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Repurchase intention in B2C e-commerce—A relationship quality perspective
Information & Management 48 (2011) 192–200
Contents lists available at ScienceDirect
Information & Management journal homepage: www.elsevier.com/locate/im
Repurchase intention in B2C e-commerce—A relationship quality perspective§ Yixiang Zhang a,b, Yulin Fang c,*, Kwok-Kee Wei c, Elaine Ramsey d, Patrick McCole e, Huaping Chen f a
School of Management and Economics, Beijing Institute of Technology, Beijing, PR China Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, PR China Department of Information Systems, City University of Hong Kong, Hong Kong, China d School of Business, Retail and Financial Services, University of Ulster, Northern Ireland, UK e Queen’s University Management School, Queen’s University Belfast, Northern Ireland, UK f School of Computer Science and Technology, University of Science and Technology of China, Hefei, PR China b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 19 September 2008 Received in revised form 17 November 2010 Accepted 30 December 2010 Available online 19 May 2011
Information systems professionals must pay attention to online customer retention. Drawing on the relationship marketing literature, we formulated and tested a model to explain B2C user repurchase intention from the perspective of relationship quality. The model was empirically tested through a survey conducted in Northern Ireland. Results showed that online relationship quality and perceived website usability positively impacted customer repurchase intention. Moreover, online relationship quality was positively influenced by perceived vendor expertise in order fulfillment, perceived vendor reputation, and perceived website usability, whereas distrust in vendor behavior negatively influenced online relationship quality. Implications of these findings are discussed. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Repurchase intention Online relationship quality Business-to-customer e-commerce Trust Distrust Satisfaction
1. Introduction In general, online buying behavior can be understood in two stages: the first stage is primarily concerned with encouraging people to purchase online and the second is to encourage them to repurchase, which is critical if the e-commerce vendor is to succeed. It costs more time and effort to acquire new customers than to retain existing ones. Indeed, customer retention is often seen as a means to gaining competitive advantage [24]. However, only about 1% of online visitors return to carry out repeated purchases [10]. It is therefore important to delve into the drivers of online customer repurchase behavior [20]. Repurchase intention is a manifestation of customer loyalty. Although the literature identifies other dimensions of customer loyalty [12], repurchase behavior has a more direct effect on the vendor’s profit. Careful scrutiny of the literature indicates that it is only relatively recently that studies have considered online customer repurchase behavior [14]. Indeed, few studies have examined repurchase intention through a relationship quality
§ This work was partially supported by Strategic Research Grant at City University of Hong Kong, China (No. CityU 7002521), and the National Nature Science Foundation of China (No. 70773008). * Corresponding author at: P7722, City University of Hong Kong, Hong Kong, China. Tel.: +852 27887492; fax: +852 34420370. E-mail address: [email protected] (Y. Fang).
0378-7206/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2011.05.003
lens though relationship quality is regarded as an important factor in the relationship marketing literature. We therefore decided to examine antecedents of relationship quality and understand how it relates to customer repurchase behavior in an online context. Much of the relationship marketing literature looks at ways of sustaining buyer–seller relationships in traditional business situations. It focuses on buyer satisfaction and trust in the vendor by considering these two factors as key dimensions of relationship quality. It asserts that both are indispensable in maintaining a good buyer-seller relationship. Satisfaction reflects a state resulting from the buyer’s evaluation of the vendor’s past performance, while trust reflects the buyer’s confidence in the vendor’s future performance. Thus a buyerseller relationship is of high quality only if both the vendor’s past and future performance are perceived to be favorable. It is posited that the relationship quality construct may play a significant role in retaining buyers and increasing buyer loyalty in business context [19]. We focused on the relationship quality in online customer’s repurchase intention in the B2C context (the online relationship quality) and examined several key antecedents of online relationship quality: vendor characteristics (perceived website usability, perceived expertise in order fulfillment and perceived reputation) and vendor behavior factor (distrust in vendor behavior). We therefore posed two questions: (1) to what extent
Y. Zhang et al. / Information & Management 48 (2011) 192–200
does online relationship quality influence customers’ online repurchase intention; and (2) what factors influence online relationship quality? 2. Conceptual background Recently, we have seen an explosive growth of relationship marketing research in the traditional business context [11]. The area has considered all activities that establish, develop, and maintain relational exchange in order to generate long-term customer relationships. Sanchez-Franco et al. [22] investigated relationship quality between customers and service providers and found that it positively influenced loyalty towards the service provider. Lages et al. [16] developed a scale (RELQUAL) to measure relationship quality in the export market. Similarly, Rauyruen and Miller [21] studied relationship quality in the B2B context and found that it positively influenced business customer loyalty. Most prior studies were performed in the traditional marketing context. We focused on the impact of online relationship quality on repurchase intention in a B2C e-commerce context. Although earlier research in relationship marketing investigated and tested relationship quality in various contexts, the definitions and conceptualization remained subtly different. Johnson et al.’s conceptualization of relationship quality focused on trust, commitment, and relationship stability [13], but it has been considered to also include satisfaction, commitment, and service quality. However, despite inconsistencies, most of the literature agrees that satisfaction and trust are the key sub-constructs of relationship quality. Prior work has argued that a good relationship is developed only when buyers feel satisfied and have trust in their relationship with the vendor. Extending this definition, we view online
193
relationship quality as a second-order construct composed of a customer’s trust on and satisfaction with an online vendor. Two categories of factors are usually considered to be antecedents of relationship quality: salesperson characteristics (qualities such as domain expertise) and behavior. However, in the B2C e-commerce context, it is the website, that represents the vendor, not a salesperson. To build an online relationship quality in B2C e-commerce, a well-designed, highly usable website becomes the equivalent of a competent salesperson. Hence, we included website usability as a major factor influencing online relationship quality. In the online context, fulfilling an order is completed by the online vendor either through the website (if the product is digital) or by offline means. Vendor expertise in fulfilling the order is critical in both instances. We therefore included this expertise as part of our model. In the traditional business context, salespeople promote relationship quality by demonstrating certain behaviors including relational selling behavior, service recovery, relationship investment, social support, communication and relationship management. We focused on customer’s distrust in vendor behavior (e.g., promising to do something but not doing it); this can severely damage a vendor’s relationship quality with its customers. Distrust is, of course, an important and distinct factor that can influences online customer’s behavior [6]. Most prior research has focused only on positive factors, such as trust, familiarity, service level, perceived usefulness, and perceived ease of use. By including this important yet ignored negative factor, we hoped to help develop a more comprehensive understanding of the effect of online relationship quality and how to avoid destroying it.
Vendor Characteristics
Perceived Website Usability
H3
H2
Perceived Expertise in Order Fulfillment
Perceived
H4
Online
Online
H1
Relationship
Repurchase
Quality
Intention
H5
Reputation
Control variables: Gender; Income; education H6
Vendor Behavior
Expertise in using internet Familiarity with the vendor
Distrust in
Privacy concern
Vendor Behavior
Fig. 1. Research model.
