the impact of consumer self-confidence and hedonic value
TRANSCRIPT
ESCOLA SUPERIOR DE PROPAGANDA E MARKETING
PROGRAMA DE MESTRADO E DOUTORADO EM GESTÃO INTERNACIONAL
JOANNA CAROLINA GUARITA DOUAT
THE IMPACT OF CONSUMER SELF-CONFIDENCE AND HEDONIC VALUE ON
ANCHORED WILLINGNESS TO PAY ASSESSMENTS
SÃO PAULO
2019
THE IMPACT OF CONSUMER SELF-CONFIDENCE AND HEDONIC VALUE ON
ANCHORED WILLINGNESS TO PAY ASSESSMENTS
Tese de Doutorado apresentada como requisito para obtencao do titulo de Doutor em Administracao, com enfase em Gestao Internacional, pela Escola Superior de Propaganda e Marketing – ESPM.
Orientador: Prof. Dr. Mateus Canniatti Ponchio
SÃO PAULO
2019
THE IMPACT OF CONSUMER SELF-CONFIDENCE AND HEDONIC VALUE ON
ANCHORED WILLINGNESS TO PAY ASSESSMENTS
Tese de Doutorado apresentada como requisito para obtençao do titulo de Doutor em Administracao pela Escola Superior de Propaganda e Marketing – ESPM.
Data de aprovação: ____/____/_____
Banca Examinadora:
___________________________________________ Prof. Dr. Mateus Canniatti Ponchio (orientador)
ESPM - SP
___________________________________________ Prof. Dr. Eduardo Eugênio Spers
ESALQ - USP
___________________________________________ Prof. Dr. Felipe Zambaldi
EAESP – FGV
___________________________________________ Prof.a Dr.a Thelma Valéria Rocha
ESPM - SP
___________________________________________ Prof.a Dr.a Luciana Florêncio de Almeida
ESPM - SP
ACKNOWLEDGMENTS
First, my distinct gratitude goes out to my parents, João Carlos Douat and Celeste
Encarnação Indio Guarita, for their love, faith and support. They have always encouraged
me to get back on track when my motivation has wavered.
I must also express my sincere gratitude to my advisor Prof. Dr. Mateus Canniatti Ponchio
for his patience and guidance throughout this challenging journey.
I am also very thankful to CAPES for providing funding for this work.
Finally, I am also very grateful to my brother, Carlos Henrique Guarita Douat, and his
wife, Teodora Szabo-Douat, for hosting me in New York City, for facilitating my access to
data and for enlightening me with helpful advice.
I also must not forget to mention my little niece, Natalia Douat, who I still have not met
due to geographic distance and professional commitments.
“The possession of knowledge does not kill
the sense of wonder and mystery.
There is always more mystery.”
― Anais Nin
ABSTRACT
Prior research on anchoring indicates that arbitrary values can influence human judgment
and decision making. This thesis suggests that this behavior is not universal, and it
attempts to identify how consumer self-confidence and the hedonic value of goods may
shape consumers’ susceptibility to anchoring effect on participants’ willingness to pay. A
quantitative approach was adopted in order to demonstrate the association between
consumers’ declared willingness to pay (dependent variable), consumer self-confidence
(independent variable), hedonic value and anchor as a reference price (manipulated
independent variables). Data was collected from experiments performed by 350
international students in New York City. For this study, linear and multiple regressions
were considered and two moderation models - simple moderation and a three-way
interaction model - were tested, in order to answer the hypotheses. This thesis offers
evidence that the hedonic value of products moderates the relationship between
anchoring and willingness-to-pay. The results also indicate a three-way interaction
between hedonic value, consumer self-confidence, and anchoring effect on willingness-
to-pay. Overall, this thesis contributes to the literature of consumer behavior by shedding
light on personal traits and product features that can shape anchoring responses. These
findings will help researchers and decision makers by suggesting that a product’s
monetary valuation is based not only on its perceived value and that it can be affected by
variables that were not related. Regarding managerial implications, with the anchoring
phenomena and its determinants uncovered, consumers can become conscious of this
process to overcome this behavioral bias.
Keywords: Willingness to pay; Anchoring; Hedonic consumption; Consumer self-
confidence, Consumer behavior.
RESUMO
Estudos anteriores sobre ancoragem evidenciam que valores arbitrários podem
influenciar o julgamento humano e a tomada de decisões, mesmo em situações de
avaliação de um produto e de estimativa de seu preço máximo. Esta tese sugere que o
viés de ancoragem não representa um comportamento universal e busca identificar como
a autoconfiança do consumidor e o valor hedônico dos bens podem moldar a
suscetibilidade dos consumidores ao efeito de ancoragem na sua disposição a pagar.
Adotou-se uma abordagem quantitativa para demonstrar a relação entre a predisposição
a pagar (variável dependente), a autoconfiança do consumidor (variável independente),
o valor hedônico e a âncora como preço de referência (variáveis independentes
manipuladas). Os dados foram coletados em experimentos realizados com 350
estudantes internacionais na cidade de Nova York. Para este estudo, foram consideradas
regressões lineares e múltiplas e foram testados dois modelos de moderação
(moderação simples e um modelo de interação de tripla) com o intuito de responder as
hipóteses. Esta tese oferece evidências de que o valor hedônico dos produtos modera a
relação entre ancoragem e predisposição a pagar. Os resultados também apontam para
uma interação tripla entre o valor hedônico de um produto, a autoconfiança do
consumidor e o efeito de ancoragem na predisposição a pagar. No geral, esta tese
contribui para a literatura do comportamento do consumidor, ilustrando que traços
pessoais e características do produto podem moldar as respostas de ancoragem. Essas
descobertas ajudarão pesquisadores e tomadores de decisão ao sugerir que a avaliação
monetária de um produto é baseada não apenas em seu valor percebido, mas que
também pode ser afetada por variáveis não relacionadas. Em relação às implicações
gerenciais, com os fenômenos de ancoragem e seus determinantes elucidados, os
consumidores podem se tornar conscientes desse processo a fim de superar esse viés
comportamental.
Palavras-chave: Disposição a pagar; Ancoragem; Consumo hedônico; Autoconfiança
do consumidor; Comportamento do consumidor.
LIST OF TABLES
Table 1: Prior studies on dimensions of CSC related to the WTP and/or price anchoring
.................................................................................................................................. 3030
Table 2: Declared levels of consumer self-confidence by gender. ............................ 4545
Table 3: Number of respondents ................................................................................... 47
Table 4: Manipulated anchor values.............................................................................. 48
Table 5: Descriptive statistics .................................................................................... 5353
Table 6: WTP average .............................................................................................. 5353
Table 7: Statistics regression – hedonic value (pen) .................................................... 57
Table 8: Statistics regression – hedonic value (bathtub) .............................................. 60
Table 9: Moderated moderation model predicting anchoring effect on WTP from hedonic
value and CSC (bathtub) ............................................................................................... 63
Table 10: Synthesis of results ....................................................................................... 64
LIST OF FIGURES
Figure 1: The relationship between consumers’ hedonic and utilitarian value, customer
satisfaction and behavioral intentions ……………………………………………………....23
Figure 2: The anchoring adjustment process ................................................................ 25
Figure 3: Methods for measuring the WTP .................................................................... 35
Figure 4: Expected conceptual diagram for CSC, anchoring and the WTP ................... 37
Figure 5: Expected statistical diagram of the model comparing CSC, anchoring and the
WTP .............................................................................................................................. 38
Figure 6: Expected results for hedonic value moderating anchoring effects on the WTP
...................................................................................................................................... 39
Figure 7: Expected conceptual diagram for hedonic value, anchoring and the WTP .... 40
Figure 8: Expected statistical diagram of the model comparing hedonic value, anchoring
and the WTP ................................................................................................................. 41
Figure 9: Summary of expected conceptual diagram for simple moderation hypotheses
on CSC and hedonic value ............................................................................................ 43
Figure 10: Expected conceptual diagram for the model comparing CSC, hedonic value,
anchoring and the WTP ................................................................................................. 43
Figure 11: Expected statistical diagram for the model comparing CSC, hedonic value,
anchoring and the WTP ................................................................................................. 43
Figure 12: Hedonic-utilitarian versions of products tested ............................................. 50
Figure 13: Anchor, hedonic value and WTP .................................................................. 54
Figure 14: WTP, anchor and hedonic value (pen) ......................................................... 55
Figure 15: Moderation effects – hedonic value (pen) .................................................... 58
Figure 16: WTP, anchor and hedonic value (bathtub) ................................................... 59
Figure 17: Moderation effects – hedonic value (bathtub) ............................................. 61
LIST OF ABBREVIATIONS
ALP Ariely, Loewenstein, and Prelec
BDM Becker-DeGroot-Marschak
BHR Bearden, Hardesty, and Rose
CAPES Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
CSC Consumer self-confidence
CSF Consideration set formation
CCT Consumer Choice Theory
ESPM Escola Superior de Propaganda e Marketing
IA Information Acquisition
MI Marketplace interfaces
PK Persuasion Knowledge
SO Social Outcome
WTP Willingness to pay
TABLE OF CONTENTS
1. Introduction .......................................................................................................................... 14
1.1 Problem statement .......................................................................................................... 16
1.2 Purpose ............................................................................................................................. 17
1.3 Objectives ......................................................................................................................... 17
1.4 Research questions ........................................................................................................ 18
1.5 Expected theoretical and managerial contributions ................................................... 18
1.6 Definitions of terms .......................................................................................................... 19
1.7 Thesis outline ................................................................................................................... 19
2. Literature review ................................................................................................................. 21
2.1 Hedonic consumption ..................................................................................................... 21
2.1.1 Hedonic value .................................................................................................... 21
2.2 Anchoring .......................................................................................................................... 24
2.3 Consumer self-confidence ............................................................................................. 28
2.4 Consumer choice theory ................................................................................................ 31
2.5 Degree of familiarity ........................................................................................................ 32
2.6 Price and the WTP .......................................................................................................... 33
2.7 Research gaps ................................................................................................................. 36
2.8 Proposed conceptual model and hypotheses ............................................................. 36
3. Methodology ........................................................................................................................ 44
3.1 Participants ....................................................................................................................... 44
3.2 Procedure ......................................................................................................................... 45
3.3 Measures .......................................................................................................................... 46
4. Results ................................................................................................................................. 52
4.1 Preliminary data analysis ............................................................................................... 52
4.2 Study 1 (pen) .................................................................................................................... 55
4.3 Study 2 (bathtub) ............................................................................................................. 58
4.4 The interaction of anchor, CSC, hedonic value and WTP ........................................ 61
4.5 Research synthesis ......................................................................................................... 64
5. Discussion and final considerations ................................................................................ 66
5.1 The impact of hedonic value on the strength of anchoring the WTP ...................... 66
5.2 The impact of CSC on the strength of anchoring the WTP ...................................... 67
5.3 The impact of CSC and hedonic value on the strength of anchoring the WTP ..... 67
5.4 Theoretical and managerial implications ..................................................................... 68
5.5 Limitations ......................................................................................................................... 68
5.6 Future research ................................................................................................................ 69
References .................................................................................................................................. 71
Appendices .................................................................................................................................. 80
Appendix A: Bearden, Hardesty and Rose scale (2001) of CSC ................................... 80
Appendix B: Survey on anchoring ....................................................................................... 81
Appendix C: Linear Regression - pen ................................................................................. 88
Appendix D: Simple moderation CSC - pen ...................................................................... 89
Appendix E: Simple moderation: hedonic value for pen .................................................. 91
Appendix F: Linear Regression - bathtub ........................................................................... 93
Appendix G: Simple moderation CSC – bathtub ............................................................... 94
Appendix H: Simple moderation: hedonic value for bathtub ........................................... 96
Appendix I: Moderated moderation model - pen ............................................................... 98
Appendix J: Moderated moderation model - bathtub ..................................................... 100
Appendix K: WTP, CSC, and Familiarity (average) ........................................................ 103
14
1. Introduction
Consumer behavior is a multidisciplinary area of increasing relevance that intersects with
several fields, including economics, marketing, finance and psychology. According to
Nagle and Holden (2002), the most critical aspects of consumer behavior include product
prices and consumer judgements.
The Neoclassical Theory of Consumer Choice assumes that consumers are perfectly
rational and have complete information from which to formulate their judgements and
evaluations. However, contemporary research has confirmed that consumers do not
always satisfy neoclassical assumptions due to cognitive biases that can jeopardize the
decision-making process, expanding the discussion to other areas of research.
Prior studies on consumer behavior have identified that price perceptions are influenced
not only by consumers’ perceived value of products (e.g., Woodall, 2003), but also by
reference prices or even random numbers that consumers have in mind when evaluating
a product (e.g., Kahneman & Tversky, 1974; Krishna, A., 1991). This phenomenon
reflects a cognitive bias known as the anchoring effect. The anchoring effect can be
defined as the first piece of information decision makers have access to, which will
influence, as an “anchor”, the future choices they make (Furnham & Boo, 2011).
Studies on anchoring have recently attracted widespread attention. This is attributed not
just to its utility but rather on its robust effect sizes. According to Alevy, Landry and List
(2015), price approximation can be influenced by inappropriate numerical baits.
Nonetheless, studies by Sugden, Zheng and Zizzo (2013) highlight that the anchoring
effect may not necessarily impact consumer behavior. While it is true that the anchoring
effect may have far-reaching consequences, the cognitive workings that trigger the effect
remain unknown (Drolet & Wood, 2017).
