Dissertation_Dimitar_Filipov_101008835

Royal Holloway
University of London
MBA International Management
Dissertation
May 2014
The role of bundling as a means of exploiting bounded
rationality in consumers’ behavioural patterns
Dimitar Filipov
101008835

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Table of Contents
ABSTRACT................................................................................................................................ 5
CHAPTER 1: INTRODUCTION.................................................................................................. 6
1.1. Introduction.................................................................................................................. 7
1.2. Reasons for undertaking this research......................................................................... 7
1.3. Background.................................................................................................................. 8
1.4. Current development of prospect and bundling theories .............................................. 9
1.5. Unexplored areas .......................................................................................................10
1.6. Objective.....................................................................................................................10
1.7. Research method .......................................................................................................11
1.8. Obtained results..........................................................................................................12
1.9. Structure of the paper .................................................................................................12
CHAPTER 2: LITERATURE REVIEW.......................................................................................14
2.1. Introduction.....................................................................................................................15
2.2. Commercial Bundling and Consumers’ Irrational Behaviour ...........................................15
2.2.1. Bundling – general theory ........................................................................................16
2.2.2. Bundling and the psychology of consumers’ decisions.............................................17
2.2.3. Bundling and irrational behaviour .............................................................................18
2.3. Prospect Theory ............................................................................................................21
2.3.1. Principals of prospect theory ...................................................................................21
2.3.2. Application of prospect theory ..................................................................................25
2.4. Current paper approach..................................................................................................27
2.5. Complex bundles and impulse buying ............................................................................27
CHAPTER 3: RESEARCH METHOD........................................................................................30
3.1. Introduction.....................................................................................................................31

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3.2. Research philosophy ......................................................................................................31
3.3. Research approach ........................................................................................................31
3.4. Research method ...........................................................................................................31
3.5. Research Strategy..........................................................................................................32
3.6. Research question..........................................................................................................32
3.7. Research objectives .......................................................................................................33
3.8. Questions construction and underlying logic...................................................................35
3.8.1. Demographic information .........................................................................................36
3.8.2. Complex bundles .....................................................................................................36
3.8.3. Impulse buying.........................................................................................................39
3.9. Organisation and availability of the survey ..................................................................40
CHAPTER 4: OBTAINED RESULTS ........................................................................................41
4.1. Introduction.....................................................................................................................42
4.2. Demographic information about respondents. ................................................................42
4.3. Complex bundles............................................................................................................46
4.4. Impulse buying ...............................................................................................................54
CHAPTER 5: ANALYSIS OF OBTAINED RESULTS ................................................................57
5.1. General results ...............................................................................................................58
5.1.1. Objective 1 – Complex bundles................................................................................59
5.1.2. Objective 2 – Decision making under impulse buying of bundles .............................60
5.2. Additional results ............................................................................................................61
5.2.1. Comparison on a geographic distribution base.........................................................62
5.2.2. Comparison on a gender base .................................................................................64
5.2.3. Comparison on an age group base ..........................................................................67
CHAPTER 6: DISCUSSION......................................................................................................70
6.1. Introduction.....................................................................................................................71
6.2. Conclusions....................................................................................................................71
6.2.1. Complex bundles .....................................................................................................71

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6.2.2. Impulse buying of bundles........................................................................................71
6.2.3. Additional findings....................................................................................................72
6.3. Applications of complex bundles results .........................................................................74
6.3.1. National regulatory bodies........................................................................................74
6.3.2. Dynamic or customized bundling..............................................................................75
6.4. Further directions for research........................................................................................77
REFERENCES .........................................................................................................................79
APPENDIXES...........................................................................................................................89
Appendix 1 - Questionnaire ...................................................................................................90

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ABSTRACT
Bundling as a marketing strategy has become extremely pervasive for the last half a century.
Amongst the academic community there is a discussion about the reasons for its success.
Some of the scholars believe that there is a purely rational explanation of this phenomenon,
whereas others argue that its drivers are predominantly irrational. In this paper the author
assumes the second view and tries to answer the question whether, how and to what extent
does bundling exploit consumers’ irrationality. This question has been answered by
investigating consumers’ decision-making process on commercial bundles through the prism of
prospect theory. A quantitative monomethod with a survey strategy has been chosen, using a
self-completed, Internet based questionnaire. The results show undoubtedly that consumers are
heavily irrational when deciding on bundles, affected by most of the prospect theory’s
components. Some of the major areas of possible implementation of these findings are the
regulatory bodies surveying monopolistic and oligopolistic structures, all businesses using
dynamic bundling and more specifically, most of the Internet businesses. Additional findings
concerning the relation between the homogeneity of respondents, their reactions and some
socio-demographic factors have been reached in the course of the study.

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“…There is a principle in human perception, the contrast principle, that affects
the way we see the difference between two things that are presented one after
another. Simply put, if the second item is fairly different from the first, we will tend
to see it as more different than it actually is…” – Cialdini (2009)
1.1. Introduction
For the last several decades bundling has become an extremely pervasive practice. Bundle
strategies have been used in retail, communications, automotive industry, financial services and
many other industries. Bundling is a viable marketing strategy for goods as well as for services.
The commercial success of this strategy is recognised both by the academia and the business.
1.2. Reasons for undertaking this research
The importance of understanding the drivers behind the success of this phenomenon are
unquestionable. There are several key reasons behind this necessity. Firstly, consumers’
reactions to different bundles are in the core of marketing strategies of a waste range of
businesses. Understanding the underlying principles would then directly result in increased
sales and respectively, profits. Secondly, state regulatory agencies need a deeper
understanding of it in order to prevent market inefficiencies or market externalities on regulated
markets in the conditions of natural monopolies and oligopolies. For such a problem signal
Xavier and Ypsilanti (2010) in the area of telecom business, which is rich on natural monopolies
and regulated markets. Last but not least, an important area of interest is the growing market of
Internet sales. The development of IT capabilities allows higher level of customization of the
operations, which in turn increases the options for tailoring individual packages for any particular
customer. Similar applications are the dynamic bundles, created individually according to the

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taste of the unique consumer ( Zhengping and An, 2010, Kolay and Shaffer, 2003, Kannan and
Kopalle, 2001, Grentzi and Watts, 2007, Yang and Lai, 2006).
1.3. Background
Current paper explores the underlying principles of commercial bundling through the scope of
prospect theory framework. Bundling has a long history as a marketing strategy and it has been
extensively analysed and discussed by the business and the academia. It has an established
body of knowledge with several key mainstreams of thought. These are summarised in table 1.
Bundling - general CRM approach Irrational approach
Stingler (1963),
Adams and Yellen (1976),
Schmalensee (1984),
Tesler, (1979),
Harris et al. (2006),
Jeuland, (1984),
Kopalle et al. ,(1999),
McAfee et al., (1989),
Tellis and Stremersch (2002),
Economides (2003),
Long, (1984),
Fang and Norman (2006)
Yadav and Monroe (1993),
Yadav (1994; 1995),
Titheesawad and Kijboonchoo
(2004),
Harlam, et al. (1995),
Herrmann et al. (1997),
Bagchi and Davis (2012),
Heeler et al. (2007),
Janiszewski and Cunha
(2004)
Sheng et al. (2007)
Puri (1998),
Sharpe and Staelin (2010),
Harris (1997),
Arkes and Blumer (1985),
Prelec and Loewenstein
(1998),
Soman and Gourville (2001),
Bar-Gill (2006),
(2004),
Goh and Bockstedt (2013),
Gilbride et al. (2008)
Table 1 – Summary of the mainstreams in bundling research

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Prospect theory of Kahneman and Tversky (1979), on the other hand, is a relatively new theory,
which appears as an alternative of the theory of perceived utility (Von Neumann, 2007) about
decision making under uncertainty and risk. Kahneman and Tversky are undoubtedly the major
contributors on the field of prospect theory. However, for the last 30 years it has been a highly
popular topic amongst both the academia and business and numerous contributors have added
to this theory valuable parts. Some of the most notorious scholars on the field are Richard
Thaler, David Shkade and Norbert Swartz, to name a few. Daniel Kahneman was awarded with
the Nobel prize for economics in 2002 for his work on prospect theory.
1.4. Current development of prospect and bundling theories
As it has already been noted, bundling is a marketing strategy of selling different products
together in a bundle. It has been actively in use for the last half a century. According to some of
the pioneers in the research of this strategy (Stigler, 1963, Adams and Yellen, 1976) one of its
main purposes is extracting consumers’ surplus. This theory was dominant for more than 20
years. Although logical, however, this view supposes rational decision-making from the part of
consumers.
Further development on the field of consumers’ decision-making psychology, however, has
found that sometimes consumers could react irrationally. For instance, Harris (1997) argues that
bundling could change perceived utility of the products under bundles. This is an important
milestone in the development of bundling strategy as it clearly implies the existence of
significant irrational element in this approach.
Similarly, the prospect theory has been extensively developed. The main areas of development
are undoubtedly the areas where decisions are taken on a daily basis. These are the sectors of

