ArticlePDF Available

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

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,,,
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
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
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
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
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
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
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,
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)
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
2.2. 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
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
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
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
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
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
Private sector
Public sector
Experience in Years
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
Quality of
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
Accepted 18 August 2016
Liu & Forsythe (2011)
Cesario et al. (2004)
Price sensitivity
González-Benito et al.
Service quality
Store Brand
Intention to
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
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
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
Mean (SD)
3.99 (1.22)
Brand sensitivity
4.88 (1.53)
3.38 (1.36)
Service quality sensitivity
Promotion sensitivity
4.48 (1.53)
Price sensitivity
5.40 (1.58)
Shopping enjoyment
3.86 (1.51)
Quality of Personalization
4.24 (1.41)
Intention to purchase
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
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
Online shopping experience
Quality of Personalization
Shopping enjoyment
Online Shopping motivations
Price sensitivity
Promotion sensitivity
Service quality sensitivity
Store Brand sensitivity
Raw Coverage
Unique Coverage
Overall solution consistency
Overall solution coverage
Note: Black circles (! ) indicate the presence of a condition, and circles with “x” (
) 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
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
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
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.
This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship
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
Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). eTransQual: A transaction process-
based approach for capturing service quality in online shopping. Journal of Business
Research, 59(7), 866-875.
Berkovsky, S., Freyne, J., & Oinas-Kukkonen, H. (2012). Influencing individually: fusing
personalization and persuasion. ACM Transactions on Interactive Intelligent Systems
(TiiS), 2(2), 9.
Bosnjak, M., Galesic, M., & Tuten, T. (2007). Personality determinants of online shopping:
Explaining online purchase intentions using a hierarchical approach. Journal of
Business Research, 60(6), 597-605.
Brashear, T. G., Kashyap, V., Musante, M. D., & Donthu, N. (2009). A Profile of the Internet
Shopper: Evidence from Six Countries. Journal of Marketing Theory and Practice,
17(3), 267-282. doi:10.2753/MTP1069-6679170305
Bridges, E., & Florsheim, R. (2008). Hedonic and utilitarian shopping goals: The online
experience. Journal of Business Research, 61(4), 309-314.
Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing? An exploration of shopping
orientations and online purchase intention. European Journal of Marketing,
37(11/12), 1666-1684.
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations
for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
Close, A. G., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers'
online shopping cart use. Journal of Business Research, 63(9), 986-992.
Dabrowski, M., & Acton, T. (2013). The performance of recommender systems in online
shopping: A user-centric study. Expert Systems with Applications, 40(14), 5551-5562.
Danaher, P. J., Wilson, I. W., & Davis, R. A. (2003). A comparison of online and offline
consumer brand loyalty. Marketing Science, 22(4), 461-476.
Dennis, C., King, T., Jayawardhena, C., Tiu Wright, L., & Dennis, C. (2007). Consumers
online: intentions, orientations and segmentation. International Journal of Retail &
Distribution Management, 35(6), 515-526.
Duquenne, M.-N., & Vlontzos, G. (2014). The impact of the Greek crisis on the consumers’
behaviour: some initial evidences? British Food Journal, 116(6), 890-903.
Eastlick, M. A., Lotz, S. L., & Warrington, P. (2006). Understanding online B-to-C
relationships: An integrated model of privacy concerns, trust, and commitment.
Journal of Business Research, 59(8), 877-886.
Enrique, B. A., Carla, R. M., Joaquín, A. M., & Silvia, S. B. (2008). Influence of online
shopping information dependency and innovativeness on internet shopping
adoptionnull. Online Information Review, 32(5), 648-667.
Fiss, P. C. (2007). A set-theoretic approach to organizational configurations. Academy of
management review, 32(4), 1180-1198.
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
Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in
organization research. Academy of Management Journal, 54(2), 393-420.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables
and measurement error: Algebra and statistics. Journal of Marketing Research, 382-
Ganesh, J., Reynolds, K. E., Luckett, M., & Pomirleanu, N. (2010). Online shopper
motivations, and e-store attributes: an examination of online patronage behavior and
shopper typologies. Journal of Retailing, 86(1), 106-115.
Giannakos, M. N., Pateli, A. G., & Pappas, I. O. (2011). Identifying the Direct Effect of
Experience and the Moderating Effect of Satisfaction in the Greek Online Market.
