific product.

Academic insights into these targeting strategies are limited though. For example, the current

MSI Research Priorities call for “new ways to leverage information about customer

preferences … to enhance or supplant conventional … market segmentation, and targeting

approaches … to allocate … resources more effectively to influence a shopper along the

entire ‘path to purchase’” (MSI 2011, p. 4). Shankar and colleagues (2011, p. S40) similarly

ask: “How can shopper segmentation be improved and the results be better interpreted and

utilized?”

Unfortunately, related literature offers only incomplete or inconclusive answers. For example,

retailing research on targeted marketing mix activities has mostly centered on the retailer’s

effort to bring customers into the store; few studies adopt an in-store perspective to


14

investigate the effectiveness of in-store promotions (Ailawadi et al. 2006; Srinivasan et al.

2004). Those that do, however, provide mixed results (for an overview, see Ailawadi et al.

2009). Furthermore, in-store targeting research tends to ignore the retailer perspective and

investigate instead topics such as brand switching, taking the manufacturer’s point of view.

But retailers do not benefit from brand switching unless the manufacturer chooses to align its

interests with those of the retailer (Ailawadi et al. 2009). Another stream considers the

optimal granularity for targeting (e.g., mass market, segments, individuals). Analytically,

greater granularity should be more profitable (Grewal et al. 2011; Zhang & Krishnamurthi

2004), but empirical evidence remains inconsistent: Rossi, McCulloch, and Allenby (1996)

confirm this result, Zhang and Wedel (2009) cannot.

Noting this lack of insight into in-store targeting approaches that benefit a retailer, we propose

a novel targeting approach based on recent developments that suggest conceptualizing

consumers’ willingness to pay (WTP) as a range (Wang, Venkatesh, & Chatterjee 2007; Dost

& Wilken 2012). For a retailer, WTP is obviously crucial information: Its relation with the

price of a specific product or service determines the consumer’s purchase choice. Moreover,

WTP ranges can reveal not only if a consumer is willing to purchase at a given price, but also

if he or she might be uncertain about purchasing at that price. We argue that information

about consumers’ uncertainty may enhance targeting, because uncertain consumers can be

influenced more easily than those who are certain about their preferences. In turn, marketing

mix activities, such as price promotions, might be more effective (i.e., modify choice behavior

more) when targeted at uncertain consumers. In contrast, targeting other consumers would be

a waste of resources, because they certainly would or certainly would not have purchased

anyway.

In three studies, we establish the usefulness of a targeting approach based on WTP ranges.

Study 1 tests whether uncertain customers are more reactive to price promotions than certain

buyers or certain non-buyers. Study 2 generalizes the findings of Study 1 by (a) featuring a

different product category (higher price level, durable instead of fast moving consumer good),

(b) extending the analysis to marketing mix activities beyond price promotions, and (c)

establishing the predictive validity of choice behavior. Then we extend Study 1 further to a

competitive setting with two products in Study 3, to generalize the approach and compare our

approach with prevailing targeting practices. Compared with (a) brand customer promotions

(e.g., loyalty promotions), (b) competitive brand customer promotions (e.g., competitive


15

promotions), or (c) a combination, the WTP range–based targeting approach leads to a greater

increase in total choice rates per targeted consumer across both products we study.

5.2 Theoretical Foundations

5.2.1 Willingness to pay as a range

According to Wang, Venkatesh, & Chatterjee (2007), consumers do not know their true

willingness to pay (WTP), because they suffer from uncertainty. Instead they conceptualize

WTP as a range of reservation prices, each with a corresponding choice probability. We

modify and advance this conceptualization and define a WTP distribution that represents the

distribution of choice probability around a true, yet latent, individual WTP (Dost & Wilken

2012; see also Park, MacLachlan, & Love 2011; Schlereth, Eckert, & Skiera 2011).

The individual WTP distribution can be specified by an expected individual WTP value,

which corresponds with a traditional definition of WTP, and variance in the individual WTP,

which corresponds to the “range” introduced by Wang et al. (2007). Individual choice (or

buying) probability is therefore a function of preference (expected WTP) and uncertainty

(WTP range or simply range). Figure 5.1 illustrates the range-based WTP concept, as well as

a corresponding function of individual purchase probability. For clarity, we use the linearly

decreasing probability function introduced by Wang, Venkatesh, and Chatterjee (2007).

This novel conceptualization of individual WTP distributions offers a new dimension,

relevant for individual consumer choice. However, other than measurement issues, we know

little about the usefulness of the WTP range conceptualization, including how the marketing

mix activities that aim to influence WTP also might influence WTP range, or vice versa. How

might WTP ranges affect a firm’s or a retailer’s sales and profitability? Should WTP ranges

be increased or reduced? In their more general approach, Dost and Wilken (2012) show that

failing to control for WTP ranges can lead to misspecifications of the respective demand

function.


16

Figure 5.1: Willingness to Pay as a Range

5.2.2 WTP range–based targeting approach

Most targeting studies investigate price promotions (e.g., Grewal & Levy, 2007), because

price is the one marketing mix element that can be directly influenced by a retailer. But

retailers also can affect consumers’ preferences and attain the same market response

indirectly. The range-based WTP concept offers three alternatives: change the price, influence

a consumer’s WTP, or influence a consumer’s WTP range. All these options only affect the

consumer’s purchase probability if the retail price is within his or her WTP range or moves

into that range through the application of one of the alternatives. We thus offer the following:

Proposition 1: Only choice behavior by uncertain consumers (i.e., whose ranges include the

current retail price) are affected by a change in (a) price, (b) consumers’ WTP, or (c)

consumers’ WTP range.

Proof: We assume a linear decrease in purchase probability within the WTP range (Wang,

Venkatesh, & Chatterjee, 2007), such that it equals 1 for any price below the floor reservation

price (FP); 0 for any price beyond ceiling reservation price (CP); and a value between 1 and 0

for any price between FP and CP (WTP range), with a linear decrease between FP and CP.

That is,


17

Pr(푏푢푦|푝) =

⎩⎨

⎧0 ;푝 ≥ 퐶푃

푊푇푃 − 푝푅푎푛푔푒

+ 12

;퐹푃 < 푝 < 퐶푃;퐶푃 > 퐹푃

1 ;푝 ≤ 퐹푃

Partial derivatives reveal sensitivities in purchase probabilities to changes in price, individual

WTP, and individual WTP range, respectively:

∂Pr(푏푢푦|푝)휕푝

=

⎩⎨

⎧0 ;푝 ≥ 퐶푃

−1

푅푎푛푔푒;퐹푃 < 푝 < 퐶푃;퐶푃 > 퐹푃

0 ;푝 ≤ 퐹푃

∂Pr(푏푢푦|푝)휕푊푇푃

=

⎩⎨

⎧0 ;푝 ≥ 퐶푃1

푅푎푛푔푒;퐹푃 < 푝 < 퐶푃;퐶푃 > 퐹푃

0 ;푝 ≤ 퐹푃

∂Pr(푏푢푦|푝)휕푅푎푛푔푒

=

⎩⎨

⎧0 ;푝 ≥ 퐶푃

−푊푇푃 − 푝푅푎푛푔푒

;퐹푃 < 푝 < 퐶푃;퐶푃 > 퐹푃

0 ;푝 ≤ 퐹푃

These equations demonstrate that changes in purchase probability due to changes in price,

individual WTP, or individual WTP range only occur if the current price appears in the

individual range. Therefore, purchase probability declines as price increases, increases as

individual WTP increases, and can decrease or increase when range increases, depending on

whether the current price p is lower or higher, respectively, than WTP. Analytically, when

targeting is based on consumers’ WTP ranges, a retailer should focus on the segment of

uncertain consumers. Is this prediction consistent with empirical observations? We conduct

three studies to answer this question.


18

5.3 Empirical Studies

5.3.1 Study 1: Price promotions in the FMCG category

5.3.1.1 Design

With this first study, we empirically test Proposition 1 by examining the effect of a price

promotion on choices by three consumer groups: certain non-buyers, uncertain, and certain

buyers. We designed a quasi-experiment in an online store setting, which holds appeal for

targeting studies. Because it does not rely on coupons, it achieves 100% redemption rates

(Zhang & Wedel 2009), and redemption rates strongly affect targeting results.

The stimuli were fake online offers, illustrated by a picture and complemented with a textual

description, for a 100 fl. oz. bottle of Ultra Purex Coldwater liquid detergent, a medium

priced liquid detergent developed for use with energy-saving washing at low temperatures.

For this fast moving consumer good (FMCG), we expected a considerable portion of

uncertain consumers, because it was only recently introduced to the market. At the time of the

study, the online price asked by Walmart was $5.97. After participants provided their floor

and ceiling reference prices (Dost & Wilken 2012), they were assigned to one of the three

consumer groups, with the online retail price of $5.97 as a differentiator. For example, a

participant with a ceiling reservation price of $4.00 entered the certain non-buyers group,

whereas another participant with a floor price of $6.00 belonged to the certain buyers. Lastly,

a participant with a floor reservation price of $5.00 and a ceiling reservation price of $7.00

was labeled “uncertain.” The $5.97 price falls approximately in the middle of all available

prices for comparable detergents, so we expected consumers to be relatively equally

distributed across the three groups. In addition, we directly manipulated the price for the

stimulus (regular vs. discounted): Independent of their membership in one of the consumer

groups, participants were randomly assigned to a stimulus with either the current price of

$5.97 or a discounted price of $4.97. Altogether, this approach yielded a 3 (consumer group)

2 (price level) study design.

