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The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach

Authors:
Telematics*and*Informatics*34*(2017)*730742
The interplay of online shopping motivations and experiential factors on
personalized e-commerce: a complexity theory approach
Ilias O. Pappas1,2*, Panos E. Kourouthanassis2, Michail N. Giannakos1, George Lekakos3
ilpappas@ntnu.no, pkour@ionio.gr, michailg@ntnu.no, glekakos@aueb.gr
1Department of Computer and Information Science, Norwegian University of Science and
Technology (NTNU), Trondheim, Norway
2Department of Informatics, Ionian University, Corfu, Greece.
3Department of Management Science and Technology, Athens University of Economics and
Business, Athens, Greece
* Corresponding author
Abstract
The present study aims to examine customers’ purchase behavior in personalized online
shopping by employing complexity theory, based on their online shopping experience and
online shopping motivations are identified. To address its objectives, a conceptual model is
proposed along with research propositions. The research propositions are validated through a
survey on 401 customers’ experience with online shopping, by using the data analysis tool
fsQCA (fuzzy-set Qualitative Comparative Analysis). The results indicate nine
configurations of online shopping experience and online shopping motivations that lead to
high purchase intentions. This study takes a step further the literature of online shopping and
the theoretical ground of how customers’ online shopping experience combines with their
online shopping motivations in order to predict and explain increased intention to purchase.
The findings offer implications for both researchers and online retailers, regarding the
development of new theories in personalized e-commerce and the provision of personalized
services.
Keywords: e-commerce, online shopping motivation, online shopping experience,
personalization, configurational analysis; fsQCA.
1. Introduction
Online retailers have been implementing various strategies to attract and retain
customers. Web personalization has been identified as an important factor in the area of
marketing and information systems (Salonen & Karjaluoto, 2016). Personalization in online
shopping is a strategy that may aid in persuading customers to select a product or service and
lead to a purchase. Research in the area has focused on examining customers’ online
shopping experience in order to identify ways to convince customers to visit an online shop
and increase their purchase intentions (Ho & Bodoff, 2014; Pappas, Kourouthanassis,
Giannakos, & Chrissikopoulos, 2016; Xu, Luo, Carroll, & Rosson, 2011). However, it is
important to identify the reasons that customers choose to visit an online store (i.e., their
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
2
shopping motivations) and how their behavior differs based on their predisposition towards
personalized online shopping. The traditional personalized strategies use customers’ past
purchases or browsing history to offer tailored content. Nonetheless, such strategies should
be extended by taking into account customers’ shopping motivations, as these motivations are
most likely to influence customers’ overall online behavior.
Shopping motivations are a critical factor in personalized online shopping and
important determinants of purchase behavior. Customers may have various motivations and
depending on how they combine with each other they may lead to a different behavior
(Ganesh, Reynolds, Luckett, & Pomirleanu, 2010). Such motivations include finding the best
price, searching for product promotions, online shopping convenience, stimulation from the
interaction with the websites, receiving personalized services, quality of the received
services, perceived value, information availability (Close & Kukar-Kinney, 2010; Ganesh et
al., 2010; To, Liao, & Lin, 2007). Based on their motivations, customers express different
behaviors and they may be categorized into different categories (Lim & Cham, 2015; Rohm
& Swaminathan, 2004). Customers have only some types of motivations that they consider
more important from the others indicating the complex interrelationships among online
shopping motivations. Such complexity leads to the creation of multiple unique combinations
of motivations that all are able to explain purchase behavior. Although some motivations may
be more important than others when examined separately, identifying the more complex
combinations of variables may lead to a better understand of online shopping behavior.
To this end, in offline shopping, a recent study in the Spanish market has identified
customers’ shopping motivations, which are able to influence their perceptions and in turn,
affect their behavior (González-Benito, Martos-Partal, & Fustinoni-Venturini, 2014). Thus, it
is interesting to examine these motivations in cultures with similar characteristics, such as the
Greek market, and in the context of online shopping. Furthermore, the economic recession
reshaped the shopping habits of offline and online consumers. Consumers tend to be more
price conscious and avoid impulse purchases trying to pre-organize their purchases
(Hampson & McGoldrick, 2013). We expect that this behavior will also be evinced in the
context of online shopping, therefore this study will inform the findings of online shopper
typologies that have been executed prior to the economic recession by identifying different
types of customers, based on their online experience and online shopping motivations.
The majority of the studies examining motivations in online shopping, almost
uniformly, employ variance-based statistical approaches (e.g. regression-based Structural
Equation Modeling) that rank the hypothesized predictive adoption factors based on their
regression ‘weights’, suggesting that behavior on personalized online shopping may be
explained through a single hierarchy (or configuration) of these factors. Consequently, such
approaches offer one single solution, considered as the best solution, that explains the
outcome, leaving however a significant amount of the outcome unexplained. Furthermore,
focusing on net effects may be misleading (Woodside, 2013), since besides the main relation
among the variables, an opposite relationship will exist for some cases in the same sample,
thus creating the need to test the data for such contrarian cases (Woodside, 2014). To this
end, different configurations of the examined variables may lead to the same outcome
depending on how they combine with each other. Such configurations lead to multiple
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
3
solutions, which in total represent a larger part of the sample, and are likely to explain a
larger amount of the outcome.
This work builds on configuration theory and complexity theory in order to identify
specific causal patterns among factors that may predict customers’ purchase behavior when
using personalized services. In detail, the objective of this research is to identify how online
shopping experience and online shopping motivations combine together to form patterns that
explain high purchase intentions. To this end, this study aims to answer the following
research question
R.Q.: What configurations of online shopping experience and online shopping motivations
lead to increased intention to purchase from online stores during the period of economic
recession?
