Journal of International Consumer Marketing, 23:5–20, 2011 Copyright c Taylor & Francis Group, LLC ISSN: 0896-1530 print / 1528-7068 online DOI: 10.1080/08961530.2011.524571
ARTICLES
Repeat Purchase Intentions in Online Shopping: The Role of Satisfaction, Attitude, and Online Retailers’ Performance
Alhassan G. Abdul-Muhmin
ABSTRACT. A model of determinants of repeat purchase intentions of consumers who have
previously bought online is developed and empirically tested. Data for the model test comes from responses to a structured self-administered survey provided by a sample of 436 consumers in Saudi Arabia. The results confirm hypothesized positive effects of overall satisfaction with previous online purchases and attitude toward online purchasing on repeat purchase intentions. In turn, attitude is positively determined by overall satisfaction and negatively by experience with online purchase problems, while overall satisfaction is determined positively by satisfaction with the following online retailers’ performance dimensions: product prices, product quality, customer service, required payment methods, perceived payment security, and delivery time. Contrary to expectations, delivery cost is not significantly related to overall satisfaction. Neither does experience with online purchase problems. Theoretical and marketing strategy implications of the findings are outlined and discussed.
KEYWORDS. Online shopping, e-shopping, repeat purchase intentions, satisfaction, attitudes, e-commerce, e-satisfaction Across national contexts and demographic segments, consumers are increasingly adopting the phenomenon of buying products online. Accordingly, research interest in consumers’ online shopping behavior has shifted from the initial descriptive focus on identifying enabling product/service characteristics that facilitate their adoption for online purchasing (e.g., Phau and Poon 2000) and delineating the demographic and psychographic characteristics of online shoppers (e.g., Dholakia and Uusitalo 2002; Eastlick and Lotz 1999; Lee and Johnson 2002; Sin and Tse
Alhassan G. Abdul-Muhmin is Associate Professor of Marketing, Department of Management and Marketing, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Address correspondence to Alhassan G. Abdul-Muhmin, Department of Management and Marketing, King Fahd University of Petroleum and Minerals, P.O. Box 1185, Dhahran, 31261, Saudi Arabia. E-mail:
[email protected]
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2002; Vrechopoulos, Siomkos, and Doukidis 2001). The focus now is increasingly on developing models to explain various aspects of online purchase behavior, such as information searching (Kulviwat, Guo, and Engchanil 2004), online purchase satisfaction (Evanschitzky et al. 2004; Szymanski and Hisse 2000), attitudes toward online shopping (e.g., Jayawardhena 2004), and online purchase adoption (e.g., George 2002). In this regard, one phenomenon that has attracted significant modeling interest is online purchase intention (Shim et al. 2001). As a construct, the importance of intention lies in the fact that according to the theories of reasoned action (Fishbein and Ajzen 1975) and planned behavior (Ajzen 1991), it is an important precursor to actual behavior. Accordingly, online purchase intentions are believed to be important precursors to actual online purchasing, and a significant amount of research effort has been devoted to identifying the determinants of online purchase intentions. Drawing on theories like innovation diffusion theory (Rogers 1983), the theory of reasoned action (Fishbein and Ajzen 1975), the theory of planned behavior (Ajzen 1991), and the technology acceptance model (Davis 1989; Moon and Kim 2001), researchers have modeled online purchase intention as a function of online shopping attitude (e.g., George 2002; Monsuwe, Dellaert, and Ruyter 2004), innovativeness (e.g., Goldsmith 2001), online purchase risk perceptions (Bhatnagar, Misra, and Rao 2000; Liao and Cheung 2001; Salisbury et al. 2001), online store environment cues (Chang and Chen 2008), product presentation formats (Kim and Lennon 2008), culture (Moon, Chadee, and Tikoo 2008), and even personality (Bosnjak, Galesic and Tuten 2007). The present study focuses on a special category of online purchase intentions, namely, repeat purchase intentions of consumers who have previously bought products/services online. The focus on this construct is timely. With increasing consumer adoption of online purchasing, the key for sustained growth of the industry lies more in repeat purchases than initial purchases. This is because repeat purchase customers constitute the pool for developing a base of loyal patrons who tend to spend more, buy more frequently, and are more likely to spread positive word of mouth
(Dick and Basu 1994; Jiang and Rosenbloom 2005; Smith 2002). The study’s theoretical contribution is threefold. First, by examining repeat purchase intentions of consumers who have previously made online purchases, it complements the literature on consumers’ online purchase behavior in general, and online purchase intentions in particular. Second, it extends this literature by including the impact of experiences with online purchase problems. Few other studies have examined the impact of this construct. Yet, consumers in many countries outside the industrialized world often face a variety of online purchase problems resulting from their particular national circumstances. These include inadequate communications, transportation, and financial infrastructures. Experiences with such problems can have significant effects on the consumers’ satisfaction and attitudes toward online purchasing as well as their intentions to buy online again. Third, beyond the online purchase context, the study contributes to the broader literature on the relationships among overall satisfaction, attitudes, intention, and actual repeat purchase (e.g., Ajzen 1991; Fishbein & Ajzen 1975; Fornell 1992; Hellier et al. 2003). From a managerial perspective, it provides online retailers with guidance on which dimensions of their product/service offerings have the greatest impact on satisfaction and repeat purchase intentions. Given the current dearth of domestic online retailers in the study region (Saudi Arabia), this guidance could be particularly useful to start-up domestic online retailers as they build systems to target consumers in the country. The rest of the article is organized as follows. The next section presents the conceptual framework for the study, justifications for including specific variables, rationale for hypothesized relationships in the model, and formal statements of the hypotheses to be tested in the empirical study. The section following this describes the study methodology. The analysis and results are then presented in a separate section, followed by another section discussing their theoretical and managerial implications. A final section outlines some limitations of the study and offers suggestions for future research.
