■
Research Paper
Flow Experience and Internet Shopping
Behavior: Investigating the Moderating
Effect of Consumer Characteristics
Chia-Lin Hsu
1, Kuo-Chien Chang
2* and Mu-Chen Chen
31Department of Business Administration, National Taiwan University of Science and Technology, Taipei, Taiwan
2Department of Sports, Health and Leisure, Chihlee Institute of Technology, Banciao City, Taipei County 220, Taiwan
3National Chiao Tung University, Institute of Traffic and Transportation, Taipei, Taiwan
Researchers have recognized thatflow is a constructive construct for elucidating consumer behavior in the context of computer-mediated environments. Accordingly, this paper endeavours to investigate the relationship betweenflow experience and Internet shopping behavior to which the moderating role of consumer characteristics (trust propensity, willingness to buy and self-confidence) is concerned. Data collected from 395 customers of an online shopping store provide support for the proposed research model. The results show that flow experience is significantly and positively related to Internet shopping behavior (continuance intention, purchase intention and impulsive buying). In addition, it also suggests that the relationship between flow experience and Internet shopping behavior is moderated by consumer characteristics. Specifically, when the extent of a customer’s trust propensity, willingness to buy or self-confidence is relatively high, the influence of flow experience on Internet shopping behaviors is maximized. According to the findings, the implications and future research suggestions are provided. Copyright © 2011 John Wiley & Sons, Ltd.
Keywords flow experience; internet shopping behaviors; consumer characteristics
INTRODUCTION
Flow has been described as a state of optimal psychological experience (Novak et al., 2000),
resulting from engagement in a variety of activities, such as sports, writing, work, games, hobbies and website use. When inflow state, an individual becomes entirely focused on his or her activity and experiences many positive ex-periential characteristics, including great enjoy-ment and loss of self-consciousness (Jackson and Marsh, 1996). Accordingly, flow experience has * Correspondence to:Kuo-Chien Chang, Department of Sports, Health
and Leisure, Chihlee Institute of Technology, No. 313, Sec. 1, Wunhua Rd, Banciao City, Taipei County 220, Taiwan.
E-mail: kcchang@mail.chihlee.edu.tw Syst. Res. 29, 317–332 (2012)
Published online 1 August 2011 in Wiley Online Library (wileyonlinelibrary.com)DOI: 10.1002/sres.1101
been viewed as a crucial determinant of online customers’ subjective enjoyment of website use (Csikszentmihalyi, 1993; Koufaris, 2002; Lu et al., 2009; Siekpe, 2005; Wu and Chang, 2005). Researchers have also found that
computer-mediated environments facilitate flow
experi-ences because they require high concentration, involve the distortion of time and bring about increased levels of pleasure (Csikszentmihalyi, 1990; Hoffman and Novak, 1996). Hoffman and Novak (1996) extend the general applicability of flow to computer-mediated environments by sug-gesting that the success of online marketers depends on their ability to create opportunities for consumers to experienceflow. If the use of the web can potentially serve as entry intoflow state (i.e. an enjoyable experience), web users should ultimately improve their subjective well-being by accumulated ephemeral moments. Numerous
researchers have investigated flow in various
conditions, including human–computer
inter-action (Csikszentmihalyi, 1990; Ho and Kuo, 2010; Hsu and Lu, 2004; Trevino and Webster, 1992; Webster et al., 1993) and web use (Chen et al., 1999; Chen et al., 2000; Pace, 2004), and the concept has been regarded as useful insight into consumer behavior (Chen et al., 1999; Hoffman and Novak, 1997; Shin and Kim, 2008).
Moreover, as proposed by Smith and Sivakumar (2004), no two consumers are alike. Thus, to probe into purchase behavior on the Internet, consumer characteristics (e.g. trust propensity, willingness
to buy and self-confidence) have been
recom-mended as factors to consider in determining what
influences consumers during online shopping
(Smith and Sivakumar, 2004). Accordingly, along
with the investigation of the link between flow experience and Internet shopping behavior, this paper also seeks to verify the moderating role of
consumer characteristics in the flow–Internet
shopping behavior relationship.
RESEARCH MODEL AND HYPOTHESES Hypotheses Development
Internet shopping behaviors are modelled as con-sequences offlow experience, whereas consumer characteristics are functioned as moderators between the flow experience and Internet shop-ping behavior. Figure 1 displays the research model. The related hypotheses are further detailed.
Flow Experience and Internet Shopping Behavior
Flow experience has been shown to increase learning and changes in attitudes and behaviors (Webster et al., 1993). In an online context,
researchers have theorized that such flow
ex-perience can attract consumers and signi ficant-ly affect subsequent attitudes and behaviors (Novak et al., 2000). Specifically, researchers have
revealed that flow experience is a significant
determinant of consumer attitudes toward the focal website and the focalfirm (Mathwick and Rigdon, 2004), thus increasing the intention to revisit and spend additional time on the website (Kabadayi and Gupta, 2005). Numerous previous studies have also presented a strong
Flow experience
Internet shopping behavior
Continuance intention Purchase intention Impulsive buying Consumer characteristics Trust propensity Willingness to buy Self-confidence
relationship between onlineflow experience and subsequent online behaviors (Chen et al., 1999; Skadberg and Kimmel, 2004; O’Cass and Carlson, 2010). Celsi et al. (1993) found that people who experience flow have a tendency to replicate or re-experience that state. Cyr et al. (2005)
sug-gested that customers who experience flow
while shopping online would be likely to con-sider return visits to the website or purchasing from it in the future. Therefore, a consumer who
experiences flow will attempt to reengage and
revisit the activity that delivered theflow experi-ence. Accordingly, the following hypothesis is constructed:
H1a: Flow experience will be positively related to continuance intention.
Furthermore, Nel et al. (1999) and Rettie (2001)
indicated that flow experience appeared to
prolong Internet and website use. Hsu and Lu
(2004) demonstrated that flow experience is
positively and significantly related to intention to play an online game. Korzaan (2003) found that that experiencing flow affects behavioral intention such as an increase in the likelihood of
purchasing from a website. As confirmed by
Wu and Chang (2005), theflow experience can in-crease the transaction intentions of members when they are in the online travel communities.
Specifically, consumers who experience flow
while shopping online would be likely to gener-ate transaction intentions. Hence, the following hypothesis is constructed:
H1b: Flow experience will be positively related to purchase intention.