International Journal of E-Services and Mobile Applications (IJESMA), 3(2), 39-58.
González-Benito, Ó., Martos-Partal, M., & Fustinoni-Venturini, M. (2014). Retailers’ Price
Positioning and the Motivational Profiling of Store-Brand Shoppers: The Case of
Spain. Psychology & Marketing, 31(2), 115-125.
Ha, H. Y., & Perks, H. (2005). Effects of consumer perceptions of brand experience on the
web: Brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour,
4(6), 438-452.
Hampson, D. P., & McGoldrick, P. J. (2013). A typology of adaptive shopping patterns in
recession. Journal of Business Research, 66(7), 831-838.
Harris, L. C., & Goode, M. M. H. (2004). The four levels of loyalty and the pivotal role of
trust: a study of online service dynamics. Journal of Retailing, 80(2), 139-158.
Herstein, R., Tifferet, S., Luís Abrantes, J., Lymperopoulos, C., Albayrak, T., & Caber, M.
(2012). The effect of personality traits on private brand consumer tendencies: A cross-
cultural study of Mediterranean countries. Cross Cultural Management: An
International Journal, 19(2), 196-214.
Ho, S. Y., & Bodoff, D. (2014). The Effects of Web Personalization on User Attitude and
Behavior: An Integration of the Elaboration Likelihood Model and Consumer Search
Theory. MIS quarterly, 38(2), 497-520.
Hostler, E. R., Yoon, V. Y., & Guimaraes, T. (2012). Recommendation agent impact on
consumer online shopping: The Movie Magic case study. Expert Systems with
Applications, 39(3), 2989-2999. doi:
Kau, A. K., Tang, Y. E., & Ghose, S. (2003). Typology of online shoppers. Journal of
Consumer Marketing, 20(2), 139-156. doi:doi:10.1108/07363760310464604
Kim, J., Jin, B., & Swinney, J. L. (2009). The role of etail quality, e-satisfaction and e-trust in
online loyalty development process. Journal of Retailing and Consumer Services,
16(4), 239-247. doi:
Kim, S., & Eastin, M. S. (2011). Hedonic Tendencies and the Online Consumer: An
Investigation of the Online Shopping Process. Journal of Internet Commerce, 10(1),
68-90. doi:10.1080/15332861.2011.558458
Kwon, K.-N., Lee, M.-H., & Jin Kwon, Y. (2008). The effect of perceived product
characteristics on private brand purchases. Journal of Consumer Marketing, 25(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
Lee, G.-G., & Lin, H.-F. (2005). Customer perceptions of e-service quality in online
shopping. International Journal of Retail & Distribution Management, 33(2), 161-
Lee, K. C., & Kwon, S. (2008). Online shopping recommendation mechanism and its
influence on consumer decisions and behaviors: A causal map approach. Expert
Systems with Applications, 35(4), 1567-1574.
Legewie, N. (2013). An introduction to applied data analysis with qualitative comparative
analysis. Paper presented at the Forum Qualitative Sozialforschung/Forum:
Qualitative Social Research.
Leischnig, A., & Kasper-Brauer, K. (2015). Employee adaptive behavior in service
enactments. Journal of Business Research, 68(2), 273-280.
Lim, Y. M., & Cham, T. H. (2015). A profile of the Internet shoppers: Evidence from nine
countries. Telematics and Informatics, 32(2), 344-354.
Liu, Y., Mezei, J., Kostakos, V., & Li, H. (2015). Applying configurational analysis to IS
behavioural research: a methodological alternative for modelling combinatorial
complexities. Information Systems Journal.
Montgomery, A. L., & Smith, M. D. (2009). Prospects for Personalization on the Internet.
Journal of Interactive Marketing, 23(2), 130-137.
Morgan-Thomas, A., & Veloutsou, C. (2013). Beyond technology acceptance: Brand
relationships and online brand experience. Journal of Business Research, 66(1), 21-
27. doi:
Mosteller, J., Donthu, N., & Eroglu, S. (2014). The fluent online shopping experience.
Journal of Business Research, 67(11), 2486-2493.
O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user
engagement: The case of online shopping experiences. Interacting with Computers,
22(5), 344-352. doi:
O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors.
Quality & Quantity, 41(5), 673-690.
Ordanini, A., Parasuraman, A., & Rubera, G. (2014). When the recipe is more important than
the ingredients a Qualitative Comparative Analysis (QCA) of service innovation
configurations. Journal of Service Research, 17(2), 134-149.