194
Y. Zhang et al. / Information & Management 48 (2011) 192–200
3. Research model and hypotheses Fig. 1 shows the research model for our study. It is intended to explain customer repurchase intention in B2C e-commerce. Relationship quality and perceived website usability are considered to be the main predictors of repurchase intention. Factors relating to vendor characteristics (perceived expertise in order fulfillment, perceived website usability, and perceived reputation) and vendor behavior (distrust in vendor behavior) are the assumed antecedents of relationship quality. 3.1. Online relationship quality A buyer–seller relationship is considered high quality only when previous interaction with the vendor has been positive and future interactions with the seller are expected. A good relationship is developed only when buyers feel satisfied and have trust in their relationship with the vendor. We thus hypothesized: Hypothesis 1. Online relationship quality is positively related to customer online repurchase intention. 3.2. Online vendor characteristics 3.2.1. Website usability Consumers use a website to find product information, make online payments, and complete purchases. Thus a website should provide the consumer with rich product information [25]. In addition, a well-designed website with high usability (e.g., ease of navigation) can improve a consumer’s online buying experience and their perception of the vendor [4,7]. Cyr [5] found that consumer loyalty was positively related to website usability design. Other research has highlighted a significant relationship between loyalty and website usability when consumers are familiar with the website [3]. Similarly, we expected a positive relationship between website usability and online repurchase behavior and hypothesized: Hypothesis 2. Perceived website usability is positively related to online repurchase intention. Perceived website usability influences online customer perceptions towards the website. Yoon found that certain properties of a website (e.g., adequacy of product description and width of product selections) significantly influenced customer trust and that navigation functionality was a significant antecedent of customer satisfaction. Zviran et al. [26] studied the effect of website usability and user-based design on customer satisfaction and found that website usability influenced user satisfaction. Flavian et al. found that perceived website usability influenced customer satisfaction and trust positively. Casalo´ et al. also found that perceived website usability positively influenced customer satisfaction. Therefore, we expected a significant relationship between perceived website usability and online relationship quality and hypothesized: Hypothesis 3. Perceived website usability is positively related to online relationship quality.
also important for online customers to receive the product for which they have paid in a timely, efficient, and safe manner. When a customer’s perception of online vendor expertise in order fulfillment is high, the customer believes that the vendor has the ability and relevant competencies associated with order fulfillment and is confident that he/she will obtain the product on time. This, in turn, increases his/her satisfaction and trust. Therefore, customers tend to develop long-term relationships when they perceive high vendor expertise in order fulfillment. Thus we hypothesized: Hypothesis 4. Perceived vendor expertise in order fulfillment is positively related to online relationship quality. 3.2.3. Vendor reputation Vendor reputation involves customer perceptions of the vendor’s public image, innovativeness, quality of product and service, and commitment to customer satisfaction [15]. Customers can determine vendor reputation based on an evaluation of the vendor’s past performance and behavior. Reputation is associated with brand equity and firm credibility; it is also viewed as a sign of trustworthiness. It is, however, difficult to build, but easy to lose. This requires the vendor to stay motivated to maintain a good reputation once it is established. Furthermore, customers tend to trust vendors with a high reputation because they believe that such firms will not risk their reputation by acting opportunistically. Empirical research has shown that reputation is an important trustbuilding factor for online vendors and is significantly related to trust. Therefore: Hypothesis 5. Perceived vendor reputation is positively related to online relationship quality. 3.3. Distrust in vendor behavior Increasing research efforts are being paid to studying the concept of distrust [6]. We studied the effect of online customers’ distrust of a vendor’s behavior on relationship quality. When customers have distrust in vendor behavior, they believe that the vendor will not keep their promises. Distrust also implies violations of customer expectations. For example, an online vendor may deliver low quality products. Accordingly, we posit a negative relationship between distrust in vendor behavior and online relationship quality: Hypothesis 6. Distrust in vendor behavior is negatively related to online relationship quality. 3.4. Control variables Several control variables were included in our model to rule out the possibility that empirical results were due to covariance with other variables. Some variables have been found to influence online customers directly or indirectly, such as gender, levels of income and education, expertise in using the Internet, and familiarity with the vendor. We therefore included these factors as control variables. 4. Research methods and analyses
3.2.2. Vendor expertise in order fulfillment There seems to be a significant relationship between salesperson expertise and relationship quality. In B2C e-commerce, order fulfillment is an important characteristic of the online vendor [2]. We therefore studied vendor expertise in order fulfillment as perceived by online customers. Customer perceptions of order processing is important in influencing e-commerce success [23]. It is
4.1. Measurement development Most of our constructs have been established in prior literature and we drew on and adapted these measures to enhance the validity of our study. Appendix A lists the items for each measure and provides the sources of our measures. Individual meetings
Y. Zhang et al. / Information & Management 48 (2011) 192–200 Table 1 Demographic information of respondents. Measure
Items
Frequency
Percent
Gender
Female
251
69.7
Age
19–25 26–35 36–45 >45
200 74 40 46
55.6 20.5 11.1 12.8
Occupation
University student Staff
205 155
56.9 43.1
Income
<20 k 20–40 k >40 k
141 114 105
39.2 31.7 29.1
were held with two colleagues to discuss: the appropriateness of the questionnaire items; whether there was any ambiguity surrounding the questionnaire items; and the appearance and layout of the instrument. Based on this, a revised questionnaire was developed and sent to the same individuals for a second review. Some additional suggestions were made and minor revisions resulted. The revised questionnaire was then piloted among 10 staff and 12 students in a large university before being accepted as the final version. 4.2. Data collection To test the model, we conducted a survey based on respondents’ retrospective online purchasing experience. This is appropriate because online purchase behaviors are memorable events that can be recalled by customers [9]. The participants were instructed to complete the survey questionnaire only if they had online buying experience, and the product was for personal use. This solved the problem of respondents answering questions related to purchases they had carried out on behalf of the university. We asked respondents to ‘‘please think of a vendor you have purchased from recently via the Internet.’’ They were asked to write down the vendor’s name and website address before answering survey questions. The survey data was collected from a sample of students and staff at a university located in Northern Ireland. We are confident that the study can be generalized to the population in Northern Ireland in terms of the age and socio-economic profiles of online consumers represented by our sample. The feasibility of using a student sample has been demonstrated in prior studies [8]; a large portion of them have online shopping experience. We re-analyzed the sample by splitting it into Student (sample size 155) and University Staff (sample size 205). Overall results for the two sub-samples showed that the significance level remained the same and no substantial differences were found in the inferences between the student sub-sample and the staff sub-sample, despite slight differences
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in the magnitude of coefficients. The final questionnaire was emailed to 170 randomly chosen university staff. A printed questionnaire was distributed to a sample of 695 students attending business courses at the university. We received 360 usable responses, yielding a response rate of 42%. Table 1 shows the respondents’ demographic information. Our sample covered a broad collection of online vendors, with the four most frequently cited vendors being (1) easyJet (21%; n = 77); (2) Amazon (16%; n = 58); (3) eBay (6%; n = 23); and (4) Play.com (6%; n = 23). A non-response bias check was performed by comparing the first and last 10% of the responses on all the main constructs using a Mann–Whitney U-test. None of the tests were statistically significant, indicating that non-response bias was not a problem. 4.3. Data analysis We used a two-stage analytical procedure to analyze the data. First, a confirmatory factor analysis was done to assess the measurement model. Second, the structural model was examined. We used SmartPLS to conduct the analysis. Bootstrapping with 500 re-samples and 360 cases per sample was used to assess the path significance. 4.3.1. Measurement model We first checked for reliability and construct validity. Online relationship quality was conceptualized as a second-order factor containing satisfaction and trust. We used the factor score of the first-order construct as items of online relationship quality. The construct loadings were examined, with values greater than 0.7 being retained and items with loadings lower than 0.7 being dropped. The model was then re-examined. All of the remaining items loaded sufficiently on the relevant construct (P < 0.01). Table 2 shows the number of items, composite reliability, and average variance extracted in the final measurement. The composite reliability was then checked: the lowest value was 0.84, which is larger than the recommended value of 0.7, showing good reliability. Convergent validity was assessed by examining average variance extracted (AVE) from the measures; it ranged from 0.61 to 0.78, well above the recommended value of 0.5, thus showing convergent validity. In Table 3, the square root of the AVE for each construct was larger than the correlation of any specific construct with any of the other constructs, showing that we had discriminant validity. As shown in Appendix B, all the items loaded significantly on their target construct, and the loadings were larger than the cross loadings, which also confirmed construct validity. In order to address any possible common method bias, we followed the procedure suggested by Liang et al. [17]. We calculated each indicator’s variance, as explained by their construct and by this method. As shown in Appendix C, the
Table 2 Results of confirmatory factor analysis. Construct
Number of items
Composite reliability
Average variance extracted
Perceived expertise in order fulfillment Online relationship quality Satisfaction Trust Perceived reputation Perceived website usability Distrust in vendor behavior Online repurchase intention Customer expertise in using Internet
3 2 3 8 6 8 4 3 4
0.89 0.84 0.92 0.95 0.90 0.94 0.90 0.84 0.93
0.73 0.72 0.78 0.69 0.61 0.65 0.69 0.64 0.77
Y. Zhang et al. / Information & Management 48 (2011) 192–200
196 Table 3 Correlation between constructs. Mean, SD PEOF SAT TRU ORI PR DIST PWU CEUI
5.68, 5.85, 5.45, 5.94, 5.55, 3.70, 6.03, 4.71,
PEOF
1.02 1.08 1.02 1.06 0.96 1.43 0.88 1.34
0.85 0.41 0.66 0.45 0.47 0.24 0.52 0.20
SAT
TRU
0.88 0.46 0.40 0.50 0.37 0.43 0.29
ORI
0.83 0.49 0.55 0.47 0.55 0.19
0.80 0.29 0.26 0.49 0.19
PR
0.78 0.40 0.43 0.25
DIST
0.83 0.32 0.13
PWU
CEUI
0.81 0.28
0.88
Note: SD, standard deviation; PEOF, perceived expertise in order fulfillment; SAT, satisfaction; TRU, trust; PR, Perceived reputation; DIST, distrust in vendor behavior; PWU, perceived website usability; CEUI, customer expertise in using Internet; Values in the diagonal row are square roots of the average variance extracted. The other cells contain the correlations between constructs.
