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INCORPORATING PROSPECT THEORY INTO DECISION-MAKING
FOR CUSTOMER SATISFACTION
Yu-Cheng Lee1, Feng-Han Lin2*, Cheng-Fu Hsiao3 1Department of Technology Management, Chung Hua University, No.707, Sec.2,
WuFu Rd., Hsinchu 300,Taiwan
E-mail address: ycl@chu.edu.tw 2Ph.D. Program of Technology Management, Chung Hua University, No.707, Sec.2,
WuFu Rd., Hsinchu 300,Taiwan
E-mail address: yes.me@yulin.com.tw 3Department of Hospitality Management, Hsing Wu University, No. 101, Sec.1,
Fenliao Rd., LinKou District, New Taipei City 24452, Taiwan
E-mail address: haiso085009@yahoo.com.tw
*Corresponding author
Feng-Han Lin
Ph.D. Program of Technology Management, Chung Hua University, No.707, Sec.2,
WuFu Rd., Hsinchu 300, Taiwan. Tel: +886-932781043; Fax: +886-35726580
E-mail address: yes.me@yulin.com.tw
ABSTRACT
In the customer-oriented trend of the last three decades, customer satisfaction has increasingly
become the object of study. Although studies have adopted the Kano model and
importance-performance analysis (IPA) to explore the influence of quality attributes on customer
satisfaction, the role of consumer behaviour has not be considered in this association. The present
study proposes a novel methodology for identifying quality attributes by exploring customer
requirements, and focuses on effectively evaluating customer satisfaction by employing prospect
theory. This methodology integrates the quantitative Kano model and dual IPA to identify asymmetric
and nonlinear relationships between attribute-level performance and customer satisfaction. In addition,
the incorporation of prospect theory assists users in effectively evaluating satisfaction. Finally, a case
study is presented in this paper to highlight the management implications. The current findings
contradict the reasoning of the Kano model and IPA. The novel methodology provides quality attributes
categorisation to precisely fulfil customer needs and help managers make strategic decisions to meet
these needs.
KEYWORDS: Customer satisfaction, Prospect theory, Kano model, Importance-performance analysis,
Quality attribute
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INTRODUCTION
In the highly competitive global market, a product or service must fulfil meet customer
needs. Several current studies are increasingly focusing on customer satisfaction.
The Kano model (Kano, Seraku, Takahashi, & Tsuji, 1984) has been widely used to
satisfy customer needs by identifying and classifying the quality attributers; however,
it cannot provide decision-making support because the Kano category remains
qualitative (Riviere, Monrozier, Rogeaux, Pages, & Saporta, 2006; Xu, Jiao, Yang,
Helander, Khalid, & Opperud, 2009; Shahin, Pourhamidi, Antony & Hyun Park, 2013).
Attribute performance and importance, obtained through importance-performance
analysis (IPA)—originally introduced by Martilla and James (1977) —can facilitate
decision-making (Ting & Chen, 2002; Matzler, Bailom, Hinterhuber, Renzl, & Pichler,
2004; Matzler & Renzl, 2007). However, the relationship between attribute
performance and importance is asymmetric; this problem can be resolved using few
sustainable solutions.
Several related studies have considered the positive potential of integrating the Kano
model with IPA to accurately identify key quality elements and explore customer
satisfaction (Kuo, Chen, & Deng, 2012; Yin, Cao, Huang & Cao, 2016; Meng, Jiang, &
Bian, 2015). However, these studies have not considered the role of consumer
behaviour in customer satisfaction. To fill this gap in behavioural economics
knowledge, the present study incorporated prospect theory in customer satisfaction to
predict consumer behaviour.
The purpose of this study was to examine customer satisfaction with regard to
psychology through a behavioural economics analysis. The specific aims of this study
were (a) to identify the impact of attribute-level performance on customer satisfaction
and (b) to elucidate the concept of prospect theory to construct a behavioural
decision-making model. Although the current research type remains in its infancy, this
study may aid in further predicting customer satisfaction. Moreover, compared with
the conventional method, the current alternative method should make
decision-making model much more desirable. Finally, this study was concluded with a
case study of bicycle user satisfaction to demonstrate the current method.