Overby, J. W., & Lee, E.-J. (2006). The effects of utilitarian and hedonic online shopping
value on consumer preference and intentions. Journal of Business Research, 59(10–
11), 1160-1166. doi:
Pappas, I. O., Giannakos, M. N., & Sampson, D. G. (2016). Making Sense of Learning
Analytics with a Configurational Approach. Paper presented at the Proceedings of the
workshop on Smart Environments and Analytics in Video-Based Learning (SE@
VBL), LAK2016.
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2014).
Shiny happy people buying: the role of emotions on personalized e-shopping.
Electronic Markets, 24(3), 193-206.
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
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2016).
Explaining online shopping behavior with fsQCA: The role of cognitive and affective
perceptions. Journal of Business Research, 69(2), 794-803.
Pappas, I. O., Mikalef, P., & Giannakos, M. N. (2016, 2016). Video-Based Learning
Adoption: A typology of learners. Paper presented at the Proceedings of the workshop
on Smart Environments and Analytics in Video-Based Learning (SE@VBL),
Park, D.-H., & Kim, S. (2009). The effects of consumer knowledge on message processing of
electronic word-of-mouth via online consumer reviews. Electronic Commerce
Research and Applications, 7(4), 399-410.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method
biases in behavioral research: a critical review of the literature and recommended
remedies. Journal of applied psychology, 88(5), 879.
Ragin, C. C. (2000). Fuzzy-set social science: University of Chicago Press.
Ragin, C. C. (2006). Set relations in social research: Evaluating their consistency and
coverage. Political Analysis, 14(3), 291-310.
Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond (Vol. 240): Wiley
Online Library.
Ragin, C. C., & Davey, S. (2014). fs/QCA [Computer Programme], version 2.5. Irvine, CA:
University of California.
Rogers, E. M. (2010). Diffusion of innovations: Simon and Schuster.
Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on shopping
motivations. Journal of Business Research, 57(7), 748-757.
Salonen, V., & Karjaluoto, H. (2016). Web personalization: The state of the art and future
avenues for research and practice. Telematics and Informatics, 33(4), 1088-1104.
Thirumalai, S., & Sinha, K. K. (2013). To personalize or not to personalize online purchase
interactions: Implications of self-selection by retailers. Information Systems Research,
24(3), 683-708.
To, P.-L., Liao, C., & Lin, T.-H. (2007). Shopping motivations on Internet: A study based on
utilitarian and hedonic value. Technovation, 27(12), 774-787.
Turk, T., Blažič, B. J., & Trkman, P. (2008). Factors and sustainable strategies fostering the
adoption of broadband communications in an enlarged European Union.
Technological Forecasting and Social Change, 75(7), 933-951.
Vis, B. (2012). The comparative advantages of fsQCA and regression analysis for moderately
large-N analyses. Sociological Methods & Research, 41(1), 168-198.
Wang, H.-C., Pallister, J. G., & Foxall, G. R. (2006). Innovativeness and involvement as
determinants of website loyalty: III. Theoretical and managerial contributions.
Technovation, 26(12), 1374-1383.
Wang, Y. J., Hernandez, M. D., & Minor, M. S. (2010). Web aesthetics effects on perceived
online service quality and satisfaction in an e-tail environment: The moderating role
of purchase task. Journal of Business Research, 63(9–10), 935-942.
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
Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling
for adoption of a paradigm shift from symmetric to asymmetric thinking in data
analysis and crafting theory. Journal of Business Research, 66(4), 463-472.
Woodside, A. G. (2014). Embrace• perform• model: Complexity theory, contrarian case
analysis, and multiple realities. Journal of Business Research, 67(12), 2495-2503.
Woodside, A. G., Prentice, C., & Larsen, A. (2015). Revisiting problem gamblers’ harsh gaze
on casino services: Applying complexity theory to identify exceptional customers.
Psychology & Marketing, 32(1), 65-77.
Wu, P.-L., Yeh, S.-S., & Woodside, A. G. (2014). Applying complexity theory to deepen
service dominant logic: Configural analysis of customer experience-and-outcome
assessments of professional services for personal transformations. Journal of Business
Research, 67(8), 1647-1670.
Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy
paradox: An exploratory study of decision making process for location-aware
marketing. Decision Support Systems, 51(1), 42-52.