In addition, we also realize fromflow research that intrinsic enjoyment can enhance a user’s ex-ploratory behavior (Ghani and Deshpande, 1994). Beatty and Ferrell (1998) found an increase in im-pulse purchasing urges for shoppers with positive feelings during shopping. Specifically, the signifi-cance of positive emotional responses is likely to facilitate consumers’ impulsive purchases. Consu-mers’ impulsive nature implies that they depend a lot on consumer feelings. Accordingly, in the con-text of online shopping, if online consumers enjoy their shopping experience, they may engage in more exploratory browsing in the web store,
leading to more impulsive buying (Koufaris, 2002). Moreover, we learn fromflow theory that
when flow experience occurs, an individual
becomes entirely focused on their activity. As proposed by Koufaris (2002), consumers that are able to focus their attention at a web store should also be more likely to notice marketing promo-tions on the site. In other words, if consumers are not paying full attention to the contents of the web-site when buying online, they are less likely to no-tice products that they might otherwise buy on impulse. Consequently, the following hypothesis is constructed:
H1c: Flow experience will be positively related to impulsive buying.
Trust Propensity as a Moderator
Internet shopping involves trust not only be-tween the consumer and the Internet mer-chant, but also between the consumer and the computer system through which transactions are performed. Trust in the online store has previously been verified as an essential ante-cedent to online buying and repeat buying behaviors (Gefen and Straub, 2004; Reichheld and Schefter, 2000). Thus, trust has a critical impact on consumer activities and thereby on e-commerce success (Corbitta et al., 2003). In short, e-commerce success, particularly in the business-to-consumer area, is determined partly by whether consumers trust sellers and prod-ucts they cannot see or touch and electronic sys-tems with which they have no prior experience (Lee and Turban, 2001).
Yang et al. (2009) indicated that consumer char-acteristics such as individual trust propensity will influence the consumer trust in web shop-ping. Trust propensity is a personality trait that is defined as a ‘general willingness based on
extended socialization to depend on others’
(McKnight and Chervany, 2001/2002; Ridings et al., 2002). Trust propensity characterizes an individual’s inclination to trust or distrust other individuals. Those who typically trust others, under conditions of uncertainty, believe that they will be treated reasonably and that over time
their good acts will be reciprocated in some way (Smith et al., 1983). Trusting individuals are less suspicious and less concerned about monitoring the behavior of others (Van Dyne et al., 2000). McKnight et al. (1998) suggested that high trust propensity individuals believe ‘that things turn out best when one is willing to depend on others, even though others may or may not be trust-worthy’. Trust propensity intensifies or reduces the signals provided by cues (e.g. trustworthiness attributes) (Lee and Turban, 2001). Individuals vary in their readiness to trust others (people or other entities), and this individual characteristic has been shown to have an effect on customer trust in online shopping as well (Lee and Turban, 2001). Cheung and Lee (2001) indicated that trust propensity will affect trust in Internet shopping. Limerick and Cunnington (1993) also believed that trust can reduce uncertainty concerning the future and is a necessity for a continuing relation-ship with participants who have opportunistic behavior. Thus, the formation of trust, in turn, reduces consumers’ perceived risk of Internet shopping. In summary, consumers’ general trust-ing disposition will play a key role in determin-ing their Internet shoppdetermin-ing behavior.
In addition, because consumers incline to use their prior experience as decision-making heuris-tics, consumers purchasing on the Internet can also be predicted to use their previous experience to formulate strategies for repurchasing behavior. Furthermore, consumers often need a lot of time and a pleasurable environment to foster ongoing search for products. Through aflow experience, consumers’ behavioral intention will be improved by the positive feelings related to aflow experience state. Also, the flow experience can ensure that consumers give their attention for longer periods, consequently, facilitating more possibility of con-tinuance intention. Consequently, the following hypothesis is constructed:
H2a: The greater the trust propensity, the stronger the relationship betweenflow experi-ence and continuance intention.
Furthermore, as shown in the study of McCole et al. (2010), trust in the vendor and Internet has a positive influence on attitude towards online purchasing. That is, individuals with a high level
of trust propensity will selectively attend to infor-mation congruent with their level of trust in hu-manity, as well as interpret new information based on their natural tendency (Limerick and Cunnington, 1993). Ferrin and Dirks (2003) pro-vided a similar explanation and suggested that perhaps people with a low propensity to trust are more likely to have a‘suspicion’ bias when proces-sing information concerning one’s trustworthi-ness. So, trust in the vendor and Internet enables the consumers to concentrate and focus on the undertaking. Besides, in the online context, when flow experience occurs, an individual becomes en-tirely focused on their activity and is likely to feel joyful and pleasant, which has been found to facili-tate a more positive experience (Hoffman and Novak, 1996). Wu and Chang (2005) verified that flow experience is positively related to transac-tion intentransac-tions in the study of online tourism. Therefore, this moderation effect can be viewed positively in the sense that the greater the trust propensity, the stronger the impact of flow ex-perience on purchase intention. Accordingly, the following hypothesis is constructed:
H2b: The greater the trust propensity, the stronger the relationship betweenflow experi-ence and purchase intention.
In addition, the flow experience would
im-prove consumers’ satisfaction while facilitating feelings of pleasure and control, and also enable consumers to reduce the amount of time spent on deliberation to purchase online, as consumers will probably need less time to decide because of their trust propensity with the web store (Smith and Sivakumar, 2004). Therefore, theflow experi-ence allows consumers to make their purchasing decision in an expedient fashion. Accordingly, the following hypothesis is constructed:
H2c: The greater the trust propensity, the stronger the relationship betweenflow experi-ence and impulsive buying.
Willingness to Buy as a Moderator
In traditional transactions, consumers typically have to expend physical energy and time to move to a retail site; however, the Internet
tenders consumers’ immediate access. Because consumers have less at stake when determining to explore different Internet sites, they may be more likely to undertake browsing and/or infor-mation collecting without having actual purchase intentions. The Internet provides consumers with the unparalleled opportunity to shop purely for obtaining pleasure. Thus, true purchasing inten-tions may be lower for Internet shoppers than traditional brick-and-mortar retailers. This may be the reason that consumer characteristics, such as willingness to buy, need to be examined in terms of the formulation of consumers’
decision-making regarding specific shopping behaviors
(Smith and Sivakumar, 2004).
Baker et al. (1992) indicated that individuals are more likely to shop at a particular site and buy gifts for others when willingness to buy is high.