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finance and insurance. To a lesser extent there are development in the areas of consumption-
savings decisions, organisation and labour supply. According to Barberis (2012), however, the
high-scale implementation of prospect theory frameworks and techniques has been impeded by
the lack of clear algorithm for practical implementation.
1.5. Unexplored areas
Although the irrational element in decision-making is widely acknowledged, quite a limited
number of research approaches apply the prospect theory framework in exploring the causality
of this irrationality. Although most approaches claim to use behavioural science in the analysis,
in fact just a few of them use selected aspects of prospect theory. For instance, Goh and
Bockstedt (2013) research the relation between bundling and reference dependence, which is
just one of the elements of Barberis’ framework. The author could not find any research to use
the full set of elements of the prospect theory. Apparently, one major reason for that is the
finding of Barberis (2012) that pure prospect theory could not be easily translated in practical
principles.
1.6. Objective
The research question of this paper is to establish whether, how and to what extent does
bundling exploit consumers’ irrationality. The answer of this question has been sought by testing
two observed cases of anomalies in consumers’ rationality in decision making related to
bundles. It comes to so called “complex bundles” and bundles under conditions of impulse
buying. A number of scholars have observed irrational behaviour in these cases. For instance,
Soman and Gourville (2001) find that bundling causes ambiguity in consumers’ perception of
bundle. Xavier and Ypsilanti (2010) argue that such confusion in consumers’ perception could
be dangerous in conditions of natural monopolies and regulated markets. Impulse buying, on

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the other hand, is a well known phenomenon amongst marketers and psychologists. By
definition, the impulse purchase is “an unplanned purchase” (Patterson, 1963). Rookh (1987)
suggests that buying urges are triggered by multiple psychological and biochemical factors.
1.7. Research method
According to Saunders et al. (2011) classification, the character of this research manifests a
positivistic philosophy and use a deductive approach. Similarly to the research approach of the
underlying theory (Kahneman and Tversky 1979; 1992), a quantitative monomethod has been
chosen for the current research. Budget and longitude limitations have determined the research
strategy to be a survey, performed by a questionnaire. Hypothetical polar questions concerning
decision-making on bundles have been created with conditions and underlying logic according
to the main elements of prospect theory as formulated by Kahneman and Tversky (1979) and
further distillated by Barberis (2012). The four components of the prospect theory chosen to be
tested are reference dependence, loss aversion, diminishing sensitivity and probability
weighting.
According to Stern (1962) goods subject to impulse buying must possess several key attributes.
They must be relatively inexpensive and relatively small. These specific characteristics of
impulse buying induce limitation of the application of some of the elements of the prospect
theory in the case of impulsive buying. The low price of impulse purchases makes the elements
“loss aversion” and “diminishing sensitivity” inapplicable in the case of impulse buying. Loss
aversion becomes an insignificant factor because consumers become less sensitive to losses.
Similarly, the effect is the same on “diminishing sensitivity” factor.
On the base of the reflections above, two questions have been added to the questionnaire,
representing the tests of the influence of two of the elements defined by the prospect theory.

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These are the reference dependence and probability weighting under the condition of impulse
buying.
1.8. Obtained results
Results from the research clearly indicate that it exists a strong irrational element in consumers’
decision- making process on bundled goods and services. This element could be satisfactory
explained by the principles of prospect theory. The research, however, failed to find a
correlation between impulse buying of bundles and any irrational element as viewed by prospect
theory. It has been recommended impulse buying objective to be tested through a real time
experiment as the negative correlation could be due to the limitations of the method used.
Interesting additional conclusions have been reached using analysis on a demographic
principle. The findings challenge the established homogeneity assumptions on the respondent
agent and suppose different behavioural reactions by different consumers’ subgroups on a
demographic basis.
1.9. Structure of the paper
The paper begins with an Abstract, outlining the main purpose of the research, the reasons for
undertaking this research, the methods used in order to obtain the necessary data and the
results of the research. Next follows an Introduction part, which aims to introduce the reader into
the main subjects of research, the current level of research in the explored areas and to briefly
outline the used methods and findings. A Literature review follows where all key theories and
achievement in the areas of research, along with the names of the key contributors are
discussed in detail. Then it follows the Method part, which outline the chosen method and
techniques of research. Finally, there are the Result and Analysis of Results parts, followed by

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the Discussion part, where some suggestions for further development and possible implication
of current findings are discussed.

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CHAPTER 2: LITERATURE REVIEW

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2.1. Introduction
This study evaluates commercial bundling and its effects through the prism of prospect theory.
In the course of evaluation, two main topics have been discussed and analysed – the theory of
bundling and the prospect theory. Additionally, two areas of bundling strategy have been
explored, areas where a deviation from rationality has been observed. These are impulse
buying and complex bundles, where under “complex bundles” are comprised bundles with more
than two components.
2.2. Commercial Bundling and Consumers’ Irrational Behaviour
Historically, the theory of bundling has undergone through several stages. Tellis and
Stremersch (2002) offer a good classification. They divide the historical development of
academics’ view on bundling into three main stages, according to the focus of scholars on
different aspects of bundling. These stages are the focus on optimality of bundling, focus on
consumer evaluation of bundles and firm’s pricing and promotion of bundles. The author uses
theirs classification with a slightly modified headings. The first two stages coincide with the
ideas of this paper. The third one, however, could include both rational and irrational views on
the firm’s pricing and promotion strategies, whereas the current paper is focused on the
irrational element in decision-making under bundling. This is the reason the third stage to be
replaced with one treating only irrationalities related to bundling. The chosen classification thus
includes the general theory of bundling describing issues related to optimality of bundles, CRM
view of bundling, treating common psychological issues related to customer decisions on
bundles and irrationalities in bundling, which presents different cases where deviations from
rationality have been observed. The last part is on the focus of this paper.

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2.2.1. Bundling – general theory
Bundling is a widely used marketing strategy. Initially, the benefits of bundling have been
assumed to be the economies of scale, scope, distribution and aggregation (Bakos and
Brynjolfsson, 2000). The first observations on bundling as a commercial practice have been
performed by Stigler (1963). He analyses the “block-booking” in film industry in the US. In this
short analysis he introduces the idea that bundling could be effectively used for extraction of
consumer surplus under condition of monopoly. Adams and Yellen (1976) take a deeper
analysis of this strategy. Using examples they prove that by bundling the reservation prices
limits could be optimised and a consumer surplus extracted. Further on, Schmalensee (1984)
deepens this analysis by studying the bundling where the products under bundle have bivariate
Gaussian (normal) distribution. He performs analysis of the profitability of bundles where their
components have different correlation relationships. Tesler, (1979) researches the relation
between bundles, composed by complimentary products and those, composed by substitute
products. His findings are that the first type of bundles is more profitable. Furthermore,
significant attention was paid to different type of bundle strategies (e.g. mixed bundling strategy
and pure bundling strategy), the market condition under which they could be used and their
market success (Jeuland, 1984, Economides, 2003, Kopalle et al. ,1999). In the following
years, several scholars find that bundling is more profitable than selling separate products and
further developed the underlying theory (Long, 1984, McAfee et al., 1989, Fang and Norman,
2006). Tellis and Stremersch (2002) try to introduce a standard for bundles evaluation and offer
a set of bundling definition, rules for bundle’s evaluation and a framework of 12 rules for optimal
bundling. Harris et al. (2006) introduce another factor in support of the benefits of bundling,
precisely, that bundling reduces the search costs for the consumer.

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2.2.2. Bundling and the psychology of consumers’ decisions
With the development of marketing science and customer relationship management, higher
attention was paid to customer’s perspective of bundling. Factors that affect consumer’s
preferences towards different type of bundles become a focus of the academia and business.
The decision making process become the dominated approach for bundling analysis.
Increasingly, scholars turned their attention towards the psychological motivation behind
consumer’s decisions
Yadav and Monroe (1993), Yadav (1994; 1995) research the process of customer’s decision
making and its different components. One important new observation in these studies is that the
price bundling could affect consumers’ price sensitivity and positively influence their decision to
purchase. Titheesawad and Kijboonchoo (2004) go further and explore five different factors that
shape consumers’ preference, precisely, the bundle composition, the price sensitivity, the price
level, the frame and the familiarity of the products in the bundle. In addition to the benefits of the
bundle already cited, they provide two more – namely, that the bundle could strengthen market
power and that bundling could be used as a short-term promotional market strategy to stimulate
purchase. Similar studies have been performed by Harlam, et al. (1995). They examine the
relation of four factors with the profitability of the bundle, precisely, the composition, the price,
the semantic presentation and the individual difference. Herrmann et al. (1997) perform similar
research and examine the relation of four different factors and consumer’s purchase intention.
The same are the findings of Simonin and Ruth (1995). Bagchi and Davis (2012) go deeper and
examine consumer’s perception of different size of multi-items bundles. Alternatively,
customer’s knowledge on bundled goods is the focus of Basu and Vitharana (2009) research.
Heeler et al. (2007) introduce the inferred bundle savings hypothesis, suggesting that
consumers have savings expectations when the approach to bundle evaluation, whereas

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Janiszewski and Cunha (2004) examine the effect of price discount of different bundle’s
components on the overall evaluation of the bundle by the consumer. Similarly, Sheng et al.
(2007) turn their attention to the relations between price discount, product complimentary and
consumer’s evaluation of the bundle.
Any of these studies recognises the dominant influence of multiple psychological factors on
consumers’ evaluation of bundles. Most of them, however, assume a pure rational reasoning
from consumer’s part.
2.2.3. Bundling and irrational behaviour
As noted in the previous section, although the psychology was admitted to play a major role in
consumer’s choice, rationality unquestionably stayed in the core of most researches.
Predominantly, the analysis of the customer’s perspective used traditional tools and approaches
and assumed traditional or rational underlying. Not until recently did scholars turn to some
theories based on the behavioural science.
Gradually, different scholars have noticed irrational elements in some cases of consumer’s
behaviour. Puri (1998), for instance, argues that bundling increases perceived value of the
products. In a field study of fast food chains bundling practices, Sharpe and Staelin (2010)
reach the same conclusion. Similarly, Harris (1997) believes that bundling can change the cost
and/or the perceived utility of the products under bundle.
In the works above, authors look for alternative explanation of consumers’ preference. One
possible explanation of these anomalies scholars have found in prospect theory.
In a search of better understanding of consumers’ behaviour, increasing number of scholars
have turned to prospect theory. Arkes and Blumer (1985) first introduce the term “sunk cost
effect” in bundling terminology. Their study is based on the work of Thaler (1980) and expresses