Yoon, V. Y., Hostler, R. E., Guo, Z., & Guimaraes, T. (2013). Assessing the moderating
effect of consumer product knowledge and online shopping experience on using
recommendation agents for customer loyalty. Decision Support Systems, 55(4), 883-
893. doi:
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
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
Scale items with mean, standard deviation and standardized loading
Construct and scale items
Price Sensitivity, CA = 0.93
1. I always compare prices between different brands
before I choose one, when I shop online.
2. I compare prices to take advantage of special offers,
when I shop online.
3. I visit different online stores to take advantage of the
best prices.
Promotion sensitivity, CA = 0.77
1. I like to take part in package promotions from online
2. I use the discount coupon from online shops when I have
the chance.
3. I like to take part in promotions from online shops that
offer an extra amount of product or a different product.
4. I stay informed about promotions from online shops by
store feature and displays.
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.
2. When I shop online, I prefer to visit a more caring store,
even if it is more expensive.
3. When I shop online, I prefer to visit a store with better
online support, even if it is more expensive.
Shopping enjoyment, CA = 0.92
1. Shopping online with personalized services is enjoyable
2. Shopping online with personalized services is exciting
3. Shopping online with personalized services makes me
feel good
4. Shopping online with personalized services is boringR*
Innovativeness, CA = 0.88
1. In general, I am one of the first one to buy a new product.
2. I used to be one of the first one to try a new brand.
3. I like to try new products.
4. I like to try new brands.
5. I enjoy taking risks by buying new products.
Store Brand sensitivity, CA = 0.79
1. I tend to buy products from well known e-shops.
2. To me, it is important from which e-shop I buy.
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
Persuasion, CA = 0.76
1. Personalized services are persuasive. (i.e. Based on
appeals made to the will, moral sense or emotions).
2. Personalized services are convincing. (i.e., Based on
evidence or arguments made to the intellect)
3. Personalized services are compelling. *
4. Personalized services are influential.
5. When I go shopping, I prefer to go to a store with kind
salespeople, even if it is more expensive.
Quality of personalization, CA = 0.87
1. Online vendors can provide me with personalized
deals/ads tailored to my activity context.
2. Online vendors can provide me with more relevant
promotional information tailored to my preferences or
personal interests.
3. Online vendors can provide me with the kind of deals/ads
that I might like.
Intention to purchase, CA = 0.78
1. In the future I intend to continue shopping online based
on personalized services.
2. My general intention to buy online based on personalized
services is very high.
3. I will think about shopping online based on personalized
4. I will shop online in the future based on personalized
R Reversed question, CA; Cronbach alpha, * Deleted due to low loading
... His research advocates surveying 400 customers on their online shopping experience using data analysis tools. Although the scholar carried out specific experiments and described the experimental objects, he did not get the experimental results, which made the experiments less meaningful [5]. Nisar and Prabhakar's goal is to analyze customer satisfaction in the EC Supply Chain market, and the results of his experiments show that customer satisfaction affects sales. ...
Full-text available
As an emerging industry, e-commerce mainly relies on online platforms to provide products and services for enterprises and consumers, and it has its own risks in the development of supply chain finance. When developing an online business, it can rely on the support of developed logistics and physical stores, which has its special advantages in operating supply chain finance, but at the same time, it also brings special business risks. With the rapid development of economic level in recent years, the application of EC Supply Chain has become more and more extensive. The emergence of EC Supply Chain has injected strong vitality into the financial market, but at the same time, the EC Supply Chain financial business is also facing huge risks. For cross-border import e-commerce companies, the core of the supply chain is the source of goods and logistics. Enterprises have more resources, which means that they have more initiative in the fierce competition, and a stable and perfect supply chain will be the core competitiveness of the cross-border import e-commerce industry. The purpose of this paper is to study how to prevent risks in the EC Supply Chain financial market based on high-performance computing. This paper puts forward the importance of high-performance computing and the significance of EC Supply Chain development and proposes preventive measures against financial market risks in EC Supply Chain. From the data in the experimental part of this paper, it can be known that the development trend of the risk generated by the EC Supply Chain in 2010 accounted for 6.9%. By 2015, the percentage of the development trend of EC Supply Chain risk was 17.9%, an increase of 11%. It can be seen that the development of EC Supply Chain risk is very rapid, so how to prevent the financial market risk of EC Supply Chain is very urgent. From the data, it can be seen that the exogenous risk in the e-commerce supply chain has a score of 7.4–8.4 for market risk, a score of 7.7–9.1 for economic risk, and the highest score for economic risk. Risk is the biggest risk factor. The characteristics of supply chain risk are not only complex, diverse, and uncertain but also transitive and virtual.