Hoffman and Novak (1996) showed that flow
experience facilitates exploratory behavior, which in turn increases the amount of time spent on the particular site. Specifically, flow experience presents itself as a mechanism by which low purchasing intention can be transformed into site/store loyalty during shopping activities (Smith and Sivakumar, 2004). As shown by Rice (1997), whether or not consumers will return to an Internet site depends on the factors of content, enjoyment, layout and uniqueness. Thus, in fluen-cing a consumer to return to a particular site is based on their previous interaction with the site (Smith and Sivakumar, 2004). Therefore, when willingness to buy is high, continuance and purchase intention will be facilitated by flow experience. Consequently, the following hypoth-eses are constructed:
H3a: The greater the willingness to buy, the stronger the relationship betweenflow experi-ence and continuance intention.
H3b: The greater the willingness to buy, the stronger the relationship betweenflow experi-ence and purchase intention.
Furthermore, pleasure and arousal derived from a store are positively related to willingness to buy and the amount of time spent in the store environment (Donovan and Rossiter, 1982). That is, with a high degree of willingness to purchase,
consumers will approach the shopping experience with strong shopping motivations (Smith and Sivakumar, 2004). Thus, flow experience, which results in feelings of pleasure and control, moves customers to the act of purchasing in an expedient manner while reducing the amount of deliberation time necessary before purchase. Consequently, the following hypothesis is constructed:
H3c: The greater the willingness to buy, the stronger the relationship betweenflow experi-ence and impulsive buying.
Self-confidence as a Moderator
Confidence, recognized as an important
con-sumer characteristic in this study, is originated with consumers’ attitudes and directly affects their purchasing intentions (Howard, 1977). Through repeat purchases, the individual can affirm his or her self-identity through the per-formance of specific shopping behavior (Sparks and Shepherd, 1992). The more an individual purchases from a particular site, the more helpful his or her self-perception may be (Smith and Sivakumar, 2004). As individuals with a higher level of self-confidence shift from novices to more experienced consumers on a particular site, they will feel more assured about past purchasing deci-sions, which will make them more likely to return to a particular store or site (Smith and Sivakumar, 2004). Furthermore, whenflow experience occurs, people are likely to feel enjoyable (Hoffman and Novak, 1996) and they have a tendency to replicate or re-experience that state (Celsi et al., 1993). Thus, the following hypothesis is constructed:
H4a: The greater the self-confidence, the stron-ger the relationship between flow experience and continuance intention.
Individuals with a high level of self-confidence will feel very driven and confident in their search for product details (Sirgy, 1982). Highly self-confident individuals may feel that their skills allow them to meet or supersede the difficulty of the tasks at hand. In addition, when in flow state, individuals will prolong the engagement in exploratory behaviors during the online shop-ping process (Hoffman and Novak, 1996). Chou
and Ting (2003) also proposed that whileflow ex-perience occurs, a consumer exex-periences a sense of happiness, accompanied by a feeling of con fi-dence and an exploratory desire. In addition, in an online shopping context, researchers found that flow experience can attract consumers and
significantly influence subsequent behavioral
intention (Novak et al., 2000). Consequently, the following hypothesis is constructed:
H4b: The greater the self-confidence, the stron-ger the relationship between flow experience and purchase intention.
When in flow state, an individual becomes en-tirely focused on their activity and experiences many positive experiential characteristics includ-ing great enjoyment and loss of self-consciousness (Jackson and Marsh, 1996). When consumers
ex-perience flow, they are likely to obtain the
increased pleasure that has been found to facili-tate positive affect or mood (Hoffman and Novak, 1996). Researchers have found that individuals who are in a good mood will be more likely to en-gage in purchasing behavior (Bloch et al., 1986). Individuals with a high degree of confidence per-ceive that their own abilities and skills will help them manage the risks typically associated with Internet shopping; thus, decreasing the amount of time they need to make a purchase decision (Smith and Sivakumar, 2004). Therefore, a
con-sumer with a higher level of self-confidence
may be more likely to engage in impulsive buy-ing because of the positive affect created byflow experience, as well as the individual’s percep-tions regarding his or her ability to manage the risks associated with Internet shopping. Conse-quently, the following hypothesis is constructed: H4c: The greater the self-confidence, the stron-ger the relationship between flow experience and impulsive buying.
METHODOLOGY
Participant Data Collection
The web store selected for this study was Yahoo Shopping Center (http://buy.yahoo.com.tw/) because it is thefirst choice for online customers
in Taiwan according to the industry reports of Market Intelligence Center. This study selected respondents who were consumers of the site in order to examine the relationships hypothesized in this study. Following this, all respondents were asked to indicate the extent to which they agreed with the statements in the questionnaire. Before distributing the survey questionnaire, the respon-dents were queried about whether they used the site to shop. If the respondents answered af firma-tively, the interviewer provided them with the survey questionnaire. If not, the respondents were not offered the survey questionnaire. All survey questionnaires were distributed to respondents in person via the interviewer between March 1 and April 30 of 2010 (about 2months). Respondents who participated in this study were considered a convenience sample. Before the start of the study, three postgraduate students were trained as interviewers to fully understand the content of the questionnaire in order to answer questions from respondents. Respondents who participated in the study and completed the questionnaire were provided a small gift (a ballpoint pen) as a token of gratitude. A total of 412 responses were received. After eliminating incomplete and inappropriate responses (e.g. duplicates), a total of 395 usable responses were included in the sample for analysis (a net response rate of 95.9%). As noted in Table 1, among the 395 usable responses, the majority were females (53.9%) and unmarried (75.4%) individuals. Respondents from 18 to 29years old (43.8%) and 30 to 39years old (37.2%) account for the largest portion of the sample, followed by individuals 40 to 49years old (16.5%). More than 95% of the respondents indicated education level at college and above. In addition, 46.6 % of the respondents were stu-dents and 20.5% were from the public servants. This study used the extrapolation technique, equating late responses to nonrespondents in order to test the nonresponse bias (Armstrong and Overton, 1977). Responses were separated into two groups, specifically, those received be-fore the second distribution and those received after the second distribution. A t-test of difference was conducted on demographic variables, in-cluding gender, marital status, age, education level and occupation. No statistically significant
differences were identified at p<0.05, leading the researchers to conclude that respondents are not different from nonrespondents. Similarly, using the extrapolation method, no significant differences in either mean scores or variances were found for any key constructs between early (i.e. before second distribution) and late (i.e. after second distribution) respondents, indicating that nonresponse bias is a relatively minor concern.
Measurement
As shown in the Appendix, the measurement scales used to operationalize the research con-structs involved in this study were adapted from the existing literature, and others were developed based on the extant conceptual studies. Control, attention focus, curiosity and intrinsic interest are used to measure flow experience by using three items from Huang (2003). Among the three mea-sures of Internet shopping behavior, continuance intention is measured using three items from Liao et al. (2006). Purchasing intention is mea-sured using four items from Maxham (2001).