Choice was the dependent variable, because it should not be very prone to biases, even in a

hypothetical setting without the obligation to purchase (Miller et al. 2011). More important,

choice reflects the ultimate goal of in-store targeting activities, that is, to influence

consumers’ purchase decisions at the point of purchase.


19

As control variables and to confirm well-balanced samples, we used one-item measures of

category knowledge and category involvement before showing the stimulus to participants.

We also collected demographic information at the end of the survey. A one-item measure of

deal attractiveness served as the manipulation check for the price promotion. All items used

seven-point Likert scales. Finally, we asked for participants’ predictions of the study’s

purpose.

5.3.1.2 Procedure

We recruited 198 respondents through Amazon Mechanical Turk, a crowdsourcing platform

for human tasks. We followed Mason and Suri’s (2011) guidelines for research on this

platform to ensure reliable and valid results. The pool of potential respondents was restricted

to U.S. residents. Each respondent received a payment between $.30 and $.40. The study

design required them to open a link for the survey, hosted on another survey platform, and

then transfer a unique code back onto Mechanical Turk. The average survey duration was

approximately five minutes; we excluded respondents who spent less than three minutes (i.e.,

the click-through benchmark) on the task. Two fail-check items helped us ensure attentive

reading. First, we asked respondents to check the third box displayed from the left. Second,

we asked if the study was about cars, and respondents answered on an agree–disagree array.

We excluded respondents who did not answer both questions correctly. Finally, we excluded

two participants who stated a ceiling price of more than $50 (one later indicated that he forgot

the decimal). After eliminating 30 respondents (15.1%), 168 data sets remained for the

analysis.

5.3.1.3 Results

A MANOVA (Pillai’s Trace p = .731 for the model; ps > .26 for the model parameters) on

age, gender, income, education, pre-stimulus knowledge about the detergent category, attitude

toward the detergent category, category involvement, and distribution of self-selected

certainty–uncertainty groups showed no significant differences across experimental groups.

There was no effect on the balance of the samples of either the random assignment or the fail-

check based elimination. None of the participants guessed the actual purpose of the study.

The manipulation check revealed that the promoted price had a significant effect on deal

attractiveness (Mregular = 5.19, SE = 1.67; Mdiscount = 5.84, SE = 1.45; F(1, 166) = 7.07; p <

.01). Across all experimental groups, the average WTP was $5.55 (SE = $2.24), calculated as

the mean between the floor and ceiling reservation prices. The average WTP range, calculated


20

as the difference between floor and ceiling reservation prices, was $2.38 (SE = $1.75). The

distribution of respondents across the three consumer groups was reasonably similar, with 56

certain non-buyers, 73 uncertain consumers, and 39 certain buyers. Table 5.1 displays the

resulting choice behavior for each group, divided by the stimulus.

Table 5.1: Results of Study 1

CHOICE EFFECTS OF PRICE PROMOTION BY CONSUMER GROUPS Certain non-buyersa Uncertain buyersb Certain buyersc Regular

price ($5.97)

Discounted price

($4.97)

Regular price

($5.97)

Discounted price

($4.97)

Regular price

($5.97)

Discounted price

($4.97) N 28 28 43 30 17 22

Choice rate

.32 .43 .67 .90 .88 .77

(Standard Error)

(.09) (.10) (.07) (.06) (.08) (.09)

Δ Choiced +.11 +.23 –.11 Te .818 2.293 –.870 p .417 .025 .390

a All participants with ceiling reservation price < $5.97. b All participants with floor reservation price < $5.97 ≤ ceiling reservation price. c All participants with floor reservation price ≥ $5.97. d Change rate from regular to discounted price, expressed as a percentage, with significant differences in bold. e Two-tailed t-test.

Pairwise t-tests of differences in choice rate reveal significant differences only in the

uncertain group. It is not surprising that a small (not significant) share of non-buyers chose

the detergent, because the price reduction of 17% is more than marginal, which likely moved

the group criterion from non-buyer to uncertain. Overall, Study 1 thus provides empirical

support for the analytical suggestion that in-store targeting based on consumers’ WTP ranges

should focus on the uncertain segment.

5.3.2 Study 2: Different marketing mix activities in a high-involvement

category

Although Study 1 supports our proposition, it has several limitations. First, it pertains only to

a reduction in price. To broaden retailers’ alternatives, it also is interesting to investigate

whether other marketing mix activities overwhelmingly affect the uncertain. Second, the

stimulus belonged to a FMCG category (detergents), which generally demands little cognitive


21

effort to purchase. This factor might limit the results to more impulse-driven purchase

decisions. We believe this test enhances support for our proposition, because an impulse

buyer is generally more likely to change choice behavior in response to a price promotion;

however, the matter still demands empirical validation. Study 2 aims to address these

limitations and generalize the results.

5.3.2.1 Design

We designed another quasi-experiment in an online store setting. The durable product used

was the Amazon Kindle Touch, a new variant of a medium-priced e-reader. Although the

purchase of such a product likely invokes intense thought, we expect a considerable

proportion of uncertain consumers, because of the product’s newness. Furthermore, the

Kindle is for sale only online, so it enhances the realism of the research setting. To measure

WTP range according to reservation prices, after the display of the stimulus, we revised the

identification of the three groups of non-buyers, uncertain, and buyers. Specifically, we asked

participants whether they would certainly buy, certainly not buy, or were uncertain about

buying the product for $100. This self-selection variable directly identified the uncertain

segment and generated the three consumer groups for our subsequent analysis. Furthermore,

the self-selection mechanism increases the generalizability of our findings, because the use of

alternate methods reduces common method bias, and generates an even stricter test of our

proposition. The uncertain group in this case might include respondents who are simply too

lazy to decide, those who always opt for the “middle,” or any others who exhibit behavior that

leads to measurement biases. Thus the group of “truly uncertain” consumers might overlap

with those of certain buyers and certain non-buyers, which reduces discrimination between

groups and ultimately might partially hide the choice behavior effect in the uncertain group.

We used manipulated, fake online store product pages for the Kindle as stimuli. Respondents

were randomly assigned to one of five different stimuli in a between-subjects design. The first

stimulus showed a headline description and picture of the product, at the retail price of $99.

This stimulus represented the control group. Then the price promotion stimulus changed the

displayed price to $79, with no reference to the previous normal price. The word of mouth

stimulus included four short, positive user comments, taken from the actual product site on

Amazon. With the information stimulus, we included a bulleted list of the features and

advantages of the product, also taken from the actual product site. Finally, the visual stimulus

advertised the e-reader with a picture of a young, attractive woman using the product while on

a beach vacation. Choice again served as the dependent variable for all stimuli conditions.


22

For control variables, we again used three-point, single-item measures for knowledge about

and attitude toward the Kindle before the stimulus, and we collected demographic information

at the end of the survey. Knowledge and attitude provided a manipulation check for the self-

selected groups. A single-item, seven-point Likert scale measure of offer attractiveness served

as the manipulation check for the four marketing mix stimuli (which were intended to

enhance offer attractiveness, compared with the control group). Finally, we asked for the

participants’ suspicions about the study’s purpose.

5.3.2.2 Procedure

We recruited 645 respondents through Amazon Mechanical Turk, restricted to U.S. residents.

Each respondent received a reward between $.30 and $.90. The actual survey was again

hosted on another survey platform and required the transfer of a unique code back to

Mechanical Turk. The average survey duration was approximately seven minutes, and we

excluded respondents who spent less than three minutes (click-through duration). We also

excluded respondents who guessed that the usual price for a Kindle in a store would be $0 or

more than 200% the actual market price. In two fail-check items, to ensure attentive reading,

we asked respondents to check the second box from the left and whether the study was about

a false product variant of the same brand (a Kindle Fire Tablet). These checks required the

elimination of 98 respondents (15.2%), which left 547 data sets for analysis.

5.3.2.3 Results

A MANOVA (Pillai’s Trace p = .357 for the model; ps > .24 for the model parameters) on

age, gender, income, education, pre-stimulus knowledge about the Kindle, attitude toward the

Kindle, e-reader category involvement, and distribution of the self-selected certainty–

uncertainty groups showed no significant differences across stimuli groups. The random

assignment and the fail-check elimination thus had no effect on the balance of the samples for

the marketing mix manipulation. Nor did any of the participants guess the actual purpose of

the study. Four participants suggested that the study’s purpose was to find out how people

react to positive customer reviews, which is close to the true purpose, but we decided not to

exclude them.

An analysis of the two manipulation check items for the group selection revealed that attitude

toward the Kindle was significantly better among the uncertain consumers compared with

non-buyers (Muncertain = 2.55; Mnon-buyer = 2.09; T = 8.130; p < .001). In contrast, buyers

showed a significantly better attitude than the uncertain (Mbuyer = 2.86; T = 6.062; p < .001).


23

Knowledge about the Kindle was only marginally higher for the uncertain compared with the

non-buyers though (Muncertain = 1.51, Mnon-buyer = 1.43; T = 1.636; p = .103), and there was no

difference between the uncertain and buyers (Mbuyer = 1.57; T = 1.122; p = .262). These results

suggest that the self-selection into the three consumer groups was driven by preference, not

by the level of information possessed, which might have confounded the subsequent results.