... It can also be used as a sales channel through agricultural fruit and major restaurants. Agricultural fruits are a necessity for restaurants, and businesses can also sell agricultural fruit through other products [8]. ...
Full-text available
With the rapid development of China, the marketing mode of agricultural fruit is also keeping pace with the times. The old sales model of agricultural fruit is also affected, which has a huge impact on marketing models, channels, and sales amount. And the sales methods of agricultural fruit have also undergone great changes, from offline transactions to online platform transactions. Comparing the profit, popularity, proportion of agricultural fruits, and turnover of agricultural fruits in e-commerce with the traditional marketing model, the smart marketing model in the data age increases the turnover by 50% and the profit by 15%. And in the network environment that extends in all directions, the probability of people knowing about agricultural fruits will be greater. It can better build the agricultural product marketing system in the information age and make it conform to the characteristics of contemporary marketing. Through the marketing mode in the information age, the judging project was set up, using estimated calculations to draw conclusions. In the era of rapid network information, e-commerce of agricultural fruits can help agriculture to advance rapidly and slowly go to the world.
... Another research work [16] defined customer engagement as experience gained by assessing the hedonic information values of various products and services available on eWOM media. As eWOM is considered to be a more complex and hedonic system [17], from the marketer's perspective, it is vital to find the reasons for customer engagement in eWOM media and their selection behaviour [18]. ...
Full-text available
In this contemporary world, electronic word of mouth (eWOM) platforms (or media) have become a prerequisite information source for online surfers, especially when planning excursions. However, tourists refer to the reviews of these platforms based on utilitarian and hedonic aspects. The utilitarian value enhances users’ task performance, whereas the hedonic value is related to pleasure and inner feelings. The present work was undertaken to study the importance of various utilitarian and hedonic determinants, and analyses their influence on the perceived usefulness (PU) of eWOM media and subsequent online booking decisions (OBD) for tourist destinations in India. In addition, the study investigates whether the influence of PU of eWOM media on OBD varies according to gender. A conceptual model was introduced based on data analysis done through SPSS 23 and AMOS 23. The model was empirically validated based on sample data comprising 338 Indian tourists. The purposive sampling technique was used in the current study, and only those samples who referred to eWOM media for information search were accepted. The findings indicate that utilitarian and hedonic determinants significantly influence tourist decision-making. TripAdvisor was the most popular web portal, followed by other social networking sites among the preferred sources of tourist destination information. The moderating analysis revealed that the impact of eWOM media PU on OBD was higher in males than in females. The study suggests that website designers and administrators design the contents according to the needs identified.
... The data required for RS comes from obvious user ratings when seeing the product picture, implicit searches, buying histories, or some other facts about the customers or product (Qiu et al. 2015;Greenstein-Messica and Rokach 2018). Companies utilizing RS emphasize growing trades through highly customized suggestions and better user experience (Pappas et al. 2017 access contents or items that are interesting to them, as well as provide them with offers they would have never looked for (Isinkaye et al. 2015). Moreover, companies can attract and retain clients with movies and TV shows that match their profiles. ...
Full-text available
The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when the interaction is made among a limited number of items. These issues not only pose a negative impact on the recommendation but also significantly condense the diversity of choices available on the particular platform. To tackle these issues, a novel methodological approach called sparsity and cold start aware hybrid recommended system (SCSHRS) has been designed to suppress data sparsity and CSP in RS. The performance of the proposed SCSHRS method is tested on MovieLens-20 M, Last.FM and Book-Crossing data sets and compared with the prevailing techniques. Based on the evaluation reports with the standards, the proposed SCSHRS system gives Mean Absolute Percentage Error of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain of 0.65. This study completely describes the SCSHRS mechanism and its comparison with other pre-proposed historic and traditional processes based on collaborative filtering.
... Apart from these, the marketing community feels that factors such as product/service quality, interpersonal experiences that are similar to offline personalized shopping (Wright et al., 2019), communication, ambiance, website design, trust factors, packaging, product display, [AQ2] labelling, sensory expressions and so on influence the decision-making of the consumer (Chi, 2018;Hultén, 2011;Milhart, 2012;Stankevich, 2017). Online shoppers are motivated to shop online for both utilitarian and hedonic reasons (Pappas et al., 2017). This, further, means that online hedonic shoppers seek enjoyment, recreation, excitement and pleasure by sifting through the online shelves. ...