Yu-Cheng Lee, Feng-Han Lin, Cheng-Fu Hsiao, Int. Eco. Res, 2016, v7i6, 26 – 38 ISSN:2229-6158
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LITERATURE REVIEW
Kano Model
The Kano model (Kano et al., 1984) was developed as a two-dimensional quality
model based on Herzberg’s motivation-hygiene theory (1959). This model classifies
quality attributes into five categories on the basis of their impact on customer
satisfaction: must-be, one-dimensional, attractive, indifferent, and reverse quality.
In recent years, several studies have described the use of the Kano model during
product development and design (Kano, 2000; Atlason, Oddsson & Unnthorsson,
2014; Dominici, Roblek, Abbate, & Tani, 2016). Furthermore, several studies have
examined the relationship between attribute-level performance and customer
satisfaction (Mittal, Ross, & Baldasare, 1998; Tontini, Søilen, & Silveira, 2013). Tontini
et al. (2013) suggested that before attractive quality attributes are improved,
improvement of must-be quality attributes or one-dimensional quality attributes
should be prioritised.
IPA
IPA uses a two-dimensional matrix, based on the customer-perceived importance of
quality attributes and attribute performance, to develop marketing strategies (Martilla
& James, 1977). The matrix is divided into four quadrants by using attribute
performance and importance:
(1) Keep Up the Good Work: In this area, quality attributes are highly regarded by
customers and the provided performance is satisfactory. IPA suggests that the quality
attributes in this quadrant should be maintained.
(2) Concentrate Here: Customers value the importance of the quality attributes
beyond the provided performance; therefore, customers are not satisfied with the
quality attributes. Quality attributes in this quadrant should be improved first.
(3) Low Priority: Customers think that the quality attributes in this area are not of great
worth. Nevertheless, owing to low performance customers are not satisfied with the
quality attributes. These quality attributes should be improved, but with low priority.
(4) Potential Overkill: Customers perceive that the quality attribute performance is
high and are satisfied with all attributes; hence, these quality attributes are of low
importance.
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Matzler et al. (2004) developed both IPAs for both satisfied and dissatisfied
customers to estimate the relative impact of each attribute for high and low
performance. Kuo et al. (2012) proposed the IPA–Kano model to provide specific
strategies for definite attributes; however, this model only classified more quality
attribute types through IPA.
Prospect Theory
Kahneman and Tversky (1979) proposed the concept of a value function and
established the psychological codes of gain and loss. The three basic principles of the
theory are as follows (Tversky & Kahneman, 1981):
1. Most people tend to avoid risk when facing “gain.”
2. Most people tend to prefer risk when facing “loss.”
3. People are more sensitive to loss than to gain.
Thaler (1985) proposed the model of consumer behaviour by using the prospect
theory value function. Transaction utility uses a hybrid of cognitive psychology and
microeconomics to evaluate purchases. This model derives compound outcomes of
gains and losses, which are used to evaluate single outcomes with a value function.
The outcomes of prospect theory could then be valued jointly or separately to yield
greater utility. They are valued jointly as )( yxv and separately as )()( yvxv . Four
possible combinations can be considered for the joint outcome: multiple gains,
multiple losses, mixed gain, and mixed loss. The value function of the prospect theory
was denoted as )(v . Segregation is preferred in multiple gains, )()()( yxvyvxv ,
whereas integration is preferred in multiple losses, )()()( yxvyvxv . Mixed gain,
)()()( yxvyvxv , involving net gain, prefers integration, whereas mixed loss,
)()()( yxvyvxv , involving loss gain, prefers segregation.
Several authors (e.g., Mittal et al., 1998; Ting & Chen, 2002) have hypothesised that
the S-shaped value function curve of prospect theory is applicable to customer
satisfaction. Mittal et al. (1998) indicated that the impact of negative attribute
performance on customer satisfaction is greater than that of the positive attribute
performance; moreover, utility-preserving attributes are applicable to the S-shaped
value function curve of prospect theory. Thus, prospect theory may be useful for
constructing a decision-making model of satisfaction utility.