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the sunk cost effect as “greater tendency to continue an endeavour once an investment in
money, time, or effort has been made” (Arkes and Blumer, 1985, p124). Furthermore, Thaler
(1980; 1985) introduce the theory of mental accounting. Prelec and Loewenstein (1998)
compliment this theory with introducing the term “coupling”, which, they argue, is the
psychological linking of the transaction costs to benefits.
Using the theory above, Soman and Gourville (2001) research the post decision making
process under bundles and reach to several interesting conclusions. Firstly, they find that
bundling causes ambiguity in consumers’ perception of the price of the bundle. This is an
extremely important finding, as it clearly signals that prospect theory could be applied to
bundling. From the definition of this theory (Kahneman and Tversky ,1979) it is apparent that it
could be applied under conditions of decision making under risk and uncertainty. Additionally,
they find that under this ambiguity, customers could decouple the price paid from the benefits,
which could further turn into irrational behaviour according to prospect theory formulation.
Similar findings were published by Bar-Gill (2006).
The results from the research of different mainstreams of bundling approaches have been
summarised in table 2.

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Bundling - general CRM approach Irrational approach
Stingler (1963),
Adams and Yellen (1976),
Schmalensee (1984),
Tesler, (1979),
Harris et al. (2006),
Jeuland, (1984),
Kopalle et al. ,(1999),
McAfee et al., (1989),
Tellis and Stremersch (2002),
Economides (2003),
Long, (1984),
Fang and Norman (2006)
Yadav and Monroe (1993),
Yadav (1994; 1995),
(2004),
Harlam, et al. (1995),
Herrmann et al. (1997),
Bagchi and Davis (2012),
Heeler et al. (2007),
Janiszewski and Cunha
(2004)
Sheng et al. (2007)
Puri (1998),
Sharpe and Staelin (2010),
Harris (1997),
Arkes and Blumer (1985),
Prelec and Loewenstein
(1998),
Soman and Gourville (2001),
Bar-Gill (2006),
(2004),
Goh and Bockstedt (2013),
Gilbride et al. (2008)
Table 2 – Summary of the mainstreams in bundling research
Very little literature could be found on the direct implementation of prospect theory on bundling
strategy. Titheesawad and Kijboonchoo (2004) suggest that consumers react irrationally in
relation to price sensitivity factor. This is a direct use of the cumulative prospect theory’s third
postulate, the “diminishing sensitivity”. Another study of Goh and Bockstedt (2013) examines
the bundling in relation with the “referencing”, or the first postulate of prospect theory. Gilbride et
al. (2008) examine the framing effect on bundles success in relation with single products.

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Although there are segmented researches on these effects, a full testing on the four postulates
of prospect theory against the bundling strategies has never been performed.
2.3. Prospect Theory
2.3.1. Principals of prospect theory
Prospect theory appears as an alternative to the existing perceived utility theory of Von
Neumann (2007) for analysis of decisions under risk. It has been first formulated by Kahneman
and Tversky (1979). They use as a base the work of Allais (1953), produce several experiments
and reach surprising conclusions. According to this theory, the choices of decision makers
substantially divert from the expected results according to the perceived utility theory, where
they are determined solely by the utility function. Precisely, they found that people underweight
probable outcomes compared to certain outcomes. The authors call this effect the “certainty
effect”. Additionally, they observe two more effects which they call “reflection effect”, accounting
for the fact that reflection of prospects around zero reverses the preference order and the
“isolation effect”, respectively meaning that people focus on elements of the alternatives that
differ and disregard the common ones.
Kahneman and Tversky (1979) offer a two-phase decision-making framework model. The first
phase they name “editing” and the second one – “evaluation”. The “editing” phase, according to
the framework, consists of four major operations: coding, combination, segregation and
cancellation. During the second phase prospects are evaluated, after being edited. Further on,
they argue that choice under uncertainty depends on two core factors. These they call the value
and the weighting function. According to the results of the experiments, they build these
functions graphically. The results show that the value function is concave for gains and convex

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for losses. For the weighting function they find that it is under the normal function under the
expected utility theory with exception in the case with very low probability. Overall, they
associate the value function with people’s attitude towards outcomes and the weighting function
with the attitude towards probabilities. These functions are illustrated in fugure 1.
Value function Weighting function (prospect theory)
Figure 1 – Weighting and value functions according to original prospect theory
Source: Kahnemen and Tversky (1979)
The original theory suffers of several disadvantages. The main of them are firstly that instead of
an individual probability function it uses a cumulative one, satisfying the stochastic dominance
and secondly, the theory is now applicable for uncertain or risky prospects with any number of
outcomes (Kahnemann and Tversky, 1992, Fennema and Wakker, 1997). After a decade of
considerable analysis and with the contribution of multiple scholars (Quiggin, 1982, Schmeidler,

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1989, Yaari, 1987, Weymark, 1981), eventually in 1992 the theory appears in its final state
under the name of cumulative prospect theory (Kahnemann and Tversky, 1992).
In the improved prospect theory Kahneman and Tversky clearly determine the five major
phenomena of choice which divert from the perceived utility theory. These are the framing
effect, nonlinear preferences, source dependence, risk seeking and loss aversion. With the help
of Quiggin (1982), using the formulated by him “rank-dependant functional”, they introduce a
new inversed S-shaped weighting function, which, they believe, holds most of the cases. The
form of the weighting function for both positive and negative gambles is shown in figure 2.
Figure 2- Positive and negative weighting function (cumulative prospect theory)
Source: Kahnemen and Tversky (1992)

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It should be noted, that prospect theory is an extremely discussed topic amongst both academia
and business. There have been hundreds of thousands of publications, respectively, there have
been its proponents as well as its opponents. According to Edwards (1996), all discussions
concern the two main differences from the expected utility theory. First, the framing effect, that
is, that consumers use a reference point to value a prospect in terms of profits and losses
instead of an overall wealth increase and second, that customers attribute a decision weight to
outcomes, which is non-linear in respect to probability.
Prospect theory serves as a base for multiple advancements in the decision-making analysis.
As already suggested in the previous section, the “sunk cost” theory developed by Arkes and
Blumer (1985) is a direct consequence of the prospect theory. Thaler (1985) introduces the
“mental accounting” effect. Along with the sunk cost effect, this represent a new foundation for
analysis of consumer behaviour patterns. Based on the risk aversion effect are the findings of
Knetsch (1989). He introduce the term of “exchange asymmetry”. Kahneman et al. (1990)
extend this study to another similar effect – the “willingness – to-accept- willingness-to-pay
gaps”. Both studies form the “endowment effect” – another important practical consequence of
prospect theory.
Thaler (1980) add another important correction – precisely, that some aspects of prospect
theory could be used not only in risky or uncertain circumstances, but practically, in any other
area of decision making. This gave an immense impulse in the development of prospect theory
and widely spread its areas of implication.

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2.3.2. Application of prospect theory
In an insightful review of the applications of prospect theory, Barberis (2012) lists a number of
areas of practical implementation of this theory. These areas are finance, insurance,
consumption-savings decisions, industrial organisation and labour supply.
2.3.2.1. Finance
The area of highest use, undoubtedly, remains finance. The prospect theory gives new insights
when analysing portfolios behaviour and give a possible explanations in some shortcomings of
Capital Asset Pricing Model (CAPM). Boyer et al. (2010) and Bali et al. (2011) analyse average
returns deviations from the model, whereas Green and Hwang (2012) research the relations
between companies’ IPOs and the expected long-term return. Furthermore, Benartzi and Thaler
(1995) explore the aggregate stock market using prospect theory. They introduce the term
“narrow framing”, which is further examined by Barberis et al. (2006). Another application of
prospect theory in finance is related to the “disposition effect”, which present the fact that most
investors, both individual and professional are tending to sell winning assets and unwilling to sell
losing ones (Odean, 1998, Frazzini, 2006).
2.3.2.2. Insurance
Insurance is an area in which requires a vast amount of consumers’ decisions making. Bundling
is widely used in this industry, and due to the nature of the products, highly customizable
bundles are possible. Not surprisingly, this is the second area implementing prospect theory
Syndor (2010) performs an exhaustive study of a large number of insurance buyers. He finds
that outstanding numbers of decisions deviate from the nominal under expected utility theory

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ones and the factor, which affect mostly consumers decisions is the weighting probability
function. Hu and Scot (2007), on the other hand, find some close relations between annuities
and risky gamble pay offs according to prospect theory. They attribute the unpopularity of
annuities to this relation.
2.3.2.3. Labour supply
Prospect theory helps scholars and business understand the processes through which undergo
workers when assessing their wages and particularly the extra hours payments. Camerer et
al.(1997) perform a research amongst taxi drivers in New York. The reach a conclusion, with the
help of Koszegi and Rabin (2006) theory workers measure the utility of their extra work using a
frame, that is, compared to their normal work day. This theory has received further detailed
analysis by Crawfors and Meng (2011). This theory could bring about new insights into different
types of agency problems.
2.3.2.4. Consumption-savings decisions
This area is the closer one to the subject of this paper. Koszegi and Rabin (2006) offer a an
important milestone in consumers’ decision making analysis by suggesting a reference point for
consumers’ decision process. They argue that such point is consumers’ expectation. Using
these ideas, the offer a dynamic model for analysis of consumption through the prism of
prospect theory (Koszegi and Rabin , 2009). The claim, that at each time consumer extracts
utility from two sources. These are, they argue, the difference between the actual consumption
and the previously expected consumption and second, the difference between the expected
consumption and the projected future expected consumption. Pagel (2012) deepens further this
analysis and offers a more comprehensive framework.