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FIGURE 1. Conceptual Model and Hypothesized Relationships
Satisfaction with Online Product Prices Satisfaction with Delivery Time
+ +
Satisfaction with Delivery Cost
+ +
Overall Satisfaction with Previous Online Purchase
Satisfaction with Online Product Quality Satisfaction with Online Customer Service Satisfaction with Required Online Payment Method Satisfaction with Online Payment Security
+ + + −
+
+
Online Repeat Purchase Intentions
+
Attitudes toward Online Purchase
−
Experience with Online Purchase Problems
CONCEPTUAL FRAMEWORK
Figure 1 shows a conceptual model of the constructs and hypothesized relationships. The focal construct is online repeat purchase intention. The study proposition is that this is directly determined by two constructs—overall satisfaction with previous online purchases (overall satisfaction) and attitude toward online purchasing (attitude). In turn, overall satisfaction is determined by two factors—experience with previous online purchase problems (experience) and satisfaction with online retailers’ performance on the key marketing-related dimensions (attribute satisfaction), while attitude is impacted by experience and overall satisfaction. The subsections that follow outline justifications for including the specific determining constructs as well as for the hypothesized relationships among the constructs.
Attitudes, Overall Satisfaction, and Repeat Purchase Intentions
The terminal dependent variable in the study is online repeat purchase intention. This is conceptualized simply as the likelihood that a consumer who has previously made an online purchase will buy online again. This conceptualization views intentions as “expectations,” in contrast to other views of intentions as “wants” or “plans” (Soderlund and Ohman 2003), and has implications for how intentions are subsequently measured. The conceptualization also applies to general online repeat purchase intention, rather than intention to buy again from a specific retailer (as in, e.g., Hellier et al. 2003). As indicated earlier, its key hypothesized determinants are attitudes toward online purchasing and overall satisfaction with previous online purchases. Attitudes have been defined as general and enduring positive or negative feelings (or learned
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dispositions) about a person, object, or issue (Eagly and Chaiken 1993). Consistent with this definition, in this study attitude toward online purchasing is viewed as a consumer’s level of affect (positive, negative, or neutral learned predisposition) toward the idea of buying products online. Both theoretical and empirical evidence in the literature support the hypothesized impact of online purchase attitudes on online repeat purchase intentions. Theoretically, postulations of the theory of reasoned action (TRA) (Azjen and Fishbein 1980; Fishbein and Ajzen 1975) and the theory of planned behavior (TPB) (Ajzen 1985, 1991) inform this hypothesis. Both theories are premised on the postulation that attitude toward a behavior is the key determinant of intention to perform the behavior. Empirically, a large body of studies in psychology, consumer behavior, and marketing confirm the potency of attitude in explaining behavioral intention in general, and repeat purchase intention in particular (e.g., Hellier et al. 2003). In the realm of consumers’ online purchase behavior, several studies have also demonstrated a positive effect of attitude toward online shopping on online purchase intention (e.g., George 2002; Griffith, Krampf, and Palmer 2001; Monsuwe et al. 2004; Shim et al. 2001). Therefore, on the basis of this evidence, a formal hypothesis for the present study is that: H1a: Consumers’ online repeat purchase intentions are positively determined by their attitudes toward online purchasing. Overall satisfaction has been defined as “an affective state that is the emotional reaction to a product or service experience” (Spreng, MacKenzie, and Olshavsky 1996). It has also been viewed as an overall evaluation of performance based on all prior experiences with a focal firm, product, or service (Jones, Mothersbauch, and Beatty 2000), and as the overall level of customer pleasure and contentment resulting from experience with a product or service (Hellier et al. 2003). In this study, all three definitions are integrated into a view of online purchase satisfaction as an overall positive evaluation of previous online purchases and an associated
positive affective emotional state resulting from such evaluation. Empirically, overall satisfaction has been found to be positively related to repeat purchase intention (e.g., E. W. Anderson and Sullivan 1993; Fornell 1992; Hellier et al. 2003; Patterson and Spreng 1997; Selnes 1998). In the online context, a positive link between online purchase satisfaction and willingness to continue patronizing online purchasing has been found by R. E. Anderson and Srinivasan (2003) and Hellier and colleagues (2003). Thus, the available theoretical and empirical evidence suggests that overall satisfaction with online purchasing should be positively related to online repeat purchase intention, and provide a basis for the hypotheses that: H1b: Consumers’ online repeat purchase intentions are positively determined by their overall satisfaction with previous online purchases.