Yu-Cheng Lee, Feng-Han Lin, Cheng-Fu Hsiao, Int. Eco. Res, 2016, v7i6, 26 – 38 ISSN:2229-6158
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METHODOLOGY
This study performed a survey to identify asymmetric and nonlinear relationships
between attribute-level performance and customer satisfaction. As reported
previously (e.g., Mittal et al.,1998; Matlzer et al., 2004), regression analysis with
dummy variables was applied to identify the (asymmetric) impact of attribute-level
performance on (overall) customer satisfaction to indicate attribute importance. The
Kano model and IPA were employed to detect the differences among the attributes.
Finally, prospect theory was applied to assist managers in decision-making.
Therefore, the steps of the methodology are given as follows:
Step 1: Identify the impact of attribute-level performance on customer satisfaction
Attribute performance ratings are recoded to yield the dummy variables such as
‘high-level performance’ (1,0), ‘average performance’ (0,0), and ‘low-level
performance’ (0,1). A regression analysis is conducted to estimate the impact of
different level performance on customer satisfaction. The high-level performance
regression coefficient is used to measure the importance degree of satisfaction,
whereas the low-level performance regression coefficient is applied to measure the
importance degree of dissatisfaction.
Step 2: Categorise the quality attributes on the basis of the Kano model
The quality attributes of a product or service are categorised on the basis of the
intensity of the impact on customer satisfaction, indicated by the regression
coefficient, namely importance.
Step 3: Construct a pair of IPA matrices
A pair of IPA matrices is constructed using performance and paring importance. Both
IPA for satisfaction and dissatisfaction are integrated to evaluate quality attributes that
are crucial to customers and require improvement.
Step 4: Market strategic decision-making
Different attributes vary in their impact on satisfaction. The level of importance is
served as a gain or loss. The Kano model and IPA are integrated to strategically
analyse and use prospect theory to make decisions.
The current methodology considers customer behaviour to suggest how decision
makers should prioritise resource allocation.
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CASE STUDY
Statistical Analysis and Classifications
The global bicycle market has been growing in recent years. Here the
aforementioned methodology was applied to the function requirements of a bicycle for
sports. As suggested by an expert, eight questions were presented in the
questionnaires: Q1 = appearance, Q2 = colour, Q3 = cushion comfort, Q4 = brake
system, Q5 = shift system, Q6 = wheel set and transmission, Q7 = weight, and Q8 =
accessories. These questionnaires were then used to measure attribute performance
and overall satisfaction. The quality attribute performance was evaluated on a scale
of 1 (extremely low) to 9 (extremely high) and overall satisfaction on a scale of 1–100.
Data from 192 respondents were collected through face-to-face interviews.
The impact of attribute-level performance on satisfaction was next evaluated using a
regression analysis with dummy variables (Table 1). The two regression coefficients
served as the importance of attribute-level performance: regression coefficients of
high- and low-level performance were used to measure the degree of importance of
fulfilling and unfulfilling quality attributes, respectively.
Table 1 Impact of attribute-level performance on customer satisfaction
Item Regression coefficients Quality attribute
category High performance Low performance
Q1 appearance 0.316*** -0.288*** one-dimensional
Q2 color 0.326* -0.178*** one-dimensional
Q3 cushion comfort 0.253(ns) -0.103* must-be
Q4 brake system 0.232*** -0.286** one-dimensional
Q5 shift system 0.270*** -0.296*** one-dimensional
Q6 wheel set and transmission 0.188*** -0.381** one-dimensional
Q7 weight 0.167(ns) -0.217(ns) indifferent
Q8 accessories 0.152** -0.280(ns) attractive
ns = not significant. * P < .05. ** P < .01. *** P < .001.