27
2.4. Current paper approach
According to Barberis (2012), one of the major disadvantages of prospect theory is that
although it has been in the public space and although its theoretical supremacy, there have
been very little practical implementations of it. Barberis attributes this to the difficulties related to
the translation of this theory into practice. He offers four main practical attributes of this theory.
These are as follow:
- Reference dependence
- Loss aversion
- Diminishing sensitivity
- Probability weighting
Compared to Goh and Bockstedt (2013) who only undertake a research on the relation between
bundling and framing (reference dependence), Barberis’ framework represents consistently
larger and more detailed foundation for analysis. The current paper’s approach is based on this
framework.
2.5. Complex bundles and impulse buying
As it has been discussed in the previous section, there are multiple evidences that bundling
causes confusion in customer’s evaluation process. Bar-Gill (2006) and Soman and Gourville
(2001) talk about ambiguity related to bundle value perception. For some anomalies in valuation
of large bundles inform Bagchi and Davis (2012). They find that with the augmentation of the
complexity of the bundle, irrationality into choices rises. Sheng et al. (2007) express the opinion
that price discount on bundle hurts consumer perception. Similar findings reach Harris et al.
(2006).

28
Xavier and Ypsilanti (2010) go further by exposing some negative effects of bundling on the
regulated retail telecommunication markets. They argue that complex bundles on the field of this
highly regulated industry leads to sever deformations in consumers’ perception of services
prices. This in turn, leads to an increase of the dead weight effect on these markets, inhabited
mainly by natural monopolies.
As evidenced by the above proves, the common denominator in these cases is uncertainty. The
involvement of uncertainty is a clear signal that prospect theory could be used in the research of
the causality of these phenomena. Apparently, the complexity of the bundle has a positive
correlation with the uncertainty induced in customers (Bagchi and Davis, 2012).
Another interesting phenomenon where the consumers’ rational decision-making is challenged
is impulse buying.
Impulse buying is a widely discussed topic amongst both academia and business. There have
been hundreds of thousands of publications on the topic, discussing almost every aspect of it.
One of the first comprehensive study has been performed by Stern (1962). He introduces four
different types of impulse buying in relation with the influencing factors, precisely, pure impulse
buying, reminder impulse buying, suggestion impulse buying and planned impulse buying. Stern
believes that the first one is performed completely under emotional impulse, the second – when
the customer associates given product with some products she needs, the third one he
attributes to buying behaviour associated with advertisements and the last one with impulse
purchases the shopper has a mental readiness to when entering the store. The analysis goes
further with determining of nine main factors influencing the buying impulse. These are,
according to Stern, the low price, the marginal need, the mass distribution, the self-service,
mass advertising, prominent store display, short product life, small size and light weight and
ease of storage. From particular interest of this study is the factor “prominent store display” as it

29
includes the influence of different package bundles on consumer decision making. Any of these
elements has received a decent amount of academic attention. For instance, Paterson (1963)
and Cox (1964) examine in details how the shelf location and the shelf space affect the impulse
whereas Kolat and Willett (1967) explore the influence of social and demographic environment
on impulse buying patterns. Rookh (1987) attempts to investigate the psychology behind the
impulse buying. Precisely, he goes into details about different subjective onsets, research
consumers’ reactions to their own impulses and finally, analyses the consequences of impulse
buying on consumers.
Although there is an abundance of study materials on impulse buying, very few of them have
been focused on the relation between bundling and impulse buying and even less on their
analysis through the prism of prospect theory. There are several evidences that decision making
processes which undergoes a consumer during an impulse buying of bundles could be
considered a sub-case of these which she experiences under evaluating complex bundles. As
already mentioned above, Stern (1962) believes that bundle type influences consumer
behaviour. Additionally, Mishra and Mishra (2011) research consumers’ preferences and reach
a conclusion that consumers prefer a bonus on the bundle of virtue food than price discount.
Janakiraman et al. (2006) explores different framing paradoxes during impulse buying through
the scope of prospect theory. Similar study has been performed by Chen et al. (2012) with
similar findings.
In the context of prospect theory, very few studies focus on behavioural elements outside of
framing (referencing) both, within bundles studies and impulse buying studies.

31
3.1. Introduction
According to Saunders et al. (2012), the current paper methodology could be classified as one
with positivism philosophy, using deduction approach and applying a quantitative mono method
for collecting the data through a survey. A questionnaire has been created and distributed to
respondents through the Internet.
3.2. Research philosophy
The positivistic philosophy has been chosen as best suited to the precise conditions. After
analysing the existing literature, there have been found some anomalies in the majority of
current academia’s views on bundling. These anomalies have been further explored and made
subject of detailed analysis with the help of framework of research questions. The framework is
based on the prospect theory (Kahneman and Tversky, 1979).
3.3. Research approach
The deduction approach is best suited for the size, the time horizon and the specifics of the
topic. As there have been already some observations on the specific deviations from the
normality, according to currently accepted theory of perceived utility (Von Neumann, 2007).
However, for a further investigation of this topic it could be used an abduction approach in order
to collect some additional information from the field of practice (Suddaby, 2006).
3.4. Research method
A quantitative monomethod has been chosen for the research. The research is using the
original prospect theory (Kahneman and Tversky, 1979) as a base , which is the reason the
core questions to be polar ones presenting exclusive disjunction situations. However, under

32
cumulative prospect theory (Kahneman and Tversky, 1992) it could be used non-polar
questions. This aspect has been discussed later in the discussion part of this paper.
3.5. Research Strategy
The strategy is a survey conducted by a questionnaire. The type of the questionnaire is self-
completed, Internet based one (Saunders et al. 2012, p.420). According to Dillman (2011) the
type of data variables are behavioural data variables. Investigative questions (Bloomberg et al.,
2008) are the questions of how consumers would react on different elements of prospect theory,
which in this case coincide with the research objectives (see below). The questionnaire consists
of eleven questions. The first three of them cover demographic information on the respondents
and according to Fink (2009) could be classified as closed questions. On this information it
could be performed an analysis of the answers of the rest of the questions on demographic
principles. The rest of the questions are forced choice closed polar questions (deVaus, 2002),
generating dichotomous data. The first six of them are hypothetical questions covering the
complex bundles case. It should be noted here, that “complex” is used in this paper in the sense
of causing complexity in the decision-making process. The questionnaire ends with two
hypothetical questions examining the impulsive buying phenomenon. As the target group is
general, any human is part of the research population. All of the hypothetical questions are
individually designed type (Bourque and Clark, 1994). Before distributed to respondents, the
questionnaire had been pilot tested.
3.6. Research question
This research is attempting to answer the question of whether, how and to what extent does
bundling exploit consumers’ irrationality. In answering the question, a prospect theory
framework has been used, based on the work of Kahneman and Tversky (1979; 1992) and

33
Barberis (2012). The induced reaction of bundles in consumers’ decision making process has
been measured against the four components of the prospect theory by answering a specific sets
of questions for each component. Two observed cases of irrational behaviour serve as a base
for the construction of the key questions. These are the complex bundles and the impulsive
buying of bundles. The answers of these questions shape the objectives of the study.
3.7. Research objectives
The main variables in this research are the theory of bundles (Stigler,1963, Adams and Yellen,
1976) and the prospect theory Kahneman and Tversky (1979; 1992). Research objectives are
the result of combining the different components of these variables. There are two main
objectives and six secondary ones altogether.
3.7.1. Determining whether, how and to what extent does bundling affect consumers’
rational choices. This objective is related to the “complex bundles” case. The
suggestion here is that bundles cause ambiguity and confuse consumer
perceptions. This is the common case of bundling and it is applicable for the vast
majority of real life cases of bundles of goods and services. This research objective
has been achieved by answering the following four questions:
3.7.1.1. How and to what extent does reference dependence affect consumers’
decision-making process. In the questionnaire this is question No 4 which
tests these levels
3.7.1.2. How and to what extent does loss aversion affect the process. Answer to
this question gives the answer of question 5

34
3.7.1.3. How and to what extent does diminishing sensitivity affect this process. The
set of questions 6 and 7 is designed to clarify this point
3.7.1.4. How and to what extent does probability weighting affect the process. The
set of questions 8 and 9 is trying to establish whether such a relation exists
3.7.2. Determining whether, how and to what extent does impulse buying of commercial
bundles affect consumers’ rational choices. This is a sub-case of objective one.
Some restrictions are applied in this case due to the nature and the specifics of
impulse buying. Precisely, the low price of bundles sold under impulse buying
conditions imposes limitations to the loss aversion and diminished sensitivity
components. Similar limitations are present for the negative part of weighting
function. For the reasons above only two questions are included in the research
related respectively to reference dependence component and the positive part of
weighting function component.
3.7.2.1. How and to what extent does reference dependence affect consumers’
decision-making process under impulse buying of bundles. Question 10
measures this parameter.
3.7.2.2. How and to what extent does weighting probability function affect
consumers’ decision-making process under impulse buying of bundles.
Question 11 is exploring for possible answers.
The research objectives and survey questions by which these objectives have been achieved
are summarised in table 3.