Determinants of Attitude toward Online Purchase
The conceptual model posits two determinants of attitudes toward online purchasing, namely overall satisfaction with previous online purchases and experience with online purchase problems. As will be discussed shortly, postulations of attitude formation theories provide strong unequivocal evidence in support of an antecedent effect of experience on attitudes. However, the same cannot be said of the effect of overall satisfaction. Its antecedent effect on attitudes is not unequivocal. Whereas some researchers treat it as an antecedent to attitudes (e.g., Garbarino and Johnson 1999), others treat it as a consequence (e.g., Hellier et al. 2003). Some insights from Roest and Pieters (1997) suggest that these differences arise because attitudes can be formed either pre- or postpurchase. Overall satisfaction, however, is always a postexperience (purchase) construct. Accordingly, prepurchase attitudes must necessarily be antecedents to overall satisfaction. However, although the possibility exists for postpurchase
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attitudes to be antecedent to overall satisfaction, they are more likely shaped by the latter, making them the consequence rather than the antecedent (see, for example, the model in Roest and Pieters 1997). Indeed some researchers even suggest that attitude toward a product can be assessed as the sum of satisfactions with various attributes of the product (Churchill and Surprenant 1982; Oliver 1981). This essentially makes satisfaction an antecedent of attitude. Since the present study focuses on consumers who have previously made online purchases, and the data is collected postpurchase, attitudes reported must necessarily be postpurchase attitudes, and therefore a consequence of overall satisfaction (see also arguments in Hellier et al. 2003). The formal hypothesis then is that: H2a: Consumers’ attitudes toward online purchasing are positively determined by their overall satisfaction with previous online purchases. Experience with online purchase problems is conceptualized as the extent to which the consumer has encountered purchase barriers and other negative experiences during previous online purchase attempts. Its expected impact on postpurchase attitude is grounded in postulations of attitude-formation theories and models (e.g., the expectancy-value model, the theory of reasoned action, and the theory of planned behavior). These models often include knowledge and/or beliefs as determinant of attitude toward an object. Knowledge and/or beliefs are themselves usually shaped by experiences with the relevant attitude object. This argument is subsumed in Roest and Pieters’s (1997) definition of attitude as a customer’s positive, neutral, or negative learned disposition with respect to a good, service, or company that is often the result of past evaluative experiences (emphasis added). A related basis for expecting that previous online purchasing experience affects online purchasing attitude is findings in the psychology literature that past behavior is a significant predictor of future behavior (e.g., Bentler and Speckart 1979, 1981; Conner et al. 2000; Ouellete and
Wood 1998). In an effort to untangle the exact processes by which this effect occurs, Albarracin and Wyer (2000) concluded that past behavior affects future behavior mainly indirectly through its effect on present attitudes. In other words, the previous performance of a behavior helps in shaping attitude toward the behavior. This result supports the general contention that such past behavior contributes in shaping learned predispositions to respond to future behavioral situations in a manner that is not readily explained by attitude formation theories alone (Bentler and Speckart 1979). Thus, previous online purchasing experience should in itself shape online purchasing attitude and eventually repeat purchase intention. In the realm of online purchasing, researchers have also demonstrated that previous online purchasing experiences have a direct impact on online purchasing attitudes (e.g., Eastlick 1996; Weber and Roehl 1999). But attitude is a valenced construct that can be positive, negative, or neutral. The specific direction it takes depends on the direction of its determinants. Accordingly, consumers who have encountered problems with previous online purchases will (a) develop negative attitudes toward online purchasing, or (b) have any previously held negative attitudes reinforced, or (c) have any previously held positive attitudes neutralized. The opposite will hold for consumers who have not encountered such problems. Accordingly, the formal hypothesis is that: H2b: Consumers’ attitudes toward online purchasing are negatively determined by their past experiences with online purchase problems.
Mediating Role of Attitude
When considered jointly, hypotheses H1a, H1b, and H2a imply that attitudes mediate the relationship between satisfaction and repeat purchase intention. Thus, in addition to the direct effect of satisfaction on repeat purchase intention hypothesized earlier, an indirect effect through (postpurchase) attitudes is also expected, resulting in the following mediating hypothesis:
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H3: Attitude toward online purchasing mediates the positive impact of overall satisfaction with previous online purchasing on online repeat purchase intentions.