How can marketers create an illusion of touch through expanding the roles of the visual sense to better present products on online shopping platforms? This study is a conceptual attempt to apply cross-modal mental imagery in the context of generating illusions of tactile sense through stimulating visual sense by the use of sensory-aided descriptions (SAD). We establish that these perceptual illusions can enhance the purchase intention of online shoppers when they are making purchases. Furthermore, we introduce that this linkage is moderated by the unique imaginative capability of the customer. The proposed model in this paper provides thoughtful insights for marketing managers to consider during the process of designing online product presentations. Theoretically, it contributes by establishing that cross-modal mental imagery, when applied over SAD, can serve as effective stimuli in the generation of tactile mental imagery.
... We explicitly engage the intermediary with opacity-related firm parameters including sector, size, and age (vector Wk), while accounting for the chance rate's susceptibility (Pappas et al. 2017). Analyzing the interaction factors reveals when the probability of default for bank-guaranteed loans is greater or lower than for MGIguaranteed loans. ...
Full-text available
This study empirically investigates the impact of natural resource revenues on China’s financial sector’s indicators, financial intermediaries, economic and institutional quality indicator; China, a nation that is supposedly immune to the resource curse, is used as a case study in this research from the duration 2001 to 2020. In this investigation, Fourier ADF and FGL were used together with the structural break unit root test for cointegration as well. In order to assess the long-term link, a newly created bootstrapped ARDL was used. As a result of the newly suggested Fourier ARDL, BARDL’s resilience is further enhanced. It has also been calculated using Dynamic Ordinary Least Squares (DOLS). Single and cumulative Fourier frequency methods are used to study how parameters respond in a casual manner. In this paper, fresh and substantial empirical evidence is presented to demonstrate the financial services and natural resources curses. Although the research indicated a favorable correlation between natural resources and financial sector development, it also confirmed the financial position capacity sustenance theory. Administrative qualities and natural resources are intertwined, as are natural resources and financial development. When natural resource revenues are properly channeled via financial institutions and stock returns, they may stimulate economic development.
... It can also foster satisfaction (Rust and Chung, 2006), improve attitude toward websites (Ho and Bodoff, 2014;Kalyanaraman and Sundar, 2006), increase intentions to purchase Pappas et al., 2016) and influence e-impulse buying (Ampadu et al., 2022). Prior research has also shown that personalization is positively correlated to customer experience (McLean et al., 2018;Pappas et al., 2017;Rose et al., 2012;Tyrväinen et al., 2020). ...
Retailers develop personalized websites with the aim of improving customer experience. However, we still have limited knowledge about the effect of personalization on customer experience and the underlying processes. With a lab experiment, this research specifically examines the effect of actual personalization and perceived personalization on playful customer experience using both subjective and objective measures, with the support of eye-tracking techniques. We show that personalization, regardless of whether it is perceived or not, enhance the playful customer experience of a retailing website. In addition, we highlight the presence of two concomitant processes. Content needs to be perceived as personalized to influence the subjective playful customer experience, but actual personalization does influence objective playful customer experience. Although customers spend the same time on the website, they focus more of their attention on their favorite products when content is personalized. Such focused attention leads them to select their favorite products for purchase.
Purpose The purpose of this paper is to analyze the sustainable development of e-commerce. Design/methodology/approach This paper proposes to analyze the development of rural e-commerce based on the perspective of big data of the Internet of things, in order to achieve the purpose of the development of e-commerce in China. In the analysis, this paper selects the data envelopment analysis (DEA) model for analysis and filters the parameters of the model to improve the accuracy of the model. Findings The experimental results of this paper show that various efficiency values of the improved model have decreased, and the scale efficiency has decreased by 0.319 on average. From the decline of technical efficiency, pure technical efficiency and scale efficiency of rural e-commerce in each province after excluding environmental variables, it can be concluded that environmental variables have a greater impact on the development efficiency of rural e-commerce. Originality/value In the analysis, this paper selects the DEA model for analysis and filters the parameters of the model to improve the accuracy of the model.