35
Reference
Dependence
Loss Aversion Diminishing
Sensitivity
Weighting
Function
Complex
Bundles
Question 4 Question 5 Question 6,
Question 7
Question 8,
Question 9
Impulse Buying
of Bundles
Question 10
-- --
Question 11
Table 3 – Questions from the questionnaire in relation to the main objectives covered
The answers of the questions above would then be analysed and compared to the results
obtained by Kahneman and Tversky (1979) for the base questions. If a correlation exists then
for the corresponding component could be said that it is a factor in shaping consumers’ irrational
choice. The magnitude of the difference between the answers of each question would give an
answer to the question for the extent to which this component influences the irrational decision
making. Respectively, this would give an answer to the question of the relative weight of each
component in the total mix. Some demographic relations would be examined.
3.8. Questions construction and underlying logic
In this section are discussed the survey questions and their underlying logic. All questions
present hypothetical situations where a decision must be taken. For simplicity all questions have
been chosen to be polar, that is, the logic of the original prospect theory is applied. Some of the
questions are extreme (highly idealized ) for the purpose of survey. Practically, humans face
similar situations but with lower degree of severity on a daily basis. For further studies the

36
cumulative prospect theory could be applied, including more than two possible answers and
offering cumulative values for different answer.
3.8.1. Demographic information
The first three questions are designed to collect demographic information about the
respondents. On the base of this information would be performed an analysis of any possible
deviations between the answers of people of different genders, age and geographic location.
3.8.2. Complex bundles
This is the first part of the research. It treats the general bundling and the irrationalities it causes
in consumers’ decision-making process. These are the questions covering reference
dependence, loss aversion, diminishing sensitivity and probability weighting effects. These
effects cover bundles of goods as well as bundles of services.
Question 4
This is an attempt to measure the extent to which referencing play a role into consumers’
evaluation of complex bundles. Consumer is faced with a choice where there are two options.
The first option is worse in terms of value for money but is better in absolute price. The second
option, on the contrary, represents better value for money. However, it is more expensive than
the reference one in absolute terms. In this case, the reference is based upon present
conditions, that is, the current agreement with the telecom. The initial plan is taken as a
reference point. All components weighting equally in the bundle, there is VA=(-0.25+0.2-0.25-
0.25+0)/2= -11% change in the value of bundle A and VB=(-0.25+0.2+0.25-0.25+1)/2= +21%

37
change in bundle B. However, the price of bundle A changes -5%, whereas the price of bundle
B changes +14.3%.
Question 5
This question is intended to capture the loss aversion effect. It represents precisely the
Kahneman and Tversky (1979) finding, that most people don’t accept the (-100, ½ ; 110, ½)
gamble. The alternatives are a 50% chance for a loss of 25 Euro and 50% chance for a profit of
27.50 Euro.
Question 6 and question 7
This question as the next one treat the problem with the diminishing sensitivity. This is the
finding, that most people attribute more utility to a change in a positive gamble when the change
is similar to the base compared to the same change with a larger base. For instance, 25 Euro
positive change in the gamble compared to a 25 Euro base is considered more valuable than 25
Euro change with a 1,000 base. In the original work of Kahneman and Tversky (1979) the
diminishing sensitivity effect is described by problems 13 and 13’. It is difficult, however, to find
a direct application of these gables in the practice. Barberis (2012) offers a more practical view
of this effect. He claims, that the effects of concavity over gains in the value function are
expressed by the fact that humans are risk averse then they are faced moderate probability
gains, for instance, most would accept a sure gain of 500 Euro than a probability of 50% of
gaining 1,000 Euro, though from the perceived utility perspective, these are identical offers.
However, most people are risk seeking when it comes to losses; most would prefer a 50%
probability of losing 1,000 Euro than a sure loss of 500 Euro. The second phenomenon
determines the convexity over losses. This view of Barberis’ gives a field of much more practical
implementations.

38
Questions 5 and question 6 are based on the conclusions above. In question 5 the “buy two get
one free” promotion gives a sure 50% gain over the purchase of two glasses, whereas the
scratching tickets game gives a 50% probability of gaining an additional glass on any glass
purchased. Statistically, both offers are identical, that is, two bundles of 2+1 glasses are equal
to buying 4 glasses with 50% probability of gaining an additional glass on any purchase.
Question 6 offers the same dilemma in the loss area. Consumers are faced the choice of
loosing for sure 500 Euro (the insurance premium) and the 50% chance of a loss of 1,000 Euro.
Question 8 and question 9
These question are designed to verify the probability weighting function. According to
Kahneman and Tversky (1979; 1992) and Barberis (2006; 2012) humans attribute different
weights to probabilities, which divert from the objective probability according to perceived utility
theory (Von Neumann, 2007). It is originally described by Kahneman and Tversky (1979) by
problems 14 and 14’. Barberis (2012) summarises this as the tendency for humans to
overweight the tails in any distribution, that is, to overweight extreme outcomes. For instance,
when faced with the choice of 1/1,000 probability of winning 5,000 Euro and sure gain of 5 Euro
most people choose the gambling, whereas when they are faced with the choice between the
1/1,000 probability of losing 5,000 Euro and the sure loss of 5 Euro they choose the sure loss.
Question 7 covers Kahneman and Tversky ‘s problem 14. Practically, customer is offered to
participate in a monthly lottery with a 1/1,000 chance of winning a 500 Euro device for 0,50 Euro
on its monthly bill.

39
Question 8 covers the opposite case (Kahneman and Tversky‘s problem 14’). Here the
customer is offered a half a percent insurance over his/her initial purchase, bundled with the
purchase itself.
3.8.3. Impulse buying
This is a private case of the complex bundle case. There are only two questions here. The first
one is exploring the reference dependence under impulse buying of bundles and the second
one – the probability weighting function.
Question 10
The logic structure of this question is very similar to the one in question 11. Customer is faced to
a choice of losing 40 cents of not indulge the urge of impulse buying of a favourite chewing
gum. The reference base is the sum of 200 Euro expected to be spent on convenience goods.
The irrational choice here is to by the bundle. However, the prospect theory would predict that
the customer would buy the bundle, as the relative change of 0.40 Euro is very small to the
base of 200 Euro.
Question 11
This is a very common promotion strategy, very popular between FMCG businesses (Nestle,
2014, Milka, 2014). This question is testing the positive part of probability weighting function.

40
The customer is given a choice to buy a single bag instant coffee and win a sure gain of 76
cents (assuming this is an impulsive buy and he/she doesn’t need the other 4 bags) on one
hand and the opportunity to gamble with a low probability of winning a tablet or smart phone.
The question’s logic is similar to one of question 7, but under the conditions of the impulse
buying.
3.9. Organisation and availability of the survey
The survey has been conducted online through the site of surveymonkeys.com (2014). The
survey could be found online at :
https://www.surveymonkey.com/s/79C58JZ
A copy of the questionnaire could be found in the appendixes section.

41
CHAPTER 4: OBTAINED RESULTS

42
4.1. Introduction
The questionnaire was distributed through the Internet between 22 February and 4 March 2014
and was completed by 131 global respondents.
The questionnaire contains eleven questions, within which there are eight core ones measuring
the susceptibility of consumers to various irrationalities described by prospect theory and three
questions describing the demographic profile of the respondents. All of the core questions are
polar questions generating dichotomous categorical data.
The analysis and the visualization of the results has been performed using the Exploratory Data
Analysis (EDA) (Turkey, 1977). Bar chart are used when appropriate, respectively, multiple and
percentage component bar charts where required. Questions’ answers have then been
compared and analysed. Additionally, demographic filters have been applied on the base of
location, gender and age. Additional summary charts have been created and analysed.
4.2. Demographic information about respondents.
These are the first three questions shaping the respondent’s demographic profile.

43
Question 1
“How old are you?”
This question determines the respondent’s age. There are people from different age groups.
However, two principals groups have been formed – the age group of people between 25 and
34 and the age group of people between 35 and 44. The results are presented in figure 3.
Figure 3 – Age groups of the respondents
A. Less than
18 years old
18-24 years
old
25-34 years
old
35-44 years
old
45-54 years
old
Over 55
years old
Series1 0.0% 5.3% 33.6% 31.3% 11.5% 18.3%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Percents
Participants' age

44
Question 2
“What gender are you?”
This question determines the gender of survey’s participants. 54% of the interviewed were
female while 46% - male. Results are visualized in figure 4.
Figure 4 – Respondents’ gender
Female Male
Series1 54.3% 45.7%
40.0%
42.0%
44.0%
46.0%
48.0%
50.0%
52.0%
54.0%
56.0%
Percent
Respondents' gender

45
Question 3
“Where do you live?”
This question is meant to locate the geographical distribution of participants. Respondents are
globally distributed. However, two main groups are observed, the group of North America with
48.5% and the group of Europe with 35.4%. Results are shown in figure 5.
Figure 5 – Geographic distribution of respondents
Europe North America Asia Africa Other
Series1 35.4% 48.5% 10.8% 1.5% 3.8%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Percent
Respondants' location

46
4.3. Complex bundles
The following group of questions is attempting to capture the rationality of consumers’ decision-
making process in relation to decisions on commercial bundles. These are six questions
covering all four structural elements of prospect theory according to Barberis (2012).
Question 4
“Your current contract with the local telecom has just expired. So far, you find
your package satisfactory. An officer from the telecom informs that you need to
change your billing plan as the current one is no longer supported. He offers you
a couple of options for the new contract.
Which option would you choose?”
Current
conditions
Option A
conditions
(Economy pack)
Option B
conditions
(Golden pack)
Cable TV –
number of
channels
96 72 72
A land line phone,
number of
minutes included
500 600 600
Unlimited Cable
Internet, speed
20 MBit/sec 15 MBit/sec 25 MBit/sec
Mobile phone,
number of
minutes included
2,000 1,500 1,500
High Speed
Internet for mobile
phone, MB
512 512 1,024
Price, Euro 79,90 76.40 89.90
This questions has been designed to test the reference dependence. 48% of the participants
choose option A, whereas the rest 52% choose option B, as shown in figure 6.