average substantively explained by variance of the indicators was 0.67, the average method explained by variance was 0.01, and the ratio was 67:1. In addition, all the method factor loadings were not significant. Thus common method bias was not a major concern in our study. 4.3.2. Structural model After checking the validity, we tested our hypotheses with PLS. Fig. 2 shows the results of the structural model, including the path coefficients and their significance, along with the R2. We found that our model explained 34% of the variance of online repurchase intention and 62% of the variance of online relationship quality. Hypothesis 1 posited that online relationship quality positively influences repurchase intention. The path coefficient of 0.34 (p < 0.001), supporting this hypothesis. The influence of perceived website usability on online repurchase intention was positive and significant (coefficient of 0.24, p < 0.001), providing support for Hypothesis 2. Hypothesis 3 posited that perceived website usability positively influenced online relationship quality. This
was supported by the significant path coefficient of 0.20 (p < 0.001). The positive impact of perceived vendor expertise in order fulfillment on relationship quality was also confirmed (coefficient of 0.36, p < 0.001), supporting Hypothesis 4. Our data also supported Hypothesis 5 (coefficient of 0.26, p < 0.001). Our result also showed a negative influence of distrust in vendor behavior on online relationship quality (coefficient of 0.24, p < 0.001), which confirmed Hypothesis 6. We did not find any significant relationship between the control variables and online repurchase intention. 5. Discussion, implications, and limitations 5.1. Research implications Most prior research on online repurchase studied satisfaction and trust separately. We have shown why they should be examined together when studying online repurchase intention. Using the second-order construct of relationship quality, we
Vendor Characteristics
Perceived Website Usability
H3 (0.20***) Perceived Expertise in Order Fulfillment
H4 (0.36***)
H2 (0.24***)
Online Relationship Quality
H1 (0.34***)
(R2=0.62) Perceived Reputation
H5 (0.26***)
H6 (-0.24***) Vendor Behavior
*p≤0.05,**p≤0.01;***p≤0.001 Distrust in Vendor Behavior
Fig. 2. Results of PLS analysis.
Online Repurchase Intention (R2=0.34)
Y. Zhang et al. / Information & Management 48 (2011) 192–200
obtained a more parsimonious model with the same predictive power as with the two separate models; indeed, when we broke the relationship quality into two first-order constructs (satisfaction and trust) and re-examined the model, we found that the firstorder model explained the same variance of repurchase intention as did the higher order model. Marketing research has posited a paradigm shift from transactional marketing to relationship marketing and has called for more focus on buyer–seller relationships; this was true for e-commerce in our study. Our study identified and synthesized several important antecedents of online relationship quality: vendor characteristics (website usability, expertise in order fulfillment and reputation) and vendor behavior (distrust in vendor behavior). Also online relationship quality was positively influenced by all three vendor characteristics and negatively influenced by perceived malevolent vendor behavior. Website usability depends on product information presentation and ease of conducting transactions. Order fulfillment should focus on post-purchase service. The empirical data showed that perceived vendor expertise in order fulfillment had a greater impact on online relationship quality than perceived website usability. This is important. There have been very few studies on B2C e-commerce retention that have focused on the post-purchase stage of order fulfillment. We showed the importance of considering the postpurchase stage of order fulfillment. Furthermore, the greater impact of order fulfillment implies that vendor post-purchase expertise is more important for online relationship quality than expertise in online activities. Product delivery is important. We also studied another vendor characteristic: perceived vendor reputation. This was not included as an antecedent of relationship quality but is considered very important. Prior research on B2C ecommerce found that vendor reputation was important as it can influence customer. Our study revealed that perceived reputation was important: it positively impacts online relationship quality, extending the original offline relationship quality model. The study also revealed the negative influence of distrust in vendor behavior on online relationship quality. We empirically demonstrated its detrimental role on online relationship quality. 5.2. Managerial implications Our study poses certain implications for the management. It suggests that online vendors should adopt a relationship-oriented
197
view when conducting business online and evaluate ongoing attempts to do so by using the concept of online relationship quality in the pursuit of customer loyalty. Website usability was found to positively influence both relationship quality and repurchase intention, showing that online vendors should be more considerate of the role of the website and work to improve user experience by providing rich product information, improving website navigation functions, and making online purchasing easier. Expertise in order fulfillment was found to be an important antecedent of relationship quality, showing that online vendors should demonstrate their expertise in order to increase customer loyalty and retention. For example, they could provide professional testimonies and publish on-time delivery statistics. Mangers of e-commerce website can also improve online relationship quality by establishing and sustaining a good reputation thus promoting customer loyalty and retention. 5.3. Limitations Our research had certain limitations. First, our data were collected only in Northern Ireland, UK. Caution must therefore be exercised when attempting to generalize our results to other locations. Also, since the respondents self-selected the online vendor from whom they had purchased material in order to answer the questionnaire, social desirability bias may be present – although it may be observed that the data had enough variability to make model testing possible.
6. Conclusion Our study developed and tested a model explaining B2C ecommerce customer repurchase intention from a relationship quality perspective. By including vendor characteristics (perceived website usability, perceived expertise in order fulfillment and perceived reputation) and vendor behavior (distrust in vendor behavior), our model explained the importance of online relationship quality to online re-purchase intention. To a certain degree, our study demonstrated the value of using relationship marketing theory to account for online customer repurchasing behavior. Its results should provide useful implications for ecommerce practitioners.