Determinants of Overall Satisfaction
Researchers distinguish between overall satisfaction with a product/service and satisfaction with its attributes (attribute satisfaction). While overall satisfaction is concerned with a consumer’s postpurchase evaluation of the total product/service experience, attribute satisfaction is concerned with evaluations of specific aspects of the product or service. According to Oliver (1993), attribute satisfaction is “the consumer’s subjective satisfaction judgment resulting from observations of attribute performance” (p. 421). This distinction implies that a consumer may be satisfied (dissatisfied) overall with a product/service and yet be dissatisfied (satisfied) with certain aspects (attributes) of it. Accordingly, it has been cited as a major explanatory account of the phenomenon of mixed feelings toward a product/service (Mittal, Ross, and Baldasare 1998). Available empirical evidence strongly supports such a distinction (Mittal et al. 1998; Oliver 1993; Spreng et al. 1996). In particular, Mittal and colleagues (1998) not only demonstrate the importance of this distinction, but even conclude that positive and negative attribute satisfactions have asymmetric effects on overall satisfaction. On the basis of this distinction, the present study examines the impact of online retailers’ performance on key online purchase dimensions (attribute satisfaction) on overall satisfaction with online purchasing (overall satisfaction). Specific performance dimensions included are satisfaction with online product prices, delivery time, delivery cost, product quality, customer service, payment method required, and payment security (see figure 1). These specific dimensions are chosen based on previous research showing that consumers generally evaluate online shopping experiences in terms of product information, form of payment, delivery terms, services offered, and payment security, among others (Monsuwe et al. 2004; Shim et al. 2001).
Drawing on both theoretical and empirical evidence, a set of positive independent linear relationships is posited between overall satisfaction and satisfaction with each of the performance dimensions. Theoretically, the dimensions represent online retailers’ marketing program variables (e.g., Kotler 2003)—unique aspects of their offers on the basis of which customers develop preferences for online purchasing over traditional shopping. Academic discourses relating to the marketing mix suggest a positive link between satisfaction with elements of a seller’s marketing mix and overall satisfaction with the seller’s offer (e.g., Abdul-Muhmin 2002; Biong 1993; Ghosh et al. 1997; Schellhase, Hardock, and Ohlwein 2000). From a practitioner point of view, customer satisfaction programs are often implemented by manipulating relevant aspects of this marketing mix to increase the overall appeal of the seller’s offering. This is based on the logic of a positive link between satisfaction with each element of the mix and overall satisfaction with the purchase. The formal hypothesis then is that: H4: Consumers’ overall satisfaction with previous online purchasing is positively determined by their satisfaction with online (a) product prices, (b) product delivery time, (c) product delivery cost, (d) product quality, (e) customer service, (f) required payment method, and (g) perceived online payment security. For any given product or service, satisfaction judgments are relevant only for consumers who have experienced the focal product or service. That is, in order to form a satisfaction judgment, the customer must have consumed the product in question (Soderlund and Ohman 2003). Indeed, some researchers have defined overall satisfaction as an overall evaluation of performance based on all prior experiences with the focal firm or product (Jones et al. 2000). Consequently, overall satisfaction should be related to experiences with the focal product or service, and overall satisfaction with online purchasing should be related to specific experiences with previous online purchases. Experience also is a valence construct that can be bad or good,
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positive or negative. Its impact on satisfaction (or dissatisfaction) is a consequence of the direction of this valence, such that positive experiences result in satisfaction while negative experiences result in dissatisfaction. Accordingly, for the present study, there is an expectation that experiences with online purchase problems will be negatively related to overall satisfaction with online purchasing, leading to the following hypothesis: H5: Consumers’ overall satisfaction with previous online purchases is negatively determined by their experience with online purchase problems.
of 30 undergraduate students. Minor modifications were then made in the main survey before administration.
Sample and Data Collection
Proper sampling frames are virtually nonexistent in Saudi Arabia, making it impossible to obtain adequate probability samples. Consequently, a nonprobability sampling and data collection procedure was devised for the study. Research assistants distributed the questionnaires in coffee shops, doctors’ offices, banks, Internet cafes, and shopping centers in the major cities of the Kingdom. Respondents mostly filled out the questionnaires on the spot and returned them to the research assistants. A total of 1,637 completed questionnaires were collected in the process. Of these, 436 respondents (26.6% of the sample) indicated that they had ever bought products online.2 These constitute the subsample of interest to the present study because the conceptual model and hypotheses can only be tested on a sample that has experience with online purchasing. Summary demographic characteristics of this subsample are: 75.1% Saudi (the remainder are expatriates); 88.2% male; 65.4% in the age group 18–30 years; 52.1% in the income bracket SR 0–SR 5,000 (SR is for Saudi Riyal; SR 3.75 = US$1.00); and 79.5% with a university degree or some university education. The dominance of male respondents in the sample reflects the realities of the cultural and economic situation in Saudi Arabia, where females are virtually out of the job market and most aspects of public life.