47
Figure 6 – Results generated by question 4
Question 5
“You are about to subscribe for a new roaming plan. You are not aware of your
current consumption. You can either be billed on what you actually use, which in
most cases is expensive, or accept a new billing plan. The statistics of those,
using the plan are as follow:
Monthly fixed charge Customers using 25
Euro and less monthly
Customers using
77.50 and above
monthly
50 Euro 50% 50%
Would you accept the plan?”
Option A Option B
Series1 47.9% 52.1%
45.0%
46.0%
47.0%
48.0%
49.0%
50.0%
51.0%
52.0%
53.0%
Percent Question 4

48
Question 5 tests the loss aversion effect. 59% answers negatively to this question, whereas
41% answer positively. Results are depicted in figure 7.
Question 6
“You would like to buy some wine glasses. In the store you are offered two
promotions: “Buy two get one free” and a scratching tickets promotion “Every
glass gets a ticket. Every second ticket wins an extra glass”.
What option would you choose?
A. “Buy two get one free”
B. The scratching ticket game.”
No Yes
Series1 59.2% 40.8%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Percent
Question 5

49
This is the first question from the set of two, which measures diminishing sensitivity effect. As
evident from figure 8, 72% of participants choose “Buy two get one free” promotion, whereas the
rest 28% choose the scratching game.
“Buy two get one free” promotion The scratching ticket game.
Series1 71.9% 28.1%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
Percent
Question 6

50
Question 7
“You are about to buy a new piece of furniture from a DIY store. The price of the
furniture is 1,000 Euro. However, at the cash desk you get an unusual warning
from the officer. He claims that precisely this piece of furniture apparently is quite
sophisticated for DIY assembling as almost half of the clients report causing a
total damage while assembling it. For that reason, the management has decided
to offer a 500 Euro additional package of professional assembling, performed by
store’s staff.
What option would you choose?
A. 1,000 Euro DIY package, assuming the 50% risk of ruining your furniture
B. 1,500 Euro package guaranteeing safe assembling”
Question 7 is the second question from the set, exploring the diminishing sensitivity effect.
Results are in a close range, though the majority of the participants choose option A (55%),
whereas the minority choose option B (45%). Results are shown in figure 9.

51
Question 8
“Your current contract with the telecom is about to expire. You are currently
paying 97.10 Euro per month. The help desk officer offers you a new plan, giving
you the opportunity to win every month a 500 Euro worth product of your choice.
The new billing plan includes all features of you current plan and only costs 97.60
Euro per month and according to the telecom’s employee every thousandth of
their customers with this plan, every month wins the claimed prize above.
Would you accept the new plan?”
1,000 Euro DIY package, assuming the
50% risk of ruining your furniture
1,500 Euro package guaranteeing safe
assembling
Series1 55.2% 44.8%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Percent Question 7

52
Question 8 is the first one from the set covering the probability weighting effect. 60% of the
respondents answer positively, whereas the rest 40% - negatively. Results are illustrated in
figure 10.
Question 9
“You are about to buy a new smart phone. The price is 1,000 Euro. The officer
tells you, that the company also offers insurance for their products. Although not
quite common, insurance events do occur. According to the officer, they have 0.5
Yes No
Series1 59.5% 40.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Percent
Question 8

53
% cases of a total damage for the past year. The cost of the total damage
insurance for a year time is 5 Euro.
Would you accept to buy insurance along with your new device?”
This is the second question mapping the probability weighting function. 65% of the respondents
answer positively to this question, against 35% who answer negatively. Results are presented in
figure 11.
Question 9 is the last question of the group of questions exploring consumers’ decision-making
on bundles. It follows two more questions trying to spot inefficiencies in consumers’ decision
under impulse buying of bundles.
Yes No
Series1 64.7% 35.3%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Percent
Question 9

54
4.4. Impulse buying
The final pair of questions examines consumer’s decision-making process under impulse buying
of bundles. Due to the nature of impulse buying and the characteristics of the goods and
services which could be sold under this conditions, there are only two questions which cover the
reference dependence factor and the positive part of probability weighting factor.
Question 10
“You are lining at the cash desk in a big convenience store. In your shopping
trolley there is some merchandise for about 200 Euro. On the cash desk you see
some of your favourite peppermint chewing gums. Unfortunately, they are
bundled with another package with a taste you don’t like. The usual price of the
pack is about 0.50 Euro and the bundle is priced 0.90 Euro. You look for a single
pack of peppermint chewing gums, but on this desk you cannot see such. Would
you buy the bundle?”
Question 10 is attempting to represent a possible reference dependence under conditions of
impulse buying of bundles. Only 16% of the participants answer positively on this question,
whereas 85% answer negatively. Results are presented in figure 12.

55
Question 11
“You are about to pay some goods you have just bought in the store. Suddenly,
on the cash desk you see some of you favourite instant coffee one-doze bags.
You feel like drinking a cap of it. The coffee could be bought on a promotion “Buy
five, get a code, play for a tablet!”. The price of a single bag is 0.19 Euro.
Would you buy the 5 dozes pack? “
This is the second question on impulse buying and the final question of the questionnaire. It
tests the relation between the probability weighting factor and consumers’ decisions under
Yes No
Series1 15.5% 84.5%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
Percent Question 10

56
impulse buying conditions. 43% of the respondents answer positively and 53% - negatively.
Results are displayed in figure 13.
The following section analyses the obtained results.
Yes No
Series1 43.0% 57.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Percent
Question 11

57
CHAPTER 5: ANALYSIS OF OBTAINED RESULTS

58
5.1. General results
The results obtained by the survey are summarised in figure 14. The questions in the
questionnaire have been designed in a way so their answers support or not the prospect theory
approach. In figure 1 there have been presented the main tenets of prospect theory and the
results of the tests against these tenets. For convenience, the answers in support of prospect
theory have been presented in a blue colour and the ones that do not support it – in red. That is,
the predominance of the blue answer means mostly irrational consumers’ behaviour from the
perspective of perceived utility theory, whereas the predominance of red colour – rational
behaviour.
Figure 14 – Summarised results from the survey
47.9%
59.2%
71.9%
55.2% 59.5% 64.7%
15.5%
43.0%
52.1%
40.8%
28.1%
44.8% 40.5% 35.3%
84.5%
57.0%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - general
Supports Doesn't support

59
5.1.1. Objective 1 – Complex bundles
5.1.1.1. Reference dependence
The results show that consumers are only moderately affected by this element. Although
a high percentage of irrational behaviour, the consumers’ part acting rationally is overall
predominant. This result is congruent with the findings of Gilbride (2008) who argues
that the majority of consumers do not use price reference when assessing bundless.
5.1.1.2. Loss aversion
According to the results of question 5 of the questionnaire, consumers are mainly loss
aversive. The results clearly indicate that most consumers are loss aversive. There is
nearly 20% difference in the answers in support to the irrational consumers’ behaviour.
5.1.1.3. Diminishing sensitivity
The results unambiguously show that diminishing sensitivity factor plays a major role in
shaping irrational behavioural patterns in consumers’ decision-making process on
commercial bundles. Both in the areas of convexity and concavity there are strong
evidences of irrational decisions by consumers.
5.1.1.4. Probability weighting
Probability weighting is definitively a major factor in shaping consumers’ decision-making
process. Consumers react irrationally, precisely in the way predicted by prospect theory.
Results are explicitly clear about this fact for both the losses part and the gains part.

60
The overall conclusion is that consumers react in a heavily irrational way when taking decisions
on bundled products. In that sense, bundling could be viewed as a means of exploiting the
bounded consumers’ rationality, as described by the prospect theory. This view is in contrast
with the current theories which regard bundling solely as a means of extracting consumers’
surplus. This is so because these theories assume rational behaviour as described by Von
Neumann’s theory of perceived utility and in their core is the presumption of rational decision-
making by consumers. However, the results of the research demonstrate in a categorical way
that this is not the case.
5.1.2. Objective 2 – Decision making under impulse buying of
bundles
5.1.2.1. Reference dependence under impulse buying of bundles
The test of reference dependence factor under impulse buying of bundles failed to spot
any irrationality in consumers’ behaviour. On the contrary, consumers reacted
categorically in a rational way.
5.1.2.2. Probability weighting under impulse buying of bundles
Consumers’ reactions on probability weighting factor tests were mostly rational,
according to test’s results. The degree of rationality was lesser that to one of reference
dependence test, however, but still, rational results were significantly more than irrational
ones.
The results from the tests of the two factors under impulse buying conditions were both
categorically positive in relation to rational behaviour. This is an unexpected result, as but

61
strategies, used as a base for questions 10 and 11 are real strategies, widely used by retailer
for selling FMCGs. (for instance, Nestle, 2012). Possible explanation are suggested in the
conclusion part.
Results of the research are summarised in table 4. The field highlighted in red do not support
the prospect theory tenets, whereas those in green support them.
Reference
Dependence
Sensitivity
Weighting
Function
Complex
Bundles
Question 4 Question 5 Question 6,
Question 7
Question 8,
Question 9
Impulse Buying
of Bundles
Question 10
-- --
Question 11
Legend: red – failure to support prospect theory tenets
green – support prospect theory tenets
Table 4 – Summary of the research results
5.2. Additional results
Under the course of the experiment, additional interesting findings have been discovered.
Results have been filtered on a demographic principle and then the answers of the core
questions have been compared.
For main fields of comparisons have been chosen geographical location, gender and age group.