Prior research has demonstrated that price estimations can be affected by irrelevant
numerical baits (e.g., Kahneman & Tversky, 1979; Krishna, A., 1991; Ariely, Loewenstein,
& Prelec, 2003; Simonson & Drolet, 2004; Adaval & Wyer Jr, 2011; Fudenberg, Levine,
& Maniadis, 2012; Sugden, Zheng, & Zizzo, 2013; Alevy, Landry, & List, 2015; Hu & He,
15
2018). However, studies have not reached to a congruent outcome, achieving mixed
results, suggesting that some conditions can enhance or diminish anchoring phenomena,
almost reducing it to zero.
An experiment in which participants were randomized within a marketplace established
that the anchor effect does not affect experienced individuals (Alevy, Landry and List,
2015). However, since the experiment was a reflection of the market environment,
participants could have been motivated by products with functional value as opposed to
those with hedonic value. This therefore creates the possibility that anchoring effects may
be manipulated based on product attributes. Sugden, Zheng and Zizzo (2013) point to
the scarcity of research focused on factors that may trigger the power and degree of
anchoring effects. According to Nobuyuki (2018), experience with a product may also
determine a consumer’s willingness to buy it.
Although several studies have previously examined anchoring effects on price evaluation,
ongoing studies have been devoted to evaluating underlying cognitive motivations in
comprehending the association between anchoring effects and individual characteristics
or product attributes (Plott & Zeiler, 2005). This thesis is devoted to appraising the
influence of consumer self-confidence and hedonic value on anchoring processes.
According to a study by Plott and Zeiler (2005), respondents that were well-informed of
this subject were the least manipulated by anchors when approximating this value.
By contrast, Lee et al. (2016) have established that expert knowledge does not inoculate
consumers against the anchoring effect. According to Jürgestnsen and Guesalaga
(2018), hedonic consumption, hastiness, consumer self-confidence and the pursuit of
status and happiness may trigger the purchasing of hedonic products among young
consumers. Another study by Ahmetoglu et al. (2014) suggests that the scale of the
anchoring effect on price evaluation is determined by the knowledge that consumers have
regarding a product’s attributes. Nevertheless, Hu, Jiang and He (2018) affirm that biases
emerge among consumers when making decisions simply because adjustments
concerning an anchor value as a point of reference are usually inadequate. For instance,
a popular handset brand has a substantially stronger anchoring effect on buyers than a
new brand attempting to penetrate the market.
16
Quintal, Phau, Sims and Cheah l. (2016) indicate that extrinsic factors may also be
responsible for the anchoring effect. Studies on anchoring have indicated that people are
often compelled to make decisions based on external forces, and thus the relevance of
the decision does not seem to matter. Comprehending how consumers integrate or
disregard random references when making decisions can contribute to the development
of a critical pricing approach to understanding consumer behavior.
For instance, Sailors and Heyman (2019) demonstrate that the scale of anchoring effects
is positively related to the semantic association between comparison and estimation
objects, which runs in tandem with preferences developed out of convenience.
The fact that past studies discussing subjective values and consumer appraisal have not
profoundly explored consumer self-confidence and how human attributes manipulate the
degree of anchoring reasserts the significance of this thesis. Although a variety of findings
discuss the causal mechanisms of anchoring effects, this has created a new opportunity
for research that should help explain anchoring effects in more detail due to at least two
factors. The central objective of this thesis is to examine whether consumer self-
confidence and hedonic value affect the power of anchoring and the willingness to pay
(WTP).
1.1 Problem statement
When making decisions, people tend to depend heavily on the little information they have
at their disposal. The cognitive bias that comes with a reliance on the first piece
information encountered can adversely impact decisions that people may end up making.
Consumers, for instance, use anchoring to determine whether to make a purchase or not.
In most cases, since anchoring bias favors the first piece of information received, people
can be easily lured into buying products that they do not necessarily need. As a result,
they may become critical of themselves. Inasmuch as anchoring bias has far-reaching
consequences, researchers have not established the driving force behind it (Sugden et
al., 2013).
17
According to Sugden et al. (2013), there has been little systematic investigation of the
determinants that might affect the power and strength of anchoring effects. Most studies
do not consider consumers’ personality traits when investigating price-anchoring
intensity, suggesting that there is a gap in consumer behavior literature.
Even though there are numerous previous studies regarding anchoring effects in price
evaluation, the existent investigation focus on reasons for the psychological phenomena,
rather than understanding the relationship between anchoring effects and personal traits
or product characteristics (e.g., Brown & Gregory, 1999; Peters, Slovic, & Gregory, 2003;
Plott & Zeiler, 2005).
1.2 Purpose
The thesis intends to determine how consumer self-confidence and hedonic value prompt
anchoring in consumer purchasing. In particular, the thesis aims to investigate whether
internal and external factors may inform consumers’ decision-making processes. The
thesis also examines how consumers can overcome the anchoring effect. Although
previous studies have evaluated the anchoring effect in terms of authority and aesthetic
attributes of products, there has been a dearth of findings on this theme in relation to
market-place environments (Quintal et al., 2016). This research will therefore broaden the
subject matter by endeavoring to determine how confidence and product hedonistic value
can impact consumer decision making.
1.3 Objectives
The main objective of this thesis is to examine whether consumer self-confidence and the
hedonic value of products affect the role of anchoring. As secondary objectives, the thesis
explores the relationship between these two factors (hedonic value and consumer self-
confidence) in the heuristic bias of anchoring given that how consumers incorporate or
18
ignore arbitrary references plays an essential role in the development of pricing
strategies. In other words, this thesis assesses and tests the influence of hedonic value
and consumer self-confidence in purchasers’ susceptibility to price-anchoring.
1.4 Research questions
The key objective of this thesis is to answer whether consumer self-confidence and
hedonic value affect the power of anchoring. To do this, four issues are examined. First,
the impact of consumer self-confidence on the strength of anchoring is investigated.
Then, in the same way, how the hedonic value of a good may affect the degree of
anchoring is verified.
Next, this thesis speculates on the relationship between consumer self-confidence,
hedonic value and anchoring. The two issues explored concern the impact of consumer
self-confidence on the hedonic value of a good and the effect of the hedonic value of a
good on the degree of consumer self-confidence.
1.5 Expected theoretical and managerial contributions
While anchoring bias shows that people make decisions based on exterior forces even
when they are totally inappropriate in terms of their judgements, the findings of this thesis
will help future studies determine not just how consumers use external reference points
when making decisions but also how anchoring plays a fundamental role in pricing. The
impact of unrelated reference prices on a consumer’s WTP for a product is relevant for
researchers and decision makers (Adaval & Wyer Jr 2011) especially because it suggests
that a product’s monetary valuation is not based only on the perceived value.
Nonetheless, as managerial contributions, the findings of this study may also help identify
ways of reversing the anchoring bias.
19
1.6 Definitions of terms
The most important concepts explored in this thesis include the anchoring effect,
willingness-to-pay, consumer self-confidence and hedonic value. To be better situated
within the consumer behavior literature, it is important to define the meaning of these
terms.
According to Koças and Dogerlioglu-Demir (2013), anchoring occurs when individuals’
numeric decisions are affected by a reference number activated before a decision is
made.
The willingness to pay (WTP) is the cost a certain buyer is willing to pay for a product or
service (Le Gall-Ely, 2009). In other words, the WTP denotes the maximum price a
consumer is willing to pay for a given product.
Consumer self-confidence reflects subjective evaluations of one’s ability to generate
positive experiences as a consumer in the marketplace (Adelman, 1987) and, according
to Bearden, Hardesty, and Rose (2001, p.122), it is defined as “the extent to which an
individual feels capable and assured with respect to his or her marketplace decisions and
behaviors”.
Hedonic value, on the other hand, represents the degree of pleasure merchandise
provides. According to Khan, Dhar and Wertenbroch (2004), hedonic goods are
multisensory, and their consumption is fun, pleasurable and exciting. The consumption of
these products enhances emotional pleasure and evokes feelings of happiness within the
consumer (Khan; Dhar & Wertenbroch, 2004, p. 4).
1.7 Thesis outline
The thesis includes five chapters. The first chapter introduces the topic by describing its
background. It also presents the study’s research questions, objectives, purpose
statement, problem statement and expected contributions. The second chapter provides
a critical literature review on consumer self-confidence, hedonic consumption, consumer
20
choice and the WTP. The third chapter describes the quantitative methodology applied,
involving the method, sampling, data collection and analysis. The fourth chapter presents
the study results, and the last chapter provide a discussion and conclusion. The
discussion incorporates the study’s results as supported by the literature review.
21
2. Literature review
Studies present divergent findings regarding anchoring effects. Whereas some studies
have found significant effects of anchoring, others have found associations between
anchoring price and consumer’s WTP. While Alevy, Landry and List (2015) justify that no
association can be a result of type of anchoring effect, these differences may be caused
by various factors. For instance, Alevy, Landry and List (2015) investigated peanuts and
functional commodities, concluding that these products might influence buyers’
vulnerability to anchoring effects. Sugden, Zheng and Zizzo (2013) also acknowledge the
insufficiency of studies that explore determinants of the power of anchoring effects.
2.1 Hedonic consumption
In fact, a variety of studies do not consider the consumption of a product as an indicator
of anchoring power. However, a study by Koças and Dogerliouglu-Demir (2014) proposed
that a product-based view can be used to investigate the effects of various products on
anchoring in terms of the WTP. Hedonic products are multisensorial, and their
consumption offers enjoyment, pleasure and fun (Khan, Dhar and Wertenbroch, 2007).
In addition, the consumption of hedonic goods enhances consumers’ levels of emotional
excitement and triggers happiness. Unlike hedonic consumption, utilitarian consumption
relates to basic products essential to survival and stimulated by the functional aspect such
as food (Khan, Dhar and Wertenbroch, 2007).
Furthermore, the categorization of utilitarian products is becoming more sophisticated due
to technological development; societies have embraced computers, phones and so forth
(Khan, Dhar and Wertenbroch, 2007). The classification of utilitarian-hedonic
consumption greatly affects consumer behavior. Essentially, products’ features influence
consumers’ decisions regarding how to choose from available options. Okada (2005)
reported that consumers are more likely to consume hedonic products than utilitarian
products, especially when they are presented individually. Nonetheless, when utilitarian
22
products and hedonic goods are presented together, consumers are likely to select
utilitarian goods while trying to justify their judgments (Okada, 2005).
According to Kapferer (2016), hedonic consumption involves one or more objects and a
service. The authors continue to assert that a hedonic product is always accompanied by
a service if not an expression of a service (Sun et al., 2017). These products are also
elegant, exhibiting exceptional richness in tone and aesthetic features (Bennett et al.,
2018). According to Sun et al. (2017), hedonic consumption is related to a dream and
relates not to needs but is rather motivated by the desire to belong to a higher class (Seo
and Buchanan-Oliver, 2015).
Saran, Roy and Sethuraman (2016) contend that sentiments, motivations, endorsement
and innuendos serve as strong motivators for hedonic consumption. In addition, Ding and
Tseng (2015) affirm that the social and psychological nature of hedonic goods is seen as
the central factor that influences consumers’ decisions. According to Kim and Johnson
(2015), many anthropologists and sociologists have indicated that hedonic consumption
is employed as a semiotic maneuver, suggesting the influence of rank and competition,
harmony and community, distinctiveness or exclusion. Consumers buy hedonic products
not for their utility but due to their connotations (Kim and Johnson, 2015).
2.1.1 Hedonic value
The hedonic value is defined as the value a customer receives based on the subject
experience of fun and playfulness (Babin et al. 1994). It is a dimension of consumer
perceived value associated with senses, pleasures, feelings, and emotions (Chen and
Hu, 2010).
The definition of value is multiple, inasmuch as researchers interpret it differently. In
scientific literature it is agreed that the conceptualization of value could vary dependably
on the context of the performed research (Babin et al., 1994). Rintamaki et al., (2006)
support this opinion and claim that the variety of perceived value definitions should not
surprise because all marketing researches propose their results after performed
23
researches and analysis in different environments. According to Rintamaki et al., (2006)
and Chen and Hu (2010) the most universe conceptualization and widely used in scientific
literature is that value represents all quantitative, qualitative, subjective, objective factors
that create and / or determine buying experience. Value is provided by the whole shopping
experience, not simply by product acquisition. This definition recognizes explicitly value's
subjective nature and this consideration explains the variety of suggested value
definitions in the scientific literature
According to Kazakeviciute and Banyte (2012), utilitarian and hedonic values of shopping
have huge impact on buying process in the. The utilitarian value of consumer buying is
related to the usage and represents a rational purchase of a particular product or service,
while the hedonic value includes various different purchasing reasons that are related to
pleasure seeking (Kang & Park-Poaps, 2010). Ryu et al., (2010) researched the
relationship between consumers’ perceived hedonic and utilitarian values and behavioral
intentions. The results of this research are shown in Figure 1.
Figure 1: The relationship between consumers’ hedonic and utilitarian value, customer
satisfaction and behavioral intentions
Source: Adapted from Ryu et al., 2010.
24
Ryu et al., (2010) claim that both the consumers’ perceived utilitarian and the hedonic
values significantly influence consumers’ satisfaction and future intentions. Satisfaction
plays an important role in formation or change of future intentions. Also, it is known that
hedonic and utilitarian values directly and indirectly influence on consumer buying
intentions but the impact on consumers’ satisfaction is stronger on hedonic value.