METHOD
Data for the study were collected as part of a larger study using a structured self-administered questionnaire that was distributed in selected cities in Saudi Arabia. In the questionnaire, respondents first indicated whether or not they had ever bought any products online. Then those who reported having ever bought indicated the types of products bought, reasons for buying online rather than through conventional channels, their experiences with selected online purchase problems, overall satisfaction with the online purchase experience, and their intention of buying online in the future. Those who had never bought indicated their reasons for not buying. All respondents then completed a set of attitudinal statements about online product purchasing and provided demographic information. As input into the questionnaire design, an exploratory study was conducted to identify online purchase problems that respondents typically encounter in the study context. A combination of depth interviews and group discussion was used. Students of an undergraduate marketing research course taught by the author conducted the depth interviews among their friends and relatives for course credit.1 Subsequently, with the author as moderator, the whole class discussed the factors generated from the interviews and developed a final list of online purchase problems. These were incorporated in the final questionnaire, which was pretested on a sample
Measurement
The key constructs in the conceptual model are online repeat purchase intention, overall satisfaction with online purchasing, attitude toward online purchasing, experience with online purchase problems, and satisfaction with online retailers’ performance on selected dimensions. Overall satisfaction and repeat purchase intention were each measured using single-item measures with 7-point bipolar anchors. For overall satisfaction, respondents indicated their general satisfaction or dissatisfaction with their previous online purchases (1 = very dissatisfied;
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7 = very satisfied) while for repeat purchase intentions, they indicated how likely they were to purchase products online again in the near future (1 = very unlikely; 7 = very likely). In the broader satisfaction literature, there are precedents for using similar single-item measures. Mittal and colleagues (1998) use single-item measures of overall satisfaction and repeat purchase intention, citing earlier work by Yi (1990) that shows acceptable test–retest reliabilities of single-item measures in satisfaction research, and work by LaBarbera and Mazursky (1983) that shows that use of multi-item measures of overall satisfaction in large-scale surveys may actually decrease measurement quality. Attitude toward online purchasing was measured using five Likert statements to which respondents expressed agreement or disagreement on a 5point category scale (1 = strongly disagree; 5 = strongly agree). Satisfaction with online retailers’ performance was elicited by asking respondents to indicate how satisfied or dissatisfied they were with the following seven dimensions of online retailers’ performance: online product prices, delivery time, delivery cost, product quality, customer service, payment method required, and payment security associated with their previous online purchases. Satisfaction judgments were obtained on a 7-point scale, also anchored only at the ends (1 = very dissatisfied; 7 = very satisfied). For experience with online purchasing problems, respondents indicated the frequency with which they had experienced each of 13 online purchasing problems uncovered from the exploratory study (see the Appendix for the list of problems). A 4-point category response format was used with the following category labels: 1 = never; 2 = once; 3 = more than once; and 4 = always. A summated experience score was calculated using the following procedure. For each of the 13 possible problems, each respondent was assigned a score of 1 if s/he ever experienced it and a 0 if otherwise. Next a total experience score was computed by summing across the 13 possible problems. The outcome is an experience variable with a range from 0 (for respondents who have never experienced any problem) to 13 (for those who have experienced
all problems). All constructs and their respective measures are shown in the Appendix.
ANALYSIS AND RESULTS
Prior to the hypothesis testing, psychometric properties of the attitude scale were assessed using exploratory principal component analyses. Only one dimension was extracted, which accounted for 61.4% of the variance. The Cronbach’s alpha reliability coefficient was 0.72, which is considered acceptable. With only one multi-item construct in the model, discriminant validity could not be assessed using the traditional causal modeling approaches (i.e., by comparing the squared correlations between constructs with the average variance extracted [AVE] from each, or by establishing that the confidence intervals of interconstruct correlations do not include 1). However, predictive validity of the attitude scale was assessed by conducting a one-way analysis of variance in which attitudes of respondents who had ever purchased online were compared with attitudes of respondents who never had. The difference was statistically significant and in the expected theoretical direction. Specifically, those who purchased online had significantly more positive attitudes (mean = 3.59) than those who had not (mean = 3.11) (F1,1611 = 155.88; p < .0001). Thus, in terms of its ability to discriminate between online purchasers and nonpurchasers, the scale has acceptable validity. Table 1 shows descriptive statistics (means and standard deviations) and interitem correlations for all constructs used in the hypothesis testing. The hypotheses were tested using ordinary least squares (OLS) multiple regression.3 Three separate OLS regression equations were estimated (see table 2). The first equation was formulated to test hypotheses H1a and H1b relating to the effects of overall satisfaction and attitude on repeat purchase intentions. Parameter estimates for this equation (shown in the first column of table 2) provide support for the hypotheses. Repeat purchase intention is positively related to both overall satisfaction (β = .441; p <
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TABLE 1. Means, Standard Deviations, and Interitem Correlations for Study Constructs
1(a) 1. Satisfaction with . . . (a) Product prices (b) Delivery time (c) Delivery cost (d) Product quality (e) Customer service (f) Payment methods (g) Payment security 2. Negative experiences 3. Overall satisfaction 4. Attitude 5. Repeat purchase intention M SD 1(b) 1(c) 1(d) 1(e) 1(f) 1(g) 2. 3. 4. 5.