Appendix A. Measurement items Construct
Items
Source
Online repurchase intention
Likelihood/probability that you will purchase online from the same vendor. . . In the medium term In the long term I will never purchase from the same vendor again
[20]
Satisfaction
Overall extremely dissatisfied/overall extremely satisfied Overall extremely displeased/overall extremely pleased My expectations were not met at all/my expectations were exceeded
[20]
Trust
I believe that this vendor is consistent in quality and service I believe that this vendor is keen to fulfill my needs and wants I believe that this vendor is honest I believe that this vendor wants to be known as one that keeps promises and commitments I believe that this vendor has my best interests in mind I believe that this vendor is trustworthy I believe that this vendor has high integrity I believe that this vendor is dependable
[20]
Y. Zhang et al. / Information & Management 48 (2011) 192–200
198 Appendix A (Continued ) Construct
Items
Source
Perceived expertise in order fulfillment
I believe that this vendor has knowledge and expertise in distribution (i.e. how to deliver products/services) I believe that this vendor has efficiently integrated all necessary departments/systems that are needed to deliver products or services I believe that this vendor has an efficient system for processing orders received
[20]
Perceived reputation
Poor/excellent public image Not/extremely committed to customer satisfaction Not innovative at all/extremely innovative Products and/or services are extremely poor/excellent Has an extremely poor/excellent reputation. Extremely unreliable/reliable
[20]
Distrust in vendor behavior
I believe that this vendor could sometimes fail to deliver product/ service as and when promised I believe this vendor is sometimes unable to deliver what they promise to I believe that this vendor is sometimes unable to meet expectations I believe that this vendor sometimes promises more than they can deliver
Developed based on [8,23]
Perceived website usability
Extremely difficult/easy to use Extremely unprofessional/professional Extremely poorly organised/well organised Extremely poor/excellent breadth of product/service selection Extremely poor/excellent description of product/service selection Extremely difficult/easy to navigate Extremely difficult/easy to find information that I want Extremely difficult/easy to conduct online shopping
[1,4,25]
Customer expertise in using internet to conduct tractions
I I I I
know a lot about conducting purchases via the Internet am experienced in conducting purchases via the Internet am informed about conducting purchases via the Internet am an expert buyer of products/services via the Internet
[18]
Appendix B. Item loadings and cross loadings #
Construct
Item
1
2
3
4
5
6
7
8
1
Perceived expertise in order fulfillment
PEOF1 PEOF2 PEOF3
0.86 0.86 0.85
0.17 0.24 0.20
0.33 0.36 0.46
0.34 0.31 0.38
0.52 0.50 0.66
0.44 0.38 0.39
0.39 0.38 0.54
0.13 0.19 0.19
2
Distrust in vendor behavior
DIST1 DIST2 DIST3 DIST4
0.16 0.20 0.23 0.20
0.78 0.87 0.86 0.81
0.15 0.25 0.17 0.29
0.22 0.30 0.33 0.35
0.32 0.37 0.38 0.46
0.27 0.35 0.38 0.33
0.26 0.25 0.24 0.31
0.04 0.16 0.13 0.11
3
repurchase intention
ORI1 ORI2 ORI3
0.31 0.43 0.34
0.19 0.24 0.21
0.78 0.89 0.71
0.33 0.40 0.20
0.34 0.48 0.34
0.13 0.33 0.22
0.38 0.47 0.31
0.14 0.20 0.11
4
Satisfaction
SAT1 SAT2 SAT3
0.41 0.32 0.35
0.31 0.33 0.34
0.38 0.35 0.31
0.89 0.91 0.86
0.44 0.38 0.38
0.47 0.48 0.35
0.40 0.36 0.37
0.25 0.25 0.27
5
Trust
TRU1 TRU2 TRU3 TRU4 TRU5 TRU6 TRU7 TRU8
0.60 0.59 0.57 0.54 0.35 0.56 0.57 0.55
0.46 0.33 0.40 0.29 0.35 0.38 0.40 0.47
0.46 0.46 0.37 0.44 0.24 0.39 0.38 0.46
0.47 0.33 0.32 0.39 0.32 0.33 0.38 0.46
0.85 0.83 0.83 0.78 0.72 0.84 0.87 0.88
0.59 0.45 0.43 0.42 0.31 0.39 0.46 0.53
0.50 0.45 0.47 0.50 0.33 0.38 0.47 0.47
0.18 0.12 0.19 0.14 0.17 0.11 0.14 0.18
6
Perceived reputation
PR1 PR2 PR3 PR4 PR5 PR6
0.27 0.38 0.34 0.43 0.35 0.41
0.25 0.36 0.19 0.39 0.29 0.35
0.22 0.23 0.17 0.29 0.15 0.29
0.36 0.39 0.25 0.43 0.39 0.45
0.39 0.48 0.33 0.46 0.41 0.47
0.70 0.82 0.72 0.84 0.83 0.75
0.29 0.41 0.31 0.39 0.28 0.32
0.30 0.15 0.21 0.22 0.16 0.15
Y. Zhang et al. / Information & Management 48 (2011) 192–200
199
Appendix B (Continued ) #
Construct
Item
1
2
3
4
5
6
7
8
7
Perceived website usability
PWU1 PWU2 PWU3 PWU4 PWU5 PWU6 PWU7 PWU8
0.46 0.43 0.43 0.41 0.34 0.47 0.34 0.43
0.20 0.24 0.29 0.22 0.30 0.27 0.27 0.27
0.46 0.36 0.41 0.34 0.31 0.43 0.42 0.43
0.37 0.37 0.41 0.32 0.33 0.33 0.30 0.31
0.41 0.50 0.48 0.44 0.41 0.46 0.37 0.44
0.25 0.46 0.46 0.40 0.36 0.29 0.28 0.28
0.79 0.81 0.85 0.78 0.75 0.87 0.82 0.78
0.18 0.18 0.21 0.24 0.29 0.27 0.20 0.23
8
Customer expertise in using internet
CEUI1 CEUI2 CEUI3 CEUI4
0.16 0.19 0.21 0.14
0.10 0.12 0.11 0.16
0.19 0.20 0.14 0.13
0.27 0.26 0.30 0.18
0.14 0.17 0.19 0.16
0.22 0.23 0.29 0.14
0.25 0.26 0.28 0.17
0.91 0.93 0.87 0.79
Appendix C. Common method bias analysis
Construct
Item
Substantive factor loading (R1)
R12
Perceived expertise in order fulfillment
PEOF1 PEOF2 PEOF3
0.686 0.947 0.930
0.471 0.897 0.865
0.181 0.098 0.077
0.033 0.010 0.006
Relationship quality
RQ1 RQ2
0.982 0.736
0.964 0.542
0.182 0.164
0.033 0.027
Online repurchase intention
ORI1 ORI2 ORI3
0.824 0.816 0.769
0.679 0.666 0.591
0.064 0.096 0.048
0.004 0.009 0.002
Perceived website usability
PWU1 PWU2 PWU3 PWU4 PWU5 PWU6 PWU7 PWU8
0.825 0.685 0.717 0.750 0.739 0.963 0.981 0.786
0.681 0.469 0.514 0.563 0.546 0.927 0.962 0.618
0.049 0.141 0.144 0.041 0.021 0.107 0.177 0.01
0.002 0.020 0.021 0.002 0.000 0.011 0.031 0.000
Perceived reputation
PR1 PR2 PR3 PR4 PR5 PR6
0.739 0.769 0.801 0.777 0.952 0.640
0.546 0.591 0.642 0.604 0.906 0.410
0.042 0.074 0.084 0.086 0.155 0.117
0.002 0.005 0.007 0.007 0.024 0.014
Distrust in vendor behavior
DIST1 DIST2 DIST3 DIST4
0.835 0.896 0.876 0.718
0.697 0.803 0.767 0.516
0.051 0.023 0.015 0.095
0.003 0.001 0.000 0.009
0.813
0.671
0.001
0.011
Average
References [1] G. Balabanis, N.L. Reynolds, Consumer attitudes towards multi-chennel retailers’ web sites: the role of involvement, brand attitude, internet knowledge and visit duration, Journal of Business Strategies 18 (2), 2001, pp. 105–131. [2] Y. Cao, T.S. Gruca, B.R. Klemz, Internet pricing, price satisfaction, and customer satisfaction, International Journal of Electronic Commerce 8 (2), 2003, pp. 31–50. [3] L. Casalo´, C. Flavia´n, M. Guinalı´u, The role of perceived usability, reputation, satisfaction and consumer familiarity on the website loyalty formation process, Computers in Human Behavior 24 (2), 2008, pp. 325–345. [4] G. Chakraborty, V. Lala, D. Warren, An empirical investigation of antecedents of B2B websites’ effectiveness, Journal of Interactive Marketing 16 (4), 2002, pp. 51–72. [5] D. Cyr, Modeling web site design across cultures: relationships to trust, satisfaction, and E-loyalty, Journal of Management Information Systems 24 (4), 2008, pp. 47–72. [6] A. Dimoka, What does the brain tell us about trust and distrust? evidence from a functional neuroimaging study MIS Quarterly 34 (2), 2010, pp. A373–A377. [7] C. Flavian, M. Guinaliu, R. Gurrea, The role played by perceived usability, satisfaction and consumer trust on website loyalty, Information & Management 43 (1), 2006, pp. 1–14. [8] D. Gefen, Customer loyalty on e-commerce, Journal of the Association for Information Systems 3, 2002, pp. 27–51. [9] Y. Gre´goire, R. Fisher, The effects of relationship quality on customer retaliation, Marketing Letters 17 (1), 2006, pp. 31–46.