Through these configurations managers and decision makers of online shops will be
able to gain a better understanding on how their customers experience the purchase process,
and more importantly, what types of customers are more likely to purchase based on how
they are motivated. The configurations depict the different combinations of the examined
factors, and each configuration describes a unique combination of customers’ online
shopping experience and motivations, which can be comprehended as customers’ behavioral
group. Multiple configurations will lead to multiple behavioral groups. The identified
configurations have unique values which explain the same outcome, and each configuration
shows how a group of customers behaves. To this end, a configuration analysis is performed
with the data analysis tool fsQCA (fuzzy set Qualitative Comparative Analysis) (Ragin,
2008). This study connects configurational analysis with complexity theory in the area of
personalized online shopping, because when fsQCA is applied together with complexity
theory researchers are able to gain a better and deeper insight on their (Leischnig & Kasper-
Brauer, 2015; Ordanini, Parasuraman, & Rubera, 2014; Woodside, 2014). Complexity theory
“can explain any kind of complex system—multinational corporations, or mass extinctions,
or ecosystems such as rainforests, or human consciousness. All are built on the same few
rules” (Lewin 1992, back cover). Complexity theory and configuration theory are appropriate
for explaining the complex interrelations existing among variables, since the way they
combine and their interdependencies are the ones leading to the desired outcome (Fiss, 2007;
Woodside, 2014; Wu, Yeh, & Woodside, 2014). We expand on the contributions of other
studies from the areas of sociology (Ragin 2008), business management (Pappas et al. 2016),
marketing (Wu et al., 2014), learning (Pappas, Giannakos, & Sampson, 2016; Pappas,
Mikalef, & Giannakos, 2016) and others.
The paper is organized as follows. The next section presents related work and the
conceptual model along with the research propositions. Section 3 presents the applied
measures for data collection, and section 4 describes the research methodology. Section 5
presents the empirical results derived, and the final section of the paper includes discussion of
the findings and conclusions highlighting theoretical and practical implications.
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
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2. Conceptual model and research propositions
2.1. Determining the online shopping experience
The advent of the Internet as a vital channel that fuels sales has driven scholars to seek
the predominant customer motives for shopping online. In effect, studies have documented
that online shoppers are driven by both utilitarian and hedonic dimensions, which together
formulate the online shopping experience (Childers, Carr, Peck, & Carson, 2001; H. L.
O’Brien, 2010; Overby & Lee, 2006) and influence online shopping decisions (Bosnjak,
Galesic, & Tuten, 2007). Utilitarian dimensions are mostly relevant for specific goals and
tasks of online shopping; such as purchase deliberation. For example, early studies
showcased that online customers plan their online purchases through the collection of
information pertaining product/ brand features and prices (Kau, Tang, & Ghose, 2003; Rohm
& Swaminathan, 2004). Likewise, online shoppers tend to seek convenience during their
online shopping sessions (Brashear, Kashyap, Musante, & Donthu, 2009). To reinforce
convenience, contemporary online shoppers employ a variety of personalization features in
order to support shoppers’ online judgments pertaining purchasing decisions. In effect, the
quality of personalization (i.e., the accuracy and overall fidelity of the personalization
message according to shoppers’ needs and expectations) represents an important motivational
factor towards persuading the shopper to purchase from the online store (Pappas,
Kourouthanassis, et al., 2016). Personalization aids may effectively enhance product search
in online stores (Dabrowski & Acton, 2013) and, depending on their quality, increase
customer loyalty (Yoon, Hostler, Guo, & Guimaraes, 2013). Similarly, the degree of
perceived persuasion stemming from the application of a personalization strategy will also
likely affect in a positive way the propensity of online purchases (Berkovsky, Freyne, &
Oinas-Kukkonen, 2012; K. C. Lee & Kwon, 2008; Park & Kim, 2009). Persuaded customers
would evoke feelings of satisfaction from their shopping sessions within the online retailers,
which, in turn, may lead to repurchase behavior (Hostler, Yoon, & Guimaraes, 2012)
although such benefits are more apparent to large retailers which require to organize a greater
assortment of products and associated information in their websites (Thirumalai & Sinha,
2013).
However, online shopping also incorporates hedonic elements. Indeed, online shoppers
may obtain hedonic value through arousal, playfulness, and positive affect from interacting
with an online store (Bridges & Florsheim, 2008). In effect, hedonic shopping motivations
may positively influence the time shoppers browse within the online store, which in turn may
positively affect the propensity of online purchases (S. Kim & Eastin, 2011). Moreover,
online hedonic motivations are usually associated with customers’ desire for entertainment,
enjoyment, and escapism (Childers et al., 2001; To et al., 2007). In the context of online
personalized services, the degree of personalization is positively associated with the
formulation of positive emotions (Pappas, Kourouthanassis, Giannakos, & Chrissikopoulos,
2014). Specifically, online personalization features allow customers to perceive the presented
information about products and promotions in a more easy and fluent way, which results to
greater shopping enjoyment (Mosteller, Donthu, & Eroglu, 2014) and increased likelihood to
purchase from the online store (Pappas et al., 2014)
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Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
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2.2. Online shopping motivations
A shopping motivation defines the overall dispositions of online consumers towards the
task of shopping online. Such motivations may include a variety of shopping motivations that
are manifested by the expected benefits that one consumer seeks to receive from the online
store. Along this line, extant studies describe a number of shopper types based on generic
shopping motives and varying patterns of conditions that drives consumers during the
consumer behavior stages of information search, alternative evaluation, and product selection
(Brown, Pope, & Voges, 2003; Dennis, King, Jayawardhena, Tiu Wright, & Dennis, 2007;
Ganesh et al., 2010; Kau et al., 2003; Rohm & Swaminathan, 2004).