1.000 .341 .354 .351 .241 .310 .256 −.234 .417 .326 .331 4.65 a 1.58
1.000 .486 .258 .345 .273 .299 −.256 .319 .239 .251 4.13 a 1.75
1.000 .198 .265 .223 .296 −.244 .230 .141∗ .166∗ 3.87 a 1.68
1.000 .386 .261 .313 −.227 .351 .227 .181 5.37 a 1.39
1.000 .321 .349 −.242 .345 .157∗ .217 4.90 a 1.73
1.000 .434 −.291 .373 .299 .258 5.16 a 1.71
1.000 −.282 .363 .345 .267 4.47 a 1.99
1.000 −.185 −.236 −.175 6.12 b 3.23 1.000 .413 .519 5.23 a 1.36
1.000 .372 3.59 c .65
1.000 5.55 d 1.54
Note. Measurement scales: a: 7-point scale (1 = very dissatisfied; 7 = very satisfied); b: Total number experienced out of problems listed in the Appendix (0 = never experienced a problem; 13 = ever experienced all 13); c: 5-point scale (1 = totally disagree; 5 = totally agree); d: 7-point scale (1 = very unlikely; 7 = very likely). ∗ p < .005; for all remaining coefficients p < .001.
.005) and attitude (β = –.189; p < .005), and the two predictors account for 30% of the variance in repeat purchase intention. In the second equation, attitude was the dependent variable and the predictors were overall satisfaction and experience with online purchase problems. It was used to test hypotheses H2a and H2b. The relevant coefficients are
shown in the second column of table 2, and provide support for the two hypotheses. Attitude toward online purchasing is positively related to overall satisfaction (β = .383; p < .005), and negatively related to experience with online purchase problems (β = −.165; p < .005). The two variables account for almost 20% of the variance in attitudes.
TABLE 2. Regression Results (Standardized Regression Coefficient [t -Value])
Dependent Variable Predictor Variables 1. Attitude toward online purchase 2. Overall satisfaction with online purchase 3. Experience with online purchase problems Satisfaction with online . . . 4. Prices 5. Delivery time 6. Delivery cost 7. Product quality 8. Customer service 9. Payment method 10. Payment security Intercept Overall F (df ) R 2 (Adj R 2 ) Online repeat purchase intention .189 (4.23)∗∗∗∗ .441 (9.86)∗∗∗∗ — — — — — — — — 1.33 (3.59)∗∗∗∗ 90.22 (2, 424)∗∗∗∗ .300 (.296) Attitude toward online purchase — .383 (8.62)∗∗∗∗ −.165 (−3.71)∗∗∗∗ — — — — — — — 2.84 (20.99)∗∗∗∗ 51.73 (2, 424)∗∗∗∗ .197 (.193) Overall satisfaction with online purchase — — .032 (.721) .245 (5.24)∗∗∗∗ .101 (2.06)∗ −.029 (−0.61) .121 (2.60)∗∗ .121 (2.57)∗∗ .153 (3.25)∗∗∗ .141 (2.95)∗∗∗ 1.77 (5.18)∗∗∗∗ 24.13 (8, 430)∗∗∗∗ .314 (.301)
Note. One-tail probabilities: ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .005; ∗∗∗ p < .001.
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The third equation was used to test the effects of satisfaction with online retailers’ performance (H4a to H4g) and experiences with online purchase problems (H5) on overall satisfaction. Parameter estimates for this equation are shown in the last column of table 2. At α = 0.01, five of the eight hypothesized determinants have statistically significant effects on overall satisfaction. Delivery time is additionally significant at α = 0.05. Experience with online purchase problems and delivery cost are the only factors with nonsignificant effects on overall satisfaction. Together the significant variables account for 31% of the variance in satisfaction. Thus, except for hypothesis H4c, all subhypotheses under H4 are supported by the data. No support is found for H5. In terms of relative effects, satisfaction with online product prices has the largest and most distinct effect on overall satisfaction. Finally, the mediating role of attitudes on the impact of overall satisfaction on repeat purchase intention (H3) was examined using the procedures suggested by Judd and Kenny (1981) and Baron and Kenny (1986).4 The results show that attitude partially mediates the satisfaction–repeat purchase intention relationship. The amount of mediation (i.e., reduction in the effect of satisfaction on repeat purchase intention after controlling for attitude) is 0.089. A Sobel test led to rejection of the null hypothesis that the indirect effect (through attitude) of satisfaction on repeat purchase intention is zero (t = 3.85; p < .001).5 Thus, there is evidence in support of H3 and the satisfaction → attitude → intention causal ordering suggested in some sections of the literature (e.g., Hellier et al. 2003).
of attitude toward online purchasing, while online product prices, product quality, customer service, payment methods, payment security, and to a marginal extent, delivery time are the significant determinants of overall satisfaction. The study findings have implications for both theory and practice related to online purchasing.