Method factor loading (R2)
R12
[10] S. Gupta, K. Hee-Woong, The moderating effect of transaction experience on the decision calculus in on-line repurchase, International Journal of Electronic Commerce 12 (1), 2007, pp. 127–158. [11] M.J. Harker, J. Egan, The past, present and future of relationship marketing, Journal of Marketing Management 22 (1–2), 2006, pp. 215–242. [12] L.C. Harris, M.M.H. Goode, The four levels of loyalty and the pivotal role of trust: a study of online service dynamics, Journal of Retailing 80 (2), 2004, pp. 139–158. [13] J.L. Johnson, R.S. Sohi, R. Grewal, The role of relational knowledge stores in interfirm partnering, Journal of Marketing 68 (3), 2004, pp. 21–36. [14] M. Khalifa, V. Liu, Online consumer retention: contingent effects of online shopping habit and online shopping experience, European Journal of Information Systems 16 (6), 2007, pp. 780–792. [15] M. Koufaris, W. Hampton-Sosa, The development of initial trust in an online company by new customers, Information & Management 41 (3), 2004, pp. 377–397. [16] C. Lages, C.R. Lages, L.F. Lages, The RELQUAL scale: a measure of relationship quality in export market ventures, Journal of Business Research 58 (8), 2005, pp. 1040– 1048. [17] H. Liang, N. Saraf, Q. Hu, Y. Xue, Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management, MIS Quarterly 31 (1), 2007, pp. 59–87. [18] P. McCole, E. Ramsey, J. Williams, Trust considerations on attitudes towards online purchasing: the moderating effect of privacy and security concerns, Journal of Business Research 63 (9–10), 2010, pp. 1018–1024.
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[19] R.W. Palmatier, R.P. Dant, D. Grewal, K.R. Evans, Factors influencing the effectiveness of relationship marketing: a meta-analysis, Journal of Marketing 70 (4), 2006, pp. 136–153. [20] I. Qureshi, Y. Fang, E. Ramsey, P. McCole, P. Ibbotson, D. Compeau, Understanding online customer repurchasing intention and the mediating role of trust—an empirical investigation in two developed countries, European Journal of Information Systems 18 (3), 2009, pp. 205–222. [21] P. Rauyruen, K.E. Miller, Relationship quality as a predictor of B2B customer loyalty, Journal of Business Research 60 (1), 2007, pp. 21–31. [22] M.J. Sanchez-Franco, A.F.V. Ramos, F.A.M. Velicia, The moderating effect of gender on relationship quality and loyalty toward Internet service providers, Information & Management 46 (3), 2009, pp. 196–202. [23] G. Torkzadeh, G. Dhillon, Measuring factors that influence the success of Internet commerce, Information Systems Research 13 (2), 2002, pp. 187–204. [24] H.-T. Tsai, H.-C. Huang, Determinants of e-repurchase intentions: an integrative model of quadruple retention drivers, Information & Management 44 (3), 2007, pp. 231–239. [25] S.-J. Yoon, The antecedents and consequences of trust in online-purchase decisions, Journal of Interactive Marketing 16 (2), 2002, pp. 47–63. [26] M. Zviran, C. Glezer, I. Avni, User satisfaction from commercial web sites: the effect of design and use, Information & Management 43 (2), 2006, pp. 157–178. Yixiang Zhang is an Assistant Professor in the School of Management and Economics, Beijing Institute of Technology. He received his Ph.D. from City University of Hong Kong and University of Science and Technology of China. His current research is focused on knowledge management, and electronic commerce. He has published papers in International Journal of Information Management.
Yulin Fang is an Assistant Professor in the Department of Information Systems, City University of Hong Kong. He earned his Ph.D. at Richard Ivey School of Business, University of Western Ontario. His current research is focused on knowledge management, virtual teams, and open source software projects. He has published papers in major journals such as the Strategic Management Journal, Journal of Management Information Systems, Journal of Management Studies, Organizational Research Methods, Journal of the Association for Information Systems, European Journal of Information Systems, Journal of the American Society for Information Science and Technology, Information & Management, among others. He has won the 2009 Senior Scholars Best IS Publication Award, and was the Samsung Best Paper Award Finalist and the Carolyn Dexter Award Finalist at the 2008 Academy of Management Conference.
Kwok-Kee Wei is Chair Professor in the Department of Information Systems at the City University of Hong Kong. He obtained his Ph.D. from the University of York and B.S. from Nanyang University. His research focuses on human–computer interaction, innovation adoption and management, electronic commerce, and knowledge management. He has published widely in the information systems field with articles appearing in many international journals.
Dr. Elaine Ramsey is a Senior Lecturer in the department of Business, Retail and Financial Services, University of Ulster (UU). She received her Ph.D. in SME E-Marketing from UU in 2005. Her main research interests relate to the adoption and diffusion of IT among SMEs, and the inherent issues relative to ecommerce deployment. She has published in the European Journal of Information Systems, Journal of Business Research, Journal of Marketing Management, International Journal of Innovation Management, Service Industries Journal, and others.
Patrick McCole is a Lecturer in Management at Queen’s University Management School, Queen’s University Belfast. He received his BA (Hons) and PhD degrees from the University of Ulster. His main research interests include the role of trust in e-commerce, new media and customer-connectivity, as well as empirical evidence relating to the Service-Dominant Logic.
Huaping Chen is a Professor in School of Computer Science and Technology at University of Science and Technology of China. His research interests include information strategies, business intelligence and application.