Based on these studies there are several motives that drive consumers to shop online. A
particular shopper cluster that stands out is economic shoppers. Such shoppers are price
sensitive and they are concerned with purchasing products at the lowest price or identifying
good bargains for the money they are willing to spend online (Brown et al., 2003; Kau et al.,
2003). Apart from product price, online shoppers also value the brand image of the store as a
major determinant of their decision to purchase online and a factor that affects the perceived
user experience of online shoppers (Morgan-Thomas & Veloutsou, 2013). In effect, positive
perceptions and familiarity of shoppers with the online store are likely to generate positive
trust perceptions because they mitigate concerns about possible mishandlings of personal
information and security (Eastlick, Lotz, & Warrington, 2006; Ha & Perks, 2005). Similar to
brand store image, online customers may value specific brands in the products’ portfolio of
the online store. Brand loyalty and sensitivity is an established factor that influences purchase
choices and dictates a consumer behavior that is evinced in both the offline and online
environments (Danaher, Wilson, & Davis, 2003).
Perceived service quality reflects another important online shopping motivator,
especially during initial service encounters, since it captures shoppers' overall evaluation of
the excellence online stores (Y. J. Wang, Hernandez, & Minor, 2010). Service quality is a
multidimensional concept that includes both utilitarian and hedonic judgments (Bauer, Falk,
& Hammerschmidt, 2006). These judgments go beyond simple evaluations pertaining the
variety of products the online store offers, but also include consumer perceptions on whether
their needs are fulfilled through online/ offline support in terms of order fulfillment and
delivery (J. Kim, Jin, & Swinney, 2009). Unsurprisingly, scholars report that the provided
service quality by an online store has been shown to influence the degree of shoppers’
perceived value, trust, and satisfaction for the store (Harris & Goode, 2004) as well as
develop loyalty cues (J. Kim et al., 2009). Finally, the degree of perceived personal
innovativeness is likely to affect the propensity of shoppers to purchase from an online store.
Personal innovativeness is a personality trait that dictates individuals’ predisposition towards
innovations (Rogers, 2010). Early studies posit that innovators are more likely to shop online
and are usually heavier shoppers than non-innovators (Enrique, Carla, Joaquín, & Silvia,
2008) and such behavior may lead to website loyalty (H.-C. Wang, Pallister, & Foxall, 2006).
2.3. The importance of capturing the typology of online shoppers
With the increasing importance of online sales and the growing number of shoppers
visiting online stores, it is imperative for retailers, to develop a better understanding of the
factors influencing online shopping behavior. Although online retailing literature includes a
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
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multitude of such efforts (Bridges & Florsheim, 2008; Childers et al., 2001; Ganesh et al.,
2010; Kau et al., 2003) we posit that the economic recession requires a revisit of these
factors. Indeed, the economic recession has changed the shopping behavior both offline and
online. For example, Hampson and McGoldrick (2013) highlight that shoppers during the
recession period have become more price conscious, less store loyal, shop less frequently,
and perform more planned purchases while at the same time reduce their impulse purchases.
This pattern is more evident to countries that are highly affected by the economic recession,
such as Greece, in which consumers have significantly reduced their shopping expenses
(Duquenne & Vlontzos, 2014). To this end, this research aims at revisiting online shoppers’
behavior by taking into account factors from two interweaved perspectives: the online
shopping experience and internal motivators that drive online shopping. The results of this
work will contribute to comprehending the new online customer that is affected by the
economic crisis and assist retailers to devise more effective marketing and communication
strategies.
This study adopts the shopping motivations, as identified in the Spanish market, and
examines their role on personalized online shopping in Greece and their relation with
customers’ online shopping experience. As parts of the Mediterranean markets, the Greek
market shares some similarities with the Spanish one, due to their character, lifestyle, cultural
characteristics and their level of e-commerce adoption (Giannakos, Pateli, & Pappas, 2011;
Turk, Blažič, & Trkman, 2008). Although countries in the Mediterranean area also have some
differences when it comes to personality traits of their customers (Herstein et al., 2012), they
experience the purchase process more as a social exchange and interaction, compared to other
European countries (Giannakos et al., 2011).
Relationships between two variables (e.g., A, B) are complex, and the presence of one
(i.e., A) may lead to the presence of the other (i.e., B), suggesting sufficiency. However, at
the same time, variable B may be present even when variable A is absent, suggesting that the
presence of A is a sufficient but unnecessary condition for variable B to occur. For example,
customers may have high intentions to purchase if part of their shopping motivations are met,
regardless of the quality of the personalized service. Also, customers’ that enjoy shopping
online will be motivated by a promotion message or a low price, suggesting that their
combination will be a sufficient condition for high purchase intentions. Customers may be
more willing to overcome a low service quality if the price is good, or on the other hand they
may prefer to pay a rather higher price for better service quality. Furthermore, customers that
receive high quality services and are persuaded to proceed to a purchase will probably
experience positive emotions as well. It is thus evident that complex interrelations exist
among customers’ online shopping experience and motivations.
To this end, we posit that there is a synergy between online shopping motivations and
online shopping experience, explaining online purchase intentions. In particular, we theorize
that there is not one single, optimal, configuration of such values. Instead, multiple and
equally effective configurations of causal individual adoption factors may exist, which may
include different combinations of adoption perceptions (i.e. combinations of high and low
perceptions). Depending on how they combine they may or may not explain customers’ high
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
7
intention to purchase. To this end, in order to conceptualize these relationships, we propose a
theoretical model (Figure 1) illustrating three sets of constructs and their intersections.