The offer attractiveness item revealed that the marketing mix stimuli had significant effects

on deal attractiveness. Offer attractiveness was significantly worse for the control stimulus

(Mcontrol = 4.66) compared with all four other stimuli: price promotion (M = 5.12; T = 2.344; p

< .05), information (M = 5.42; T = 3.988; p < .001), visual (M = 5.11; T = 2.309; p < .05), and

positive word of mouth (M = 5.20; T = 2.653; p < .01). The marketing mix stimuli all worked

in the intended direction; Table 5.2 displays the resulting choice behavior for each group,

divided by stimulus, as well as WTP and WTP range values.

Table 5.2: Results of Study 2 WTP, WTP RANGES, AND CHOICE RATES BY CONSUMER GROUP AND STIMULUS

Groupa Stimulus N

Mean WTP

in US$ (SE)

in US$

Mean Range in US$

(SE) in US$

Choice Rate (SE)

Δ choiceb Tc p

Certain non-buyers

Control 29 32.62 (23.84) 20.69 (21.41) .03 (.19) Promotion 29 28.67 (26.12) 15.90 (21.90) .03 (.19) .00 .00 1.00

Information 20 43.13 (33.35) 33.85 (49.87) .05 (.22) +.02 .264 .793 Visual 22 34.55 (33.23) 21.45 (34.41) .00 (.00) -.03 .869 .389 PWOM 38 51.07 (27.99) 26.82 (29.91) .08 (.27) +.04 .753 .454

Uncertain buyers

Control 55 85.96 (22.72) 63.78 (42.31) .25 (.44) Promotion 56 80.16 (25.07) 51.68 (45.93) .64 (.48) +.39 4.425 <.001

Information 57 90.25 (31.51) 53.89 (45.27) .42 (.50) +.17 1.873 .064 Visual 68 90.31 (36.81) 65.85 (54.13) .43 (.50) +.17 2.004 .047 PWOM 49 88.37 (32.73) 48.53 (32.45) .59 (.50) +.34 3.674 <.001

Certain buyers

Control 21 123.71 (30.70) 63.90 (42.60) .95 (.22) Promotion 30 115.65 (24.66) 36.57 (36.89) 1.00 (.00) +.05 1.200 .236

Information 27 123.94 (28.42) 57.89 (35.08) 1.00 (.00) +.05 1.137 .261 Visual 21 125.71 (39.64) 46.29 (37.73) .81 (.40) -.14 1.430 .160 PWOM 25 120.92 (33.90) 42.00 (35.61) .92 (.28) -.03 .434 .666

Notes: SE = standard error. PWOM = positive word of mouth. a Self-selected groups. b Difference in choice rate between stimulus and control group, with significant differences in bold. c Two-tailed t-test.

Pairwise t-tests of mean demand between the control group and respective marketing mix

stimuli revealed significant differences only for the uncertain group. Even though the certain

buyers included consumers whose WTP ranges included the price, they exhibited no

significant change in demand as a result of any of the stimuli. The certain non-buyer group


24

also did not adapt its choice behavior in any case. Figure 5.2 illustrates the mean differences

in demand values and significance levels from the pairwise t-tests.

Figure 5.2: Differences in Choice Rate by Consumer Group

Overall the results support our proposition that the uncertain segment should be targeted by

any marketing mix activity. The additional product information stimulus exerted no

significant impact, though its size and direction were comparable. Our subsequent analyses of

changes in WTP and WTP ranges demonstrate how the stimuli account for these results.

Similar to extant studies using WTP as a range, we adopted the shift-in-choice likelihood

(SCL) criterion to assess the predictive validity of the WTP measures for each group (Wang,

Venkatesh, & Chatterjee 2007; Dost & Wilken 2012). We applied SCL as an absolute

difference between actual choice and calculated choice probability. The mean SCL values for

each group should be generally low, to indicate predictive validity, and not differ across

groups, which would rule out the possibility that choice differences across stimuli are caused

by variables other than price, WTP, or WTP range. SCL results are shown in Table 5.3. The

SCL values were comparable to those in previous studies (absolute SCL values in Dost and

Wilken [2012] ranged between .03 and .25), and they did not differ for any marketing mix

stimuli compared with the control group. Therefore, the relationship among WTP as a range,

price, and resulting choice is unaffected by the stimulus type. However, the price promotion

stimulus indicated a significantly higher SCL than that for the control group, perhaps because

of a reference price effect, in that a different price slightly changes the relationship, compared

with the other stimuli. Reference price can influence both preference and perceived cost.


25

Table 5.3: Predictive Validity in Study 2

MEAN WTP, MEAN WTP RANGES, AND SCL SCORES, BY STIMULUS Group: Control Promotion Information Visual PWOM

N: 105 115 104 111 112 Price in US$: 99 79 99 99 99

Mean WTP in US$: 78.78 76.43 89.93 85.96 82.98 (SE) in US$: (40.39) (40.21) (40.96) (46.52) (40.72)

Mean Range in US$: 51.90 38.71 51.08 53.35 39.70 (SE) in US$: (42.16) (41.21) (44.25) (50.79) (33.47)

Mean SCL: .19 .33 .20 .20 .18 (Standard Error): (.26) (.40) (.29) (.27) (.29)

Δ SCLa: - +.14 +.01 +.01 -.01 Tb: - 3.074 .312 .216 .215 p: - .002 .755 .829 .830

Notes: PWOM = positive word of mouth. SCL = shift-in-choice likelihood. a Difference in SCL between stimulus and control group, with significant differences in bold. b Two-tailed t-test.

5.3.3 Study 3: Price promotions in the FMCG category (competitive setting)

5.3.3.1 Design

The two previous studies proved that increased choice caused by various marketing mix

stimuli overwhelmingly occurs among uncertain consumers. However, targeting often entails

competitive settings with more than one brand. To empirically confirm the applicability of our

novel targeting procedure to competitive settings, as well as compare our proposed approach

with extant practices (e.g., loyal customer, competitive targeting), Study 3 features a

competitive setting with two brands.

The actual WTP values for two brands might be correlated, due to income or category

preference, so for this study, we randomly assign respondents to buyer groups, independent of

the brands. We thereby manipulate the consumer group (non-buyers, uncertain, or buyers) for

both brands and on the basis of the corresponding reservation prices (floor and ceiling). We

also manipulate targeting activity (20% price discount vs. regular price) separately for each

brand. This method ultimately yielded a 3 (consumer group brand A: buyer, uncertain, non-

buyer) 3 (consumer group brand B: buyer, uncertain, non-buyer) 2 (price for brand A:

regular vs. discounted) 2 (price for brand B: regular vs. discounted) between-subjects

design. Similar to Study 1, the stimuli were fake, online FMCG offers, illustrated by a picture

and textual description. In addition to the medium-priced 100 fl. oz. bottle of Ultra Purex

Coldwater, a competitive product was represented by the higher-priced Tide brand.


26

The displayed prices for both products were individually calculated for each participant, on

the basis of their stated floor and ceiling reference prices (Dost & Wilken 2012) and

according to their randomly assigned consumer group. For example, a participant randomly

assigned to the non-buyers group received a regular price that was 25% above his or her

stated ceiling price, whereas a member of the uncertain group saw a regular price that

reflected the midpoint between floor and ceiling reservation prices. Finally, the certain

buyers’ regular price was 25% below their floor price. Then for each group, the discounted

price was reduced by 20% off the individual regular price. A participant assigned to the

certain non-buyers group who stated a floor (ceiling) reservation price of $4.00 ($8.00) would

consider either a regular price of $4.00 – 25% = $3.00 or a discounted price of $3.00 – 20% =

$2.40. In contrast, if this participant belonged to the certain non-buyers group, he or she

received either a regular price of $8.00 + 25% = $10.00 or a discounted price of $10.00 – 20%

= $8.00. Lastly, if this participant belonged to the uncertain group, then he or she considered a

regular price of ($4.00 + $8.00)/2 = $6.00 or a discounted price of $6.00 – 20% = $4.80.

The response options for the choice dependent variable were none, Tide, or Ultra Purex. This

single-unit choice among several brands and no choice reflected the in-store perspective of a

retailer. It also provided choice values of interest for retailers (total choice, whether Tide or

Ultra Purex), as well as for each brand manufacturer. For this case, we assume the retailer

equally benefits from each bottle of detergent sold, irrespective of the brand.

To complement our data collection, we used the controls from the previous studies (category

knowledge, category involvement, shown before the stimulus), demographic information, and

manipulation checks (deal attractiveness for both brands), measured on seven-point Likert

scales. We also asked for participants’ guesses about the study’s purpose.

5.3.3.2 Procedure

We recruited 1,568 respondents via Amazon Mechanical Turk, restricted to U.S. residents.

Each respondent received a reward between $.30 and $.82. The actual survey was again

hosted on another survey platform and required the transfer of a unique code back to

Mechanical Turk. The average survey duration was approximately six minutes. To increase

attention and time to think, we disabled the continue button for several seconds, equal to a

reading speed of 250 words per minute (Kapelner & Chandler 2010). The resulting minimum

time to complete the survey was 3:20 minutes. We excluded all respondents who guessed that

the usual price for a liquid detergent in a store would be either $0 or more than $100. For this


27

study, three fail-check items helped us ensure attentive reading. The first asked respondents to

check the third box from the left, the second asked them to state the number 0.12 to receive a

$.12 bonus, and the third asked whether the study was about cars. With these checks, 412

respondents (26.3%) were eliminated, which left 1,156 data sets for the analysis.