Contents lists available at ScienceDirect
Information & Management journal homepage: www.elsevier.com/locate/im
Repurchase intention in B2C e-commerce—A relationship quality perspective§ Yixiang Zhang a,b, Yulin Fang c,*, Kwok-Kee Wei c, Elaine Ramsey d, Patrick McCole e, Huaping Chen f a
School of Management and Economics, Beijing Institute of Technology, Beijing, PR China Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, PR China Department of Information Systems, City University of Hong Kong, Hong Kong, China d School of Business, Retail and Financial Services, University of Ulster, Northern Ireland, UK e Queen’s University Management School, Queen’s University Belfast, Northern Ireland, UK f School of Computer Science and Technology, University of Science and Technology of China, Hefei, PR China b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 19 September 2008 Received in revised form 17 November 2010 Accepted 30 December 2010 Available online 19 May 2011
Information systems professionals must pay attention to online customer retention. Drawing on the relationship marketing literature, we formulated and tested a model to explain B2C user repurchase intention from the perspective of relationship quality. The model was empirically tested through a survey conducted in Northern Ireland. Results showed that online relationship quality and perceived website usability positively impacted customer repurchase intention. Moreover, online relationship quality was positively influenced by perceived vendor expertise in order fulfillment, perceived vendor reputation, and perceived website usability, whereas distrust in vendor behavior negatively influenced online relationship quality. Implications of these findings are discussed. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Repurchase intention Online relationship quality Business-to-customer e-commerce Trust Distrust Satisfaction
1. Introduction In general, online buying behavior can be understood in two stages: the first stage is primarily concerned with encouraging people to purchase online and the second is to encourage them to repurchase, which is critical if the e-commerce vendor is to succeed. It costs more time and effort to acquire new customers than to retain existing ones. Indeed, customer retention is often seen as a means to gaining competitive advantage [24]. However, only about 1% of online visitors return to carry out repeated purchases [10]. It is therefore important to delve into the drivers of online customer repurchase behavior [20]. Repurchase intention is a manifestation of customer loyalty. Although the literature identifies other dimensions of customer loyalty [12], repurchase behavior has a more direct effect on the vendor’s profit. Careful scrutiny of the literature indicates that it is only relatively recently that studies have considered online customer repurchase behavior [14]. Indeed, few studies have examined repurchase intention through a relationship quality
§ This work was partially supported by Strategic Research Grant at City University of Hong Kong, China (No. CityU 7002521), and the National Nature Science Foundation of China (No. 70773008). * Corresponding author at: P7722, City University of Hong Kong, Hong Kong, China. Tel.: +852 27887492; fax: +852 34420370. E-mail address: [email protected] (Y. Fang).
0378-7206/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2011.05.003
lens though relationship quality is regarded as an important factor in the relationship marketing literature. We therefore decided to examine antecedents of relationship quality and understand how it relates to customer repurchase behavior in an online context. Much of the relationship marketing literature looks at ways of sustaining buyer–seller relationships in traditional business situations. It focuses on buyer satisfaction and trust in the vendor by considering these two factors as key dimensions of relationship quality. It asserts that both are indispensable in maintaining a good buyer-seller relationship. Satisfaction reflects a state resulting from the buyer’s evaluation of the vendor’s past performance, while trust reflects the buyer’s confidence in the vendor’s future performance. Thus a buyerseller relationship is of high quality only if both the vendor’s past and future performance are perceived to be favorable. It is posited that the relationship quality construct may play a significant role in retaining buyers and increasing buyer loyalty in business context [19]. We focused on the relationship quality in online customer’s repurchase intention in the B2C context (the online relationship quality) and examined several key antecedents of online relationship quality: vendor characteristics (perceived website usability, perceived expertise in order fulfillment and perceived reputation) and vendor behavior factor (distrust in vendor behavior). We therefore posed two questions: (1) to what extent
Y. Zhang et al. / Information & Management 48 (2011) 192–200
does online relationship quality influence customers’ online repurchase intention; and (2) what factors influence online relationship quality? 2. Conceptual background Recently, we have seen an explosive growth of relationship marketing research in the traditional business context [11]. The area has considered all activities that establish, develop, and maintain relational exchange in order to generate long-term customer relationships. Sanchez-Franco et al. [22] investigated relationship quality between customers and service providers and found that it positively influenced loyalty towards the service provider. Lages et al. [16] developed a scale (RELQUAL) to measure relationship quality in the export market. Similarly, Rauyruen and Miller [21] studied relationship quality in the B2B context and found that it positively influenced business customer loyalty. Most prior studies were performed in the traditional marketing context. We focused on the impact of online relationship quality on repurchase intention in a B2C e-commerce context. Although earlier research in relationship marketing investigated and tested relationship quality in various contexts, the definitions and conceptualization remained subtly different. Johnson et al.’s conceptualization of relationship quality focused on trust, commitment, and relationship stability [13], but it has been considered to also include satisfaction, commitment, and service quality. However, despite inconsistencies, most of the literature agrees that satisfaction and trust are the key sub-constructs of relationship quality. Prior work has argued that a good relationship is developed only when buyers feel satisfied and have trust in their relationship with the vendor. Extending this definition, we view online
193
relationship quality as a second-order construct composed of a customer’s trust on and satisfaction with an online vendor. Two categories of factors are usually considered to be antecedents of relationship quality: salesperson characteristics (qualities such as domain expertise) and behavior. However, in the B2C e-commerce context, it is the website, that represents the vendor, not a salesperson. To build an online relationship quality in B2C e-commerce, a well-designed, highly usable website becomes the equivalent of a competent salesperson. Hence, we included website usability as a major factor influencing online relationship quality. In the online context, fulfilling an order is completed by the online vendor either through the website (if the product is digital) or by offline means. Vendor expertise in fulfilling the order is critical in both instances. We therefore included this expertise as part of our model. In the traditional business context, salespeople promote relationship quality by demonstrating certain behaviors including relational selling behavior, service recovery, relationship investment, social support, communication and relationship management. We focused on customer’s distrust in vendor behavior (e.g., promising to do something but not doing it); this can severely damage a vendor’s relationship quality with its customers. Distrust is, of course, an important and distinct factor that can influences online customer’s behavior [6]. Most prior research has focused only on positive factors, such as trust, familiarity, service level, perceived usefulness, and perceived ease of use. By including this important yet ignored negative factor, we hoped to help develop a more comprehensive understanding of the effect of online relationship quality and how to avoid destroying it.
Vendor Characteristics
Perceived Website Usability
H3
H2
Perceived Expertise in Order Fulfillment
Perceived
H4
Online
Online
H1
Relationship
Repurchase
Quality
Intention
H5
Reputation
Control variables: Gender; Income; education H6
Vendor Behavior
Expertise in using internet Familiarity with the vendor
Distrust in
Privacy concern
Vendor Behavior
Fig. 1. Research model.
194
Y. Zhang et al. / Information & Management 48 (2011) 192–200
3. Research model and hypotheses Fig. 1 shows the research model for our study. It is intended to explain customer repurchase intention in B2C e-commerce. Relationship quality and perceived website usability are considered to be the main predictors of repurchase intention. Factors relating to vendor characteristics (perceived expertise in order fulfillment, perceived website usability, and perceived reputation) and vendor behavior (distrust in vendor behavior) are the assumed antecedents of relationship quality. 3.1. Online relationship quality A buyer–seller relationship is considered high quality only when previous interaction with the vendor has been positive and future interactions with the seller are expected. A good relationship is developed only when buyers feel satisfied and have trust in their relationship with the vendor. We thus hypothesized: Hypothesis 1. Online relationship quality is positively related to customer online repurchase intention. 3.2. Online vendor characteristics 3.2.1. Website usability Consumers use a website to find product information, make online payments, and complete purchases. Thus a website should provide the consumer with rich product information [25]. In addition, a well-designed website with high usability (e.g., ease of navigation) can improve a consumer’s online buying experience and their perception of the vendor [4,7]. Cyr [5] found that consumer loyalty was positively related to website usability design. Other research has highlighted a significant relationship between loyalty and website usability when consumers are familiar with the website [3]. Similarly, we expected a positive relationship between website usability and online repurchase behavior and hypothesized: Hypothesis 2. Perceived website usability is positively related to online repurchase intention. Perceived website usability influences online customer perceptions towards the website. Yoon found that certain properties of a website (e.g., adequacy of product description and width of product selections) significantly influenced customer trust and that navigation functionality was a significant antecedent of customer satisfaction. Zviran et al. [26] studied the effect of website usability and user-based design on customer satisfaction and found that website usability influenced user satisfaction. Flavian et al. found that perceived website usability influenced customer satisfaction and trust positively. Casalo´ et al. also found that perceived website usability positively influenced customer satisfaction. Therefore, we expected a significant relationship between perceived website usability and online relationship quality and hypothesized: Hypothesis 3. Perceived website usability is positively related to online relationship quality.