Figure 1. Conceptual Model
The Venn diagram illustrates three sets of constructs and their intersections. The three sets of
constructs reflect the outcome of interest (dependent variable) of this study and two sets of
causal conditions to predict the outcome (independent variables). Specifically, the outcome of
interest is customers’ intention to purchase, and the two sets of causal conditions are online
shopping experience (i.e., quality of personalization, shopping enjoyment, persuasion) and
online shopping motivations (i.e., price sensitivity, promotion sensitivity, service quality
sensitivity, brand sensitivity, innovativeness). The intersections represent factor
configurations, which are higher-level interactions.
2.4. Research Propositions
Configuration theory and complexity theory, build on the principle of equifinality (von
Bertalanffy, 1968), which suggests that a result may be equally explained by alternative sets
of causal conditions (Fiss, 2007). These conditions may be combined in sufficient
configurations to explain the outcome (Fiss, 2011; Woodside, 2014). Online shopping
experience and motivations are essential causal conditions to understand customers’ online
purchase behavior, and may be combined in various configurations to explain it. In detail,
Pappas et al. (2014) have found that quality of personalization and positive emotions (i.e.,
enjoyment) may influence customers’ purchase intentions. Further, personalization is related
with price and promotion sensitivity, and is used as a strategy to influence customers’
behavior (Montgomery & Smith, 2009). Nonetheless, personalization may not influence
customers’ perceived service quality, which in turn may increase their purchase intentions
(G.-G. Lee & Lin, 2005). However, service quality is directly related with goal-oriented
online shopping behavior (i.e., online shopping motivations), as well as with hedonic aspects,
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
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such as shopping enjoyment (Bauer et al., 2006). Thus, configurations may include various
combinations leading to the following proposition:
Proposition 1. No single best configuration of customers’ online shopping experience and
online shopping motivations lead to high intention to purchase, but there exist multiple,
equally effective configurations of causal factors.
Further, configuration theory proposes the principle of causal asymmetry, which means
that, for an outcome to occur, the presence and absence of a causal condition depends on how
this condition combines with the other conditions (Woodside, 2014; Woodside, Prentice, &
Larsen, 2015). For example, personalization of high quality may influence customers’
attitudes and behavior (Ho & Bodoff, 2014), but depending on how it combines with positive
emotions, such as enjoyment, it may not always explain high purchase intentions (Pappas,
Kourouthanassis, et al., 2016). Furthermore, customers’ online shopping motivations may
have various effects on their behavior depending on their experience. For instance, certain
types of customers focus on quality (service or product), and thus they may not be
particularly interested in the price of the service or product. Such types of customers are
likely to value high quality personalization, leading to increased purchase intentions (Pappas,
Kourouthanassis, et al., 2016). Nonetheless, if the quality of the service is poor, the same
types of customers may express high purchase intentions, when they are influenced by other
factors, such as brand sensitivity or innovativeness (Kwon, Lee, & Jin Kwon, 2008). Hence,
configurations may be combined variously and lead to the following proposition:
Proposition 2. Online shopping experience and online shopping motivations may be present
or absent as single causal conditions for customers’ high intention to purchase, depending on
how they combine with other causal conditions.
3. Research Methodology
3.1. Data Collection
The survey was conducted in Greece, from April to June 2015. A snowball sampling
methodology was used to recruit participants, as it gives access to a representative sample with
an interconnected network of people. The research instrument controls the prospective
participants for their experience with personalized services in online shopping. The researchers
contacted people with established experience with online personalized services. Similarly, the
latter turned to their personal or business contacts (e.g., friends, relatives, colleagues etc.) with
established online shopping experience. The participants were asked to answer based on
evaluations created after using of personalized services. It was made clear that there was no
reward for the respondents, the participation was voluntary and that the study was confidential.
Data were collected by means of an online questionnaire. Respondents with no previous
experience with personalized online shopping were excluded from the study. Finally, 482
responses were collected out of which 401 have the desired previous experience.
3.2. Sample
The sample of the study consists of more women (53.1%) than men (46.9%). The
majority of the respondents (64.5%) belonged to the age group 18-28. Further, 14% belonged
to the age group 29-35, and 9% belonged to the age group 36-45. Almost 12.5% were 46 years
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
9
old or older. Regarding their occupation, the majority (47.1) of the sample consists of students,
followed by those working in the private (29.2%) or public (15.5%) sector, and 8.2% are retired
or unemployed. Finally, regarding experience with personalized online shopping, the majority
(43.2) of the sample purchases online for over 3 years, and 39.7% of the sample makes online
purchases for 1-3 years. A smaller percent (17.2) has experience of less than a year. In addition,
the respondents have made on average 3 online purchases the past six months. Based on the
most recent report that profiles the Greek online customers (Hellenic Statistical Authority,
2015), the biggest category of customers is that of 16-34 years old, followed by customers
being between 35-44. The sample of the present research approximates the distribution of the
aforementioned report, although with a skew towards younger ages, suggesting it is
representative of the Greek online market.
Table 1 Demographics of the sample
N
Percent
Gender
Female
213
53.1
Male
188
46.9
Age
18-28
259
64.6
29-35
56
14
36-45
36
9
46-55
32
8
55+
18
4.5
Occupation
Student
189
47.1
Private sector
117
29.2
Public sector
62
15.5
Retired
4
1
Unemployed
29
7.2
Experience in Years
<1
69
17.2
1-3
159
39.7
3-5
78
19.5
5-7
42
10.5
7+
53
13.2
Purchases in the past six months
Mean (S.D.)