5.3.3.3 Results

A MANOVA (Pillai’s Traces p > .25) on age, gender, income, education, pre-stimulus

knowledge about the detergent category, attitude toward the detergent category, and category

involvement showed no significant difference across experimental groups, though few of the

parameters were significant (all ps > .04)—as should be expected with 36 experimental

groups. There was no or only a marginal effect on the balance of samples by random

assignment or fail-check based elimination. None of the respondents guessed the study’s

purpose correctly.

The manipulation check for the deal attractiveness items was successful (Mregular, Tide = 4.52,

Mdiscounted, Tide = 5.29; F(1, 1154) = 48.22; p < .001; Mregular, Purex = 4.36, Mdiscounted, Purex = 5.04;

F(1, 1154) = 37.10; p < .001); the price promotion stimuli worked in the intended direction.

The choice behavior with respect to Tide and/or Purex for each consumer group, divided by

the stimulus, is displayed in Appendix A (section 5.5).

Figure 5.3 reveals the resulting differences in total choice (Tide or Purex) for each

manipulated consumer group, as well as between each manipulated promotion group (Tide

promotion, Purex promotion, or both) and the control group.

Pairwise t-tests of the differences in choice rates reveal significant differences, mainly for the

uncertain groups (Tide or Purex). Thus, our central proposition is valid in a setting in which

consumers must choose between competing brands.


28

Figure 5.3: Retailer Gains in Choice Rate by Consumer Group

For a comparison with extant practices in retail targeting, we assume that increasing choice

rates is the retailer’s goal. Therefore, for both our targeting approach and some benchmark

approaches, we calculated the relative increase in choice rate per targeted person. This

indicator represented the choice rate for the targeted groups after the promotion, minus the

choice rate of the same groups without promotion, divided by the choice rate of the groups

without promotion. We calculated these values for the Tide and Purex promotions separately,

as well as for a joint promotion of both brands. The compared targeting approaches were:


29

(1) “Target the uncertain”: Only the uncertain consumer is targeted, either exclusively

for Tide or Purex, or simultaneously for both.

(2) Loyalty targeting: All consumers who at some point in time could repurchase a

brand (i.e., consumers with a purchase history of that brand) are targeted. In our

model, these are all uncertain buyers and all certain buyers.

(3) Competitive targeting: One brand is promoted to loyal customers of the

competitor’s brand.

(4) Loyalty and competitive targeting: This combination approach features loyalty

targeting to focal brand customers and competitive targeting to competitive brands

customers.

(5) Market-level targeting: All consumer groups are targeted identically.

Table 5.4 shows the targeted groups (colored boxes indicate targeted segments; white boxes

indicate untargeted ones), along with the respective relative choice rate increase for each

targeted person. The proportion of colored boxes is lowest for our proposed targeting

approach (first line); that is, this approach generates the lowest level of effort for the retailer,

which can target relatively few consumers.

In this sense, our approach is superior when it comes to the retailer’s inputs; it also generates

the highest relative increase in choice rate per targeted person. Thus, targeting uncertain

consumers, identified by their WTP ranges, is a more efficient approach than widely

employed benchmark practices. Market-level targeting generates the second highest relative

increase in choice rates. This result highlights the importance of targeting the right groups or,

if that is impossible, targeting all groups in the same way, in line with extant empirical studies

(Zhang & Wedel 2009). In contrast, competitive targeting achieves poor performance in our

results, which is not necessarily a contradiction with extant studies that have investigated the

effectiveness of competitive targeting from a manufacturer’s perspective. High gains for a

manufacturer likely cause losses for its competitors, but the retailer’s focus is the joint gain

achieved across a number of potentially competing brands.


30

Table 5.4: Comparison of Retail Targeting Approaches RELATIVE INCREASE IN TOTAL CHOICE RATE PER PERSON BY TARGETING APPROACH

Approach Promotion Tide Promotion Purex Promotion both

“Target the uncertain”

Targeted groups (red: Tide promotion, blue Purex promotion)

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

Δ total choice rate per targeted person

(in %) +10.14% +10.08% +9.79%

Loyalty targeting


cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P


(in %) +6.52% +2.95% +6.16%

Competitive targeting


cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P


(in %) –.32% +2.82% +.80%

Loyalty and competitive

targeting


cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P


(in %) +3.70% +3.49% +3.73%

Market level Targeted groups

(red: Tide promotion, blue Purex promotion)

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P

cnb_T uc_T cb_T

cnb_P

uc_P

cb_P


(in %) +7.18% +9.28% +6.57%

Notes: cnb_T = “certain non-buyer for Tide”, uc_T = “uncertain buyer for Tide”, cb_T = “certain buyer for Tide”, cnb_P = “certain non-buyer for Purex”, uc_P = “uncertain buyer for Purex”, cb_P = “certain buyer for Purex”; Total choice rate = Tide choice rate + Purex choice rate; Δ total choice rate per targeted person = (total choice ratepromotion, targeted – total choice rateno promotion, targeted)/ total choice rate no promotion, targeted.


31

5.4 General Discussion

5.4.1 Key findings and implications

Recent developments in WTP conceptualization and measurement have encouraged us to

introduce a new targeting approach for retailers. This approach classifies consumers into three

categories, depending on whether they would certainly buy, certainly not buy, or express

uncertainty about buying a particular product at a given price. With an analytic demonstration

and across three empirical studies, we substantiate the claim that our targeting approach can

benefit retailers: Compared with predominant practices (e.g., targeting loyal consumers or

consumers of competing brands), our method demands relatively little effort, by targeting

only uncertain consumers, but achieves a relatively great effect in terms of choice behavior

changes.

The result holds across different product categories (FMCGs and high-involvement electronic

devices), varied marketing mix activities, different market settings (monopolistic or

competitive), and different ways to identify consumer groups (WTP ranges, direct elicitation).

We are thus confident in the generalizability of our results.

One of our empirical studies suggested that price promotions and positive word of mouth are

particularly beneficial for the retailer. Between 30% and 40% of uncertain consumers adapted

their choice behavior (from non-purchase to purchase) for a promoted brand. Decreased WTP

ranges accounted for these effects, which indicates the usefulness of range-based WTP,

compared with traditional point-based perspectives on WTP.

Beyond these practical benefits, our study contributes to theoretical discussions about

appropriate targeting strategies. Rather than focusing on loyal customers (mostly certain

buyers), we reveal that focusing on the uncertain group can leverage purchase decisions to a

much greater extent. This result might explain why existing empirical research has been

inconclusive regarding the benefits of individual targeting. That is, the insignificant effects of

marketing activities on sales might reflect the input of certain buyers or non-buyers who,

according to our demonstration, are immune to such activities when it comes to their adapted

choice behavior.


32

5.4.2 Limitations and further research

We also acknowledge some limitations that additional studies could address to make further

progress in this field of inquiry. First, it would be helpful to have effective methods to identify

uncertain consumers. Ongoing research should pursue new ideas about how to use individual-

level purchase history data to measure purchase uncertainty. Irregular purchase behavior

toward a specific brand or contradictory purchase decisions at various retail prices could be

meaningful indicators of uncertainty.

Second, follow-up studies could explore in more detail how marketing mix activities should

be planned. For example, our research has shown that it is preferable to decrease consumers’

WTP ranges and leverage their WTP levels. Although the information and positive word of

mouth stimuli in Study 2 worked in these directions, only one of the effects was significant in

each case. Thus, we need more information about how to design marketing mix activities to

enhance the effectiveness of our targeting approach.

Third, further research should investigate the long-term effects of our targeting approach for

retailers. The competitive setting in Study 3 noted brand-switching effects, which

approximated the retailer’s interest (i.e., store-level instead brand-level). However, our

analysis was static, and it would be interesting to analyze the effect of targeting activities over

time. If a retailer regularly engages in price promotions for one brand in a specific category,

do these promotions lead to considerably fewer purchases of high-priced brands in that

category? What are the long-term effects on the retailer’s price image, or on the images of the

brands it sells? How permanent are the effects on targeted customers’ WTP values and

ranges? How should a retailer combine different marketing mix activities (e.g., enhanced in-

store visibility together with price promotions) to reduce the risk of long-term negative effects

of price discounts? Similar to our preceding discussion, we suggest that purchase history data

could provide a useful basis for answering these pertinent questions.