Four items are adapted from Sim and Koi (2002) to measure impulsive buying. Four items are adapted from Lee and Turban (2001) to measure trust propensity. Willingness to buy is measured using four items from Jarvenpaa et al. (2000). Four items are adapted from Dash et al. (1976) to measure self-confidence. All items are measured
on five-point scales ranging from ‘1=strongly
disagree’ to ‘5=strongly agree’. DATA ANALYSIS AND RESULTS
This study used LISREL 8.54 (Scientific Software International, Chicago, IL, USA) to test the relationship between flow experience and Inter-net shopping behavior. In addition, this study used SPSS 12.0 (SPSS Inc., Chicago, IL, USA) to substantiate the moderating role of consumer characteristics and to analyse descriptive statis-tics, reliability and validity.
Reliability and Validity Analysis
To evaluate the convergent validity of the mea-surements, this study used three measures pro-posed by Fornell and Larcker (1981), including the item reliability of each measure, the compos-ite (construct) reliability of each construct and the average variance extracted (AVE) for each construct (Table 2). The item reliability of a mea-sure is evaluated by using its factor loading of the underlying construct (Shih, 2004). The results revealed that the factor loadings of all the mea-sures’ underlying constructs exceed 0.5 and thus confirm the test of item reliability (Hair et al., 1995). Furthermore, construct reliability is evalu-ated by using Cronbach’s a. The results showed that the reliabilities of all constructs were be-tween 0.761 and 0.907 and thus confirm the test of construct reliability (Nunnally, 1978). In addition, noted in Table 2, this study found that the AVE from each construct exceeds 0.5 and thus demonstrated convergent validity (Fornell and Larcker, 1981). Overall, the convergent validity test indicated that the proposed constructs of the extended model was adequate.
Furthermore, if the items in a construct correlate more highly with each other than with items mea-suring other constructs, the measure is regarded as
Table 1 Demographic characteristics
Variables Frequency (s) Percentage of total (%) Gender Male 182 46.1 Female 213 53.9 Marital status Married 97 24.6 Unmarried 298 75.4 Age 18–29 173 43.8 30–39 147 37.2 40–49 65 16.5 50 and over 10 2.5 Education level High school or below 11 2.8 College 275 69.6 Graduate school or above 109 27.6 Occupation Student 184 46.6 Public servant 81 20.5 Industrial 15 3.8 Commercial 62 15.7 Professionals 22 5.6 Miscellaneous (e.g. retired, housekeeper) 31 7.8
having adequate discriminant validity (Cho, 2006). Table 3 shows the squared intercorrelations among the variables, suggesting that the shared variance among the variables does not surpass the average variance explained (Cho, 2006). Hence, discriminant validity is justified in this study.
Structural Model
Using structural equation modelling, the hypothe-sized relationships between flow experience and Internet shopping behavior were tested and analysed. As seen in Figure 2, the overall results suggested that the research model offers an adequatefit to the data.
The Relationship between Flow Experience and Internet Shopping Behavior
As seen in Figure 3, the results supported
the relationship between flow experience and
Internet shopping behavior. H1a is supported; namely, flow experience positively and
signifi-cantly influenced the continuance intention
(b=0.78, p<0.001). Moreover, flow experience was also found to be positively related to the purchase intention (b=0.56, p<0.001) and impul-sive buying (b=0.67, p<0.001). Therefore, H1b and H1c were also supported. Based on the dis-played results, the researchers postulate that the
stronger the consumers experienced flow, the
stronger the consumers’ continuance, purchase intention and impulsive buying on the website are.
The Moderating Effect of Trust Propensity, Willingness to Buy and Self-confidence
As consistent with the way of Hsu et al. (2010), this study used partial correlation analysis to investigate whether consumer characteristics (i.e. trust propensity, willingness to buy and self-confidence) positively moderate the link between flow experience and Internet shopping behavior. First, in order to confirm the moderating effect of trust propensity, this study investigated whether trust propensity positively moderates the link
Table 2 Reliability and factor loadings Variables Factor Loading Reliabilitya Average variance extracted Control 0.876 0.713 Control 1 0.894 Control 2 0.720 Control 3 0.906 Attention focus 0.826 0.625 Attention focus 1 0.774 Attention focus 2 0.857 Attention focus 3 0.735 Curiosity 0.839 0.672 Curiosity 1 0.734 Curiosity 2 0.866 Curiosity 3 0.853 Intrinsic interest 0.810 0.616 Intrinsic interest 1 0.800 Intrinsic interest 2 0.807 Intrinsic interest 3 0.747 Continuance intention 0.761 0.543 Continuance intention 1 0.714 Continuance intention 2 0.761 Continuance intention 3 0.734 Purchase intention 0.838 0.540 Purchase intention 1 0.740 Purchase intention 2 0.707 Purchase intention 3 0.779 Purchase intention 4 0.712 Impulsive buying 0.823 0.542 Impulsive buying 1 0.711 Impulsive buying 2 0.772 Impulsive buying 3 0.726 Impulsive buying 4 0.734 Trust propensity 0.907 0.755 Trust propensity 1 0.801 Trust propensity 2 0.838 Trust propensity 3 0.915 Trust propensity 4 0.917 Willingness to buy 0.904 0.592 Willingness to buy 1 0.742 Willingness to buy 2 0.736 Willingness to buy 3 0.783 Willingness to buy 4 0.815 Self-confidence 0.862 0.602 Self-confidence 1 0.844 Self-confidence 2 0.796 Self-confidence 3 0.786 Self-confidence 4 0.667
between flow experience and Internet shopping behavior. The results indicated that if the moderat-ing role of trust propensity is not eliminated, the
correlation coefficient of flow experience and continuance intention was 0.583 (p<0.001), but if the moderating role of trust propensity was
Table 3 Squared intercorrelation among the study constructs
Dimension 1 2 3 4 5 6 7 8 9 10 1 Control 0.713 2 Attention focus 0.068 0.625 3 Curiosity 0.068 0.078 0.672 4 Intrinsic interest 0.083 0.053 0.139 0.616 5 Continuance intention 0.199 0.112 0.100 0.229 0.543 6 Purchase intention 0.065 0.037 0.070 0.163 0.124 0.540 7 Impulsive buying 0.163 0.092 0.139 0.138 0.176 0.114 0.542 8 Trust propensity 0.005 0.005 0.003 0.013 0.024 0.057 0.021 0.755 9 Willingness to buy 0.092 0.366 0.153 0.052 0.125 0.040 0.150 0.010 0.592 10 Self-confidence 0.043 0.039 0.021 0.079 0.085 0.346 0.067 0.078 0.038 0.602
All correlations are significant at the 0.05 level. The diagonals represent the average variance extracted.