62
5.2.1. Comparison on a geographic distribution base
The first comparison has been performed by filtering the results received by respondents
from North America and Europe. Results are graphically displayed in figure 15 for North
America respondents and figure 16 for Europe participants. Results for North America
consumers are prepared on the base of 63 answered questionnaires and those for Europe – on
the base of 46 answered questionnaires.
Figure 15 – Summarised results from the survey - North America
43.9%
61.0% 64.4% 63.8%
53.4% 60.3%
8.8%
38.6%
56.1%
39.0% 35.6% 36.2%
46.6% 39.7%
91.2%
61.4%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - US

63
Figure 16 – Summarised results from the survey - Europe
As it is evident from the charts, there are two striking differences. The first one is the completely
opposite results in two of the cases – the one of the reference dependence and the one of
probability weighting under impulse purchase of bundles. American respondents give answers
which could be considered overall rational, whereas European respondents give overall
irrational answers.
Additionally, for three other factors the difference in the irrational answers between American
and European respondents differs with more than 10 points. This is illustrated in figure 17.
58.1% 53.5%
79.1%
52.4%
72.1% 76.7%
25.6%
52.4%
41.9% 46.5%
20.9%
47.6%
27.9% 23.3%
74.4%
47.6%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - EU

64
Figure 17 – Comparison on irrational reactions of American and European consumers
An interesting conclusion could be drawn from the above figure. The results clearly show that
American consumers react more rationally compared to their European pairs. This paradox will
be further discussed in the discussion part.
5.2.2. Comparison on a gender base
For this comparison results have been filtered on a gender base. Male chart has been build on
the base of the responses of 61 men and the female part on the base of the answers of 70
women. Results have been displayed in figure 18 for men and in figure 19 for women.
58.1%
79.1%
72.1%
76.7%
52.4%
43.9%
64.4%
53.4%
60.3%
38.6%
Reference
Dependence
Diminishing
Sensitivity -
Concavity
Probability
Weighting - Gains
Probability
Weighting - Losses
Probability
Weighting - Impulse
Irrational behaviour - Europe compared to
North America
Europe North America

65
Figure 18 – Summarised results from the survey - Men
Figure 19 – Summarised results from the survey - Women
50.9%
60.7%
67.9%
51.9%
49.1%
64.8%
11.1%
38.9%
49.1%
39.3%
32.1%
48.1% 50.9%
35.2%
88.9%
61.1%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - men
46.0%
58.7%
75.0%
59.0%
69.4% 65.6%
19.7%
45.8%
54.0%
41.3%
25.0%
41.0%
30.6% 34.4%
80.3%
54.2%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - women

66
The gender comparison shows some inconsistencies in the answers of different genders as
well. The most obvious observation is that overall men are more susceptible to the reference
dependence factor and are overall irrational when exposed to this factor, whereas women are
more rational. For the diminishing sensitivity in both concavity and convexity areas and the
probability weighting in the area of gains, however, women show higher irrationality than men.
These results are summarised in figure 20.
Figure 20 – Comparison on irrational reactions of Men and Women
50.9%
67.9%
51.9% 49.1%46.0%
75.0%
59.0%
69.4%
Reference
Dependence
Diminishing
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting - Gains
Irrational behaviour - Men compared
to Women
Men Women

67
5.2.3. Comparison on an age group base
The last comparison is on the base of two of the major age groups of the participants. These are
the age group of respondents aged between 25 and 34 years and the group on these, aged
between 34 and 45 years. The first group consists of 44 respondents and the second one – of
41.
It could not be observed any tendency in the reactions of the consumers from both groups.
Differences in their reactions, however, do exist. The answers of both groups of participants are
depicted in figure 21 and figure 22.
57.1%
41.9%
69.8%
59.5%
76.2%
66.7%
23.8%
48.8%42.9%
58.1%
30.2%
40.5%
23.8%
33.3%
76.2%
51.2%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - 25-34 age group

68
The only conclusion, that could be drawn is that participants from different age groups are
susceptible to different irrational drivers. For instance, on the base of the data it could be stated
that younger people are more reference dependent and react more irrationally to probability
weighting over gains, whereas they are less loss aversive. These results are summarised in
figure 23.
48.6%
61.8%
82.9%
51.5% 54.5%
66.7%
14.7%
42.4%
51.4%
38.2%
17.1%
48.5% 45.5%
33.3%
85.3%
57.6%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Probability
Weighting -
Losses
Reference
Dependence
- Impulse
Probability
Weighting -
Impulse
Summary of obtained results - 35-44 age group

69
Figure 23 – Comparison on irrational reactions of 25-34 age group and 34-45 age group
The lack of homogeneity in respondents’ reactions is an unexpected finding of this study and is
discussed further in the discussion session.
57.1%
41.9%
69.8%
59.5%
76.2%
48.6%
61.8%
82.9%
51.5% 54.5%
Reference
Dependence
Sensitivity -
Concavity
Diminishing
Sensitivity -
Convexity
Probability
Weighting -
Gains
Irrational behaviour - 25-34 age group
compared to 35-44 age group
25-34 age group 35-44 age group

71
6.1. Introduction
This research has answered the research question and its objectives have been achieved.
Additionally, some important conclusions outside of the research objective have been reached
in the course of analysis.
6.2. Conclusions
6.2.1. Complex bundles
The research has found that consumers are heavily irrational when they have to make decisions
on commercial bundles. The results show that all components of prospect theory influence such
irrational behaviour. This conclusion contrasts with the initial view on bundling as a pure rational
marketing strategy aiming to extract consumer surplus as believed by the founders of the theory
of bundling strategy Adams and Yellen (1976) and Stigler (1963). It has been proven by the
results, that consumers’ decision-making process is affected by the introduction of uncertainty
and ambiguity by the bundles. In that sense, the theory of Soman and Gourville (2001) that
bundling introduces ambiguity has been confirmed by this research.
6.2.2. Impulse buying of bundles
The research on consumers’ behavioural patterns related to impulse buying of bundles failed to
demonstrate significant irrationality in consumers’ decisions according to prospect theory
principles. This finding is against the practice examples where industry leaders in FMCG heavily
use similar strategies. There could be many reasons for this result, but the author believes this
is due to inappropriate method used. The most suitable method should be a real time
experiment in a convenience store cash desk. The explanation is simple. When offered with an

72
imaginary questions, most people use their logical part of the mind to reach a decision.
Emotional/affective aspects of human estimates differ substantially from the cognitive/rational
ones, as suggested by Thompson and Dolce (1989). This is one of the reasons, which make it
highly probable for the respondent to react differently than suggested in the questionnaire.
Unfortunately, this type of experiment was outside of both the budget and the time span of this
research. However, it could be a subject to a different study.
6.2.3. Additional findings
Some interesting additional findings have been reached in the course of study. According to the
results of the research, consumers’ reactions are not homogenous. Similar problems have been
discussed by scholars in topics on the homogeneity of the responding agent. In this case the
research treats categories, which are expected to be homogenously dispersed across global
consumers. The research, however, clearly shows significant differences in the answers on the
base of demographic factors such as gender, age and geographical location. There could be
two possible explanations of this unexpected result.
Firstly, this finding could be attributed to the natural existing heterogeneity in the responding
agent, an opinion offered by Kirman (1992). Ne believes that respondents are not homogenous,
that is, their responses could significantly vary, as opposed to the traditional believing that there
is one standard type of respondents. Kirman claims that this is the case even if respondents are
utility maximizers, which, in fact, has been denied by the prospect theory and particularly, the
application of this theory on decision making on bundles (the general conclusion of this
research). Haruvy et al. (2001) deepen this research. However, they do not reach a conclusive
results. Hey (1995) tries to establish a framework for analysis of the inconsistencies in the
responses under risk in order to adjust for possible deviations from the “normal” answer. He too,
leaves the topic open for further research.

73
Secondly, the cause of this effect could be the fact that various socio-economic and cultural
factors participate in the formation of consumers’ tastes and perceptions and ultimately, shape
their reactions in a particular way. Harrison and Rutstrom (2009) suggest that there could be a
mix of factors which determines consumers’ behaviour and some of these factors could be
explained by prospect theory, whereas others by perceived utility theory. This opinion could be
viewed as a transition to the suggestion of Harrison et al. (2005; 2010) that consumers from
developing countries react to certain stimulus differently, compared to their pairs from the
developed countries. Harrison et al. perform their research in three countries of the developed
world, precisely Ethiopia, India and Uganda.
The results of current research show consistency and a clearly expressed tendency. Americans
are less irrational than Europeans and man are less irrational than women. For the age groups
the results are controversial, but the their consistency in the first two groups make it more
probable the second point to be the underlying reason for these discrepancies between the
groups’ reactions. If such differences really exist in different consumers’ subgroups decision-
making process, this signifies that global marketing strategies based on prospect theory
principles could not be applied with the same success rate in different countries. Rather, they
need to be customized for each country individually and applied only after such an adjustment.
Similar conclusions could be made about the regulatory bodies’ work. A working strategy
applied in New Zealand, for instance, could bring about completely different results if applied in
India or Uganda. A very strong evidence in support of this conclusion comes from Hartmann
(1936) who reaches similar conclusions conducting experiments in Brazil, Taiwan, Mexico and
the USA.
The conclusions above should be considered by any producer, retailer or service provider which
targets different genders and decides to use prospect theory based marketing strategies. As it is
evident from the results, women and men differ on the base of their reactions.