33

5.5 Appendix A: Results of Study 3

Table 5.5: Choice Rate Means and Comparisons of Study 3 CHOICE EFFECTS OF PRICE PROMOTION BY CONSUMER GROUP

Group manipulation Promotion manipulation Samp

le Choice rate Tide Choice rate Purex Choice rate Total

Tide Purex Tide Purex N Mean SE Δ T p Mean SE Δ T p Mean SE Δ T p

Certain non-

buyers

Certain non-

buyers

Regular price

Regular price 46 .09 .28 .09 .28 .17 .38 Discount price 27 .04 .19 -.05 .808 .422 .15 .36 .06 .800 .426 .19 .40 .01 .120 .905

Discount price

Regular price 29 .07 .26 -.02 .276 .783 .00 .00 -.09 1.640 .105 .07 .26 -.10 1.299 .198 Discount price 38 .16 .37 .07 .993 .324 .08 .27 -.01 .131 .896 .24 .43 .06 .708 .481

Un-certain buyers

Regular price

Regular price 28 .07 .26 .68 .48 .75 .44 Discount price 36 .06 .23 -.02 .256 .799 .86 .35 .18 1.768 .082 .92 .28 .17 1.841 .070

Discount price

Regular price 31 .23 .43 .15 1.657 .103 .48 .51 -.19 1.515 .135 .71 .46 -.04 .342 .733 Discount price 28 .21 .42 .14 1.532 .131 .57 .50 -.11 .818 .417 .79 .42 .04 .311 .757

Certain buyers

Regular price

Regular price 29 .07 .26 .86 .35 .93 .26 Discount price 43 .07 .26 .00 .013 .990 .81 .39 -.05 .531 .597 .88 .32 -.05 .657 .513

Discount price

Regular price 41 .15 .36 .08 .995 .323 .76 .43 -.11 1.085 .282 .90 .30 -.03 .415 .679 Discount price 26 .19 .40 .12 1.369 .177 .65 .49 -.21 1.837 .072 .85 .37 -.08 .999 .322

Un-certain buyers

Certain non-

buyers

Regular price

Regular price 26 .58 .50 .08 .27 .65 .49 Discount price 28 .71 .46 .14 1.047 .300 .07 .26 -.01 .076 .940 .79 .42 .13 1.072 .288

Discount price

Regular price 24 .88 .34 .30 2.436 .019 .00 .00 -.08 1.386 .172 .88 .34 .22 1.856 .070 Discount price 31 .84 .37 .26 2.249 .029 .03 .18 -.04 .743 .461 .87 .34 .22 1.978 .053

Un-certain buyers

Regular price

Regular price 35 .60 .50 .26 .44 .86 .36 Discount price 31 .32 .48 -.28 2.310 .024 .65 .49 .39 3.390 .001 .97 .18 .11 1.565 .122

Discount price

Regular price 35 .89 .32 .29 2.852 .006 .06 .24 -.20 2.357 .021 .94 .24 .09 1.190 .238 Discount price 25 .48 .51 -.12 .912 .365 .48 .51 .22 1.803 .077 .96 .20 .10 1.306 .197

Certain buyers

Regular price

Regular price 39 .38 .49 .59 .50 .97 .16 Discount price 32 .03 .18 -.35 3.853 .000 .91 .30 .32 3.162 .002 .94 .25 -.04 .760 .450

Discount price

Regular price 35 .43 .50 .04 .380 .705 .54 .51 -.05 .401 .689 .97 .17 .00 .077 .939 Discount price 29 .14 .35 -.25 2.295 .025 .86 .35 .27 2.514 .014 1.00 .00 .03 .861 .393

Certain buyers

Certain non-

buyers

Regular price

Regular price 25 .88 .33 .04 .20 .92 .28 Discount price 41 .88 .33 .00 .023 .982 .02 .16 -.02 .354 .725 .90 .30 -.02 .237 .813

Discount price

Regular price 36 .97 .17 .09 1.432 .158 .03 .17 -.01 .259 .796 1.00 .00 .08 1.740 .087 Discount price 50 .94 .24 .06 .896 .373 .02 .14 -.02 .501 .618 .96 .20 .04 .720 .474

Un-certain buyers

Regular price

Regular price 44 .86 .35 .05 .21 .91 .29 Discount price 27 .74 .45 -.12 1.297 .199 .19 .40 .14 1.941 .056 .93 .27 .02 .244 .808

Discount price

Regular price 32 1.00 .00 .14 2.218 .030 .00 .00 -.05 1.218 .227 1.00 .00 .09 1.765 .082 Discount price 28 .82 .39 -.04 .479 .633 .11 .31 .06 .997 .322 .93 .26 .02 .288 .774

Certain buyers

Regular price

Regular price 27 .74 .45 .26 .45 1.00 .00 Discount price 24 .38 .49 -.37 2.776 .008 .63 .49 .37 2.776 .008 1.00 .00 .00 .000 1.00

Discount price

Regular price 22 .77 .43 .03 .254 .801 .09 .29 -.17 1.518 .136 .86 .35 -.14 2.022 .049 Discount price 28 .71 .46 -.03 .216 .830 .21 .42 -.04 .386 .701 .93 .26 -.07 1.415 .163

6 Verhaltensorientierter Ansatz zur Erklärung von Preisreaktionen bei Commodities

34

6 Verhaltensorientierter Ansatz zur Erklärung von Preisreaktionen bei Commodities und Empfehlungen für die Preissetzung auf Commodity-Märkten

Manuscript No. 3 This manuscript is published as: Dost, Florian & Wilken, Robert (2011).

Verhaltensorientierter Ansatz zur Erklärung von Preisreaktionen bei Commodities und

Empfehlungen für die Preissetzung auf Commodity-Märkten. In: Enke, M. & Geigenmüller,

A. (eds.). Commodity Marketing (2nd ed.). Wiesbaden: Gabler, 2011.

DOI: http://dx.doi.org/10.1007/978-3-8349-6388-8_6

35

III. Conclusion

7 Overview of Results

36

7 Overview of Results Each of the preceding chapters presents a distinct discussion of the key results, limitations,

and further research directions, though without reference to the overarching framework. That

reference is the focus of the remaining chapters. In particular, Figure 7.1 offers an overview

of the most relevant findings.

Figure 7.1: Overview of findings in the manuscripts

Without repeating the detailed results and findings from the individual manuscripts, it is

obvious that empirical results are missing in one specific area in the framework: antecedents

of WTP ranges and the modes of their construction. The WTP-as-a-range concept relies on

two important assumptions that demand empirical validation. First, though rationally

bounded, consumers will have to undertake at least some cognitive efforts to rationalize

decision making, such as retrieval of past experiences or weighing the benefits against the

7 Overview of Results

37

cost, to exhibit choice behavior that reflects WTP as a range. Chapter 6 presents support for

this premise, in that it conceptualizes choice as especially driven by a cognitive mode of

decision making at prices in the WTP range, yet it does not offer an empirical validation.

Second, the WTP-as-a-range concept relies on the assumption that uncertainty is a main

driver of WTP ranges. However, extant results have been inconclusive (Wang, Venkatesh, &

Chatterjee 2007). Thus, to synthesize a more complete and valid collection of findings, I

subject these two questions to empirical investigation.

8 Empirical Extension to Manuscript No. 3

38


8.1 The Link of WTP Ranges and Cognitive Effort in Price-Related Choice

Chapter 6 (Verhaltensorientierter Ansatz zur Erklärung von Preisreaktionen bei Commodities)

suggested that of the two modes of processing, a cognitive processing mode is more

prominent for prices within WTP range, because it is more difficult for respondents to choose

at a price within their respective WTP range, where they cannot know with certainty whether

their WTP is truly higher than the posted price. Thus, respondents try to reduce the

uncertainty by intensive thought, or cognitive activity. In contrast, for prices lower or higher

than the floor and ceiling prices, people likely engage in rapid, experience-driven heuristic

choice behavior (Epstein 1991; Gigerenzer 2007)—the “no brainer” of choice described by

Wathieu and Bertini (2007). Thus the range defines the prices at which consumers engage in

additional thought about the exact benefits of the product, which may cause them to

reconsider their initial hunches about a current choice (Wathieu & Bertini 2007; Park,

McLachlan & Love 2011).

8.2 Study Design

The purpose of this study was to test empirically the propositions of slow, cognitively

demanding choice behavior for prices within the WTP range in contrast to a fast, cognitively

non-demanding choice behavior for prices lower or higher than the WTP range. To ensure

that the decision process is not driven by individual preference levels (i.e., levels of the WTP)

or the actual distribution of individual WTP ranges around price in a consumer group, it is

necessary to manipulate the relation of price and WTP range randomly at the individual level.

Thus in an experimental approach, respondents were assigned randomly to one of nine

experimental groups, each of which described a specific relationship between individual WTP

ranges and the price used in the decision. The groups received prices that were 50%, 30%, or

10% lower than floor reservation price; 10%, 30%, or 50% higher than ceiling reservation

price; or at the 25%, 50%, or 75% quartile of the range between floor and ceiling reservation

prices. For example, a respondent with a floor reservation price (FP) of 5 EUR and a ceiling


39

price (CP) of 10 EUR was assigned to one of the following prices: 2.5 EUR (FP – 50%), 3.5

EUR (FP – 30%), 4.5 EUR (FP – 10%), 11 EUR (CP + 10%), 13 EUR (CP + 30%), 15 EUR

(CP + 50%), 6.25 EUR (25% quartile), 7.5 EUR (50% quartile), or 8.75 EUR (75% quartile).

The stimulus product and control variable scales were similar to the study in section 5.3.3 (the

Tide liquid detergent offer).

After asking participants to provide their floor and ceiling reference prices, the study

procedure assigned them to one of the nine experimental groups. To distract respondents from

their posted reservation prices, most control variable scales appeared before the choice

options. The choice of the manipulated price followed on a single page; the time respondents

took to continue to the next survey page served as an objective measure of cognitive

engagement. The choice task was followed by a three-item measure of subjective cognitive

difficulty, using seven-point Likert scales. Demographic information and suspicions about the

study’s purpose were collected at the end of the survey.

8.3 Procedure

The procedure was similar to the studies in Chapter 5. That is, 297 U.S. residents were

recruited through Amazon Mechanical Turk. Each respondent received a payment between

$.20 and $.25. Respondents who spent less than 1:30 minutes on the survey or failed at either

of the three fail-check items (see section 5.3.3) were excluded. Altogether, 87 respondents

(29.3%) had to be eliminated, which left 210 data sets for the analysis.