5.8 (9.34)
3.3. Measures
The questionnaire consisted of two parts. The first part included questions on the
demographics of the sample. The second part included measures of the various constructs
identified in the literature review section. For testing our hypotheses, the survey included
reflective scales for the constructs of our conceptual model. Table 1 lists the operational
definitions of the constructs in this theoretical model as well as the studies from which the
measures were adopted. The appendix lists the questionnaire items used to measure each
construct, along with descriptive statistics and loadings.
Table 2. Construct definition
Construct
Source
Quality of
Personalization
Pappas et al. (2014)
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
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Shopping
enjoyment
Liu & Forsythe (2011)
Persuasion
Cesario et al. (2004)
Price sensitivity
González-Benito et al.
(2014)
Promotion
sensitivity
Service quality
sensitivity
Store Brand
sensitivity
Innovativeness
Intention to
purchase
Pappas et al. (2014)
3.4. FsQCA
The study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) using fs/QCA
2.5 (Ragin & Davey, 2014). fsQCA was developed by integrating fuzzy set and fuzzy logic
(Zadeh, 1965) with QCA (Ragin, 2000). QCA is an analytic technique that is based on
Boolean algebra and implements principles of comparison to study social phenomena (Ragin,
2000), and is able to answer questions that are based on set-theoretic notions and for
analyzing causal complexity (Legewie, 2013). FsQCA uses (fuzzy) set theory and Boolean
algebra to analyze how factors combine together, and how their presence or absence leads to
a certain outcome (Ragin, 2008). Fuzzy sets and fuzzy logic principles apply in engineering
and control theory, as well as in social sciences (Liu, Mezei, Kostakos, & Li, 2015). This tool
identifies patterns among independent and dependent variables, that explain an outcome and
more importantly differentiates from the analyses of variance, correlations and multiple
regression models, as it offers multiple solutions that can lead to the same result. A variable
that affects the outcome in only a small subset of cases cannot be identified by regression
analysis (Liu et al., 2015; Vis, 2012). The offered solutions are presented by necessary and
sufficient conditions, which offer a differentiation between core and peripheral variables.
Core variables are those with a strong causal condition with the result, while peripheral
elements are the ones with a weaker one (Fiss, 2011). Further, these conditions may be
present, absent or may not matter (i.e., “do not care”) in a solution (Fiss, 2011). In a “do not
care” situation, the conditions may either be present or absent.
3.4.1 Calibration
Firstly, the outcome (i.e., dependent variable) and the causal conditions (i.e.,
independent variables) are defined. Next, fsQCA needs all variables to be calibrated into
fuzzy sets with values ranging from 0 to 1. In detail, the value of 1 stands for the full set
membership, while that of 0 stands for the no set membership. Thus, all variables are
continuous, with scale from 0-1, which defines the level of their membership. Data
calibration may be either direct or indirect. In the direct method, the researcher chooses three
qualitative breakpoints, whereas in the indirect method, the measurements require rescaling
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
11
based on qualitative assessments. It is up to the researcher to choose a calibration method
based on the data as well as on the underlying theory, but the direct method is recommended
(Liu et al., 2015; Ragin, 2008) The transformation of variables into calibrated sets is done by
fsQCA program, by setting three meaningful thresholds; full membership, full non-
membership, and the cross-over point, which describes how much the case belongs to a set
(Ragin, 2008). The calibration requires that three points are defined, which represent the
three qualitative anchors (Fiss, 2011; Ragin, 2008). These points set the thresholds for the full
set membership, full non-set membership, and the crossover point. The calibration is based
on the survey scale (7-point Likert) that was used in this study. In detail, the full membership
threshold is fixed at the rating of 6; the full non-membership threshold is fixed at the rating of
2; and, the crossover point was fixed at 4 (Ordanini et al., 2014; Pappas, Kourouthanassis, et
al., 2016).
3.4.2 Truth table
Once the calibration is complete, the fsQCA algorithm is applied in order to produce a
truth table of 2k rows, on which k represents the number of outcomes, and every row
represents every possible combination among the causal conditions. For example, a truth
table between two variables (i.e., conditions) provides four possible logical combinations
between them. For every combination, the minimum membership value is calculated; that is,
the degree to which every case supports the specific combination. Following, each row of the
truth table is examined first based on the frequency and then on the consistency (Ragin,
2008). The frequency is the number of observations for each possible combination, while
consistency refers to “the degree to which cases correspond to the set-theoretic relationships
expressed in a solution” (Fiss, 2011). Regarding frequency, a cut-off point is set, which
provides assurance that a minimum number of empirical observations will be used for the
analysis. Ragin (2008) suggests a cut-off point of 1 for small or medium sized samples.
However, for samples with over 150 cases a higher cut-off point should be set. Fiss (2011)
suggests setting it at 3. For consistency, the minimum acceptable value is 0.75 (Ragin, 2006).
In this study, the consistency threshold is set at 0.80, higher than the recommended value.
4. Findings
4.1. Measurements
A confirmatory factor analysis (CFA) is performed to verify the factor structure of the
reflective constructs. The constructs used in this research are evaluated in terms of reliability
and validity. Reliability testing, based on the Cronbach alpha indicator, shows acceptable
indices of internal consistency since all constructs exceed the cut-off threshold of 0.70.