Virvilaite & Saladiene (2012) notice that some consumers have stronger expressed
hedonic value and therefore expectations of shopping and behavior could vary. These
arguments confirm the single-mindedness of further theoretical studies of the hedonic
value’s influence on consumers’ behavior.
2.2 Anchoring
Evidence shows that anchoring takes place when individuals’ choices are stimulated prior
to making decisions (Koças and Dogerlioglu-Demir, 2014). Available information
considerably facilitates decision making. For several decades, numerous theories on
anchoring effects have been developed while existing principles have remained relevant.
Recognition of the mechanisms of anchoring effects requires a thoughtful analysis of
suitable perceptions of emotional development using four different approaches.
Anchoring and adjustment theory acts as the foundation for estimating value, which is
biased by the anchoring perspective (Lieder, Griffiths, Huys, and Goodman, 2017). This
theory assumes that the mind performs probabilistic inference by sequential adjustment
inferring that the less time a person invests into generating an estimate, the more biased
her/his estimate will be towards the anchor. As illustrated in Figure 2, the relative
adjustment increases with the number of adjustments. When the number of adjustments
is zero, then the relative adjustment is zero and the prediction is the anchor regardless of
how far it is away from the correct value. However, as the number of adjustments
increases, the relative adjustment increases, and the predictions become more informed
by the correct value.
25
Figure 2: The anchoring adjustment process
Source: adapted from Lieder et al., 2017.
The anchoring bias, according to this theory, corresponds to the difference between the
correct estimation and the adjusted answer, which is determined by the relative
adjustment or by the proposed anchor’s value and individual adjustment. The relative
adjustment is equal to the tangent of α, which corresponds to the formula
adjustment/distance.
Conversely, Mussweiler’s selective accessibility theory is based on rational bias
confirmation (Mussweiler and Schneller, 2003). Following this theory, researchers have
investigated whether the right response is associated with the anchor value. The specific
reference involved triggers views on product attributes and costs. Hence, attributes
accessible to memory are recollected. Mussweiler’s theory (2003) reflects the prevailing
contemporary view of anchoring and demonstrates that anchoring effects are stimulated
by similar information of the presented anchor. It thus functions as a confirmatory search
strategy that allows individuals to emphasize similarities between the anchor and target.
Attitudinal perspective theory focuses on numeric anchoring resulting from attitude and
persuasion. According to this theory, individuals process numeric anchors and persuasive
26
information in the same manner (Wegener, Petty, Blankenship and Detweiler-Bedell,
2010). Furthermore, attitudinal perspective theory defines the anchor as providing
persuasive information. This is supported by Zhang and Schwarz (2013) who discovered
that anchors with accurate values (e.g., 7342) confer stronger anchoring effects than
those with round values (e.g., 7500). In other words, individuals depend on the accuracy
of the presented anchor in evaluating the quality of anchored information. As
demonstrated by Wegener et al. (2010), anchors from credible sources must have
considerable anchoring effects.
Finally, Frederick and Mochon (2012) scale distortion as another theory of anchoring.
This theory indicates that humans’ decisions are made based on the causal response
scale. Nonetheless, anchor presentation does not change individuals’ subjective
representations of given items, as rather it serves as a platform for informing them of the
scale adopted to make decisions. The theory suggests that anchoring manipulates the
scale upon which decisions are made. However, Frederick and Mochon’s theory of scale
distortion is not consistent with how anchoring affects decisions. According to the theory
of anchoring-and-adjustment, the anchor serves as the basis for an elaborative iteration.
On the other hand, selective accessibility theory states that the anchor acts as the
predictor that triggers the hypothesis test to improve the consistency of anchoring
information.
While these theories adopt different approaches, they all show that anchoring serves as
a source of information that affects decision making. Furthermore, while anchoring seems
to be a strong psychological concept, not everyone is affected by anchoring references
in the same way.
Johnson and Schkage (1989) were pioneers of experimental research on anchoring
effects on consumer valuation. In addition, the authors developed the platform upon which
experiments on anchoring effects are currently performed. While variations exist, the
experimental design is based on the adoption of a certain procedure. It begins with an
anchoring task where the participants respond to Yes or No questions about their WTP
for produce at an arbitrary price. This is followed by a valuation task where the participants
determine the highest prices they would pay for a certain good.
27
The anchoring effect is rooted in the fact that humans are sensitive to information, which
affects their decisions as determined by external factors. The first experiment conducted
by Alevy, Landry and List (2015) provided no proof to show that anchors influence
experienced agents, leading to doubts regarding whether anchoring effects can be
neutralized or enhanced based on external cues. A previous study demonstrated that
respondents familiar with the number of physicians listed in their phonebooks were less
affected by anchors. Nevertheless, the subjects were asked to report their level of
knowledge immediately after estimates were made, implying that decisions made reflect
confidence in estimates and not in the given topic. Hence, it is not clear whether these
results demonstrate that a high level of knowledge is correlated with minor anchoring
effects.
Sugden, Zheng and Zizzo (2013) explored the impacts of various anchors on the WTP
and found that anchor effects are stronger when the value is acceptable (p. 25). On the
other hand, Ariely, Loewenstein, and Prelec (2003) allege that buyers’ preferences are
marked by considerable levels of unpredictability (p.73). Furthermore, the authors
discovered a strong association between unpredictability cues and the WTP. However,
to assess the robustness of Ariely, Loewenstein, and Prelec’s (2003, p.74) experiment,
Fudenberg, Levine, and Maniadis (2012) duplicated it. They found a weak association
between anchoring effects and the WTP (p. 132). Generally, previous experiments on
anchoring effects on the WTP have found mixed results due to posing questions that
inform decision making. As such, based on CSC and anchoring effect procedures, this
thesis hypothesizes that consumer self-confidence and hedonic value affect the power of
anchoring.
28
2.3 Consumer self-confidence
Consumer self-confidence (CSC hereafter) refers to the subjective assessment of buyers’
abilities to have positive experiences (Jürgensen and Guesalaga, 2018). Consumers with
less CSC have a tendency to make unpredictable decisions due to environmental factors
(Qiu, Cranage and Mattila, 2016). Similarly, individuals make impractical judgments,
which ultimately influence consumer decisions and their WTP (Parker and Stone, 2014).
Furthermore, individuals with less self-confidence are more likely to make inconsistent
decisions than those with high self-confidence (Qiu, Cranage and Mattila, 2016).
Nonetheless, studies on how CSC influences anchoring effects are insufficient. Initially,
measures for self-esteem taken from psychology were used to investigate the role of self-
confidence in marketing and consumer buying behavior (Balabanis and Diamantopoulos,
2016). This view conflicts with that of Bearden, Hardesty and Rose (2001), who affirm
that the CSC rule may be utilized to quantify how consumers behave. This is largely the
case because consumer self-confidence reflects an individual’s subjective assessment of
his or her ability to act in certain manner in the marketplace.
Bearden, Hardesty and Rose (2001) allege that CSC can be defined as the degree at
which individuals feel that they can make informed decisions and have the power to buy
a product. Nonetheless, CSC is considered a complex, yet secondary concept closely
correlated to consumer phenomena relative to dispositions such as self-esteem
(Bearden, Hardesty and Rose, 2001). Again, CSC reflects a slightly stable self-appraisal
that is readily available to buyers as a result of the universality of their activities in
marketplace.
The CSC scale covers two main dimensions: consumer protection from being deceived
or misled and the ability to make informed consumer decisions (Appendix A). The latter
element refers to the capacity for consumers to make effective buying decisions while the
former denotes the customer’s ability to safeguard against unfair treatment, misleading
information or deceit (Bearden, Hardesty and Rose, 2001). Behavioral studies also
suggest that individuals have impractical perceptions regarding their knowledge and
29
confidence in such knowledge, which affects consumer decision making and
consequently the WTP (Parker and Stone, 2014).
Bearden, Hardesty and Rose (2001) allege that these two dimensions can be divided into
six subcategories. Information Acquisition (AI) refers to consumer confidence in terms of
acquiring and processing market information (p. 123). Even though the importance of
marketplace information varies among consumers based on product categories and
levels of experience, the capacity to access and process information and relevant content
serves as a useful predictor of informed decisions making. Again, varying degrees of
confidence in acquiring and processing marketplace information can help illuminate
differences in pre-purchase content searches even for premium products (Bearden,
Hardesty and Rose, 2001).
The subcategory of Consideration Set Formation (CSF) focuses on consumers’
capacities to identify appropriate options such as products, brands and buying venues
(Bearden, Hardesty and Rose, 2001). Under the CSF dimension, consumers compare
different brands and choose one that is relevant and commonly referred to as a
consideration set. In addition, labeling helps show how consumers may opt for
alternatives from a wide range of categories (Bearden, Hardesty and Rose, 2001).
Therefore, assumptions are made that consumers have varied confidence levels with
regard to constructing a consideration set of alternatives to fulfill consumption objectives.
The Persuasion Knowledge (PK) and Marketplace Interface (MI) subcategories are
associated with decision-making outcomes. Essentially, PK refers to buyers’ confidence
in knowledge regarding strategies used by companies to persuade consumers (Bearden,
Hardesty and Rose, 2001). As a dimension of CSC, PK represents buyers’ confidence in
their capacity to comprehend marketers’ strategies and in dealing with them. For these
reasons, PK denotes that CSC refers to consumers’ perceived levels of capacity to
recognize the cause and effect associations that shape marketers’ behaviors and abilities
to cope with efforts made to persuade them to make purchases.
On the other hand, the MI demonstrates consumers’ levels of confidence to stand up for
their rights and to express their views when dealing with other parties in the market such
as salespeople or store workers (Bearden, Hardesty and Rose, 2001). Consumers with
30
high levels of CSC are likely to present their views while engaging with salespeople.
Conversely, such consumers are assured in their ability to interact with store employees
or salesperson and call for product demonstrations or for substitutes for defective
products (Bearden, Hardesty and Rose, 2001). The last two subdimensions are the Social
Outcome (SO) and Personal Outcome (PO), which reflect social and personal benefits
and drawbacks.
Further literature was examined to relate the effects of each subdimension from the BHR
scale, available in Appendix A, regarding its relationship to the WTP and/or anchoring
effects in consumers’ pricing valuations as shown in Table 1.
Table 1: Prior studies on dimensions of CSC related to the WTP and/or price anchoring
CSC CONSTRUCT INFLUENCE ON THE WTP OR ANCHORING Information acquisition (IA) Chapman and Johnson (1994) Information availability Wilson et al. (1996) Information accessibility Mukherjee and Hoyer (2001) Simonson and Drolet (2004)
Consideration set formation (CSF) Alevy, Laundry and List (2015)
Personal outcome (PO) Customer satisfaction Alba and Hutchinson (2000) Subjective probability of winning Homburg, Koschate and Hoyer (2005)
Social outcome (SO) No relevant study relating SO to WTP or
anchoring was found
Persuasion knowledge (PK) Perceived risk No relevant study relating PK to WTP or
Perceived price dispersion anchoring was found
Marketplace interfaces (MI) Jorgensen and Syme (2000) Protest response Meyerhoff and Liebe (2006) Lo and Jim (2015)
Source: the author with data collected from prior studies.
31
2.4 Consumer choice theory
Consumer choice theory (CCT) demonstrates that consumers are rational beings with not
only thoughtful but also reliable decision-making processes (Skouras, Avlonitis, &
Indounas, 2005). According to Tuck and Riley (2017), consumer choice theory
emphasizes why individuals purchase things. People often buy hedonic products due to
the high levels of satisfaction that these products confer provided that costs remain within
their budgets. Economists almost assume that buyers behave rationally in the sense that
their preferences are stable and self-consistent and in that psychic contentment or
functions obtained through their purchases can be optimized. The optimization of utility is
often subject to certain limitations, key among which is ostensibly the income or
purchasing power at the buyer’s disposal. However, other limitations include false
information and searching costs that lead to functional optimization of a constrained
nature. Subsequently, consumers’ reactions to prices are referred to as restricted utility
optimization.
Drolet and Wood (2017) contend that at the core of CCT are three propositions about
human nature. The first hypothesis states that consumers decide to purchase items
based on predetermined decisions in terms of what would make them happy. However,
consumers make unlike decisions when faced with a comparable anomaly, as the
comparative significance of each alternative varies among individuals (Train, 1993). For
this reason, it is imperative to comprehend how consumers make their procurement
decisions and the variables most relevant to this process. Since buyers cannot process
all accessible information when making choices, their decisions are often experiential.
According to Yacan and Hazen (2016), CCT supposes that regardless of how much a
person purchases, one cannot achieve complete satisfaction once. Happiness will often
come as a result of consuming a little bit more. Nonetheless, CCT fails to provide an
accurate account of how people make choices. In fact, CCT has necessitated the
development of a new discipline known as behavioral economics to employ outcomes
from psychology to disprove the assumptions that underlie CCT (Campbell, 2016).
Moreover, Drolet and Wood (2017) indicate that people tend to comprehend available
32
options and the relative cost. While they consider alternatives, they opt for the most
preferred option. A wide range of options comes with a list of products that consumers
may choose from. This is also known as utility theory. According to Campbell (2016),
utility theory is employed to understand consumers’ behaviors in terms what they like
most.