74
6.3. Applications of complex bundles results
Multiple elements of this research could be useful in different areas of the business, regulations
and economics. The core components of the research, based on prospect theory tenets and
adapted for a practical implementation using Barberis’ (2012) interpretation of the theory, are
intuitive and easy for understanding by the majority of the practitioners in the business. The
research introduces metrics and makes the principles of prospect theory easily applicable for
real world situations. It helps assessing the impact that a chosen bundling strategy could have
on consumers and respective, give the opportunity to predict the result with a high degree of
certainty.
6.3.1. National regulatory bodies
Myron (2004) gives a wonderful explanation as to why telecoms are adopting bundling
strategies. He argues that competing on price harms telecoms consumers’ loyalty. In his study,
he announces that only 28% of telecoms clients remain loyal on a yearly basis. In the same
time, he offers a statistics how introducing a bundling strategy reduces consumers’ churn by
25% for a two-services bundle, by 38% for a three-product bundle and by 44% for a four-
product services bundle.
On the other hand, bundling increases consumers’ misperception of bundles and causes dead
weight market externalities (Xavier and Ypsilanti 2010). The need of a balance is obvious. This
is the reason one of the biggest areas of implementation of the findings of this paper to be the
regulations of telecommunication markets, where the availability of natural monopolies and
oligopolistic structures are a norm. The need of a better understanding of the processes behind
price formation, pricing of bundle offerings and the processes of interaction between the natural
monopoly and consumers has been largely discusses by the academics, such as Camerer

75
(2002), Gans (2005), Miravete (2007) and Wilson and Waddams-Price (2010). This paper
contributes to their researches by providing possible underlying reasons for the irrational
behaviour of consumers and an explanation of the way the “foggy” practices of telecoms affect
consumers’ choice. Respectively, it suggests means for correcting the market externalities by
regulating the key aspects of monopolies’ pricing on their bundles.
6.3.2. Dynamic or customized bundling
Another area of a great interest could be the rapidly growing sector of Internet sales and
dynamic bundles. With the boom of the dotcom bubble between 1998 and 2001 scholars
identified the importance of this new media for the business in general and particularly for the
Internet based businesses. The media, the changed consumers’ habits and the new paradigm
supposed new business strategies. These strategies affected sales, logistics, distribution and
production processes and offered principally new structures and dynamics of these processes.
For instance, Wu et al. (2008) perform a numerical analysis of non-linear models of dynamic
bundles and reach the conclusion that this is the best strategy, even better than the pure
bundling and individual sales in condition that customers are exposed to incomplete information.
Incomplete information leads to ambiguity and in that sense, this research offers a possible
explanation of the underlying reasons for these results.
One of the first scholars to highlight this new field of opportunities is Mahajan (2000). In his
study he analyses the new phenomenon of the Internet and attempts to indicate the existing
marketing strategies and their relation to the new paradigm. He reaches the conclusion, that a
principally new approach is necessary when applying the existing strategies to the new media.
Even more, he believes that designing completely new marketing strategies is a better option for
exploiting the new business opportunities. Kannan and Kopalle (2001) discuss the dynamic
bundles, a term showing their understanding of the fact that due to the higher level of

76
automation and customization, along with the much shortened product lifecycle, it comes a
completely new way of bundling of the products with a higher level of satisfaction of any
individual consumer’s taste and preferences. Not surprisingly, a couple of years later, Dewan
and Freimer (2003) conclude, that consumers prefer bundled ad-ins in the software industry.
Hui (2006) goes even further in his analysis and offers different marketing strategies for
software products considering the fact that software is difficult to be produced, but easy to be
distributed as there is not an incurred cost in any following copy, that is, the marginal cost is
close to zero. Not surprisingly again, one of the main, principally new strategies is product
bundling.
It should be noted, that the application of customized bundles in a mass and lean production
environment becomes possible with the development of the new computer and information
systems technologies. Their role and the implementation of rich standards in order to
accomplish the “mass customization” across a multi-seller environment has been introduced by
Grentzi and Watts (2007). Yang and Lai (2006) add to this idea by offering a study, which
proves that better results on bundled products could be achieved by incorporating the new
technologies into any traditional methods of analysis of consumers’ behaviour. In their case,
they offer collecting data from customers’ Internet browser charts using IT advanced
technologies and adding this data to the traditional POS data, collected in stores. Findings from
the current research could greatly improve the success rate of marketing campaigns based on
such new IT based business strategies.
A continuation of the ideas above is the work of Kolay and Shaffer (2003) on a strategy using
the direct involvement of customers in the bundling process. It comes to the “self selection”
strategy, where customers participate directly in choosing their bundle amongst a pool of
different bundles of services. As it has become evident from the results, different framing
according to prospect theory’s tenets has different chances of being preferred by the customers,

77
although the value of bundles being the same. That is why the current paper findings could
directly improve the “self selection” strategy.
Finally, one of the most successful areas of application of this research would be its
implementation in the Online Customized Bundling and Pricing strategy (OCBP) offered by
Zhengping and An (2010). This strategy aims e-tailers (from electronic retailers) and consists of
electronically created bundles and the respective discounts for any individual customer.
Zhengping and An use a heuristic algorithm and computer power to solve the case for the
optimal pricing of the bundle. They claim that using this strategy could raise the profit between
15% to 45 %. Incorporating the prospect theory principles discussed in this paper could
undoubtedly improve the customization process and respectively, the final financial results.
6.4. Further directions for research
Initially, prospect theory dealt with precise amounts of numerical values. In their later work,
Kahneman and Tversky (1992) introduce the cumulative element, that is, they suggest that
more than two different answers are possible and the gambling amounts are not fixed, but are
dynamic. For example, if part of the gamble is in the classic prospect theory is ” …you could win
$100”, in cumulative theory it becomes “…you could win $100 and above “. This transition is
undoubtedly a step from the laboratory experiment towards the real life situation. However, the
exact numbers still exist, though there is some deviation allowed.
In real life, however, there are rarely such precise values. Often people use comparable values
– “more expensive”, “less expensive”, “the price is almost the same” and so on. I such cases, it
is difficult to apply prospect theory’s principles as the reference point is not clear. For this

78
reason the author believes that a suitable topic for further research could be the exploration of
prospect theory through the lenses of fuzzy sets theory.
Fuzzy sets theory has been offered by Zadeh (1965) as an alternative to the traditional system
control theories. Since then, it has been successfully used in any kind of adaptive system
controls as well as in the learning process of neural networks. The theory uses fuzzy terms
instead of precise numbers in order to control more effectively a system. Some of the latest
theoretical development could on fuzzy sets are performed by Dubois and Prade (2008; 2012).
Fuzzy logic could be undoubtedly a valuable contribution to the analysis of decisions under risk.
There are already several in depth studies of fuzzy logic as a means of analysis tool for the
above theories. Liginlal and Ow (2006), for instance, use the fuzzy set theory to research the
risk attitude, whereas Aliev et al. (2012) research the generalized decisions in condition of
imperfect information through the fuzzy logic theory.
The lack of research on the relation between bundle theory, prospect theory and the fuzzy sets
theory makes this an attractive area for further investigations, especially meaning the great
practical importance of this topic described above.

79
REFERENCES
Adams, W. J. and Yellen, J. L. (1976) Commodity Bundling and the Burden of Monopoly, The
Quarterly Journal of Economics, 90(3), pp. 475-498.
Aliev, R., Pedrycz, W., Fazlollahi, B., Huseynov, O. H., Alizadeh, A. and Guirimov, B. (2012)
Fuzzy logic-based generalized decision theory with imperfect information, Information
Sciences, 189, pp. 18-42.
Allais, M. (1953) Le comportement de l'homme rationnel devant le risque: Critique des postulats
et axiomes de l'école Américaine, Econometrica: Journal of the Econometric Society, pp.
503-546.
Arkes, H. R. and Blumer, C. (1985) The psychology of sunk cost, Organizational behavior and
human decision processes, 35(1), pp. 124-140.
Bagchi, R. and Davis, D. F. (2012) $29 for 70 Items or 70 Items for $29? How Presentation
Order Affects Package Perceptions, Journal of Consumer Research, 39(1), pp. 62.
Bakos, Y. and Brynjolfsson, E. (1999) Bundling information goods: Pricing, profits, and
efficiency, Management Science, 45(12), pp. 1613-1630.
Bali, T. G., Cakici, N. and Whitelaw, R. F. (2011) Maxing out: Stocks as lotteries and the cross-
section of expected returns, Journal of Financial Economics, 99(2), pp. 427-446.
Barberis, N., Huang, M. and Thaler, R. H. (2006) Individual preferences, monetary gambles,
and stock market participation: A case for narrow framing, The American economic
review, pp. 1069-1090.
Barberis, N. C. (2012) Thirty Years of Prospect Theory in Economics: A Review and
Assessment: National Bureau of Economic Research.
Bar-Gill, O. 'Bundling and consumer misperception'. American Law & Economics Association
Annual Meetings: bepress, 52.

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