Overall Satisfaction, Attitudes, and Repeat Purchase Intentions
By far, the most significant findings of the present study are the positive effects of overall satisfaction and attitudes on repeat purchase intentions. The result for overall satisfaction confirms the association between satisfaction and intentions found in numerous previous studies (e.g., Fornell 1992). Furthermore, the positive effect of attitudes on repeat purchase intention confirms the association between attitudes and intentions in extant attitude formation theories like the theory of reasoned action (Fishbein and Ajzen 1975) and the theory of planned behavior (Ajzen 1991) and related empirical studies. However, the size of the regression coefficients indicates that, in relative terms satisfaction is a far more important determinant of repeat purchase than attitudes. A possible reason for this is that, as an essentially postpurchase construct, satisfaction is more closely related to repeat purchase intentions than attitudes, which can be formed either pre- or postpurchase. Furthermore, satisfaction is more immediate and short-term, while attitudes are more enduring and tend to change more slowly. The finding here is similar to evidence in the service marketing literature that satisfaction judgments are more closely related to outcome behaviors than quality perceptions (Baker and Taylor 1997). Another significant issue worth discussing relates to the causal ordering between overall satisfaction and attitudes. In addition to their hypothesized individual effects on repeat purchase intention, the present study also hypothesized a causal ordering between the two constructs such that satisfaction affects attitude, and therefore mediates the effect of attitudes on repeat purchase intentions. As discussed in the hypothesis development section, this particular position is contentious because some
DISCUSSION
In totality, the empirical results provide evidence in support of the conceptual model and hypothesized relationships. Overall satisfaction with previous online purchasing and attitude toward online purchasing are significant determinants of repeat purchase intention. In turn, overall satisfaction and experience with online purchase problems are significant determinants
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other researchers believe that the direction of the “causal” path is from attitude to satisfaction. The present empirical results, however, do support the hypothesized causal ordering, and have two major theoretical implications. First, the results show that attitude and satisfaction are two distinct constructs that need to be treated as such. This is contrary to some views in the literature suggesting that satisfaction is a form of attitude (Taylor 2008). Second, the present results are probably indicative of the need to model constructs at the same level of analysis, especially in cross-sectional studies. In this regard, because satisfaction is largely a postpurchase construct while attitudes can be formed pre- or postpurchase, it is important that a cross-sectional study involving the relationship between the two be based on a model in which attitudes are viewed as postpurchase. In that case, the present results suggest that satisfaction judgments influence such postpurchase attitudes. On the other hand, in a longitudinal study in which attitudes are measured prepurchase while satisfaction judgments are obtained postpurchase, it is only logical that the attitudes will be antecedent to the satisfaction judgments.
Effects of Online Purchase Problems
The negative effect of experience with online shopping problems on online shopping attitude is consistent with other aspects of attitude theories suggesting that experiences are key determinants of attitudes. However, an unexpected finding is the differential impact of these negative experiences on attitudes and overall satisfaction, i.e., negative experiences have a significant negative effect on online shopping attitudes (H2b is supported) but totally unrelated to overall satisfaction (H5 is not supported). To investigate whether the lack of support for H5 is because experience is included in a multiple regression model as one of several determinants of satisfaction, a separate simple regression model was estimated with satisfaction as dependent variable and experience as predictor. The resulting standardized regression coefficient was statistically significant and in the expected direction (β = –0.185; t = –3.918; p < .005). However, it explained only 3.4% of the variance
in satisfaction. Thus, it appears that although experience with online purchase problems does have a negative effect on satisfaction, in relative terms, its effect is weaker (and actually vanishes) in the presence of the online retailer performance dimensions included in the model. One possible explanation for this result is that most online purchases by consumers in Saudi Arabia typically involve products that are not available locally, or services (e.g., hotel bookings) in international destinations. For the consumers, the gratification in obtaining such locally unavailable products may have far outweighed any negative experiences (hurdles) encountered in the online purchasing process. The plausibility of this explanation is corroborated by a high level of overall satisfaction with online purchasing (Mean = 5.23 on a 7-point scale). This level of satisfaction notwithstanding, such problems could still trigger the development of negative attitudes toward online shopping. It is possible that, in reporting overall satisfaction, respondents may have been primed by specific previous online purchase encounters, leading to a salience of the benefits of finally obtaining the purchased products. On the other hand, in reporting attitudes toward online purchasing they may have been primed by the general idea of buying products online, leading to a salience of the problems encountered in the previous purchase. It is like saying: “Given that I have been able to obtain a product not available locally, I am quite happy with my previous online shopping experience, despite the problems encountered. However, given the problems encountered last time, I don’t think it is a good idea to buy products online unless I absolutely have no other option.”