Control Attention focus Curiosity Intrinsic interest co1 co2 co3 af1 af2 af3 cu1 cu2 cu3 ii1 ii2 ii3 Flow experience Continuance intention Purchase intention Impulsive buying Notes: ***(p<0.001); **(p<0.01);*(p<0.05).
χ2: 461.27; df: 217; GFI: 0.91; AGFI: 0.88; CFI: 0.97; NFI: 0.95; NNFI: 0.97; IFI: 0.97; RMSEA: 0.053.
0.97*** 0.62*** 0.94*** 0.73*** 0.89*** 0.74*** 0.70*** 0.83*** 0.89*** 0.80*** 0.92*** 0.63*** 0.37*** 0.23*** 0.17** 0.49*** 0.78*** 0.56*** 0.67***
ci1 ci2 ci3
0.73*** 0.74 *** 0.69*** pi1 pi2 pi3 pi4 0.73*** 0.74*** 0.76*** 0.78***
ib1 ib2 ib3 ib4
0.79***
0.73***
0.62*** 0.76 ***
eliminated, the partial correlation coefficient of flow experience and continuance intention was 0.576 (p<0.001). This result clearly showed that trust propensity moderates the link betweenflow experience and continuance intention. Further-more, if the moderating role of trust propensity was not eliminated, the correlation coefficient of flow experience and purchase intention was 0.407 (p<0.001), but if the moderating role of trust propensity was eliminated, the partial correlation coefficient of flow experience and purchase intention was 0.394 (p<0.001). This result clearly demonstrated that trust propensity moderates the link betweenflow experience and purchase intention. In addition, if the moderating role of trust propensity was not eliminated, the correlation coefficient of flow experience and impulsive buying was 0.535 (p<0.001), but if the moderating role of trust propensity was eliminated, the partial correlation coefficient of flow experience and impulsive buying was 0.527 (p<0.001). This result clearly showed that trust propensity moderates the link betweenflow experience and impulsive buying.
Furthermore, the moderating effect of willing-ness to buy was also confirmed in this study. Specifically, this study found that willingness to buy moderates the links betweenflow experience and continuance intention, between flow experi-ence and purchase intention, as well as between flow experience and impulsive buying. Finally, self-confidence also demonstrated that it will moderate the links betweenflow experience and continuance intention, between flow experience and purchase intention, as well as betweenflow experience and impulsive buying. To summarize, the foregoing results can be seen in Table 4.
In addition, to test how the different levels of trust propensity influence the links between flow experience and Internet shopping behaviors, the
data on trust propensity and flow experience
were divided into high and low groups based on their mean scores (M=3.539 for trust
propen-sity, M=3.568 for flow experience). The first
group displays high trust propensity and high flow experience (n=188); the second group dis-plays high trust propensity but lowflow experi-ence (n=40); the third group displays low trust
propensity but high flow experience (n=37);
and,finally, the fourth group displays low trust propensity and lowflow experience (n=130).
As consistent with the way of Chang et al. (2010), this study used the ANOVA analysis and Duncan post hoc test to achieve the men-tioned purposes. Results found that the F-values and p-values are all significant (F=41.356, p<0.001). According to the results of the Duncan post hoc test, the Internet shopping behaviors of the first group are higher than that of the other three groups, whereas there is no significant dif-ference between the Internet shopping behavior levels of the second and the third group.
Further-more, when trust propensity is low, high flow
experience will entail lower Internet shopping behaviors than does lowflow experience coupled with a high level of trust propensity. Thus, a customer’s trust propensity has a significant
impact on the links between flow experience
and Internet shopping behaviors. Thus, H2a, H2b and H2c are supported.
Similarly, to test how the different levels of willingness to buy influence the links between flow experience and Internet shopping beha-viors, the data on willingness to buy andflow ex-perience were also divided into high and low groups based on their mean scores (M=3.551 for willingness to buy, M=3.568 forflow experience). Thefirst group displays high willingness to buy
and high flow experience (n=196); the second
group displays high willingness to buy but low
Flow experience
Internet shopping behavior
Continuance intention Purchase intention Impulsive buying 0.78*** 0.56*** 0.67*** Path significance: *** p < 0.001
flow experience (n=42); the third group displays low willingness to buy but highflow experience (n=38); and, finally, the fourth group displays low willingness to buy and lowflow experience (n=119).
Again, using the ANOVA analysis and Duncan post hoc test, results show that the F-values and p-values are all significant (F=37.214, p<0.001). According to the results of the Duncan post hoc test, the Internet shopping behaviors of the first group are higher than that of the other three groups, whereas there is no significant difference between the Internet shopping behavior levels of the second and the third group. Furthermore, when willingness to buy is low, highflow experi-ence will entail lower Internet shopping behaviors than does lowflow experience coupled with a high level of willingness to buy. Thus, a customer’s will-ingness to buy has a significant impact on the links
between flow experience and Internet shopping
behaviors. Thus, H3a, H3b and H3c are supported. Finally, to test how the different levels of
self-confidence influence the links between flow
experience and Internet shopping behaviors, the data on self-confidence and flow experience were also divided into high and low groups based on their mean scores (M=3.547 for
self-confidence, M=3.568 for flow experience). The
first group displays high self-confidence and high flow experience (n=190); the second group displays high self-confidence but low flow experi-ence (n=39); the third group displays low
self-confidence but high flow experience (n=42);
and, finally, the fourth group displays low self-confidence and low flow experience (n=124).
Similarly, using the ANOVA analysis and Duncan post hoc test, results found that the F-values and p-values are all significant (F=43.514, p<0.001). According to the results of the Duncan post hoc test, the Internet shopping behaviors of
the first group are higher than that of the
other three groups, whereas there is no signi fi-cant difference between the Internet shopping behavior levels of the second and the third group. Furthermore, when self-confidence is low, high flow experience will entail lower Internet
shop-ping behaviors than does low flow experience
coupled with a high level of self-confidence. Thus, a customer’s self-confidence has a signifi-cant impact on the links betweenflow experience and Internet shopping behaviors. Thus, H4a, H4b and H4c are supported.