8.4 Results

A multivariate analysis of variance (MANOVA; Pillai’s Trace p = .767 for the model; ps

>.255 for the model parameters) using age, gender, income, education, pre-stimulus

knowledge about the detergent category, attitude toward the detergent category, category

involvement, floor prices, and ceiling prices showed no significant differences across nine

experimental groups. This finding indicates no effect on the balance of the samples by

random assignment or fail-check–based elimination. None of the participants was able to

guess the actual purpose of the study.

The resulting choice rates, perceived cognitive effort, and time spent on the choice are

depicted in Figure 8.1.


40

Figure 8.1: Results of extension study

At face value, Figure 8.1 confirms the prediction of higher cognitive efforts for prices within

the range. However, group sample sizes per single price were small. Thus, for statistical

testing, averages over three prices were used, adapting the group classification from Chapter

5. The mean values of the three groups (certain non-buyer, uncertain buyer, and certain buyer)

were then calculated and compared. Pairwise t-tests of the differences in perceived cognitive

effort showed that perceived cognitive effort was higher at prices within the WTP range

(Mcog_eff.uncertain = 3.01, SE = 1.31) than at prices below the floor reservation price (Mcog_eff.non-

buye r= 2.13, SE = 1.06; T=4.505, p < 0.001) or above the ceiling reservation price (Mcog_eff.buyer


41

= 2.00, SE = 1.04; T = 4.938, p < 0.001). These results support the proposition: Cognitive

effort for choices at prices within the WTP range is higher than outside WTP range.

Furthermore, the time spent on the survey page for the choice task was longer for prices

within WTP range (Mtime.uncertain = 20.67 sec, SE = 15.19 sec) that for prices higher than the

ceiling reservation price (Mtime.nonbuyers=13.81 sec, SE = 6.51 sec; T = 3.346, p < 0.001) and

for prices below the floor reservation price, though not significantly (Mtime.buyers = 17.80 sec,

SE = 12.44 sec; T = 1.250, p = .213). The time to read the choice task information is included

as a “baseline” time in the measure, which may level out some variance caused by different

processing modes. The correlation (Pearson) was significant, at r = .178 (p <.01), in support

of the proposition of cognitively demanding, slow processing when prices fall within the

WTP range, in contrast with cognitively undemanding, rapid processing for other prices.

8.5 Discussion

These results offer empirical support for a link between WTP range and cognitive efforts by

consumers to reduce their uncertainty. Cognitive effort and perceived cognitive effort are

higher for prices within the WTP range than for prices outside it. Both results may point to the

presence of different modes of choice processing: a fast decision mode with little cognitive

effort, applicable to “certain” decisions (buy or not buy) and a slow decision mode with great

cognitive effort, applicable to “uncertain” decisions at prices within the WTP range. These

results support the propositions discussed in manuscript 3 (Chapter 6). Furthermore, they

extend findings from manuscript 2 (Chapter 5), namely, that targeting should focus on the

uncertain consumer group. Because the segment of the uncertain buyers is not only more

reactive to marketing mix activities (see manuscript 2), but also more inclined to use

cognitive processing, and thus rationalized choice (Bettman, Luce, & Payne 1998), targeted

marketing mix activities should draw on the cognitive dimension by offering cognitively

persuasive arguments.

These results have implications for extant and further research as well. First, in light of the

results in Chapter 4 (“Measuring Willingness to Pay as a Range, Revisited: When Should We

Care?”), that traditional point-based WTP refers to the midpoint of WTP ranges, it is likely

that the range of thought-provoking prices promoted by Watthieu and Bertini (2007) extends

not beyond WTP, as stated by the authors, but rather around it. Second, a potential link

between a dual process of choice and individual WTP might provide a means to measure


42

WTP ranges indirectly, according to levels of cognitive engagement in evaluating a price.

Such measures would be less prone to strategic bias. Although this study provides an

indication of links among WTP range, price, cognitive effort, and time spent on the choice as

an easy-to-use measure, this issue deserves more substantive inquiry, which in turn suggests a

fruitful route for research. Third, the theoretical considerations draw heavily on behavior that

is determined by past experiences or present stimuli, so a dual process model might be useful

to combine the behavioral, experience, and stimulus-related aspects of choice with the

(boundedly) rational aspects of choice. The merits of research in this direction ultimately

might lie in unifying extant works in a single framework for price-related decision making.

9 Secondary Analysis of the Interplay Among WTP, Range, and Uncertainty

43


9.1 Introduction

Although many of the original results and propositions of Wang, Venkatesh, and Chatterjee

(2007) received support and extension during the course of this thesis and its related studies,

one of the most central, underlying assumptions they offer has not been addressed: Wang and

colleagues assume that individual WTP ranges are driven by individual levels of uncertainty.

Yet their empirical results are inconclusive. The authors even admit:

“Although we were able to demonstrate the existence of a positive, significant

relationship between a consumer’s reservation price range and associate levels of

uncertainty in the chocolate study, our results were inconclusive for the wine study

(probably because of a smaller sample and possibly greater measurement error due to

the survey-based elicitation). We acknowledge this limitation and encourage

additional investigation of the link.” (Wang, Venkatesh, & Chatterjee 2007, p. 211).

Because the theoretical considerations of this thesis also rely on this assumption, an additional

empirical investigation of the proposed relationship between uncertainty levels and range

levels seems necessary. However, no empirical studies focused on substantiating this link,

even as they featured covariates related to uncertainty—mostly to test the balance of the

subsamples for unwanted side effects. The covariates included knowledge, involvement,

experience, and also some certainty scales, all of which can reasonably be assumed to

correlate with consumer uncertainty. Therefore, to present more conclusive results about the

uncertainty–ranges link, a secondary analysis of all empirical subsamples seems appropriate.

Beyond the mere integration of empirical correlations between pseudo-uncertainty scales and

range levels, this secondary analysis also should account for two potentially confounding

relationships that Wang and colleagues ignore. First, range levels are based on the same

measures as WTP levels, namely, measured reservation prices. A strong correlation between

WTP and range levels is likely, because consumers tend to evaluate differences in price levels

in relative rather than absolute terms (Janiszewski & Liechtenstein 1999; Kahnemann &

Tversky 1979). For example, a range level of Range = 2.00 EUR at a WTP level of WTP =

4.00 EUR (ratio = .5) might be perceived as just as uncertain as a range level of Range = 3.00


44

EUR at a WTP level of WTP = 6.00 EUR (again, ratio = .5). Second, the level of uncertainty

might influence WTP levels themselves, in that an existing level of uncertainty might lead to

perceptions of risk and thus shift the WTP downward for risk-averse consumers (e.g., Park,

McLachlan, & Love 2011). Such a multi-collinear relationship between WTP and uncertainty,

in relation to range levels might confound the results presented by Wang and colleagues

(2007) and in the chapters enclosed in this thesis. The latter collinear relationship needs to be

ruled out.

9.2 Study Design

This test used the existing subsamples from the previous studies to perform the regression

analyses and integration of beta and correlation coefficients. To keep the induced variance to

a minimum, each experimental stimulus or elicitation method group constituted a separate

subsample, as Table 9.1 displays, along with their various (pseudo)certainty scales. The three

variables of interest in each subsample are:

(1) WTP levels, calculated as the midpoint between floor and ceiling reservation prices;

(2) Pseudo-certainty levels, calculated as the average of all pseudo-certainty scales used in

the respective subsample. The directions of the scale levels are labeled and interpreted

as “certainty” levels instead of uncertainty levels, because of the direction of the scales

used. For example, knowledge positively correlates with certainty, not uncertainty.

Uncertainty also is multidimensional (Wang, Venkatesh, & Chatterjee 2007), so index

building by average should cover the highest possible degree of uncertainty reflected

in the various pseudo-certainty scales; and

(3) WTP ranges as dependent variables for the regression analyses, calculated as the

difference between ceiling and floor reservation prices.

The comparable nature of the range measures as dependent variables makes this selection of

subsamples appropriate for an inter-study comparison. Large differences in scales for the

independent certainty variable will add to the generalizability of the subsequent results.


45

9.3 Results

Expected WTP and certainty served as the independent variables in a series of ordinary least

squares (OLS) regressions on WTP ranges. Standardized beta coefficients and their levels of

significance were calculated for the two independent variables in each regression model. All

resulting levels of adjusted R-square, standardized beta coefficients, p-values of the respective

two-sided t-tests, variance inflation factors, and correlations coefficients are reported in Table

9.1.

The number of significant coefficients of the same direction was then used to provide a

simple vote-count integration over subsamples (Bushman 1994). Vote counting showed that

WTP is positively and significantly (p < .05) related to the range in 20 of 21 cases, with an

average beta coefficient of +.48. The only subsample without such a strong relationship is the

ICERANGE subsample of study 1 in the first manuscript (see Chapter 4), which also was the

only regression that failed to provide a significant model fit (F = 1.776, p = .181). Altogether

these results provide strong evidence of a positive relationship between expected WTP levels

and WTP range levels. Certainty variables linked significantly and negatively to WTP range

in 12 of 19 regression models. The average coefficient was βcertainty.mean = –.17, which offers a

good indication of the existence of a theoretical relationship between uncertainty and range.

Furthermore, just one coefficient in the 19 regression models showed a non-negative sign.

Considering the importance of the relationship between uncertainty and WTP range for the

theoretic foundations of WTP as a range, further tests should corroborate this finding.