Establishing validity requires that average variance extracted (AVE) is greater than 0.50, the
correlation between the different variables in the confirmatory models does not exceed 0.8
points, as this suggests low discrimination and that the square root of each factor’s average
variance extracted (AVE) is larger than its correlations with other factors (Fornell & Larcker,
1981). The AVE for all constructs ranges between 0.53 and 0.88, all correlations are lower
than 0.80, and square root AVEs for all constructs are larger than their correlations. The
findings are illustrated in Table 2.
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
12
Next, multicollinearity (R. M. O’brien, 2007) is examined along with the potential
common method bias by utilizing the Harman's single factor test (Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003). Since the variance inflation factor (VIF) for every factor is below
the recommended value (<3), multicollinearity is not an issue in this study. Thus, common
method bias is not a problem, because the first factor does not account for the majority of the
variance and no single factor occurs from the factor analysis. Additionally, since a CFA
analysis is performed, model fit is examined as well. The comparative fit index (CFI),
Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA) serve as
indices to assess the overall measurement model fit. All values are within the recommended
range. Specifically, x2/df: 2.44, TLI: 0.93, CFI: 0.94 and RMSEA: 0.06.
Table 2. Descriptive statistics and correlations of latent variables
Construct
Construct
Mean (SD)
CR
AVE
1
2
3
4
5
6
7
8
9
Persuasion
3.99 (1.22)
0.96
0.88
0.94
Brand sensitivity
4.88 (1.53)
0.85
0.65
0.28
0.81
Innovativeness
3.38 (1.36)
0.87
0.69
0.25
0.21
0.83
Service quality sensitivity
4.51(1.53)
0.94
0.84
0.27
0.5
0.24
0.92
Promotion sensitivity
4.48 (1.53)
0.91
0.67
0.32
0.27
0.25
0.26
0.82
Price sensitivity
5.40 (1.58)
0.91
0.83
0.31
0.30
0.08
0.24
0.44
0.91
Shopping enjoyment
3.86 (1.51)
0.82
0.53
0.43
0.33
0.28
0.28
0.3
0.27
0.73
Quality of Personalization
4.24 (1.41)
0.92
0.79
0.5
0.32
0.23
0.3
0.3
0.36
0.48
0.89
Intention to purchase
4.14(1.5)
0.93
0.81
0.51
0.29
0.26
0.26
0.28
0.32
0.58
0.52
0.9
Note: Diagonal elements (in bold) are the square root of the average variance extracted (AVE). Off-diagonal
elements are the correlations among constructs (all correlations are significant, p< 0.05). For discriminant validity,
diagonal elements should be larger than off-diagonal elements.
4.2. Results from the fsQCA
The findings from the configuration analysis with fsQCA are presented in Table 3. In
detail, black circles (●) demonstrate the presence of a condition, and the crossed-out circles
() present its absence (Fiss, 2011). Also, large circles represent the core variables of a
configuration, and small circles symbolize the peripheral variables. The “do not care”
situation is indicated by a blank space on the solution. Further, the values for consistency and
coverage are presented on Table 3, for the overall solution and for each solution separately.
All values are greater from the recommended threshold of 0.75. First, consistency represents
the level that a relation has been approximated, and then coverage determines the empirical
relevance of the same consistent subset (Ragin, 2006). Thus, the overall solution coverage
presented on Table 3, indicates the extent that high intention to purchase when using
personalized services may be determines based on the set of the configurations, and may be
compared to the R-square value (Woodside, 2013). The results suggest an overall solution
coverage of 0.702, indicating that a substantial proportion of the outcome is covered by the
nine solutions. FsQCA estimates also the empirical relevance for each solution, by
calculating raw and unique coverage. The raw coverage describes the amount of the outcome
that is explained by a certain alternative solution, while the unique coverage describes the
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
13
amount of the outcome that is exclusively explained by a certain alternative solution. The
solutions presented in Table 3 explain a vast amount of customers’ intention to purchase,
ranging from 11% to 54% cases associated with the outcome.
Table 3 Configurations that lead to high intention to purchase
Solution
Configuration
1
2
3
4
5
6
7
8
9
Online shopping experience
Quality of Personalization
!
!
!
!
!
U
U
U
U
Shopping enjoyment
#
#
U
U
U
U
U
Persuasion
#
#
#
U
U
U
U
Online Shopping motivations
Price sensitivity
#
#
!
#
#
!
#
U
Promotion sensitivity
!
!
!
U
!
!
U
Service quality sensitivity
#
#
U
#
#
U
#
#
!
Store Brand sensitivity
#
#
#
#
U
#
#
!
Innovativeness
U
U
U
U
U
#
U
Consistency
0.904
0.916
0.955
0.952
0.875
0.760
0.847
0.780
0.817
Raw Coverage
0.431
0.536
0.241
0.372
0.213
0.157
0.175
0.226
0.112
Unique Coverage
0.016
0.015
0.041
0.015
0.011
0.014
0.012
0.010
0.112
Overall solution consistency
0.805
Overall solution coverage
0.702
Note: Black circles (! ) indicate the presence of a condition, and circles with “x” (
U
) indicate its absence.
Large circles indicate core conditions; small ones, peripheral conditions. Blank spaces indicate “do not care”.
For high intentions to purchase when using personalized services, the solutions 1-9
presented in Table 3 reflect of the presence or absence of online shopping experience and
online shopping motivations. In detail, quality of personalization and the online shopping
motivations appear both as core and peripheral conditions in the solutions, suggesting their
importance depending on how they combine with the other factors. Solutions from 1-5
provide combinations among the examined factors when quality of personalization is high
and considered very important by the customers. On the other hand, solutions 6-9 present
combinations explaining high intention to purchase when quality of personalization is low.