2.5 Degree of familiarity
Previous research shows that the degree of familiarity is shaped by consumer awareness
of a product (Welsh and Begg, 2017). Previous knowledge is strongly correlated with
processed information, which affects the precision of buyers’ choices. This suggests that
previous knowledge helps consumers perceive product features. For instance, relative to
knowledgeable consumers, those who are less familiar with particular products may take
more time when assessing attributes to consider when deciding whether to make a
purchase.
Similarly, Beck and Prügl (2018) allege that those with previous knowledge of or familiarity
with a product use this information when making decisions as opposed to consumers
without such knowledge. In this case, inexperienced buyers need more time to develop
knowledge and familiarity before making choices. According to Oshri et al. (2018),
previous knowledge and familiarity are essential to decision making. For instance, they
present consumers with relevant information while making purchases.
Familiarity can be defined as “the number of product related experiences that have been
accumulated by the consumer” (Alba & Hutchinson, 2000, p. 411). These experiences
come from exposure of advertising, interactions with salespersons, word of mouth
communications and consumption according to Jackie Tam (2008).
Mieres et al. (2006) believe that familiarity stimulates buyers to judge brands as of high
quality when they lack experience about certain products.
Sheau-Fen et al. (2012) report that familiarity is the most significant factor that shapes
consumer purchase intentions through perceived quality. In addition, Diallo et al. (2013)
33
have found evidence of direct effects of familiarity on purchase intentions, which means
that when people are familiar with private labels, they will consider purchasing from them.
2.6 Price and the WTP
The WTP plays an important role in consumer choices and pricing. Pricing strategies
involve setting the cost of a product based on how much consumers are WTP. However,
predicting demand for products in the market necessitates an understanding of buyers’
responses to pricing plans. The WTP or reservation price is the cost a certain buyer is
willing to pay for a product or service (Le Gall-Ely, 2009). The WTP was first introduced
in the economics literature roughly a century ago. Furthermore, the WTP concept was
designed to serve as a basis for determining the cost of public goods and services (Le
Gall-Ely, 2009). Therefore, the WTP denotes the maximum price a consumer is willing to
pay for a given product. In this respect, the reservation price denotes the difference
between buying or not buying a product.
The WTP is the value that a product is worth to individual consumers. In this study, the
WTP or reservation cost denotes the cost at which consumers are indifferent to
consuming or not consuming a given product. In the event that a product is sold at a lower
price than the WTP price, a buyer saves money, increasing the difference between the
amount paid and the WTP. The measurement of the WTP allows for the computation of
demand in accordance with prices to generate the best margins (Le Gall-Ely, 2009).
Past research demonstrates that several methods can be used to measure the WTP on
a characteristic and conceptual basis but with methodological implications (Breidert,
Hahsler and Reutterer, 2006). Miller et al. (2011) state that approaches commonly used
to measure the WTP involve direct and indirect techniques. The direct method requires
consumers to provide their WTP for a given product using open-ended questions.
Similarly, Breidert, Hahsler and Reutterer (2006) allege that the direct method involves
the use of surveys where results are referred to as stated preferences.
34
On the other hand, indirect techniques use choice-based conjoint analyses whereby the
WTP is estimated with respect to consumers’ choices of products made among different
options and nonchoice alternatives (Miller et al., 2011). Conversely, the indirect method
is used to gather relevant data from price responses (Breidert, Hahsler and Reutterer,
2006). Indirect methods are also known as revealed preference methods. Nonetheless,
none of the above methods are accurate. Previous works demonstrate that indirect and
direct techniques can produce inaccurate results for technical and psychological reasons
(Verlegh, Hendrik and Wittink, 2002).
Essentially, both techniques assess buyers’ hypothetical views rather than their actual
opinions. As such, they produce biased results (Miller et al., 2011). For a direct method
to uncover the actual WTP, buyers must purchase a product based on the expected price.
The WTP can be determined using indirect methods via an incentive-aligned choice-
based conjoint (ICBC) analysis whereby consumers are obliged to buy products from their
revealed preferences.
Generally, direct and indirect methods apply different techniques as illustrated in Figure
3. This thesis adopts the direct approach to measuring the WTP to investigate
hypothetical scenarios produced through a systematic and planned design (Louviere,
Hensher, Swait and Adamowicz, 2000). Even though many scholars propose the use of
indirect methods, the majority of studies use direct WTP techniques (Backhaus et al.,
2005). Furthermore, direct methods are better suited to this thesis due to its robustness.
35
Figure 3: Methods for measuring the WTP
Source: adapted from Breidert et al. (2006).
This thesis applies a stated preference approach (direct method) to the WTP construct,
as stated preference methods allow for the examination of hypothetical situations, which
are generated through systematic and planned design processes (Louviere et al., 2000).
Although researchers tend to recommend the use of indirect approaches (e.g., Backhaus
et al., 2005), two thirds of commercial pricing studies apply direct WTP approaches
(Steiner and Hendus, 2012). Lipovetsky et al. (2011) consider direct WTP methods suited
to practical applications due to their robustness.
WTP Measurement
Revealed Preference
(Indirect Method)
Stated Preference
(Direct Method)
Direct Surveys
Indirect Surveys
Expert Judgements
Market Data
Experiments
Laboratory Experiments (Auctions)
Field Experiments
Customer Surveys
Conjoint Analysis
Discrete Choice
Analysis
36
2.7 Research gaps
Despite a plethora of studies conducted on cognitive bias, few studies have researched
factors that motivate the power and scale of anchoring effects for consumers inclined to
purchase hedonic products. Moreover, while studies by Quintal et al. (2016) have
examined the anchoring effect perpetuated by authority and products’ physical
characteristics, few scholars have been committed to study the marketplace
environments. Nonetheless, while previous studies have concentrated on self-confidence
and consumer behavior in marketing, there has not been substantive research on how
consumer self-confidence impacts the power of anchoring. The current study is designed
to close this gap by attempting to understand how consumer self-confidence and
hedonistic value affect the power of anchoring.
2.8 Proposed conceptual model and hypotheses
As a fundamental aspect of the anchoring effect, individuals are sensitive to information
that they have experienced. This change in judgment, which is based on external cues,
seems particularly relevant and related to the consumer self-confidence personality trait.
Alevy, Landry and List’s (2015) first experiment found no evidence to show that
experienced agents are influenced by anchors, contributing to the suspicion that
anchoring effects can be neutralized or enhanced according to external factors. On the
other hand, Wilson et al. (1996) found that participants reporting to be more
knowledgeable are less influenced by anchors when they are estimating a value.
Researchers have concluded that, from the consumer’s perspective, individuals
considered to have high levels of self-confidence are less influenced by marketing tactics
(Kropp et al., 2005) and are selective and focused in their search for information (Mourali
et al., 2005; Clark et al., 2008; Bishop and Barber, 2012) inasmuch as they already
possess knowledge of a product or know where to find and access the information that
they need. Self-confident consumers also know how to evaluate product alternatives
37
(Loibl, Cho, Dieckman and Batte, 2009; Bishop and Barber, 2012). Chapman and
Johnson (1994) illustrated that a less significant anchoring effect is found for those who
are certain about a given answer. Supporting these findings, according to Bergman et al.
(2010), anchoring effects decline with higher levels of cognitive ability, which fuels the
assumption that price-anchoring effects have a lesser influence on self-confident
consumers than on average consumers. These principles are proposed by the first
hypothesis and are represented in conceptual and statistical form in Figure and Figure .
H1a: Consumer self-confidence negatively affects the power of anchoring.
Figure 4: Expected conceptual diagram for CSC, anchoring and the WTP
(-)
(+)
Source: the author based on Model 1 taken from Hayes (2018).
W
X
CSC
Anchor
(high or low)
WTP Y
38
Figure 5: Expected statistical diagram of the model comparing CSC, anchoring and the
WTP
Source: the author based on Model 1 taken from Hayes (2018).
Based on the consumer behavior theory of hedonic consumption, a moderation effect is
also expected to be found for hedonic value in terms of the relationship between
anchoring and the WTP. The categorization of hedonic-utilitarian consumption has
important implications for consumer behavior studies inasmuch as product attributes may
influence consumers’ choices. It has been demonstrated that a consumer is more likely
to consume a hedonic good than a utilitarian good when individually presented with one
(Okada, 2005, p. 45). In this case, if hedonic goods influence consumer preferences, it
could be inferred that their prices should be higher and consequentially that the WTP for
this kind of product should also be stronger, suggesting that this may also affect
consumers’ levels of susceptibility to anchoring effects as suggested by the second
hypothesis.
b3
b1
b2
XW
WTP
(Y)
Anchor
(X)
CSC
(W)
39
H1b: The hedonic value of products positively affects the power of anchoring.
As possible results of this model, anchoring is expected to be positively related to the
WTP (confirming the anchoring effect), which means that the stronger the anchoring
effect, the more willing a consumer should be to pay for a product. In this case, the
relationship between anchoring and the WTP should be moderated by the hedonic value
of a product. In other words, the impact of anchoring on the WTP is conditional to a
product’s hedonic value where the strong the hedonic value, the stronger the effect as
Figure graphically shows from moderation results and Figure 77 and Figure 88 represent
this model.
Figure 6: Expected results for hedonic value moderating anchoring effects on the WTP
Source: the author.
0
2
4
6
8
10
12
0,4 0,6 0,8 1 1,2 1,4 1,6
Will
ingn
ess to
pa
y (
WT
P, in
US
$)
Consumer self-confidence (CSC, index)
Hedonic (no anchor) Utilitarian (no anchor)
Anchoring effect (hedonic) Anchoring effect (utilitarian)
40
The anchoring effect represented on the dashed line represents the effect of a higher
anchor (upward effect) and a lower anchor (downward effect) as price references,
meanwhile the continuous line represents the expected results if no anchor were given to
the participants.
Figure 7: Expected conceptual diagram for hedonic value, anchoring and the WTP
(+)
(+)
Source: the author based on Model 1 taken from Hayes (2018).
W
X
Hedonic value
Anchor
(high or low)
WTP Y
41
Figure 8: Expected statistical diagram of the model comparing hedonic value, anchoring
and the WTP
Source: the author based on Model 1 taken from Hayes (2018).
Figure 9 illustrates the summary of the simple moderation hypotheses (H2a and H2b),
combining CSC and hedonic value in the same framework. It represents the expected
conceptual diagram, exposed on Figure 4 and Figure 7, combined in a single figure in
order to simplify the relationship between the variables.
b1
WTP
(Y)
Anchor
(X)
Hedonic
value
(W) b3
b2
XW
42
Figure 9 : Summary of expected conceptual diagram for simple moderation hypotheses
on CSC and hedonic value
Source: the author based on Model 1 taken from Hayes (2018).
H2a: CSC moderates the relationship between anchoring and the WTP, and this
relationship is stronger when consumers have less self-confidence than when they
are more confident.
H2b: Hedonic value moderates the relationship between anchoring and the WTP,
and this relationship is stronger when consumers engage in hedonic consumption
than when they purchase a utilitarian product.
Finally, a three-way interaction whereby anchoring affects the WTP (direct effects) and
where CSC and hedonic value moderate this relationship is anticipated and represented
on Figure 10 and Figure 11.
CSC / Hedonic value
(-) (+)
43
H3: There is a three-way interaction between anchoring, consumer self-confidence
and hedonic value.
Figure 10: Expected conceptual diagram for the model comparing CSC, hedonic value,
anchoring and the WTP
Hedonic value CSC (-) (+)
(+)
Anchor WTP Source: the author.
Figure 11: Expected statistical diagram for the model comparing CSC, hedonic value,
anchoring and the WTP
Source: adapted from Hayes (2018).
Z
W
X Y
WZ
XW
W
X
Z
XZ
XZW
44
3. Methodology
This chapter presents the approach employed in performing the empirical study for this
thesis. A quantitative approach was adopted to demonstrate the relationship between
dependent and independent variables. The researcher investigated and objectively tested
the association between a manipulated independent variable (anchor), the
WTP(dependent variable), CSC and hedonic value (independent variables). For this
quantitative research study, the researcher created three hypotheses and collected
information from undergraduate students to accept or reject the hypotheses. Creswell
(2014) alleges that in quantitative research, various techniques are used to gather
information, including surveys and experimental research.
3.1 Participants
The participants composed a sample of 350 international students enrolled at two North
American universities. In total, 407 students were instructed to complete the online
survey, but only 350 students completed it, resulting in a completion rate of 85.99%. The
survey was completed over 6 minutes on average. The participants varied in age from
18-54 (M = 20.38, SD = 4.4), and most were female (52.57%) while the majority
considered themselves confident (or extremely confident) as consumers, corresponding
to 83.43% of the total sample as illustrated in Table 2.
45
Table 2: Declared levels of consumer self-confidence by gender
Extremely
confident
Not
confident
Neutral Confident Extremely
confident
Total
Female 3
(50%)
3
(38%)
29
(63%)
84
(48%)
65
(56%)
184
(53%)
Male 3
(50%)
5
(63%)
15
(34%)
92
(52%)
51
(44%)
166
(47%)
Total
(%)
6
(2%)
8
(2%)
44
(13%)
176
(50%)
116
(33%)
350
(100%)
Source: the author with data extracted from surveys.
3.2 Procedure
The survey (Appendix B) was presented online with on QuestionPro tool, an online
software program, and it took approximately 6 minutes to complete. The instrument
allowed the researcher to gather views from undergraduate and graduate students to biter
understand their attitudes towards anchoring with a questionnaire including closed and
open-ended questions. To prevent multiple response submissions from the same
participant, the survey was constructed such that the link to the survey could not be
opened more than once on the same Internet server following its completion. Informed
consent to participate in the research was obtained online before the participants began
the survey. Following data collection, questionnaires completed in QuestionPro were
downloaded to an Excel database and results were analyzed with PROCESS Macro
(version 3.3) developed by Hayes (2018) in SPSS Version 26.0.