DISCUSSION
Based on the analysis of the pooled data, all of the hypotheses were supported. Specifically, the study demonstrated thatflow experience is sali-ent in influencing Internet shopping behavior. The findings imply that when a website fosters the flow experience among its customers, their continuance intention, purchase intention and impulse buying are the results. To ensure the desired shopping behavior, e-stores should seek to manage the shoppers’ flow states on an indi-vidual basis (Smith and Sivakumar, 2004). In view of this, e-stores should be mindful in how the content, organization and layout of their
Table 4 The moderating effect of consumer characteristics
Partial correlation coefficient
Trust propensity Willingness to buy Self-confidence
Not eliminated Eliminated Not eliminated Eliminated Not eliminated Eliminated
Flow experience and continuance intention
0.583*** 0.576*** 0.407*** 0.394*** 0.535*** 0.527*** Flow experience and
purchase intention
0.583*** 0.497*** 0.407*** 0.364*** 0.535*** 0.415*** Flow experience and
impulsive buying
0.583*** 0.542*** 0.407*** 0.294*** 0.535*** 0.495*** ***p<0.001.
e-sites can be configured to promote the flow experience.
This study importantly demonstrated that
the relationship between flow experience and
Internet shopping behavior is moderated by con-sumer characteristics (i.e. trust propensity, will-ingness to buy and self-confidence). Specifically, when the extent of a customer’s trust propensity, willingness to buy or self-confidence is relatively high, the influence of flow experience on his or her Internet shopping behavior is maximized. Thus, to contribute to the higher level of trust propensity by customers, e-stores can show their competence and concern by increasing communi-cation with their customers and by using web security technologies to make sure that the custo-mers are conscious of the precautions the com-pany takes to ensure that transactions are secure (Koufaris and Hampton-Sosa, 2004). Then, to enhance customers’ willingness to buy, e-stores can use virtual advisors and digital receipts to make the online experience feel as offline as possible (Freeman, 2000). To strengthen custo-mers’ self-confidence, e-stores should be able to provide information that is accurate, complete, timely and easy to understand (Shih 2004).
Implications
Ourfindings have several important implications for theory and practice relating to e-store practi-tioners and online marketing in general. First, albeit prior studies have confirmed the impact of flow experience on behavioral intention (Hsu and Lu, 2004; Qi et al., 2009; O’Cass and Carlson,
2010), the findings reported here specifically
confirm the prominent role of flow on intention in relation to continuance and purchase and im-pulsive buying. Thus, if e-store practitioners are trying to create compelling online experiences for consumers to engender online shopping behavior, managers have to pay close attention to how they design or ‘engineer’ controllable elements of the website for consumers to facilitate flow. That is, focus needs to be placed on improving attributes of the website (such as content, navigation, respon-siveness, e-commerce capabilities and supple-mentary service offers), which are considered
important by consumers to induce flow. Thus,
collecting such insights from the customer provides information that assists managers in their allocation of resources and deployment of marketing capabilities over electronic networks to
deliver flow experiences for consumers that
facilitate favourable consumer behavior outcomes.
Second, although some researchers have
assumed that flow represents an optimal state
across consumption behaviors (Koufaris, 2002; Kabadayi and Gupta, 2005; Qi et al., 2009), our research further demonstrated that the
relation-ship between flow experience and Internet
shopping behavior is moderated by consumer characteristics. Thus, to ensure the desired shop-ping behavior, e-stores should attempt to manage the shoppers’ flow states on an individual basis. Specifically, e-stores should invest in tools that enable them to develop personal profiles of their customers, while garnering information regard-ing the consumers’ skills and their perceptions of the challenges presented by shopping in the site. In addition, e-stores must determine how the content, organization and layout of their e-sites can be configured to foster the flow that is necessary to manage consumers’ willingness to buy. In addition to analysing data obtained after consumers have made a purchase, e-stores must also derive information about customer expertise, level of confidence and trust propen-sity at the beginning of the shopping experience. In this manner, e-stores will ensure that consu-mers are given appropriate cues based on their individual needs.
Third, previous studies in consumer behavior
have examined shopping motivation from
many different perspectives; however, no study
has examined the link between flow experience
and Internet shopping behaviors (Smith and Sivakumar, 2004). This study showed that there
is a link between flow and Internet shopping
behavior, and the link is moderated by consumer characteristics. Specifically, from an empirical perspective, this study extends the extant litera-ture by testing and validating a model
incorpor-ating flow and online shopping behavior, and
the link is moderating by consumer characteris-tics in the Internet environment using data from actual consumers in an online shopping context.
The findings also explained that the success of e-stores depends on their ability to create
oppor-tunities for consumers to experience flow and
must consider the individual internal factors that influence consumers during Internet shopping.
Limitations and Directions for Future Research The limitations of this research, addressed as follows, also provide the direction for future study. First, although the results successfully verified that flow experience impacts Internet shopping behavior, it is important to realize that other factors may also play a critical role in the antecedents of Internet shopping behavior. For instance, other factors include perceived useful-ness (Ha and Stoel, 2009), perceived benefit (Lee, 2009), satisfaction (Chen and Cheng, 2009), attitude toward the website (Castaneda et al. 2009) and so on. Thus, future research should continue the search for antecedents that influence Internet shopping behavior. Second, the present research contributes to online consumer behavior literature by identifying the moderating role of consumer characteristics in the relationship
between flow experience and Internet shopping
behavior. Specifically, this study sheds new light on the role of consumer characteristics in relation toflow experience and Internet shopping behav-ior. However, the model included only a subset of the variables that can potentially influence the
link between flow experience and shopping
behavior. Thus, a future study could investigate the relationship between theflow experience and other individual differences—for example, the shopper’s individualist or collectivist orientation— and the role offlow in special occasions involving gift giving (Smith and Sivakumar, 2004). Third, although convenience sampling is a way of having subjects that are selected because of their conve-nient accessibility and proximity to the researcher, it did not consider selecting subjects that are representative of the entire population. Thus, future research should use probability sampling method to recruit the respondents. If random selection was done accurately, the sample will be representative of the entire population. Fourth, in addition to the demographic characteristics
included, some of the essential characteristics of the samples, such as the amount of spending in online shopping and the number of years of experience in online shopping, are not investi-gated. Thus, future research should investigate and include them in the demographic charac-teristics to avoid skewing the results. Finally, although this study was administered with a cross-sectional research approach, a longitudinal approach should also be taken into account for future research.
REFERENCES
Armstrong SJ, Overton TS. 1977. Estimating non-response bias in mail surveys. Journal of Marketing Research 14(3): 396–402.
Baker J, Levy M, Grewal D. 1992. An experimental approach to making retail store environmental decisions. Journal of Retailing 68(4): 445–461. Beatty SE, Ferrell ME. 1998. Impulse buying: modeling
its precursors. Journal of Retailing 74(2): 69–91. Bloch PH, Sherrell DL, Ridgway NM. 1986. Consumer
search: an extended framework. Journal of Consumer Research 13(1): 119–126.