The quality of personalization, as a core construct, combined with persuasion, price
sensitivity, service quality sensitivity, and brand sensitivity, explain high intention to
purchase, regardless of shopping enjoyment and promotion sensitivity, while innovativeness
is absent (solution 1). Next, when promotion sensitivity is present, as a core construct, it may
lead to high intentions when combined with all the other factors, except shopping enjoyment
and innovativeness that do not matter (solution 2). Further, when quality of personalization,
and price and promotion sensitivity are present as core constructs, with online shopping
experience factors being present as peripheral, high intention to purchase is explained when
both service quality sensitivity and innovation are low (i.e., absent), regardless of brand
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
14
sensitivity (solution 3). Also, when only quality of personalization and promotion sensitivity
are core constructs, they explain high intentions when they combine with all the other factors,
except innovativeness and regardless of persuasion (solution 4). Finally, when quality of
personalization is present as a core factor and there is low enjoyment, high intentions occur
when price, service quality, and brand sensitivity are present as well, as long as promotion
sensitivity and innovativeness are low, and independent of persuasion (solution 5).
Next, solutions 6-9 present combinations on which online shopping experience is low
(i.e., all factors are absent), but still high intentions may be achieved. In detail, high price
sensitivity alone may lead to high intentions as long as service quality sensitivity, brand
sensitivity, and innovativeness are low, regardless of promotion sensitivity (solution 6). On
the other hand, for price sensitivity to not matter, all the other psychographic characteristics
need to be present (solution 7). Further, the presence of almost all psychographics constructs,
except innovativeness which does not matter, are able to explain high intention to purchase
(solution 8). Finally, price sensitivity, as a core construct, along with promotion sensitivity
and innovativeness may be low and still lead to high intentions, as long as customers’ service
quality sensitivity and brand sensitivity are high.
5. Discussion
The findings of this study suggest that in personalized online shopping, customers’
online shopping experience and online shopping motivations, combine to form constellations
to predict high intention to purchase. To this end, a conceptual model is constructed in order
to identify the aforementioned configurations. The findings indicate that the traditional
techniques in personalized online shopping (e.g., recommendations based on previous
purchases, tailored messages based on browsing history) are not enough to lead customers to
an online purchase, when customers are on a shopping mission. A shopping mission suggests
that customers have predefined needs that are largely based on their shopping motivations. In
detail, shopping enjoyment and persuasion are low or indifferent on the majority of the
solutions. This suggests that customers do not care about hedonic factors when using
personalized services when on a shopping mission, and persuading them is not enough to
influence their shopping behavior. Furthermore, the results identify the importance of price
sensitivity and promotion sensitivity as they are present in the majority of the solutions and
more than once as core constructs. This may be explained by the fact that the sample consists
of only Greek online shoppers, and the economic crisis in Greece is still undergoing. The
customers’ focus on finding the cheapest product or wait for promotions that will offer their
favored brands on a lower price.
This study has identified interrelations among the utilitarian and hedonic elements
included in the conceptual model. Specifically, the results show that when quality of
personalization is high and the customers are persuaded, their perceived shopping enjoyment
will either be high or indifferent. This suggests a dominating role of quality of
personalization over the other factors. On the other hand, the findings show that when quality
is not high and the customers are not persuaded, shopping enjoyment will not be high either.
This is explained mainly from customers’ being affected by the low quality of
personalization, which is directly related with positive emotions (Pappas et al., 2014), such as
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
15
enjoyment, indicating again that quality plays a dominant role in personalized online
shopping. Nonetheless, although utilitarian and hedonic values are important, their existence
is not a necessary condition for high purchase intention, as customers are influenced by their
online shopping motivations.
In an attempt to characterize customers that use personalized services to shop online,
this study focuses specifically on their motivations and benefits, and how they influence
customers’ behavior along with their online shopping experience. This research has both
theoretical and practical implications. The findings are an important step towards extending
the literature on personalized online shopping. We empirically demonstrate the importance of
synergetic nature of online shopping experience when combined with online shopping
motivations. Furthermore, by implementing complexity theory, we highlight alternative paths
that lead to high intention to purchase when specific conditions are absent. Finally, we
pinpoint the need to extend online personalization models and theories by examining at the
same time customers’ shopping motivations. Interestingly enough, the findings differentiate
from those of Rohm and Swaminathan (2004), who found that variety seeking and
convenience are the main motivators for online shopping. However, the results of this study
identify price and promotion sensitivity as the most important motivators for shopping online,
most likely explained by the fact that this study was conducted in Greece, a country in
economic recession.
This paper differentiates from the majority of previous studies on the area of online
shopping that use symmetric methods (e.g., multiple regression analysis) to analyze and
explain customer behavior. A configuration analysis is performed with the use of the data
analysis tool fsQCA, which examines asymmetric relationships among the factors. This
methodology has recently received great attention, and when it is applied with complexity
theory it may help in theory building (Leischnig & Kasper-Brauer, 2015; Woodside, 2014).
Thus, building on complexity theory, a conceptual model and research propositions are made
in order to predict customers’ online shopping behavior. The findings indicate complex
causal patterns among the predictor variables and verify the proposed asymmetric
relationships that may lead to the same outcome.