Following Shadish and Haddock’s (1994) procedure to integrate and test effect sizes from

correlation coefficients, a weighted mean correlation between certainty and ranges and a

weighted variance were calculated using respective subsample sizes as weighting factors. The

weighted mean correlation was rmean.weighted = –.1557, whereas the weighted variance was

s2weighted = .0003. The resulting Z-statistic (Eisend 2004) was Z= r/s = –8.821, indicating a

significant negative correlation. This result ultimately supports the claim that range levels are

negatively driven by levels of consumer certainty. Finally, the reported variance inflation

factors show no sign of multi-collinearity (VIF < 1.05, well below commonly used thresholds

of 4 or 10; O’Brian 2007). Uncertainty levels are not driving WTP levels, in addition to WTP

ranges.


46

Table 9.1: Secondary analysis regression results

Dependent variable: WTP range

No. Sub-sample Thesis section N Adj.

R2 WTP Certainty measures

VIF β p (pseudo-) certainty Items β p r

1 Manuscript 1, study 1, ICERANGE 46 .03 .26 .084 Product knowledge, product usage, product

expertise .04 .768 .06 1.037

2 Manuscript 1, study 1, BDM-Range 56 .16 .43 .001 Product knowledge, product usage, product

expertise -.22 .085 -.14 1.049

3 Manuscript 1, study 2, ICERANGE 40 .14 .40 .01 Product knowledge, product usage -.17 .248 -.15 1.003

4 Manuscript 1, study 2, BDM-Range 40 .27 .37 .012 Product knowledge, product usage -.36 .016 -.36 1.000

5 Manuscript 1, study 3, ICERANGE 44 .18 .45 .002 None none none none 1

6 Manuscript 1, study 3, BDM-Range 44 .44 .67 <.001 None none none none 1

7 Manuscript 2, study 1, control group 88 .16 .42 <.001 Category knowledge, category

involvement, purchase experience -.10 .339 -.05 1.010

8 Manuscript 2, study 1, price promotion 80 .16 .29 .006 Category knowledge, category

involvement, purchase experience -.32 .003 -.32 1.000

9 Manuscript 2, study 2, control group 105 .32 .54 <.001

Preference certainty, price certainty, brand choice certainty,

product benefit certainty -.25 .002 -.20 1.008

10 Manuscript 2, study 2, price promotion 115 .23 .49 <.001



11 Manuscript 2, study 2, information 104 .27 .50 <.001



12 Manuscript 2, study 2, visual 111 .30 .50 <.001



13 Manuscript 2, study 2, PWOM 112 .13 .38 <.001


product benefit certainty -.01 .886 .04 1.019

14 Manuscript 2, study 3, control; Tide 299a .34 .57 <.001 Price knowledge, category knowledge,

category involvement -.24 <.001 -.17 1.015

15 Manuscript 2, study 3, control; Purex 299a .18 .36 <.001 Price knowledge, category knowledge,


16 Manuscript 2, study 3, Tide promotion; Tide 285b .37 .60 <.001 Price knowledge, category knowledge,


17 Manuscript 2, study 3, Tide promotion; Purex 285b .44 .64 <.001 Price knowledge, category knowledge,


18 Manuscript 2, study 3, Purex promotion; Tide 289c .36 .60 <.001 Price knowledge, category knowledge,

category involvement -.15 .002 -.10 1.007

19 Manuscript 2, study 3, Purex prom.; Purex 289c .24 .47 <.001 Price knowledge, category knowledge,


20 Manuscript 2, study 3, Both promotions; Tide 283d .41 .62 <.001 Price knowledge, category knowledge,


21 Manuscript 2, study 3, Both promotions; Purex 283d .38 .60 <.001 Price knowledge, category knowledge,


Mean: .48 Mean: -.17

Notes: a,b,c,d same set of respondents. N = subsample size; β = standardized regression coefficient; p = p-value (two sided t-test); r = Pearson correlation; VIF = variance inflation factor.


47

9.4 Discussion

This secondary analysis established three results. First, the levels of WTP range are driven by

levels of uncertainty, as theorized by Wang and colleagues (2007). Second, levels of expected

WTP are not driven by uncertainty, which indicates that consumer uncertainty exclusively

drives the ranges. Third, WTP levels strongly drive WTP ranges. All these results have

implications for further research. Although an impact of consumer uncertainty on WTP

ranges can be assumed, the impact size is relatively small, which calls for additional inquiries

into the antecedents of WTP ranges. A previously unknown antecedent is the level of

expected WTP, a result that is especially important for attempts to model WTP as a range on

an aggregate demand level. The simulation-based approach in this thesis (see the simulation

study in manuscript 1, Chapter 4) conveniently assumes constant levels of range over all

subjects. However, a positive correlation between WTP and WTP ranges might further

increase differences between aggregated demand curves based on point-based WTP and

aggregated demand-curves based on range-based WTP, because the impact of the ranges

grows asymmetrically with higher WTP levels.

10 Implications of the Findings

48


10.1 WTP-as-a-Range Model

The major contributions of this thesis to the field of marketing pertain to the model, which

links WTP ranges to behavior and to the underlying organism. The model, based on Wang

and colleagues’ (2007) proposition, asserts that WTP is a range of reservation prices and not a

single point, each with a corresponding choice probability. This thesis presents a modified

and advanced conceptualization, in which a WTP distribution that represents the distribution

of choice probability around a true, yet latent individual WTP can be defined.4 This individual

WTP distribution is specified by an expected value of individual WTP, which corresponds to

the traditional definition of WTP, and a variance of individual WTP, which corresponds to the

WTP range. Individual choice probability is therefore a function of preference (expected

WTP) and uncertainty (WTP range). This novel conceptualization provides a theoretic

foundation to explain how WTP as a range relates to extant point-based WTP literature; it

further explains why traditional point-based WTP elicitation methods measure expected WTP

(see measurement synthesis, Chapter 10.2).

Acknowledging the conceptualization, it becomes apparent why individual-level, price-related

choice behavior at prices that fall within a consumers’ WTP range differ from previously

theorized behavior. Consequently, marketing mix decisions, such as pricing decisions, are

likely to be inferior when made under the traditional point-based model of WTP, because

choice rates assumed by the marketer likely differ from actual choice rates, which prevents

optimality.

Through a simulation, this thesis has demonstrated that such a bias can translate to an

aggregate level of consumer choice, making the conceptualization relevant for demand

estimation, aggregate choice models, and marketing mix activities on a more general level. It

is further revealed that the size of the bias depends on interactions with consumer

heterogeneity in WTP levels. This finding has important consequences, such as for WTP

estimation approaches that rely on choice data: Given that both heterogeneity in aggregate

WTP levels and WTP ranges (i.e., heterogeneity in individual WTP levels) affect aggregate

4 Only recently has the idea of a WTP distribution been confirmed in independent projects that also make a case for conceptually similar distributions (Park, MacLachlan & Love 2011; Schlereth, Eckert & Skiera 2011).


49

choice behavior, any approach that relies on choice data and does not account for both types

of heterogeneity may provide biased estimates of individual ranges or the differences

(heterogeneity) between consumers.

Regarding the antecedents of WTP ranges, a secondary analysis revealed that ranges are

driven by levels of individual uncertainty, remedying the inconclusive evidence provided by

Wang and colleagues (2007). However, an additional and much stronger driver is identified in

the (expected) WTP levels: Ranges are larger for high preference consumers and for higher

priced products. This finding has implications for an understanding of ranges as an indicator

of uncertainty. Specifically, direct comparisons of ranges, without accounting for an identical

level of WTP, seem invalid. Instead, relative ranges provide a more precise indicator of the

level of underlying uncertainty and a basis for comparison. The finding has also implications

for the modeling of aggregate choice behavior: The simulation approach described in Chapter

4 uses constant ranges for the sake of convenience. However, the positive relationship

between WTP and WTP ranges would not only stretch aggregated demand curves, compared

with point-based aggregated WTP data, but also asymmetrically distort the resulting demand

functions. This effect might further increase the differences between actual aggregated

demand curves based on point-based WTP and aggregated demand-curves based on range-

based WTP. Further research should inquire into whether actual aggregated choice data reflect

this result, as well as if the estimation of demand models can be improved by accounting for

it. In a related matter, more research is needed to advance knowledge on the type and shape of

individual WTP distributions. Although a symmetric WTP distribution on the individual level

seems likely, according to the findings of this thesis, actual WTP distributions have not been

investigated empirically, an effort that remains for further research.

In this light, modeling aggregated WTP as a range through simulations seems highly

compelling. For example, agent-based models provide means to implement complex,

individual-level choice models and generate aggregate-level results. These models would be

suitable for implementing various range and WTP antecedents, pre-specified correlations

between WTP levels and range levels, different specifications of WTP distributions, and even

extensions to behavioral price reaction models. Furthermore, such models would be open to

dynamic applications (see Chapter 10.4). Only recently have guidelines for rigor been

developed to foster this new class of models (Rand & Rust 2011). They also may provide

helpful guidance in the pursuit of this particular methodological route.


50

Finally, this thesis establishes that WTP ranges are linked to greater cognitive effort, in

support of the claim that a dual process choice model might better reflect consumer choice

processing. One processing mode is fast, relies on heuristics, and requires little cognitive

effort; the other one is slow, requires high cognitive efforts in sequential rational processing,

and seems more applicable to the “uncertain” decisions at prices within WTP range.