Castaneda JA, Rodriguez MA, Luque T. 2009. Attitudes’ hierarchy of effects in online user behaviour. Online Information Review 33(1): 7–21. Celsi RL, Rose RL, Leigh TW. 1993. An exploration of
high-risk leisure consumption through skydiving. Journal of Consumer Research 20(1): 1–23.
Chang KC, Chen MC, Hsu CL, Kuo NT. 2010. The effect of service convenience on post-purchasing behaviours. Industrial Management & Data Systems 110(9): 1420–1443.
Chen CW, Cheng CY. 2009. Understanding consumer intention in online shopping: a respecification and validation of the DeLone and McLean model. Behaviour & Information Technology 28(4): 335–345. Chen H, Wigand RT, Nilan M. 1999. Optimal
experience of web activities. Computers in Human Behavior 15(5): 585–608.
Chen H, Wigand RT, Nilan M. 2000. Exploring web users’ optimal flow experiences. Information Technology & People 13(4): 263–281.
Cheung C, Lee MKO. 2001. Trust in Internet shopping: instrument development and validation through classical and modern approaches. Journal of Global Information Management 9(3): 23–35.
Cho V 2006. A study of the roles of trusts and risks in information-oriented online legal services using an integrated model. Information Management 43(4): 502–520.
Chou TJ, Ting CC. 2003. The role offlow experience in cyber-game addiction. Cyberpsychology & Behavior 6(6): 663–675.
Corbitta BJ, Thanasankit T, Yi H. 2003. Trust and e-commerce: a study of consumer perceptions. Electronic Commerce Research and Applications 2(3): 203–215.
Csikszentmihalyi M 1990. Flow: The Psychology of Optimal Experience. Harper & Row: New York. Csikszentmihalyi M 1993. The Evolving Self. Harper &
Row: New York.
Cyr D, Bonanni C, Bowes J, Ilsever J. 2005. Beyond trust: web site design preferences across cultures. Journal of Global Information Management 13(4): 24–52. Dash JF, Schiffman LG, Berenson C. 1976. Risk- and personality-related dimensions of store choice. Journal of Marketing 40(1): 32–39.
Donovan RJ, Rossiter JR. 1982. Store atmosphere: an environmental psychology approach. Journal of Retailing 58(1): 34–57.
Ferrin DL, Dirks KT. 2003. The use of rewards to increase and decrease trust: mediating processes and differential effects. Organization Science 14(1): 18–31. Fornell C, Larcker DF. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1): 39–50.
Freeman L 2000. Keeping’ em happy. Marketing News 34: 21–24.
Gefen D, Straub DW. 2004. Consumer trust in B2C e-commerce and the importance of social presence: experiments in e-products and e-services. Omega 32(6): 407–424.
Ghani JA, Deshpande SP. 1994. Task characteristics and the experience of optimalflow in human-computer interaction. Journal of Psychology 128(4): 381–391. Ha S, Stoel L. 2009. Consumer e-shopping acceptance:
antecedents in a technology acceptance model. Journal of Business Research 62(5): 565–571.
Hair JF, Anderson RE, Tatham RL, Black WC. 1995. Multivariate Data Analysis with Readings, (4th edn). Prentice-Hall: Englewood Cliffs, NJ.
Ho LA, Kuo TH. 2010. How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior 26(1): 23–31.
Hoffman DL, Novak TP. 1996. Marketing in hyper-media computer-hyper-mediated environments: concep-tual foundations. Journal of Marketing 60(3): 50–68. Hoffman DL, Novak TP. 1997. A new marketing
paradigm for electronic commerce. The Information Society: An International Journal 13(1): 43–54. Howard JA. 1977. Consumer Behavior: Application of the
Theory. McGraw-Hill: New York.
Hsu CL, Lu HP. 2004. Why do people play on-line games? An extended TAM with social influences andflow experience. Information Management 41(7): 853–868.
Hsu CL, Chen MC, Chang KC, Chao CM. 2010. Apply-ing loss aversion to investigate service quality in logistics: a moderating effect of service convenience. International Journal of Operations & Production Management 30(5): 508–525
Huang MH. 2003. Designing website attributes to induce experiential encounters. Computers in Human Behavior 19(4): 425–442.
Jackson SA, Marsh HW. 1996. Development and vali-dation of a scale to measure optimal experience: the flow state scale. Journal of Sport & Exercise Psychology 18(1): 17–35.
Jarvenpaa SL, Tractinsky N, Vitale M. 2000. Consumer trust in an Internet store. Information Technology and Management 1(1–2): 45–71.
Kabadayi S, Gupta R. 2005. Web site loyalty: an empir-ical investigation of its antecedents. International Journal of Internet Marketing and Advertising 2(4): 321–345. Korzaan ML. 2003. Going with the flow: predicting
online purchase intentions. The Journal of Computer Information Systems 43(4): 25–31.
Koufaris M 2002. Applying the technology acceptance model andflow theory to online consumer behavior. Information Systems Research 13(2): 205–223.
Koufaris M, Hampton-Sosa W. 2004. The development of initial trust in an online company by new custo-mers. Information Management 41(3): 377–397. Lee MC. 2009. Factors influencing the adoption
of internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications 8(3): 130–141.
Lee MKO, Turban E. 2001. A trust model for consumer Internet shopping. International Journal of Electronic Commerce 6(1): 75–91.
Liao C, Palvia P, Lin HN. 2006. The roles of habit and web site quality in e-commerce. International Journal of Information Management 26(6): 469–483.
Limerick D, Cunnington B. 1993. Managing the New Organization. Jossey-Bass: San Francisco, CA. Lu Y, Zhou T, Wang B. 2009. Exploring Chinese users’
acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior 25(1): 29–39.
Mathwick C, Rigdon E. 2004. Play,flow, and the online search experience, Journal of Consumer Research 31(2): 324–332.
Maxham III JG. 2001. Service recovery’s influence on consumer satisfaction, positive word-of-mouth, and purchase intentions. Journal of Business Research 54(1): 11–24.
McCole P, Ramsey E, Williams J. 2010. Trust considera-tions on attitudes towards online purchasing: the moderating effect of privacy and security concerns. Journal of Business Research 63(9–10): 1018–1024. McKnight DH, Chervany NL 2001/2002. What trust
interdisciplinary conceptual typology. International Journal of Electronic Commerce 6(2): 35–59.
McKnight DH, Cummings LL, Chervany NL. 1998. Ini-tial trust formation in new organizational relation-ships. Academy of Management Review 23(3): 473–490. Nel D, van Niekerk R, Berthon JP, Davies T. 1999. Going with the flow: websites and customer involvement. Internet Research 9(2): 109–116. Novak TP, Hoffman D, Yung YF. 2000. Measuring the
customer experience in online environments: a structural modeling approach. Marketing Science 19(1): 22–42.