The use of online surveys mitigates the researcher’s influence, limits variations in
questionnaire application and allows one to quantify and determine how CSC and the
hedonic value of products influence the power of anchoring. Quantitative research was
used, as this approach allows for the use of statistical analysis to test the reliability and
validity of results while generalizing findings to the larger population. Nonetheless, when
adopting a quantitative approach, it can be challenging to understand respondents’
thoughts and to explore questions regarding the research topic. This empirical study thus
46
adopted a quantitative research approach applying two 2x2 experimental designs to the
participants.
3.3 Measures
Three self-report measures were included in the questionnaire to evaluate levels of CSC,
WTP and familiarity. Three assumed variables were manipulated to facilitate the analysis:
anchoring, hedonic value and products tested.
3.3.1 Dependent variable
The WTP, the dependent variable of this study, was measured by asking the participants
to report the maximum price they would pay for a given product (a bathtub or pen) using
a direct survey from direct methods of stated preferences (illustrated in Figure 3). This
method allows for the investigation of hypothetical conditions generated through
systematic and planned design processes (Louviere et al., 2000). For a direct method to
produce the actual WTP, buyers must purchase at the cost of a product based on the
expected price, which was facilitated through the use of a manipulated preanchored
reference question posed before declaration of the WTP.
Most pricing studies apply direct WTP approaches (Steiner and Hendus, 2012).
Lipovetsky et al. (2011) classify direct WTP methods as appropriate for practical
applications owing to their robustness. Essentially, direct and indirect techniques assess
buyers’ hypothetical views rather than their actual opinions. As such, they produce biased
results (Miller et al., 2011)
47
3.3.2 Independent variables
The products were labeled dummy variables using codes taken from the database. The
pen was coded as 0 while the bathtub was coded as 1. To further distinguish between
product categories, 0 was used to represent the low-end version and therefore utilitarian
consumption, and 1 was used for the high-end version representing hedonic
consumption. Additionally, to differentiate between anchor values, codes were used for
the lower anchor (0) and higher anchor (1), creating eight scenarios for each participant.
A summary of the collected information is illustrated in Table 3.
Table 3: Number of respondents
Low Anchor High Anchor TOTAL
Pen 87 100 187
Utilitarian 40 49 89
Hedonic 47 51 98
Bathtub 76 87 163
Utilitarian 39 39 78
Hedonic 37 48 85
TOTAL 163 187 350
Source: the author with data extracted from the survey.
48
Anchor
The anchor was manipulated as two potential values for each product version. Anchoring
manipulation was utilized to demonstrate that individuals have inconsistent preferences.
First, a random number collected from potential values given in Table 4 was assigned to
the respondent in a between-subjects study, i.e., different respondents tested the
interface; therefore, each one was only exposed to a single user condition.
Table 4: Manipulated anchor values
Low Anchor High Anchor Original price*
Pen (utilitarian) $0.10 $100.00 $0.43
Pen (hedonic) $5.00 $5.000.00 $450.00
Bathtub (utilitarian) $2.00 $2,000.00 $72.66
Bathtub (hedonic) $40.00 $40,000.00 $14,000.00
(*) prices based on websites: https://www.amazon.com/BiC-Cristal-Original-Ball-Pack/dp/B004DBHR2Q; https://www.amazon.com/ Portable-Foldable-Inflatable-Standing-Electric;https://www.therichest.com/luxury/most-expensive/10-of-the-most-expensive-hot-tubs; http://www.montblanc.com/en-us/collection/writing-instruments.filter.html?&filters=2019618787. Accessed on 19 mar 2018.
Source: the author with data extracted from the survey.
In order to better analyze the ‘anchor’ construct in the database, it was transformed into
a dichotomic variable using codes for lower anchors (0) and higher anchors (1).
49
Consumer self-confidence
To assess consumer self-confidence, a pre-existing questionnaire using Bearden,
Hardesty and Rose’s (2001) five-item scale was included in the survey. The questionnaire
included 31 Likert-scale questions scored from 1 (“extremely uncharacteristic”) to 5
(“extremely characteristic”).
Once the BHR inventory (detailed in Appendix 1) was completed, the six subdimensions
of the scale were measured, and the overall level of CSC was calculated from the
weighted average of the six items where a higher score reflected a higher level of
consumer self-confidence.
The BHR scale is widely recognized as the best available measure of consumer self-
confidence and is commonly used in consumer behavior studies (Loibl, Chi, Dieckman
and Batte, 2009).
Hedonic value
Products were selected considering their neutrality of age and gender, meaning that they
are evaluated equally for men and woman and for younger and older consumers. A high-
end or basic product, representing hedonic and utilitarian consumption, was randomly
assigned to each respondent according to the potential product versions, as illustrated on
Figure 12.
50
Figure 1: Hedonic-utilitarian versions of products tested
Utilitarian goods Hedonic goods
Sources: “A Blue Crystal Bic Pen on a White Background”, Alamy's library, 3 December 2017, https://www.alamy.com/stock-photo-a-blue-crystal-bic-pen-on-a-white-background-48197298.html; photograph. “Montblanc Starwalker Ballpoint Pen Meisterstück Montblanc Starwalker Fineliner Pen Montblanc Meisterstuck Classique Ballpoint pen”, PNGFLY, 3 December 2017, https://www.pngfly.com/png-h87wig/, photograph. “Jacuzzi bath isolated on the white background”, Alamy's library, 3 December 2017, https://www.alamy.com/stock-photo-jacuzzi-bath-isolated-102871892.html, photograph. “CO-Z Adult PVC Portable Folding Inflatable Bath Tub with Air Pump for Family Bathroom SPA”, Amazon, 3 December 2017, https://www.amazon.com/CO-Z-Portable-Folding-Inflatable-Bathroom/dp/B017GMD7YU?ref_=fsclp_pl_dp_4/, photograph.
51
These products were analyzed as a dichotomic variable (0 or 1) where a value of 0 was
assigned to the low-end version of the merchandise, while a value of 1 was assigned to
the high-end version.
Each product was randomly shown to each respondent. After that, participants were
asked whether or not they would pay a specific given price (anchor) for the product.
Subsequently, there was an open question demanding the maximum price they would
pay for that product, representing the WTP.
3.3.3 Control variable
Familiarity was measured as of a pre-defined scale developed by Casaló, Flavián and
Guinalíu. (2008). The scale included five question scored from 1 (“extremely
uncharacteristic”) to 5 (“extremely characteristic”) referring to familiarity. They were: “I am
quite familiarized with this product”; “I know to evaluate its attributes”; “comparing with
other users, I think I am quite familiarized with this product”; “I am quite familiarized with
other similar products” and “I have the habit of using this product”. The variable familiarity
was settled as a control variable inasmuch as according to many authors previous
knowledge or familiarity play an important role in influencing decision making and the
anchoring phenomena (James, Vissers, Larsson, & Dahlström, 2015; Beck and Prügl,
2018).
52
4. Results
Due to the biases that result from first access to information and the result of such
behavior on the consumer purchase trends, this thesis intends to figure out how the self-
confidence of consumers as well as their hedonic values arouse their purchase
anchoring. Such factors which can be both internal and external are critical in decision-
making among the buyers. This thesis also investigates how buyers can overcome such
an effect as anchoring. Therefore, the current study seeks to answer whether consumer
self-confidence as well as hedonic value have impacts on the power of anchoring. Such
an outcome is achieved by exploring the impact of consumer self-confidence on the
strength of anchoring WTP. Secondly, the thesis verifies how the hedonic value of good
affects the degree of anchoring WTP. Thirdly, this thesis speculates on the link between
consumer self-confidence, hedonic value, and anchoring WTP, of which the main issues
are the impact of consumer self-confidence on the hedonic value and the effect that the
hedonic value of a good has on anchoring WTP.
4.1 Preliminary data analysis
Table 5 below summarizes the descriptive data obtained from the study. The number of
participants in the study was 350. The variables under study were the Consumers’
Willingness to pay (WTP), Consumers’ Familiarity with product before purchase, and the
Consumer’s self-confidence (CSC). The other variables (anchor and hedonic value) were
manipulated in the study, making them unnecessary to be included in the descriptive
statistics.
53
Table 5: Descriptive statistics
N Minimum Maximum Sum Mean Std. Deviation
FAMILIARITY (M) 350 2.0 10.0 1733.0 7.2
2.2
CSC 350 3.1 10.0 1802.0 7.5 1.4
Valid N (list-wise) 350
Source: the author.
Willingness to Pay was categorized on Table 6 based on whether the products were for
hedonic satisfaction or utilitarian satisfaction. In Table 6, two items are used for the
analysis of hedonic and utilitarian purchase: pen and bathtub. The prices columns
indicate the average willingness to pay declared for respondents that had a low anchor,
high anchor and the original market price for the products. Based on the table, the
consumers are willing to pay more for the product whether they were anchored to a higher
value and whether the product corresponds to a hedonic consumption.
Table 6: WTP Average
Low anchor High anchor Original Price*
Pen US$ 3.35 US$ 215.38
Utilitarian US$ 0.59 US$ 24.44 US$ 0.43
Hedonic US$ 5.71 US$ 398.83 US$ 450.00
Bathtub US$ 1,019.14 US$ 4,924.68
Utilitarian US$ 142.95 US$ 451.21 US$ 80.00
Hedonic US$ 1,942.70 US$ 8,559.38 US$14,000.00
* prices based on websites: https://www.amazon.com/BiC-Cristal-Original-Ball-Pack/dp/B004DBHR2Q; https://www.amazon.com/ Portable-Foldable-Inflatable-Standing-Electric;https://www.therichest.com/luxury/most-expensive/10-of-the-most-expensive-hot-tubs; http://www.montblanc.com/en-us/collection/writing-instruments.filter.html?&filters=2019618787. Accessed on 19 mar 2018.
Source: the author.
54
The finding from the Table 6 produces the bar chart shown in Figure 13. From both the
table and the bar chart, it is evident that consumers have a higher tendency to pay more
for products based on their hedonic value as opposed to the utilitarian satisfaction.
Figure 23: Anchor, hedonic value and WTP
Source: the author.
Simple moderation analyses were performed using Hayes’ PROCESS macro (2018,
model 1) for SPSS (version 26.0) with anchor as the independent variable, hedonic value
and CSC as continuous moderators, and WTP as the dependent variable.
The simple moderation model was tested for four scenarios:
a. Hedonic value as a moderator for anchoring effect on bathtub
b. Hedonic value as a moderator for anchoring effect on pen
c. CSC as a moderator for anchoring effect on bathtub
d. CSC as a moderator for anchoring effect on pen
$0
$1.000
$2.000
$3.000
$4.000
$5.000
$6.000
$7.000
$8.000
$9.000
Low Anchor High Anchor Low Anchor High Anchor
Pen Bathtube
WTP
Utilitarian
Hedonic
55
The analysis yielded a significant anchor x hedonic value interaction for scenarios a and
b. In other words, the model was statistically significant for hedonic value as a moderator
for anchoring effect on both products/studies.
4.2 Study 1 (pen)
This section analyzes and interprets the results accomplished for the first study, that
tested the influence of consumer self-confidence and hedonic value of good on anchoring
willingness to pay’s responses for a pen. Figure 14 represents the differences in WTP’s
responses, according to a high or low anchor for hedonic and utilitarian consumptions of
a pen.
Figure 14: WTP, anchor and hedonic value (pen)
Source: the author.
US$-
US$50,00
US$100,00
US$150,00
US$200,00
US$250,00
US$300,00
US$350,00
US$400,00
US$450,00
Utilitarian Hedonic
Low Anchor
High Anchor
56
By comparing the increase rate on the declared WTP when the anchor was high vs. a low
anchor, it is evident that the WTP was higher for hedonic products (6885%) then for its
utilitarian version (4057%), even though it was reported a strong anchoring effect on
answers for both categories.
The first hypotheses (H1a and H1b) were tested for the study involving pen by performing
a linear regression, presented on Appendix C. H1a was not supported with a significance
level of 5%, since the p-value was 0.09 (>0.05). However, with a significance level of
10%, it is possible to state that the direction of the relationship was confirmed to be
negative. On the other hand, H1b was supported with a significance of 1%, inasmuch as
the p-value of hedonic value on anchoring is 0.00 (<0.01).
The hypothesis H2a for a simple moderation model, represented on the diagram in Figure
5 on section 2.8, was tested for CSC as a moderator of the strength of anchoring on
WTP’s assessments. However, the hypothesis was not supported, in as much as the
regression analysis (Appendix D) indicates that this model only explains 5% of variance
(R²=0.05) and the interaction of CSC and anchor is not significant, inasmuch as its p-
value is 0.20 (p>0.05).
H2b was also tested for a simple moderation model, represented on the diagram in Figure
7 on section 2.8, considering hedonic value as a moderator of the strength of anchoring
on WTP’s assessments. Table 7 outlines the statistics regression for this moderation
analysis.