Online retailers should always be aware of their customers’ shopping characteristics, in
order to address them based on their motivations. The findings of this study may pave the
ground for retailers and decision makers to redesign their personalization strategies, in order
to increase their effectiveness during the economic recession era. Customers behave
differently based on the foreseeable benefits regardless of how good is their shopping
experience. Despite the great evolution of personalized services and persuasion strategies, the
findings indicate that the main motivators of using personalized services are price sensitivity
and promotion sensitivity. Identifying customers’ motivations is also crucial for companies,
because it may help them increase their sales even when a recommendation message fails its
purpose by considered to be of low quality. In such cases, customers may not feel persuaded
or experience any positive affect, but may still proceed to a purchase if, for example, the
price is low. Nonetheless, the results suggest that there is still further work to be done in
order to fully exploit the benefits of personalized services in online, which may be more clear
when retailers target their marketing strategies based on customers’ motivations.
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
16
This empirical study has some limitations. Firstly, the respondents are all Greek online
shoppers; thus the generalization of the findings should be done with caution. Further
research is encouraged to verify and compare the findings with other Mediterranean
countries. Further, the study examines customers’ general perceptions towards personalized
services from online retailers, without focusing on a specific service, product, or retailer.
Future studies may focus on certain types of services or e-shops in order to offer more
specific guidelines towards retailers and managers. Also, the study controls for
personalization by presenting the respondents with various examples, of what are
personalized service are. However, future studies may ask directly the respondents to give an
example of a personalized services, thus increasing the reliability of the sample. Other online
shopping motivations may be considered as well (e.g., purpose of a purchase) (Close &
Kukar-Kinney, 2010), as well as include various demographic characteristics (e.g., gender,
age, income, etc.) to create a detailed customer profiling. Finally, from a methodological
point of view it should be noted that fsQCA is not able to identify the unique contribution of
each variable for every solution. Instead, the goal of fsQCA is to identify the complex
combinations of the independent variables, as well as the amount of the outcome that is
explained by these combinations. Future studies may combine fsQCA with regression-based
techniques to gain a deeper insight on the data and explore the effect of each variable on the
outcome, based on the configurations identified from fsQCA.
Acknowledgements
This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship
Programme.
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
17
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Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
22
Appendix
Scale items with mean, standard deviation and standardized loading
Construct and scale items
Mean
S.D.
Loading
Price Sensitivity, CA = 0.93
1. I always compare prices between different brands
before I choose one, when I shop online.
5.43
1.71
0.94
2. I compare prices to take advantage of special offers,
when I shop online.
5.42
1.65
0.95
3. I visit different online stores to take advantage of the
best prices.
5.32
1.69
0.92
Promotion sensitivity, CA = 0.77
1. I like to take part in package promotions from online
shops.*
2.78
1.76
0.55
2. I use the discount coupon from online shops when I have
the chance.
4.65
1.99
0.82
3. I like to take part in promotions from online shops that
offer an extra amount of product or a different product.
4.82
1.73
0.80
4. I stay informed about promotions from online shops by
store feature and displays.
3.99
1.77
0.79
Service-quality sensitivity, CA = 0.89
1. When I shop online, I prefer to visit a more organized
store, even if it is more expensive.
4.56
1.69
0.85
2. When I shop online, I prefer to visit a more caring store,
even if it is more expensive.
4.43
1.69
0.83
3. When I shop online, I prefer to visit a store with better
online support, even if it is more expensive.
4.54
1.66
0.81
Shopping enjoyment, CA = 0.92
1. Shopping online with personalized services is enjoyable
4.03
1.68
0.92
2. Shopping online with personalized services is exciting
3.67
1.63
0.92
3. Shopping online with personalized services makes me
feel good
3.87
1.58
0.91
4. Shopping online with personalized services is boringR*
5.43
1.61
0.26
Innovativeness, CA = 0.88
1. In general, I am one of the first one to buy a new product.
3.04
1.66
0.81
2. I used to be one of the first one to try a new brand.
2.94
1.59
0.85
3. I like to try new products.
3.99
1.68
0.85
4. I like to try new brands.
3.82
1.69
0.82
5. I enjoy taking risks by buying new products.
3.09
1.62
0.77
Store Brand sensitivity, CA = 0.79
1. I tend to buy products from well known e-shops.
4.79
1.61
0.91
2. To me, it is important from which e-shop I buy.
4.96
1.74
0.91
Pappas, I.O et al. 2017 – Telematics and Informatics
Received 14 January 2016 Telematics and Informatics
Revised 27 June 2016 DOI: 10.1016/j.tele.2016.08.021
Accepted 18 August 2016
23
Persuasion, CA = 0.76
1. Personalized services are persuasive. (i.e. Based on
appeals made to the will, moral sense or emotions).
3.34
1.62
0.72
2. Personalized services are convincing. (i.e., Based on
evidence or arguments made to the intellect)
4.73
1.68
0.69
3. Personalized services are compelling. *
3.34
63
0.54
4. Personalized services are influential.
3.64
63
0.75
5. When I go shopping, I prefer to go to a store with kind
salespeople, even if it is more expensive.
4.26
1.55
0.76
Quality of personalization, CA = 0.87
1. Online vendors can provide me with personalized
deals/ads tailored to my activity context.
4.16
1.61
0.91
2. Online vendors can provide me with more relevant
promotional information tailored to my preferences or
personal interests.
4.32
1.56
0.89
3. Online vendors can provide me with the kind of deals/ads
that I might like.
4.24
1.58
0.86
Intention to purchase, CA = 0.78
1. In the future I intend to continue shopping online based
on personalized services.
4.34
1.70
0.90
2. My general intention to buy online based on personalized
services is very high.
4.13
1.68
0.92
3. I will think about shopping online based on personalized
services.*
3.39
1.55
0.34
4. I will shop online in the future based on personalized
services.
4.94
1.57
0.88
R Reversed question, CA; Cronbach alpha, * Deleted due to low loading
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