Nunnally JC. 1978. Psychometric Theory. McGraw-Hill: New York.
O’Cass A, Carlson J. 2010. Examining the effects of website-induced flow in professional sporting team websites. Internet Research 20(2): 115–134.
Pace S 2004. A grounded theory of the flow experi-ences of web users. International Journal of Human Computer Studies 60(3): 327–363.
Qi J, Li L, Li Y, Shu H. 2009. An extension of technol-ogy acceptance model: analysis of the adoption of mobile data services in China. Systems Research and Behavioral Science 26(3): 391–407.
Reichheld FF, Schefter P. 2000. E-loyalty: your secret weapon on the web. Harvard Business Review 78 (July–August): 105–113.
Rettie R 2001. An exploration offlow during Internet use. Internet Research 11(2): 103–113.
Rice M 1997. What makes users revisit a web site? Marketing News 31: 12 .
Ridings CM, Gefen D, Arinze B. 2002. Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems 11(3–4): 271–295. Shih H 2004. An empirical study on predicting user
acceptance of e-shopping on the web. Information Management 41(3): 351–368.
Shin DH, Kim WY. 2008. Applying the technology acceptance model andflow theory to Cyworld user behavior: implication of the web2.0 user acceptance. Cyberpsychology & Behavior 11(3): 378–382.
Siekpe JS. 2005. An examination of the multidimension-ality of the flow construct in a computer-mediated
environment. Journal of Electronic Commerce Research 6(1): 31–43.
Sim LL, Koi SM. 2002. Singapore’s Internet shoppers and their impact on traditional shopping patterns. Journal of Retailing and Consumer Services 9(2): 115–124. Sirgy MJ. 1982. Self-concept in consumer behavior: a critical review. Journal of Consumer Research 9(3): 287–300.
Skadberg YX, Kimmel JR. 2004. Visitors’ flow experi-ence while browsing a web site: its measurement, contributing factors, and consequences. Computer in Human Behaviors 20(3): 403–422.
Smith DN, Sivakumar K. 2004. Flow and Internet shopping behavior: a conceptual model and research propositions. Journal of Business Research 57(10): 1199–1208.
Smith CA, Organ DW, Near JP. 1983. Organizational citizenship behavior: its nature and antecedents. Journal of Applied Psychology 68(4): 653–663. Sparks P, Shepherd R. 1992. Self-identity and the
theory of planned behavior: assessing the role of identification with “green consumerism”. Social Psychology Quarterly 55(4): 388–399.
Trevino LK, Webster J. 1992. Flow in computer-mediated communication: electronic mail and voice mail evaluation and impacts. Communication Research 19(5): 539–573.
Van Dyne L, Vandewalle D, Kostova T, Latham ME, Cummings LL. 2000. Collectivism, propensity to trust and self-esteem as predictors of organizational citizenship in a non-work setting. Journal of Organizational Behavior 21: 3–23.
Webster J, Trevino LK, Ryan L. 1993. The dimensionality and correlates of flow in human computer interac-tions. Computers in Human Behavior 9(4): 411–426. Wu JJ, Chang YS. 2005. Towards understanding
members’ interactivity, trust, and flow in online travel community. Industrial Management & Data Systems 105(7): 937–954.
Yang MH, Chandlrees N, Lin B, Chao HY. 2009. The effect of perceived ethical performance of shopping websites on consumer trust. The Journal of Computer Information Systems 50(1): 15–24.
APPENDIX: SCALE ITEMS
Scale Items
Control (adapted from Huang 2003)
Control 1. When navigating this website, I felt in control.
Control 2. I felt that I had no control over my interaction with the web. Control 3. This website allowed me to control the computer interaction. Attention focus (adapted
from Huang 2003)
Attention focus 1. When navigating this website, I thought about other things.
Attention focus 2. When navigating this website, I was aware of distractions. Attention focus 3. When navigating this website, I was totally absorbed in
what I was doing. Curiosity (adapted from
Huang 2003)
Curiosity 1. Navigating this website excited my curiosity.
Curiosity 2. Interacting with this website made me curious. Curiosity 3. Navigating this website aroused my imagination. Intrinsic interest (adapted
from Huang 2003)
Intrinsic interest 1. Navigating this website bored me.
Intrinsic interest 2. Navigating this website was intrinsically interesting. Intrinsic interest 3. This website was fun for me to use.
Continuance Intentions (adapted
from Liao et al. 2006)
Continuance intentions 1. I intend to continue using the website rather than discontinue its use.
Continuance intentions 2. My intentions are to continue using the website rather than any alternative means.
Continuance intentions 3. If I could, I would like to continue use of the website.
Purchase intention (adapted from Maxham, 2001)
Purchase intent 1. The next time I desire an online shopping, I intend to use the website.
Purchase intent 2. I will continue using the website for my online shopping. Purchase intent 3. The next time you are in the market for online shopping,
how likely are you to purchase from the website?
Purchase intent 4. The next time I make a purchase, I will not use the website as my online provider.
Impulsive buying (adapted from Sim and Koi 2002)
Impulsive buying 1. I often buy things that I never intended to buy.
Impulsive buying 2. I think I am an impulsive buyer.
Impulsive buying 3. I often go shopping without any specific need.
Impulsive buying 4. I often feel guilty for buying so many unnecessary things. Trust propensity (adapted
from Lee and Turban 2001)
Trust propensity 1. It is easy for me to trust a person/thing.
Trust propensity 2. My tendency to trust a person/thing is high.
Trust propensity 3. I tend to trust a person/thing, even though I have little knowledge of it.
Trust propensity 4. Trusting someone or something is not difficult. Willingness to Buy (adapted
from Jarvenpaa et al. 2000)
Willingness to buy 1. How likely is it that you would return to the website?
Willingness to buy 2. How likely is that you would consider purchasing from the website in the next 3months?
Willingness to buy 3. How likely is it that you would consider purchasing from the website in the next year?
Willingness to buy 4. For this purchase, how likely is it that you buy from the website?
Self-confidence (adapted from Dash et al. 1976)
Self-confidence 1. Do you ever feel bothered about what other people think of you?
Self-confidence 2. How do you feel about your abilities in general? Self-confidence 3. Just before your recent purchase of some product, how
would you have rated your ability to judge the quality of product? Self-confidence 4. Just before your recent purchase of some product,
how confident were you in your ability to make a good choice when you recently purchased some product?