Implications for future theoretical developments of this idea are far reaching and provide

many opportunities for follow-up research. Specifically, establishing this finding provides

support for the extension of the WTP as a range conceptualization to the realm of price-

related consumer behavior. Ultimately, WTP, uncertainty, the range of thought-provoking

prices (Park, McLachlan, & Love 2011; Watthieu & Bertini 2007), and reference price

reaction models (e.g. Helson 1964; Parducci 1965) may be integrated within a single model

that accounts for the different modes of consumer decision making.

10.2 WTP Range Measurement

Consistent with the theoretical findings from the proposed model, this thesis establishes

empirically in several studies that “traditional” point-based methods measure expected WTP

of WTP distributions. This finding is consistent over real purchase and hypothetical settings,

offline and online settings, and quantitative and qualitative modes of inquiry.

The simulation results in the first manuscript (Chapter 4) provide guidelines about when to

use range-based methods in market research, which is not just recommended but mandatory to

avoid a conceptual bias, even in aggregate-level data.

The simplified, lottery-based method, BDM-Range, is simpler by construction and less

restricted in terms of theoretical assumptions regarding the shape of the range than

ICERANGE, the method of Wang and colleagues (2007), or the BDM lottery for point-based

WTP elicitation. It is extensively compared to both other methods. Therefore, further studies

of the shape and type of WTP distribution do not need to adapt the mechanism of BDM-

Range. However, the method is not fully incentive-aligned, inviting further modifications, as

well as validation with real purchase choice data as a benchmark. Still, the BDM-Range

reaches comparable levels of predictive performance and internal validity and demonstrates

practical applicability at the point of purchase.

The method comparisons were restricted to direct-elicitation, lottery-based approaches. As

range-based elicitation method variants (e.g., variants of conjoint analysis) already exist


51

(Schlereth & Skiera 2009; Schlereth, Eckert, & Skiera 2011), it would be interesting to

establish a comparison with methods of indirect elicitation or methods without incentive

alignment, to gain further insights on the recommended methodology. Although the second

manuscript used a non–incentive-aligned direct elicitation of WTP range, it contained no

inquiry into the size or direction of hypothetical and/or strategic bias. Such an undertaking

would be particularly important for practical use, because marketing practitioners typically

refrain from more complex applications of lottery procedures (Hofstetter & Miller 2009).

Theoretically, stating exact floor and ceiling reservation prices might be just as challenging

for respondents as is stating an exact WTP. The underlying argument—that uncertainty about

latent, true WTP prevents a person from knowing with certainty a specific preference level—

also applies to other reservation price points. However, two findings from this thesis support

the use of current range-based methods, despite this obvious theoretical shortcoming. First,

even if the size of the range is not exact, range measured as the difference of two reservation

prices still offers an indicator of variance in the WTP distribution. It thus can be compared

with other, similarly measured indicators of said variance. Further research on the empirical

relationship between “fuzzy” (Wang et al. 2007, p. 211) measures of range and the actual size

of WTP distribution variance may remedy that shortcoming. Second, it was established that

processing mode, specifically cognitive effort, changes inside the WTP range. Behavioral

research shows that humans perceive changes in perceptions as stronger than absolute levels

(Kahnemann & Tversky 1979), so the endpoints of WTP range might be easier to detect by a

respondent than the absolute level of WTP, as required in traditional point-based methods.

The establishment of cognitive effort in ranges opens another possibility, too: Measures of

cognitive effort, such as time spent, or of brain activity might be developed to offer an

indirect measurement approach for WTP ranges. Such an approach would not only be

conceptually free of strategic bias but also might provide more realistic estimates of the exact

WTP distribution.

10.3 WTP Range Management

Positioned in the framework of marketing mix decisions, several findings relate to the

peculiarities of the WTP as a range model with respect to the marketing mix. In particular,

WTP as a range should be adopted in pricing decisions, both at individual and aggregate

levels, to avoid biased pricing. Because both uncertainty and WTP levels drive this effect, the


52

use of range-based methods to measure and model consumer choice is even more pressing for

the pricing of innovations. Innovative products are new and thus often unfamiliar to

consumers. Furthermore, price levels tend to be higher, because producers try to “skim” the

market to cover the upfront development cost early in the product life cycle. These higher

price levels are crucial for forming the first expected WTP levels (Park, McLachlan, & Love

2011) and further increasing WTP ranges. Therefore, pricing applications and studies remain

a fruitful avenue for research on WTP as a range.

Also, WTP ranges are profit-relevant, useful measures for the impact of other marketing mix

activities, both in practice and in subsequent research on marketing mix–related topics.

A relevant finding for aggregate-level marketing mix activities is the finding that the

“uncertain” consumers, whose range encompasses a given price, show the strongest reactions

to marketing mix activities in terms of choice behavior. Thus targeting should focus on

uncertain buyers in a given group of consumers. The theoretical delineation for this finding

(see Chapter 5) was based on the original model of Wang, Venkatesh, and Chatterje (2007).

Accordingly, it seems that certain buyer and non-buyer groups should not react to marginal

changes in the three dimensions: price, WTP, and range. However, assuming a differentiable

WTP distribution, as is likely, might result in small reactions to marginal changes in the three

dimensions for the certain buyer and non-buyer groups as well. This idea would be more in

line with the empirical results of the second manuscript. Together with the actual shape of

WTP distribution, a modified, more continuous targeting approach offers a compelling topic

for future investigations.

Marketing mix activities that target uncertain buyers find them in cognitive processing modes.

Thus targeted marketing mix activities should draw on the cognitive dimension by offering

rational, cognitively persuasive arguments. Lower prices as an objective benefit, as well as

convincing and credible information, such as that offered by other users, are examples of

marketing mix activities that theoretically should fare better. Empirical results in Chapter 5

suggest that price promotions and positive word-of-mouth are particularly beneficial for the

retailer. Between 30% and 40% of uncertain consumers adapted their choice behavior (from

non-purchase to purchase) for the promoted brand. However, these results are based on

experiments and hypothetical choice. Further evidence for range-based targeting and effective

in-range marketing mix activities requires the use of real purchase data or transaction choice

experiments.


53

Finally, marketing mix activities, such as branding, word of mouth, or informative

advertisements, manipulate uncertainty and WTP ranges. In presenting the range dimension

as relevant for consumer choice, the question arises: Can a marketer actively leverage this

novel dimension? The theoretical findings indicate so. First, WTP range will have a direct

impact on consumer choice. However, this impact of decreasing the range could be either

positive or negative, depending on whether expected WTP is higher or lower than the current

price (see Chapter 5.2.2; For a similar argument, see Schlereth, Eckert, & Skiera, 2011).

Second, WTP range levels interact with changes in both price and expected WTP, such that a

smaller range increases the effect of either dimension. A combination of decreased range and

increased expected WTP should be most effective. The “information” and “positive word-of-

mouth” stimuli in the second manuscript (Chapter 5.3.2) worked in these directions, though in

both cases, only one of the two effects was significant. Still, further research on effective

marketing mix activities regarding WTP range, especially on leveraging ranges for

profitability, seems fruitful.

10.4 A Call for Dynamics in WTP as a Range Research

A final suggestion for further research relates to a set of topics beyond the scope of this thesis.

The theoretical considerations of the WTP-as-a-range concept draw heavily on behavior that

is determined by either past experiences (e.g., adaption level or frequency theory; Parducci

1965), situational stimuli (Bettman, Luce, & Payne 1998), or some combination. Yet WTP as

a range thus far has been examined only in a static context. A natural and interesting route

would be to implement dynamic views. Three areas appear particularly interesting.

First, ranges are based on uncertainty, so it is highly unlikely that they remain stable over

time. Experience and learning reduce uncertainty. New pieces of conflicting information

might even increase uncertainty. Consequently, a consumer who decides to try a product or

service for the first time might feature a different expected WTP, and almost certainly a

different range, than the same customer choosing the second time. In a similar manner, WTP

elicitation studies may provide different results at different points in the product lifecycle, not

because the preference levels change, but because residual uncertainty declines as experience

in the market accumulates. Applications of such research appear promising in the area of

dynamic pricing, as well as in major marketing research areas, such as modeling product

adoption.


54

Second, the impact of situational factors on the construction of preferences in the course of a

choice remains an ongoing source of insights. Extant approaches have focused on external

reference prices and point-based WTP. However, in light of uncertainty, both the potentially

moderated impact of the situational stimulus and the stimulus impact on uncertainty itself, and

thus on WTP range, provide opportunities for extensive investigation. With respect to

learning, a further opportunity arises in inquiries about the permanence of such effects.

Third, the relation between WTP as a range and consumption over time, such as through

budgeting or saving for large ticket items, has not been investigated. Yet it is an important

route for further research; in reality, demand functions, on both aggregate and individual

consumer levels, likely relate to an underlying, latent time frame: For example, a consumer

who is generally and constantly willing to pay 1 EUR for yoghurt will not necessarily do so

every time he or she is confronted with the opportunity to buy one. Therefore, studying the

relation of WTP as a range to underlying time frames may help explain consumer choice

further. The impacts of uncertainty on budget perception and purchase frequency are closely

related topics for this route of inquiry.

Considering this vast set of opportunities for research, investigating dynamics in willingness

to pay as a range provide but one of the many exciting next steps on the path of exploration

that ultimately might help practitioners and researchers make better marketing mix decisions.

55

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