57
Table 7 : Statistics Regression – hedonic value (pen)
Regression – Simple moderator (pen)
Dependent variable: WTP
Predictor B p SE
Intercept 119.51 0.00 37.46
Anchor b1 -> 217.37 0.00 70.05
Hedonic value b2 -> 202.59 0.01 71.57
Anchor x Hedonic value b3 -> 369.26 0.01 133.83
Model R² 0.31
F 11.53 0.00
N = 187 respondents
Source: the auhor, based on PROCESS results (SPSS, version 26.0).
The results from the analysis of this study provide further evidence that, for a pen, the
hedonic value moderates anchoring effects on WTP. The analysis yielded a significant
willingness to pay x hedonic value interaction (b = 369.26; SE = 133.83; t (186) = 2.76, p
= 0.01). Complete data result for simple moderation for pen is displayed on Appendix E.
The regression analysis (R²=0.31) indicates a variance of 31%. Participants that
considered the product as hedonic were willing to pay more under the anchoring effects
(Mhedonic low anchor = 5.71 vs. Mhedonic high anchor = 398.83; b= 393.12, SE = 132.95, t (186) =
2.96, p=0.00). The anchoring effect was also evident for utilitarian products (Mutil low anchor
= 0.59 vs. Mutil high anchor = 24.44; b= 23.85, SE = 15.32, t (186) = 1.56, p=0.12). These
effects are reflected in Figure 15.
58
Figure 15: Moderation effects – hedonic value (pen)
Source: the author based on data extracted from Process Macro (2018, model 1) SPSS (version 26.0).
There is a significant effect between the moderating variable and the dependent variable,
i.e., hedonic value influences WTP (p-value <0.05). The higher the consumer perceives
the hedonic value of a product, the higher are WTP values.
4.3 Study 2 (bathtub)
This section analyzes and interprets the results accomplished for the second study, that
tested the anchoring effect on willingness to pay for a hedonic and utilitarian consumption
of a pen. Figure 16 represents the differences in WTP’s responses, according to a high
or low anchor for a bathtub.
$0
$50
$100
$150
$200
$250
$300
$350
$400
$450
Low Anchor High Anchor
WT
P
Hedonic
Utilitarian
Moderation of the effect of Anchor on WTP at values of the
moderator Hedonic value (pen)
59
Figure 16: WTP, anchor and hedonic value (bathtub)
Source: the author.
By comparing the increase rate on the declared WTP when the anchor was high vs. a low
anchor, it is clear that the WTP was higher for hedonic products (341%) the it was stated
for its utilitarian version (216%), even though, just like the first study on pens, this study
reported a strong anchoring effect on answers for hedonic and utilitarian categories.
The first hypotheses (H1a and H1b) were also tested for the study involving bathtub by
performing a linear regression, presented on Appendix F. H1a was not supported with a
significance level of 5%, since the p-value was 0.17 (>0.05). On the other hand, H1b was
supported with a significance level of 1%, inasmuch as the p-value of hedonic value on
anchoring is 0.00 (<0.01).
The hypothesis H2a for a simple moderation model was tested for CSC as a moderator of
the strength of anchoring on WTP’s assessments. However, the hypothesis was not
supported, in as much as the regression analysis (Appendix G) indicates that this model
only explains 17% of variance (R²=0.17) and the interaction of CSC and anchor is not
significant, inasmuch as its p-value is 0.48 (p>0.05).
The hypothesis H2b was also tested for bathtub and the analysis yielded a significant
willingness to pay x hedonic value interaction (b = 6,308.42; SE = 932.74; t (162) = 6.76,
US$-
US$1.000,00
US$2.000,00
US$3.000,00
US$4.000,00
US$5.000,00
US$6.000,00
US$7.000,00
US$8.000,00
US$9.000,00
Utilitarian Hedonic
Low Anchor
High Anchor
60
p = 0.00). The p-value of p(0.000) as shown in Table 8 confirms that the hedonic value of
the products and the consumers’ willingness to pay are statistically significant. Complete
data result for simple moderation for the bathtub study is displayed on Appendix H. Table
8 outlines the statistics regression for this moderation analysis.
Table 8: Statistics Regression – hedonic value (bathtub)
Regression Simple Moderation (bathtub)
Dependent variable: WTP
Predictor
B p SE
Intercept
3001.83 0.00 248.21
Anchor b1 -> 3597.92 0.00 485.31
Hedonic value b2 -> 5166.82 0.00 477.14
Anchor x Hedonic value b3 -> 6308.42 0.00 932.74
Model R²
0.55
F
47.86 0.00
N = 163 respondents
Source: The author based on PROCESS results (SPSS, version 26.0).
The regression analysis (R²=0.55) indicates a variance of 55%. As reflected in Figure 17,
participants that considered the product as hedonic were willing to pay more under the
anchoring effects (Mhedonic low anchor = 1,942.70 vs. Mhedonic high anchor = 8,559.38; b= 6,616.67,
SE = 919.45, t (162) = 7.19, p=0.00). The data also shows that the probability of obtaining
a t-value of 162 or higher is 0, which further reveals that all the consumers have high
anchoring for hedonic value. The anchoring effect was also evident for utilitarian products
(Mutil low anchor = 142.95 vs. Mutil high anchor = 451.21; b= 308.26, SE = 156.88, t (162) = 1.96,
p=0.05)
61
Figure 17: Moderation effects – hedonic value (bathtub)
Source: The author based on data extracted from Process Macro (2018, model 1) SPSS (version 26.0).
4.4 The interaction of anchor, CSC, hedonic value and WTP
The simple moderation was statistically proven between hedonic value and WTP, for both
products: bathtub and pen. As discussed before, the anchoring literature has shown
mixed findings on the robustness of its effect and the variety of results could be explained
by the type of product that was used in prior studies and supporting the main finding of
this thesis, which is that hedonic value influences the power of the anchoring effects on
WTP. However, the evidence presented and found on prior research relating CSC to
anchoring effects could not be achieved in this present study. As the simple moderation
$0
$1.000
$2.000
$3.000
$4.000
$5.000
$6.000
$7.000
$8.000
$9.000
Low Anchor High Anchor
WT
P
Hedonic
Utilitarian
Moderation of the effect of Anchor on WTP at values of the
moderator Hedonic value (bathtub)
62
model was statistically significant just for hedonic value as a moderator for anchoring
effect, it highlights the importance of different model with a deep investigation on hedonic
and utilitarian consumption, considering the CSC. The third hypothesis, whatsoever,
evaluates if CSC could still affect anchoring effect, even if in an indirect way.
A moderated moderation model predicting anchoring effect from hedonic value and CSC
was performed on Hayes’s PROCESS macro (2018, model 3) for SPSS (version 26.0)
for pen and bathtub. The three-way interaction between hedonic value, CSC, and
anchoring effect on WTP was supported only for bathtub. For the pen study, the triple-
interaction was not confirmed inasmuch as its p-value was 0.32 (p<0.05). Appendices I
and J comprise complete data for that model, regarding pen and bathtub, respectively.
The interaction between the variables for the bathtub study is predicted on the regression
analysis on Table 9. Beta factors directions were coherent to the expected results,
forecasted on Figure 10 and Figure 11, in section 2.8.
63
Table 9: Moderated moderation model predicting anchoring effect on WTP from hedonic
value and CSC (bathtub)
Regression – moderated moderation (bathtub)
Dependent variable: WTP
Predictor B P SE
Intercept 3065.56 0.00 261.39
Anchor b1 -> 3580.95 0.00 515.63
Hedonic value b2 -> 5288.36 0.00 502.36
CSC b3 -> -36.25 0.81 267.37
Anchor x Hedonic value b4 -> 6274.45 0.00 990.79
Anchor x CSC b5 -> -518.58 0.09 79.45
Hedonic value x CSC b6 -> -44.37 0.88 538.85
Anchor x Hedonic value x CSC b7 -> -970.36 0.10 178.21
Model R² 0.56
F 22.58 0.00
N = 187 respondents
Source: the auhor based on PROCESS results (SPSS, version 26.0)
The regression analysis (R²=0.56) indicates a variance of 56%.The relationship between
CSC and anchoring effect was not statistically significant (p=0.81), neither the interaction
between hedonic value and CSC (p= 0.88). However, the triple interaction between
hedonic value, CSC and anchor on WTP is an important finding. Although the interaction
has a p-value of 0.0972, a statistical fact can still be derived from an interaction with a
significance level of 10% for such a moderated moderation involving multiple variables.
64
4.5 Research synthesis
This section summarizes the findings from study 1 and study 2, associating them with
the statistical tool performed on this thesis and the hypotheses that were tested.
Table 10: Synthesis of Results
Hypotheses Statistical tool Study 1 (pen) Study 2 (bathtub)
H1a: CSC negatively affects the power of
anchoring
Linear Regression Excel
Hypothesis confirmed with a significance level
of 10% (p-value = 0.09)
The hypothesis was not confirmed
(p-value = 0.17)
H1b: The hedonic value of products positively affects the power of
anchoring
Linear Regression Excel
The hypothesis was confirmed
(p-value = 0.00)
The hypothesis was confirmed
(p-value = 0.00)
H2a: CSC moderates the relationship
between anchoring and the WTP
Model 1, Hayes (2018) PROCESS macro
SPSS (version 26.0)
The hypothesis was not confirmed
(p-value = 0.20)
The hypothesis was not confirmed
(p-value = 0.48)
H2b: Hedonic value moderates the
relationship between anchoring and the WTP
Model 1, Hayes (2018) PROCESS macro
SPSS (version 26.0)
Hypothesis confirmed
(p-value = 0.01)
Hypothesis confirmed
(p-value = 0.00)
H3: There is a three-way interaction between
anchoring, consumer self-confidence and
hedonic value.
Model 3, Hayes (2018) PROCESS macro
SPSS (version 26.0)
The hypothesis was not confirmed
(p-value = 0.32)
Hypothesis confirmed with a significance level
of 10% (p-value = 0,097)
Source: the author.
Table 10 summarizes the results from the experiments, revealing that half of the
hypotheses were confirmed. However, there was no evidence to support CSC as a
moderator of anchoring, even though the first hypothesis – that CSC negatively affects
the power of anchoring – was confirmed with a significance level of 10%. On the other
hand, the findings support that the moderating effect of the hedonic value on anchoring
65
(the relationship between anchor and WTP) is presented on both products tested, pen
and bathtub, with a significance level of 1%, confirming the hypotheses H1b and H2b.
However, the third hypothesis was just confirmed for the bathtub’s study, with a higher
significance level (10%). It means that the three-way interaction was only presented on
study 2.
66
5. Discussion and final considerations
The aim of the study was to investigate the influence of CSC and hedonic value in the
power of anchoring on the WTP. The study was conducted with 350 participants in order
to respond to research questions.
5.1 The impact of hedonic value on the strength of anchoring the WTP
According to Appendix K, on average participants were willing to spent $210.29 on the
hedonic version of a pen, compared to $13.72 on the utilitarian kind. Moreover, 85
participants were willing to pay, on average, $8,559.38 for a bathtub because of its
physical characteristics as opposed to 78 participants that were only willing to pay
$451.21 for a utilitarian bathtub.
Ariely, Loewenstein and Prelec (2003) show that anchoring may turn out to favor the first
bit of information, where people are easily lured into making purchases that they do not
necessarily need. In contrast, Koças and Dogerlioglu-Demir (2014) reassert that while
anchoring takes effect prior to making up the decision to make a purchase, accessible
information plays a vital to the consumer and determines how decisions are made.
Mussweiler (2003) contend that the strength of anchoring whether high or low is motivated
by information analogous to the presented anchor.
Positioning of hedonic products has an impact on the purchase as well. Because social
influence has a positive effect on the willingness to buy high-end products, Ding and
Tseng (2015) also indicate just how hedonic products can integrate the personal and
exterior forces. Kim and Johnson (2015) and Saran, Roy and Sethuraman (2016) show
that peoples projected view is often validated by exterior environment. In this respect, it
can be argued that the high consumption of hedonic goods is influenced by the setting
that enhances its consumption. The effect of the hedonic value on the strength of
anchoring can be perceived on the moderation results in Tables 7 and 8, respectively for
pen and bathtub.
67
5.2 The impact of CSC on the strength of anchoring the WTP
Bearden, Hardesty and Rose (2001) found that CSC presents consumers with the basis
to make buying decisions in the marketplace. According to Qiu, Cranage and Mattila
(2016), consumers with less CSC have a tendency to make unpredictable decisions due
to environmental factors. Similarly, individuals make impractical judgments, which
ultimately influence their WTP (Parker and Stone, 2014). Furthermore, individuals with
less self-confidence are more likely to make inconsistent decisions than those with high
self-confidence (Qiu, Cranage and Mattila, 2016).
Self-confidence in previous information helps consumers in perceiving product attributes.
(Lo and Jim, 2015). In this study, on average, respondents stated to be more familiar with
pens than bathtubs (Appendix K). For that reason, most of the respondents were
knowledgeable about pens hence they were confident on stating the WTP for those
products. On the issue of the influence of consumer self-confidence on anchoring the
WTP, the findings of this thesis do not support evidence for a direct relation of CSC and
anchoring effects. However, the results on Table 9 demonstrated a significant interaction
of CSC, hedonic products, and anchoring effect on WTP.
5.3 The impact of CSC and hedonic value on the strength of anchoring the WTP
The three-way interaction of CSC, hedonic products, and WTP anchoring, accomplished
for the bathtub study, is supported by previous findings indicating that knowledge plays
an important part in influencing decision making in WTP, CSC and anchoring effects
(James, Vissers, Larsson, & Dahlström, 2015). In addition, consumers with low CSC
prefer unpredictable decisions when making decisions whereas those with impractical
judgments affect WTP. This suggests that CSC lead higher hedonic value attributed to
the product and higher WTP.