As shown in Table 1, for the Q8 regression coefficients, high performance was
obviously more significant than low performance. If Q8 was adequate, customers
were satisfied; however, inadequate Q8 did not significantly influence customer
dissatisfaction, implying that Q8 is an attractive quality attribute. By contrast, for the
Q3 regression coefficients, low performance was more significant than high
performance. Therefore, if Q3 was inadequate, customers were dissatisfied;
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adequate or more favourable Q3 could not satisfy customers either. In other words,
Q3 was a must-be quality attribute. The Q1 regression coefficients of high and low
performance were significant; therefore, if Q1 was adequate or more favourable,
customers were satisfied; otherwise, they were dissatisfied. In other words, Q1 was a
one-dimensional quality attribute. Similarly, Q2, Q4, Q5, and Q6 were determined to
one-dimensional quality attributes. As for Q7, the regression coefficients of high and
low performance were nonsignificant; hence, Q7 was considered an indifferent quality
attribute.
IPA for Customer Satisfaction
The samples were divided according to high and low average overall customer
satisfaction. The average of high satisfaction was high performance for satisfaction;
by contrast, the average of low satisfaction was low performance for dissatisfaction
(Tables 2 and 3).
Table 2 IPA for customer satisfaction
Item Customer satisfaction
High importantance High performance
Q1 appearance 0.316 6.781
Q2 color 0.326 6.891
Q3 cushion comfort 0.253 6.635
Q4 brake system 0.232 6.760
Q5 shift system 0.270 6.745
Q6 wheel set and transmission 0.188 6.828
Q7 weight 0.167 6.557
Q8 accessories 0.152 6.604
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Table 3 IPA for customer dissatisfaction
Item Customer dissatisfaction
Low important Low performance
Q1 appearance -0.288 6.158
Q2 color -0.178 6.347
Q3 cushion comfort -0.103 6.116
Q4 brake system -0.286 6.200
Q5 shift system -0.296 6.126
Q6 wheel set and transmission -0.381 6.190
Q7 weight -0.217 5.937
Q8 accessories -0.280 6.105
Moreover, Fig. 1 shows the dual IPA matrix. Q3 was a must-be quality attribute, which
unidirectionally influenced customer dissatisfaction, located in ‘Concentrate Here’
quadrant of the IPA for satisfaction, implying low performance on high importance
attributes. Thus, the enhancement in satisfaction caused by improvement in Q3 was
futile. Similarly, although Q8 was an attractive attribute and unidirectionally influenced
customer satisfaction, its insufficient performance did not lead to customer
dissatisfaction. Thus, because Q3 and Q8 did not result in the expected performance,
improvements in these areas were considered unnecessary. One-dimensional
attributes, namely Q1, Q2, Q4, Q5, and Q6, influenced customer satisfaction as well
as customer dissatisfaction. In the IPA, Q1, Q2, Q4, and Q5 indicated satisfaction;
subsequently, Q1, Q4, and Q6 indicated dissatisfaction. Thus, Q1, Q2, Q4, and Q5
should be improved to enhance satisfaction; and Q1, Q4, and Q6 should be
maintained to prevent dissatisfaction.
5.8
5.9
6.0
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
7.0
-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4
Q4Q1
Q2
Q5
Q3
Q6
Q7Q8
Q1
Q2
Q3
Q4
Q5
Q6
Q7Q8
Perf
orm
ance
Importance Dissatisfaction Satisfaction
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Fig. 1 A Dual IPA Matrix
Customer Satisfaction Values
High and low importance was used to measure outcomes of gain and loss with a
value function; these values facilitate evaluating customer satisfaction probabilities.
Table 4 details the value derived from the importance values. The value function is
defined as follows:
0)(
0)(
xifx
xifxxv
(1)
where α and β are between 0 and 1; λ > 1; x > 0 indicates gain; and x < 0 indicates
loss. For our results, α = β and λ (Tversky & Kahneman, 1992).