Conference Paper
Full-text available
This research uses complexity theory to offer a deeper insight on the causal patterns of factors explaining the adoption of e-government services. To this end, we propose a conceptual model comprising of affective factors (positive and negative emotions) and cognitive factors (trust of the government, trust of the service, and perceived net benefits of e-government services) along with research propositions. Our propositions are validated by employing a fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 502 users of e-government services. Findings indicate five configurations of cognitive and affective perceptions that lead to high intention to use an e-government service. Of paramount importance are affective values and trust values since their mandatory presence or absence is incorporated in all configurations. The study has both theoretical and practical implications for academic scholars pertaining the development of new e-government adoption theories and the provision of e-government services.
Full-text available
The key to using an analytic method is to understand its underlying logic and figure out how to incorporate it into the research process. In the case of Qualitative Comparative Analysis (QCA), so far these issues have been addressed only partly. While general introductions and user's guides for QCA software packages are available, prospective users find little guidance as to how the method works in applied data analysis. How can QCA be used to produce comprehensive, ingenious explanations of social phenomena? In this article, I provide such a hands-on introduction to QCA. In the first two parts, I offer a concise overview of 1. the method's main principles and advantages as well as 2. its vital concepts. In the subsequent part, I offer suggestions for 3. how to employ QCA's analytic tools in the research process and how to interpret their output. Lastly, I show 4. how QCA results can inform the data analysis. As the main contribution, I provide a template for how to reassess cases, causal recipes, and single conditions based on QCA results in order to produce better explanations of what is happening in the data. With these contributions, the article helps prospective QCA users to utilize the full potential the method offers for social science research. URN:
Full-text available
Web personalization can achieve two business goals: increased advertising revenue and increased sales revenue. The realization of the two goals is related to two kinds of user behavior: item sampling and item selection. Prior research does not provide a model of attitude formation toward a personalization agent nor of how attitudes relate to these two behaviors. This limits our understanding of how web personalization can be managed to increase advertising revenues and/or sales revenues. To fill this gap, the current research develops and tests a theoretical model of user attitudes and behaviors toward a personalization agent. The model is based on an integration of two theories: the elaboration likelihood model (ELM) and consumer search theory (CST). In the integrated model, a user's attitude toward a personalization agent is influenced by both the number of items he/she has sampled so far (from CST) and the degree to which he/she cognitively processes each one (from ELM). In turn, attitude is modeled to influence both behaviors-that is, item selection and any further item sampling. We conducted a lab study and a field study to test six hypotheses. This research extends the theory on web personalization by providing a more complete picture of how sampling and processing of personalized recommendations influence a user's attitude and behavior toward the personalization agent. For online merchants, this research highlights the trade-off between item sampling and item selection and provides practical guidance on how to steer users toward the attitudes and behaviors that will realize their business goals.
Full-text available
An important limitation of regression-based analysis stems from the assumption of symmetric relationships between variables, which is often violated. To overcome this limitation within IS research, we propose the use of the fuzzy-set qualitative comparative analysis (FsQCA) method. The paper elaborates on the rationale for applying this approach to IS behavioural research and how to tailor FsQCA for this purpose. A systematic interpretation of the technique covering its mathematical properties and advanced features is provided. Drawing from an illustrative study of mobile government services adoption by residents of rural areas, the paper demonstrates FsQCA's potential to supplement regression-based IS be-havioural research, by (i) examining asymmetric relationships between a set of antecedents and the IS phenomenon of interest, (ii) providing nuanced coverage of necessary and sufficient conditions for emergence of an IS behavioural outcome, and (iii) identifying various configurations of conditions in association with users' demographic characteristics.
Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
Although web personalization has been examined by earlier literature reviews, an updated analysis of recent advances in the field is needed. The authors extend prior reviews of web personalization by discussing current areas of interest, research gaps and future directions. A literature review of the top 20 marketing and information systems journals published during the period of 2005–2015 (May) shows active research output and the domination of IS publications. The examined research addresses three categories: user-specific aspects, implementation, and theoretical foundations. We then analyze a total of ten themes: six on topics concerning user-specific aspects and implementation that stem from the dataset and four on theoretical foundations that are predetermined and reflected upon using the dataset. Both theme-specific and general future research suggestions are discussed. Advanced contextualization is suggested as the primary area suitable for future research and building evidence for attaining business goals as a secondary topic. Finally, we propose a conceptualization of interpolated web personalization to be tested as a potential complement to current (extrapolated) approaches.