However, the findings of this thesis (Table 9) indicate that the relationship between CSC,
the hedonic value has a negative direction, meaning that, contrarily from what was
68
expected, the lower the CSC is, the higher is the hedonic value perceived for a specific
product. This result can be attributed to the fact that when consumer has a higher self-
confidence on the purchase, his pleasure or satisfaction achieved from the product
acquisition tend to be lower, supporting the negative relationship between the hedonic
value of a good and CSC. This is consistent with the findings of Koças and Dogerlioglu-
Demir (2014) stating that anchoring occurs when buyers’ decisions are elicited before
they make decisions. Hence, the available information is of great importance in
consumers’ CSC and WTP for a given product. Nonetheless, while consumers access
product information in different ways, their ability to process such information is crucial in
making informed buying decisions.
5.4 Theoretical and managerial implications
As theorical contribution, this thesis underwrites to consumer behavior literature by
associating anchoring effects on WTP to two variables that were not related, especially
because one variable represents a personal factor, meanwhile the other one represents
an external factor. Regarding managerial implications, this thesis indicates that the
hedonic value of a product, if increased, can increase the anchoring effect on consumers
WTP, meaning that the price of the product can be increased as long as it corresponds
to a hedonic consumption that explores the anchoring bias. Another practical contribution
can be evidenced that with the anchoring phenomena and its determinants uncovered,
consumers can become conscious of this process to overcome the anchor bias.
5.5 Limitations
This study has a number of limitations, which may lead for proposals for future studies.
While the sample size of 350 participants may be a true representation of consumers and
how hedonic values and confidence impact the power of anchoring in a marketplace, it
does not represent the general population. And since this study did not have a specific
market in general, it is therefore improper to make a generalized assumption of
69
consumers in markets based on different regions. Nonetheless, the use of pen and
bathtub as the central products did not have any support in the literature review, even
though an experiment by Sugden, Zheng and Zizzo (2013) did investigate about this
product. However, since hedonic value can be attributed to a large number of products,
there is need to further research to interrogate how anchoring effect is manipulated using
a wide array of products. Nonetheless, since data was collected in an academic
environment, in where students essentially have broad knowledge about hedonic
consumption, it may not necessarily represent the views of a typical markets with a lot of
activities and players involved. Apart from these limitations, this investigation into the
nature of hedonic consumption among university students confirms just how consumer
self-confidence and hedonistic value impacts the power of anchoring.
5.6 Future research
The findings indicate that the effect of CSC and hedonic value together affect the power
of anchoring. Therefore, future research should put emphasis on investigating the effects
of various product categories or groups of individuals. Assessing the nature of changes
based on priming is another area for future exploration. Heterogeneity model that takes
into consideration cross-sectional changes in vulnerability to priming could be adopted to
investigate the manner in which priming influences buyers in line with WTP distribution.
Such a model can examine dimensions of distributions including low and high income
buyers while examining the change in WTP.
Bearden et al. (2001) posit that consumer-self-confidence is related to market mavenism.
They argue that “persons high in consumer self-confidence should be more willing to
discuss their marketplace knowledge with others” (p. 132). Market mavens are defined
by interest in and willingness to share marketplace information. A market maven is more
concerned in showing and convincing others that he or she made a good deal than in
actually making a good deal. Investigating the impact of market mavenism on the
70
anchoring effect of consumers’ price evaluation is an unexplored topic and can lead to
many interesting and relevant findings.
Besides, this thesis only investigated CSC as individual variation variable. Therefore, the
impact on other individual characteristics on anchoring the WTP is a good avenue for
future studies. One interesting individual aspect that is worthy to be investigated is
regarding how consumers mindset (abstract versus concrete) could affect their
susceptibility to anchoring effects. Based on the findings, CSC and hedonic products can
be associated with buyers’ demanding, which presents additional recommendations for
further investigations and evidences the opportunity to investigate how hedonic
consumption influences anchoring effects across cultures.
While some variables in the study rejected the null hypothesis, by indicating that
anchoring effect among consumers is largely based on their self-confidence and hedonic
value of products, more research is needed to uncover factors that trigger low and high
anchoring. Moreover, there is also a wide gap with reference to low and high anchoring
demanded by hedonic and utilitarian consumption that need to be investigated
comprehensively. As such, the current study presents a heated debate on the anchoring
concept. While some of the previous experiments fail to demonstrate any relationship
between external factors and the anchoring effect, this also indicates that the anchoring
effect can be accelerated or lessened based on individual preferences. Nonetheless,
other studies also noted that consumers may also be influenced by the amount of
information they have concerning a product may have low anchoring effect when
approximating its value.
Nonetheless, future studies should endeavor to unravel whether the familiarity with the
product has an impact on the power of the anchoring effect. In the same breadth, future
studies should determine whether exterior anchoring facets can impact the consumer
willingness to pay.
71
References
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Appendices
Appendix A: Bearden, Hardesty and Rose scale (2001) of CSC CONSUMER SELF-CONFIDENCE SCALE ITEMS INFORMATION ACQUISITION (IA) I know where to find the information I need prior to making a purchase. (.80) I know where to look to find the product information I need. (.82) I am confident in my ability to research important purchases. (.62) I know the right questions to ask when shopping. (.60)
I have the skills required to obtain needed information before making important purchases. (.64)
CONSIDERATION SET FORMATION (CSF) I am confident in my ability to recognize a brand worth considering. (.85) I can tell which brands meet my expectations. (.64) I trust my own judgment when deciding which brands to consider. (.72) I know which stores to shop. (.55) I can focus easily on a few good brands when making a decision. (.60) PERSONAL OUTCOMES DECISION MAKING (PO) I often have doubts about the purchase decisions I make. (.81) I frequently agonize over what to buy. (.67) I often wonder if I’ve made the right purchase selection. (.73) I never seem to buy the right thing for me. (.50) Too often the things I buy are not satisfying. (.65) SOCIAL OUTCOMES DECISION MAKING (SO) My friends are impressed with my ability to make satisfying purchases. (.89) I impress people with the purchases I make. (.89) My neighbors admire my decorating ability. (.53) I have the ability to give good presents. (.53) I get compliments from others on my purchase decisions. (.68) PERSUASION KNOWLEDGE (PK) I know when an offer is “too good to be true”. (.70) I can tell when an offer has strings attached. (.73) I have no trouble understanding the bargaining tactics used by salespersons. (.62) I know when a marketer is pressuring me to buy. (.68) I can see through sales gimmicks used to get consumers to buy. (.74) I can separate fact from fantasy in advertising. (.61) MARKETPLACE INTERFACES (MI) I am afraid to “ask to speak to the manager”. (.79) I don’t like to tell a salesperson something is wrong in the store. (.79) I have a hard time saying no to a salesperson. (.59) I am too timid when problems arise while shopping. (.67) I am hesitant to complain when shopping. (.77) The factor loadings based on the six-factor correlated model from the confirmatory factor analysis are shown in the parentheses to the right of each item. Source: Bearden et al. (2001)
81
Appendix B: Survey on anchoring
82
83
Then, one of the following “yes / no” questions randomly appear to the respondent:
84
85
86
87
88
Appendix C: Linear Regression - pen
Linear Regression
Regression Statistics
R 0,35
R-square 0,12
Adjusted R-square 0,11
S 500,25
N 187,
WTP (Willingness-to-pay) - (Y) = 3,35494 + 488,46091 * Anchor + 403,36113 * Hedonic value - 63,07187 * CSC
ANOVA
d.f. SS MS F p-level
Regression 3, 6.336.336,64 2.112.112,213 8,44 0,
Residual 183, 45.795.146,401 250.246,702
Total 186, 52.131.483,041
Coefficient Standard Error LCL UCL t Stat p-level
Intercept 3,35 53,63 -102,46 109,17 0,06 0,95
Anchor 488,46 285,72 -75,26 1.052,18 1,71 0,09
Hedonic value 403,36 101,47 203,15 603,57 3,98 0,00
CSC -63,07 36,62 -135,33 9,19 -1,72 0,09
89
Appendix D: Simple moderation CSC - pen
*************** PROCESS Procedure for SPSS Version 3.3 *******************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2018). www.guilford.com/p/hayes3
**************************************************************************
Model : 1
Y : wtp
X : High_anc
W : csc
Sample
Size: 187
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,21 ,05 271879,94 3,18 3,00 183,00 ,03
Model
coeff se(HC0) t p LLCI ULCI
constant 116,63 37,67 3,10 ,00 42,31 190,95
High_anc 211,82 70,44 3,01 ,00 72,84 350,80
csc -20,72 16,07 -1,29 ,20 -52,42 10,98
Int_1 -38,74 30,05 -1,29 ,20 -98,02 20,55
Product terms key:
Int_1 : High_anc x csc
Covariance matrix of regression parameter estimates:
constant High_anc csc Int_1
constant 1418,92 2653,04 -370,78 -693,39
High_anc 2653,04 4961,93 -693,39 -1296,59
csc -370,78 -693,39 258,17 482,73
Int_1 -693,39 -1296,59 482,73 902,79
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W ,00 1,66 1,00 183,00 ,20
----------
Focal predict: High_anc (X)
Mod var: csc (W)
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc csc wtp .
BEGIN DATA.
-,53 -1,39 3,36
,47 -1,39 268,97
-,53 ,00 3,35
,47 ,00 215,17
-,53 1,39 3,35
,47 1,39 161,38
END DATA.
GRAPH/SCATTERPLOT=
90
csc WITH wtp BY High_anc .
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95,0000
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
csc High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
91
Appendix E: Simple moderation: hedonic value for pen Model : 1
Y : wtp
X : High_anc
W : Hedonic
Sample
Size: 187
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,5276 ,3073 254299,385 11,5320 3,0000 183,0000 ,0000
Model
coeff se(HC0) t p LLCI ULCI
constant 119,5130 37,4619 3,1903 ,0017 45,6002 193,4258
High_anc 217,3704 70,0542 3,1029 ,0022 79,1525 355,5883
Hedonic 202,5891 71,5655 2,8308 ,0052 61,3893 343,7889
Int_1 369,2639 133,8286 2,7592 ,0064 105,2183 633,3095
Product terms key:
Int_1 : High_anc x Hedonic
Covariance matrix of regression parameter estimates:
constant High_anc Hedonic Int_1
constant 1403,3919 2624,0738 2616,9791 4893,2530
High_anc 2624,0738 4907,5964 4893,2530 9151,4535
Hedonic 2616,9791 4893,2530 5121,6276 9576,4608
Int_1 4893,2530 9151,4535 9576,4608 17910,0943
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W ,0303 7,6133 1,0000 183,0000 ,0064
----------
Focal predict: High_anc (X)
Mod var: Hedonic (W)
Conditional effects of the focal predictor at values of the moderator(s):
Hedonic Effect se(HC0) t p LLCI ULCI
-,5241 23,8524 15,3162 1,5573 ,1211 -6,3666 54,0714
,4759 393,1163 132,9493 2,9569 ,0035 130,8056 655,4269
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc Hedonic wtp .
BEGIN DATA.
-,5348 -,5241 ,5880
,4652 -,5241 24,4404
-,5348 ,4759 5,7098
,4652 ,4759 398,8261
END DATA.
GRAPH/SCATTERPLOT=
High_anc WITH wtp BY Hedonic .
92
*********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS ************
OUTCOME VARIABLE:
wtp
Coeff BootMean BootSE BootLLCI BootULCI
constant 119,5130 119,0133 37,7404 53,3520 205,2436
High_anc 217,3704 216,4261 70,5951 93,6697 377,0142
Hedonic 202,5891 202,0648 71,9182 74,4367 361,0799
Int_1 369,2639 368,2668 134,5169 130,6415 666,2464
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95,0000
Number of bootstrap samples for percentile bootstrap confidence intervals:
1000
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
Hedonic High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
93
Appendix F: Linear Regression - bathtub Linear Regression
Regression Statistics
R 0,74
R-square 0,54
Adjusted R-square 0,53
S 3.314,42
N 163
WTP (Willingness-to-pay) - (Y) = 1,019,14474 + 1,817,44875 * Anchor + 8,161,90277 * W1*X - 317,11219 * W2*X
ANOVA
d.f. SS MS F p-
level
Regression 3, 2.054.357.552,3 684.785.850,77 62,34 0,00
Residual 159, 1.746.674.618,36 10.985.374,96
Total 162, 3.801.032.170,66
Coefficient Standard Error LCL UCL t Stat p-
level
Intercept 1.019,14 380,19 268,27 1.770,02 2,68 0,01
Anchor 1.817,45 1.843,69 -1.823,84 5.458,73 0,99 0,33
Hedonic value 8.161,9 715,57 6.748,65 9.575,16 11,41 0,00
CSC -317,11 229,22 -769,82 135,59 -1,38 0,17
94
Appendix G: Simple moderation CSC – bathtub
*************** PROCESS Procedure for SPSS Version 3.3 *******************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2018). www.guilford.com/p/hayes3
**************************************************************************
Model : 1
Y : wtp
X : High_anc
W : csc
Sample
Size: 163
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,41 ,17 19960248,1 14,42 3,00 159,00 ,00
Model
coeff se(HC0) t p LLCI ULCI
constant 3124,96 353,94 8,83 ,00 2425,93 3823,99
High_anc 3908,89 679,53 5,75 ,00 2566,81 5250,97
csc -32,65 228,36 -,14 ,89 -483,65 418,35
Int_1 -305,74 435,82 -,70 ,48 -1166,49 555,01
Product terms key:
Int_1 : High_anc x csc
Covariance matrix of regression parameter estimates:
constant High_anc csc Int_1
constant 125274,27 153490,51 -6405,04 -48466,38
High_anc 153490,51 461767,02 -48466,38 -12594,57
csc -6405,04 -48466,38 52146,35 72273,86
Int_1 -48466,38 -12594,57 72273,86 189940,93
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W ,00 ,49 1,00 159,00 ,48
----------
Focal predict: High_anc (X)
Mod var: csc (W)
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc csc wtp .