also important for online customers to receive the product for which they have paid in a timely, efficient, and safe manner. When a customer’s perception of online vendor expertise in order fulfillment is high, the customer believes that the vendor has the ability and relevant competencies associated with order fulfillment and is confident that he/she will obtain the product on time. This, in turn, increases his/her satisfaction and trust. Therefore, customers tend to develop long-term relationships when they perceive high vendor expertise in order fulfillment. Thus we hypothesized: Hypothesis 4. Perceived vendor expertise in order fulfillment is positively related to online relationship quality. 3.2.3. Vendor reputation Vendor reputation involves customer perceptions of the vendor’s public image, innovativeness, quality of product and service, and commitment to customer satisfaction [15]. Customers can determine vendor reputation based on an evaluation of the vendor’s past performance and behavior. Reputation is associated with brand equity and firm credibility; it is also viewed as a sign of trustworthiness. It is, however, difficult to build, but easy to lose. This requires the vendor to stay motivated to maintain a good reputation once it is established. Furthermore, customers tend to trust vendors with a high reputation because they believe that such firms will not risk their reputation by acting opportunistically. Empirical research has shown that reputation is an important trustbuilding factor for online vendors and is significantly related to trust. Therefore: Hypothesis 5. Perceived vendor reputation is positively related to online relationship quality. 3.3. Distrust in vendor behavior Increasing research efforts are being paid to studying the concept of distrust [6]. We studied the effect of online customers’ distrust of a vendor’s behavior on relationship quality. When customers have distrust in vendor behavior, they believe that the vendor will not keep their promises. Distrust also implies violations of customer expectations. For example, an online vendor may deliver low quality products. Accordingly, we posit a negative relationship between distrust in vendor behavior and online relationship quality: Hypothesis 6. Distrust in vendor behavior is negatively related to online relationship quality. 3.4. Control variables Several control variables were included in our model to rule out the possibility that empirical results were due to covariance with other variables. Some variables have been found to influence online customers directly or indirectly, such as gender, levels of income and education, expertise in using the Internet, and familiarity with the vendor. We therefore included these factors as control variables. 4. Research methods and analyses
3.2.2. Vendor expertise in order fulfillment There seems to be a significant relationship between salesperson expertise and relationship quality. In B2C e-commerce, order fulfillment is an important characteristic of the online vendor [2]. We therefore studied vendor expertise in order fulfillment as perceived by online customers. Customer perceptions of order processing is important in influencing e-commerce success [23]. It is
4.1. Measurement development Most of our constructs have been established in prior literature and we drew on and adapted these measures to enhance the validity of our study. Appendix A lists the items for each measure and provides the sources of our measures. Individual meetings
Y. Zhang et al. / Information & Management 48 (2011) 192–200 Table 1 Demographic information of respondents. Measure
Items
Frequency
Percent
Gender
Female
251
69.7
Age
19–25 26–35 36–45 >45
200 74 40 46
55.6 20.5 11.1 12.8
Occupation
University student Staff
205 155
56.9 43.1
Income
<20 k 20–40 k >40 k
141 114 105
39.2 31.7 29.1
were held with two colleagues to discuss: the appropriateness of the questionnaire items; whether there was any ambiguity surrounding the questionnaire items; and the appearance and layout of the instrument. Based on this, a revised questionnaire was developed and sent to the same individuals for a second review. Some additional suggestions were made and minor revisions resulted. The revised questionnaire was then piloted among 10 staff and 12 students in a large university before being accepted as the final version. 4.2. Data collection To test the model, we conducted a survey based on respondents’ retrospective online purchasing experience. This is appropriate because online purchase behaviors are memorable events that can be recalled by customers [9]. The participants were instructed to complete the survey questionnaire only if they had online buying experience, and the product was for personal use. This solved the problem of respondents answering questions related to purchases they had carried out on behalf of the university. We asked respondents to ‘‘please think of a vendor you have purchased from recently via the Internet.’’ They were asked to write down the vendor’s name and website address before answering survey questions. The survey data was collected from a sample of students and staff at a university located in Northern Ireland. We are confident that the study can be generalized to the population in Northern Ireland in terms of the age and socio-economic profiles of online consumers represented by our sample. The feasibility of using a student sample has been demonstrated in prior studies [8]; a large portion of them have online shopping experience. We re-analyzed the sample by splitting it into Student (sample size 155) and University Staff (sample size 205). Overall results for the two sub-samples showed that the significance level remained the same and no substantial differences were found in the inferences between the student sub-sample and the staff sub-sample, despite slight differences
195
in the magnitude of coefficients. The final questionnaire was emailed to 170 randomly chosen university staff. A printed questionnaire was distributed to a sample of 695 students attending business courses at the university. We received 360 usable responses, yielding a response rate of 42%. Table 1 shows the respondents’ demographic information. Our sample covered a broad collection of online vendors, with the four most frequently cited vendors being (1) easyJet (21%; n = 77); (2) Amazon (16%; n = 58); (3) eBay (6%; n = 23); and (4) Play.com (6%; n = 23). A non-response bias check was performed by comparing the first and last 10% of the responses on all the main constructs using a Mann–Whitney U-test. None of the tests were statistically significant, indicating that non-response bias was not a problem. 4.3. Data analysis We used a two-stage analytical procedure to analyze the data. First, a confirmatory factor analysis was done to assess the measurement model. Second, the structural model was examined. We used SmartPLS to conduct the analysis. Bootstrapping with 500 re-samples and 360 cases per sample was used to assess the path significance. 4.3.1. Measurement model We first checked for reliability and construct validity. Online relationship quality was conceptualized as a second-order factor containing satisfaction and trust. We used the factor score of the first-order construct as items of online relationship quality. The construct loadings were examined, with values greater than 0.7 being retained and items with loadings lower than 0.7 being dropped. The model was then re-examined. All of the remaining items loaded sufficiently on the relevant construct (P < 0.01). Table 2 shows the number of items, composite reliability, and average variance extracted in the final measurement. The composite reliability was then checked: the lowest value was 0.84, which is larger than the recommended value of 0.7, showing good reliability. Convergent validity was assessed by examining average variance extracted (AVE) from the measures; it ranged from 0.61 to 0.78, well above the recommended value of 0.5, thus showing convergent validity. In Table 3, the square root of the AVE for each construct was larger than the correlation of any specific construct with any of the other constructs, showing that we had discriminant validity. As shown in Appendix B, all the items loaded significantly on their target construct, and the loadings were larger than the cross loadings, which also confirmed construct validity. In order to address any possible common method bias, we followed the procedure suggested by Liang et al. [17]. We calculated each indicator’s variance, as explained by their construct and by this method. As shown in Appendix C, the
Table 2 Results of confirmatory factor analysis. Construct
Number of items
Composite reliability
Average variance extracted
Perceived expertise in order fulfillment Online relationship quality Satisfaction Trust Perceived reputation Perceived website usability Distrust in vendor behavior Online repurchase intention Customer expertise in using Internet
3 2 3 8 6 8 4 3 4
0.89 0.84 0.92 0.95 0.90 0.94 0.90 0.84 0.93
0.73 0.72 0.78 0.69 0.61 0.65 0.69 0.64 0.77
Y. Zhang et al. / Information & Management 48 (2011) 192–200
196 Table 3 Correlation between constructs. Mean, SD PEOF SAT TRU ORI PR DIST PWU CEUI
5.68, 5.85, 5.45, 5.94, 5.55, 3.70, 6.03, 4.71,
PEOF
1.02 1.08 1.02 1.06 0.96 1.43 0.88 1.34
0.85 0.41 0.66 0.45 0.47 0.24 0.52 0.20
SAT
TRU
0.88 0.46 0.40 0.50 0.37 0.43 0.29
ORI
0.83 0.49 0.55 0.47 0.55 0.19
0.80 0.29 0.26 0.49 0.19
PR
0.78 0.40 0.43 0.25
DIST
0.83 0.32 0.13
PWU
CEUI
0.81 0.28
0.88
Note: SD, standard deviation; PEOF, perceived expertise in order fulfillment; SAT, satisfaction; TRU, trust; PR, Perceived reputation; DIST, distrust in vendor behavior; PWU, perceived website usability; CEUI, customer expertise in using Internet; Values in the diagonal row are square roots of the average variance extracted. The other cells contain the correlations between constructs.