Table 4 Customer satisfaction values
Quality elements Value
Gain Loss
Q1 appearance 0.363 -0.752
Q2 color 0.373 -0.493
Q3 cushion -0.304
Q4 brake system 0.276 -0.748
Q5 shift system 0.316 -0.771
Q6 wheel set and transmission 0.230 -0.962
Q7 weight
Q8 accessory 0.191
DISCUSSION
In the preceding case study, the quality attributes of a bicycle were categorised with
the Kano model. Table 1 shows the Kano quality attributes as well as the impact
levels. The importance of customer satisfaction indicates the impact of a quality
element on customer satisfaction. The attributes arranged in descending order of
customer satisfaction importance were Q2, Q1, Q5, Q4, Q6, and Q8, whereas those
arranged in descending order of customer dissatisfaction were Q6, Q5, Q1, Q4, Q2,
and Q3. Compared with the improvement in Q8 (accessories) performance,
improvement in Q2 (colour) performance significantly increased customer satisfaction.
Therefore, the highest priority must be given to improvement in Q2, rather than Q8
(an attractive quality) in order to improve customer satisfaction. By contrast, Q3, a
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must-be quality, need not be improved first to eliminate customer dissatisfaction;
however, Q6, with the highest importance of customer dissatisfaction, should the first
instead.
Dual IPA considers the asymmetric relationship between attribute-level performance
and customer satisfaction. A more detailed understanding of the relationship between
attribute-level performance and satisfaction can be observed Tables 2 and 3 and Fig.
1, which highlight the differences between satisfaction and dissatisfaction. As shown
in Fig. 1, attributes located in ‘Concentrate Here’ could not be improved first. If
attractive attributes are in the quadrant of dissatisfaction or must-be attributes are in
quadrant of satisfaction, performance may not impact satisfaction.
Furthermore, Table 4 presents the outcomes of gain and loss. Thaler (1985)
suggested that the outcomes of the prospect theory could be segregated or
integrated to maximise value. According to segregation of multiple gains,
improvement in Q2 and Q5 (0.689) was greater than that in Q1 and Q6 (0.593).
However, because of the integration of multiple losses, Q2 and Q5 demonstrated
lower performance than did Q1 and Q6 (0.474 vs. 0.669). When customers’
requirements are not met, Q1 and Q6 should be improved first; moreover, to further
enhance customer satisfaction, Q2 and Q5 should be improved before Q1 and Q6.
The classification criteria of the Kano model are logical and quantitative through
regression analysis; furthermore, IPA considers the asymmetrical and nonlinear
effects between quality attributes and customer satisfaction. Understanding the
asymmetrical and nonlinear relationship aids in accurately analysing the priority
ranking for improvement. The Kano model and IPA complement each other well for
detailed examination of attributes. To enhance customer satisfaction, prospect theory
provides managers with clear and effective guidance for decision-making.
CONCLUSIONS
In this study, a novel methodology was developed by incorporating the quantitative
Kano model and IPA assessing customer satisfaction. The results of the attribute
classification were more accurate than those of the conventional Kano model and IPA,
which is in agreement with previous studies (Kuo, Chen, & Deng, 2012; Yin, Cao,
Huang, & Cao, 2016). However, no study has explored behavioural economics in this
context. Employing the value function of prospect theory provides greater utility for
prioritising attributes for performance improvement. Improving attractive quality
attributes might not necessarily result in the largest increase in customer satisfaction,
and must-be quality attributes do not strongly influence dissatisfaction. Furthermore,
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attributes in ‘Concentrate Here’ need not be improved. However, these findings
contradict the reasoning of the Kano model and IPA. Managers should
comprehensively consider whether attributes in ‘Concentrate Here’ require treatment.
Through consideration of behavioural economics, the results indicated that employing
value function aided in determining how decision-makers should prioritise resource
allocation. This study incorporated the Kano–IPA model and prospect theory, without
any limitations; nevertheless, the potential application of this combination requires
further explanation and validation.
This study provides a quality attribute categorisation methodology for precisely
fulfilling customer needs and helping managers make strategic decisions to satisfy
customers. Quality attributes can change over time; in other words, a successful
attribute follows a lifecycle from indifferent quality to attractive quality to
one-dimensional quality, finally to must-be quality (Kano, 2001). Forecasting the
changes in quality attributes is crucial for providing a quality that fulfils customer
needs just in time. Thus, a more precise dynamic two-dimensional quality model will
be constructed to then increase the advantage of a product or service in highly
competitive global market.
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