BEGIN DATA.
-,53 -1,45 849,30
,47 -1,45 5201,61
-,53 ,00 1038,62
,47 ,00 4947,51
-,53 1,45 1227,94
,47 1,45 4693,42
END DATA.
GRAPH/SCATTERPLOT=
95
csc WITH wtp BY High_anc .
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95,0000
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
csc High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
96
Appendix H: Simple moderation: hedonic value for bathtub Model : 1
Y : wtp
X : High_anc
W : Hedonic
Sample
Size: 163
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,7424 ,5511 10730813,3 47,8623 3,0000 159,0000 ,0000
Model
coeff se(HC0) t p LLCI ULCI
constant 3001,8335 248,2145 12,0937 ,0000 2511,6105 3492,0566
High_anc 3597,9211 485,3088 7,4137 ,0000 2639,4373 4556,4050
Hedonic 5166,8226 477,1423 10,8287 ,0000 4224,4677 6109,1775
Int_1 6308,4159 932,7369 6,7633 ,0000 4466,2626 8150,5691
Product terms key:
Int_1 : High_anc x Hedonic
Covariance matrix of regression parameter estimates:
constant High_anc Hedonic Int_1
constant 61610,4311 44416,3555 111753,045 73712,5504
High_anc 44416,3555 235524,655 73712,5504 429068,367
Hedonic 111753,045 73712,5504 227664,754 165308,014
Int_1 73712,5504 429068,367 165308,014 869998,123
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W ,1056 45,7428 1,0000 159,0000 ,0000
----------
Focal predict: High_anc (X)
Mod var: Hedonic (W)
Conditional effects of the focal predictor at values of the moderator(s):
Hedonic Effect se(HC0) t p LLCI ULCI
-,5215 308,2564 156,8810 1,9649 ,0512 -1,5831 618,0960
,4785 6616,6723 919,4490 7,1963 ,0000 4800,7626 8432,5820
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc Hedonic wtp .
BEGIN DATA.
-,5337 -,5215 142,9487
,4663 -,5215 451,2051
-,5337 ,4785 1942,7027
,4663 ,4785 8559,3750
END DATA.
GRAPH/SCATTERPLOT=
High_anc WITH wtp BY Hedonic .
97
*********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS ************
OUTCOME VARIABLE:
wtp
Coeff BootMean BootSE BootLLCI BootULCI
constant 3001,8335 3003,5520 247,4225 2546,9479 3529,3431
High_anc 3597,9211 3591,5415 492,2137 2574,3329 4519,5215
Hedonic 5166,8226 5165,8545 468,5030 4306,3172 6104,6577
Int_1 6308,4159 6287,4935 948,3647 4344,4682 8061,3881
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95,0000
Number of bootstrap samples for percentile bootstrap confidence intervals:
1000
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
Hedonic High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
98
Appendix I: Moderated moderation model - pen
Model : 3
Y : wtp
X : High_anc
W : Hedonic
Z : csc
Sample
Size: 187
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,3569 ,1274 254136,612 6,7117 7,0000 179,0000 ,0000
Model
coeff se(HC0) t p LLCI ULCI
constant 124,5947 40,4633 3,0792 ,0024 44,7482 204,4412
High_anc 226,7090 75,6669 2,9961 ,0031 77,3950 376,0229
Hedonic 217,3409 77,2547 2,8133 ,0055 64,8938 369,7881
Int_1 396,2726 144,4672 2,7430 ,0067 111,1944 681,3508
csc -34,6143 22,8886 -1,5123 ,1322 -79,7805 10,5519
Int_2 -63,7749 42,8017 -1,4900 ,1380 -148,2359 20,6861
Int_3 -44,6728 43,7835 -1,0203 ,3090 -131,0711 41,7254
Int_4 -82,4895 81,8753 -1,0075 ,3151 -244,0546 79,0755
Product terms key:
Int_1 : High_anc x Hedonic
Int_2 : High_anc x csc
Int_3 : Hedonic x csc
Int_4 : High_anc x Hedonic x csc
Covariance matrix of regression parameter estimates:
constant High_anc Hedonic Int_1 csc Int_2 Int_3
Int_4
constant 1637,2783 3061,4336 3088,8820 5775,6894 -691,5364 -1293,1446 -1280,0963
-2393,7321
High_anc 3061,4336 5725,4758 5775,6894 10801,6567 -1293,1446 -2418,2418 -2393,7321
-4476,3822
Hedonic 3088,8820 5775,6894 5968,2866 11159,6865 -1280,0963 -2393,7321 -2525,5602
-4722,6924
Int_1 5775,6894 10801,6567 11159,6865 20870,7836 -2393,7321 -4476,3822 -4722,6924
-8831,6608
csc -691,5364 -1293,1446 -1280,0963 -2393,7321 523,8873 979,6416 950,5635
1777,5073
Int_2 -1293,1446 -2418,2418 -2393,7321 -4476,3822 979,6416 1831,9894 1777,5073
3324,0385
Int_3 -1280,0963 -2393,7321 -2525,5602 -4722,6924 950,5635 1777,5073 1916,9935
3584,6757
Int_4 -2393,7321 -4476,3822 -4722,6924 -8831,6608 1777,5073 3324,0385 3584,6757
6703,5633
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W*Z ,0028 1,0151 1,0000 179,0000 ,3151
----------
Focal predict: High_anc (X)
Mod var: Hedonic (W)
99
Mod var: csc (Z)
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc Hedonic csc wtp .
BEGIN DATA.
-,5348 -,5241 -1,3887 ,8143
,4652 -,5241 -1,3887 48,3812
-,5348 -,5241 ,0000 ,5141
,4652 -,5241 ,0000 19,5508
-,5348 -,5241 1,3887 ,2139
,4652 -,5241 1,3887 -9,2797
-,5348 ,4759 -1,3887 7,0235
,4652 ,4759 -1,3887 565,4135
-,5348 ,4759 ,0000 5,9445
,4652 ,4759 ,0000 421,2538
-,5348 ,4759 1,3887 4,8656
,4652 ,4759 1,3887 277,0941
END DATA.
GRAPH/SCATTERPLOT=
Hedonic WITH wtp BY High_anc /PANEL ROWVAR= csc .
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95,0000
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
Hedonic csc High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
100
Appendix J: Moderated moderation model - bathtub
Model : 3
Y : wtp
X : High_anc
W : Hedonic
Z : csc
Sample
Size: 163
**************************************************************************
OUTCOME VARIABLE:
wtp
Model Summary
R R-sq MSE F(HC0) df1 df2 p
,7505 ,5632 10710505,8 22,5858 7,0000 155,0000 ,0000
Model
coeff se(HC0) t p LLCI ULCI
constant 3065,5645 261,3913 11,7279 ,0000 2549,2152 3581,9139
High_anc 3580,9524 515,6343 6,9448 ,0000 2562,3742 4599,5306
Hedonic 5288,3619 502,3640 10,5270 ,0000 4295,9978 6280,7260
Int_1 6274,4475 990,7882 6,3328 ,0000 4317,2557 8231,6393
csc -36,2532 153,7047 -,2359 ,8139 -339,8798 267,3734
Int_2 -518,5779 302,7415 -1,7129 ,0887 -1116,6101 79,4542
Int_3 -44,3774 295,2464 -,1503 ,8807 -627,6037 538,8489
Int_4 -970,3595 581,4399 -1,6689 ,0972 -2118,9292 178,2103
Product terms key:
Int_1 : High_anc x Hedonic
Int_2 : High_anc x csc
Int_3 : Hedonic x csc
Int_4 : High_anc x Hedonic x csc
Covariance matrix of regression parameter estimates:
constant High_anc Hedonic Int_1 csc Int_2 Int_3
Int_4
constant 68325,3892 31983,8947 124563,663 49745,1857 6832,4598 -37508,654 14119,9160
-70062,694
High_anc 31983,8947 265878,727 49745,1857 487044,591 -37508,654 37626,2313 -70062,694
75737,2137
Hedonic 124563,663 49745,1857 252369,551 119610,987 14119,9160 -70062,694 24950,3470
-138254,28
Int_1 49745,1857 487044,591 119610,987 981661,239 -70062,694 75737,2137 -138254,28
137748,896
csc 6832,4598 -37508,654 14119,9160 -70062,694 23625,1420 12097,3929 43607,8407
20177,7313
Int_2 -37508,654 37626,2313 -70062,694 75737,2137 12097,3929 91652,4157 20177,7313
169757,721
Int_3 14119,9160 -70062,694 24950,3470 -138254,28 43607,8407 20177,7313 87170,4151
45006,4614
Int_4 -70062,694 75737,2137 -138254,28 137748,896 20177,7313 169757,721 45006,4614
338072,319
Test(s) of highest order unconditional interaction(s):
R2-chng F(HC0) df1 df2 p
X*W*Z ,0048 2,7852 1,0000 155,0000 ,0972
----------
Focal predict: High_anc (X)
Mod var: Hedonic (W)
101
Mod var: csc (Z)
Test of conditional X*W interaction at value(s) of Z:
csc Effect F(HC0) df1 df2 p
-1,4503 7681,7507 45,6308 1,0000 155,0000 ,0000
,0000 6274,4475 40,1042 1,0000 155,0000 ,0000
1,4503 4867,1443 11,3221 1,0000 155,0000 ,0010
Conditional effects of the focal predictor at values of the moderator(s):
Hedonic csc Effect se(HC0) t p LLCI
ULCI
-,5215 -1,4503 327,2202 223,4777 1,4642 ,1452 -114,2351
768,6754
-,5215 ,0000 309,0013 157,6853 1,9596 ,0518 -2,4884
620,4910
-,5215 1,4503 290,7823 165,1936 1,7603 ,0803 -35,5391
617,1038
,4785 -1,4503 8008,9709 1115,0108 7,1829 ,0000 5806,3911
10211,5507
,4785 ,0000 6583,4488 978,1598 6,7304 ,0000 4651,2029
8515,6946
,4785 1,4503 5157,9266 1437,0129 3,5893 ,0004 2319,2675
7996,5858
Data for visualizing the conditional effect of the focal predictor:
Paste text below into a SPSS syntax window and execute to produce plot.
DATA LIST FREE/
High_anc Hedonic csc wtp .
BEGIN DATA.
-,5337 -,5215 -1,4503 152,1942
,4663 -,5215 -1,4503 479,4143
-,5337 -,5215 ,0000 142,9028
,4663 -,5215 ,0000 451,9040
-,5337 -,5215 1,4503 133,6113
,4663 -,5215 1,4503 424,3937
-,5337 ,4785 -1,4503 1404,8407
,4663 ,4785 -1,4503 9413,8115
-,5337 ,4785 ,0000 2082,3264
,4663 ,4785 ,0000 8665,7752
-,5337 ,4785 1,4503 2759,8122
,4663 ,4785 1,4503 7917,7388
END DATA.
GRAPH/SCATTERPLOT=
Hedonic WITH wtp BY High_anc /PANEL ROWVAR= csc .
*********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS ************
OUTCOME VARIABLE:
wtp
Coeff BootMean BootSE BootLLCI BootULCI
constant 3065,5645 3066,5604 268,6762 2571,2162 3649,5985
High_anc 3580,9524 3580,8860 534,0163 2455,9259 4603,1717
Hedonic 5288,3619 5295,4474 519,9970 4386,5878 6437,7915
Int_1 6274,4475 6289,1305 1024,2842 4240,8968 8338,8230
csc -36,2532 -38,4670 154,6249 -320,9172 298,2492
Int_2 -518,5779 -537,3788 308,5808 -1157,7190 78,6727
Int_3 -44,3774 -46,1841 297,0175 -574,6606 593,3935
Int_4 -970,3595 -1002,3364 596,3488 -2225,9183 182,8640
*********************** ANALYSIS NOTES AND ERRORS ************************
102
Level of confidence for all confidence intervals in output:
95,0000
Number of bootstrap samples for percentile bootstrap confidence intervals:
1000
Z values in conditional tables are the mean and +/- SD from the mean.
NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator
was used.
NOTE: The following variables were mean centered prior to analysis:
Hedonic csc High_anc
NOTE: Variables names longer than eight characters can produce incorrect output.
Shorter variable names are recommended.
------ END MATRIX -----
103
Appendix K: WTP, CSC, and Familiarity (average)
WTP CSC Familiarity
Pen US$ 116.74 7.65 7.74
Utilitarian US$ 13.72 7.37 8.39
Hedonic US$ 210.29 7.90 7.14
Bathtub US$ 3.103.69 7.48 6.66
Utilitarian US$ 297.08 7.48 6.03
Hedonic US$ 5.679.18 7.48 7.24