Effects of Online Retailer Performance Variables on Overall Satisfaction
The finding that satisfaction with online product prices, product quality, customer service, payment methods, payment security, and (to a marginal extent) delivery time are positive determinants of overall satisfaction also has some implications for theory. To the extent that these factors constitute online retailers’ marketing mix elements, the study results reinforce previous
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findings on the role of suppliers’ marketing mix variables as determinants of customer satisfaction (e.g., Abdul-Muhmin 2002, 2005). In relative terms, satisfaction with product prices has the greatest impact on overall satisfaction. This result is interesting as it reinforces the popular notion that one of the key benefits of shopping online is the possibility of finding good deals, a consequence of the ability of the online shopping environment to foster easier comparison shopping. From the perspective of the study setting, anecdotal evidence suggests that this lower-price benefit is even more pronounced because most online purchases are made for products that are not manufactured locally. Such products, if available locally, are generally high-priced due to the quasi-monopoly nature of markets for most imported products, a consequence of the exclusive dealership agreements that local importing agents often have with foreign suppliers. Thus, it is not unusual for a product to be available online (typically from foreign vendors) for a price that is 20–30% less than what it is sold for in the local offline environment. The study findings also have some managerial implications, especially for European and North American online retailers from whom the majority of respondents reported having made their online purchases. The relatively large positive effect of overall satisfaction on repeat purchase intentions implies a need for these retailers to continually strive toward satisfying their Saudi customers as a means to foster future repeat purchase. The pattern of parameter estimates for the determinants of satisfaction provides a guide as to how this can be done. Specifically, they demonstrate the need for the retailers to constantly strive toward providing customer value (quality products at satisfactory prices), good customer service, payment security, and to some extent, quick delivery. Significantly, Evanschitsky and colleagues (2004) arrived at a similar conclusion. The insignificant effect of delivery cost is also interesting. Essentially, this finding suggests that consumers (at least those in the present study) make a cognitive separation between an online product’s list price and its delivery cost rather than integrate the two into a total product acqui-
sition cost. This is good news for online retailers because it suggests that these consumers are content with having to pay additional amounts for delivery of products ordered online and that this has no effect on their level of satisfaction with online purchasing. This further suggests that online retailers might benefit from two-part pricing strategies that combine low product list prices with relatively higher delivery charges to offset the low list prices.
LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH
The present study has the following two main limitations. First, the study results may be context-specific. In particular, the specific types of online purchase problems encountered in Saudi Arabia may not be present in other national contexts. This limits the generalizability of the present results, especially to national contexts that are significantly different from Saudi Arabia. Second, in explaining the finding that experiences with online purchase problems is negatively related to attitudes but unrelated to satisfaction, we have alluded to the possibility that in reporting overall satisfaction, respondents may have been primed by specific previous online purchase encounters, while in reporting attitude toward online purchase they may have been primed by the general (nonspecific) idea of buying products online. The design of the present study could not allow investigation of the plausibility of this assumption. Therefore, future studies should consider examining whether, in the online shopping context, different underlying cognitive processes in fact drive satisfaction and attitudes. Such studies will help provide useful conceptual clarity.
ACKNOWLEDGMENTS
The findings reported in this article are part of results from a study that has been funded under the CIM Accreditation Fund of King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. The author acknowledges KFUPM for the support, and for
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using its various facilities in the research and preparation of this manuscript.
NOTES
1. Students were used to conduct the personal interviews because it is the best way to reach female respondents who are otherwise impossible to reach for data collection in Saudi Arabia because of the strict separation of sexes. 2. Books, computer software, hotel reservations, and video/musical CDs were the dominant products that respondents reported having ever purchased. 3. Least squares regression was chosen over covariance structure analysis because the preponderance of singleitem measures is likely to produce unstable parameter estimates in structural equation modeling (J. C. Anderson and Gerbing 1988). 4. For a simplified exposition of the four-step procedure for testing mediation, see also http://davidakenny.net/cm/ mediate.htm 5. See http://www.danielsoper.com/statcalc/calc31.aspx for a calculator of the Sobel test statistic.
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RECEIVED: April 17, 2009 REVISED: January 16, 2010 ACCEPTED: June 14, 2010
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APPENDIX Constructs and Measures
Experiences with online purchase problems (No. of problems ever experienced; range = 0–13) a) Shipping costs were higher than you expected. b) Product took too long to arrive. c) Product was lost during shipping. d) Company will not ship to Saudi Arabia. e) Company does not accept your credit card type. f) Internet connection was lost during order processing. g) Quality of the product you received was lower than you expected. h) The ordering instructions are not clear. i) The order form is difficult to fill out. j) You spend too much time searching. k) It is difficult to find products on the Internet. l) Seller charged you additional unexpected costs. m) Saudi customs officials did not allow the product into the country. Satisfaction with online retailers’ performance dimensions (1 = very dissatisfied; 7 = very satisfied) a) Product prices b) Delivery time c) Delivery cost d) Product quality e) Customer service f) Payment methods required by sellers g) Security in the payment Attitude toward online purchase (1 = totally disagree; 5 = totally agree)—Cronbach’s alpha = 0.72 a) It is a good thing that consumers can buy products online. b) Buying products online is not a sensible thing to do. (R) c) It is exciting to buying products online. d) It is not advisable to buy products online. (R) e) Buying products online is a risky thing to do. (R) Overall satisfaction (1 = very dissatisfied; 7 = very satisfied) Overall, how satisfied/dissatisfied are you with your previous online shopping experience? Online repeat purchase intention (1 = very unlikely; 7 = very likely) How likely is it that you will continue to buy products online in the future?
(R) = Reverse-scored
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