average substantively explained by variance of the indicators was 0.67, the average method explained by variance was 0.01, and the ratio was 67:1. In addition, all the method factor loadings were not significant. Thus common method bias was not a major concern in our study. 4.3.2. Structural model After checking the validity, we tested our hypotheses with PLS. Fig. 2 shows the results of the structural model, including the path coefficients and their significance, along with the R2. We found that our model explained 34% of the variance of online repurchase intention and 62% of the variance of online relationship quality. Hypothesis 1 posited that online relationship quality positively influences repurchase intention. The path coefficient of 0.34 (p < 0.001), supporting this hypothesis. The influence of perceived website usability on online repurchase intention was positive and significant (coefficient of 0.24, p < 0.001), providing support for Hypothesis 2. Hypothesis 3 posited that perceived website usability positively influenced online relationship quality. This
was supported by the significant path coefficient of 0.20 (p < 0.001). The positive impact of perceived vendor expertise in order fulfillment on relationship quality was also confirmed (coefficient of 0.36, p < 0.001), supporting Hypothesis 4. Our data also supported Hypothesis 5 (coefficient of 0.26, p < 0.001). Our result also showed a negative influence of distrust in vendor behavior on online relationship quality (coefficient of 0.24, p < 0.001), which confirmed Hypothesis 6. We did not find any significant relationship between the control variables and online repurchase intention. 5. Discussion, implications, and limitations 5.1. Research implications Most prior research on online repurchase studied satisfaction and trust separately. We have shown why they should be examined together when studying online repurchase intention. Using the second-order construct of relationship quality, we
Vendor Characteristics
Perceived Website Usability
H3 (0.20***) Perceived Expertise in Order Fulfillment
H4 (0.36***)
H2 (0.24***)
Online Relationship Quality
H1 (0.34***)
(R2=0.62) Perceived Reputation
H5 (0.26***)
H6 (-0.24***) Vendor Behavior
*p≤0.05,**p≤0.01;***p≤0.001 Distrust in Vendor Behavior
Fig. 2. Results of PLS analysis.
Online Repurchase Intention (R2=0.34)
Y. Zhang et al. / Information & Management 48 (2011) 192–200
obtained a more parsimonious model with the same predictive power as with the two separate models; indeed, when we broke the relationship quality into two first-order constructs (satisfaction and trust) and re-examined the model, we found that the firstorder model explained the same variance of repurchase intention as did the higher order model. Marketing research has posited a paradigm shift from transactional marketing to relationship marketing and has called for more focus on buyer–seller relationships; this was true for e-commerce in our study. Our study identified and synthesized several important antecedents of online relationship quality: vendor characteristics (website usability, expertise in order fulfillment and reputation) and vendor behavior (distrust in vendor behavior). Also online relationship quality was positively influenced by all three vendor characteristics and negatively influenced by perceived malevolent vendor behavior. Website usability depends on product information presentation and ease of conducting transactions. Order fulfillment should focus on post-purchase service. The empirical data showed that perceived vendor expertise in order fulfillment had a greater impact on online relationship quality than perceived website usability. This is important. There have been very few studies on B2C e-commerce retention that have focused on the post-purchase stage of order fulfillment. We showed the importance of considering the postpurchase stage of order fulfillment. Furthermore, the greater impact of order fulfillment implies that vendor post-purchase expertise is more important for online relationship quality than expertise in online activities. Product delivery is important. We also studied another vendor characteristic: perceived vendor reputation. This was not included as an antecedent of relationship quality but is considered very important. Prior research on B2C ecommerce found that vendor reputation was important as it can influence customer. Our study revealed that perceived reputation was important: it positively impacts online relationship quality, extending the original offline relationship quality model. The study also revealed the negative influence of distrust in vendor behavior on online relationship quality. We empirically demonstrated its detrimental role on online relationship quality. 5.2. Managerial implications Our study poses certain implications for the management. It suggests that online vendors should adopt a relationship-oriented
197
view when conducting business online and evaluate ongoing attempts to do so by using the concept of online relationship quality in the pursuit of customer loyalty. Website usability was found to positively influence both relationship quality and repurchase intention, showing that online vendors should be more considerate of the role of the website and work to improve user experience by providing rich product information, improving website navigation functions, and making online purchasing easier. Expertise in order fulfillment was found to be an important antecedent of relationship quality, showing that online vendors should demonstrate their expertise in order to increase customer loyalty and retention. For example, they could provide professional testimonies and publish on-time delivery statistics. Mangers of e-commerce website can also improve online relationship quality by establishing and sustaining a good reputation thus promoting customer loyalty and retention. 5.3. Limitations Our research had certain limitations. First, our data were collected only in Northern Ireland, UK. Caution must therefore be exercised when attempting to generalize our results to other locations. Also, since the respondents self-selected the online vendor from whom they had purchased material in order to answer the questionnaire, social desirability bias may be present – although it may be observed that the data had enough variability to make model testing possible.
6. Conclusion Our study developed and tested a model explaining B2C ecommerce customer repurchase intention from a relationship quality perspective. By including vendor characteristics (perceived website usability, perceived expertise in order fulfillment and perceived reputation) and vendor behavior (distrust in vendor behavior), our model explained the importance of online relationship quality to online re-purchase intention. To a certain degree, our study demonstrated the value of using relationship marketing theory to account for online customer repurchasing behavior. Its results should provide useful implications for ecommerce practitioners.
Appendix A. Measurement items Construct
Items
Source
Online repurchase intention
Likelihood/probability that you will purchase online from the same vendor. . . In the medium term In the long term I will never purchase from the same vendor again
[20]
Satisfaction
Overall extremely dissatisfied/overall extremely satisfied Overall extremely displeased/overall extremely pleased My expectations were not met at all/my expectations were exceeded
[20]
Trust
I believe that this vendor is consistent in quality and service I believe that this vendor is keen to fulfill my needs and wants I believe that this vendor is honest I believe that this vendor wants to be known as one that keeps promises and commitments I believe that this vendor has my best interests in mind I believe that this vendor is trustworthy I believe that this vendor has high integrity I believe that this vendor is dependable
[20]
Y. Zhang et al. / Information & Management 48 (2011) 192–200
198 Appendix A (Continued ) Construct
Items
Source
Perceived expertise in order fulfillment
I believe that this vendor has knowledge and expertise in distribution (i.e. how to deliver products/services) I believe that this vendor has efficiently integrated all necessary departments/systems that are needed to deliver products or services I believe that this vendor has an efficient system for processing orders received
[20]
Perceived reputation
Poor/excellent public image Not/extremely committed to customer satisfaction Not innovative at all/extremely innovative Products and/or services are extremely poor/excellent Has an extremely poor/excellent reputation. Extremely unreliable/reliable
[20]
Distrust in vendor behavior
I believe that this vendor could sometimes fail to deliver product/ service as and when promised I believe this vendor is sometimes unable to deliver what they promise to I believe that this vendor is sometimes unable to meet expectations I believe that this vendor sometimes promises more than they can deliver
Developed based on [8,23]
Perceived website usability
Extremely difficult/easy to use Extremely unprofessional/professional Extremely poorly organised/well organised Extremely poor/excellent breadth of product/service selection Extremely poor/excellent description of product/service selection Extremely difficult/easy to navigate Extremely difficult/easy to find information that I want Extremely difficult/easy to conduct online shopping
[1,4,25]
Customer expertise in using internet to conduct tractions
I I I I
know a lot about conducting purchases via the Internet am experienced in conducting purchases via the Internet am informed about conducting purchases via the Internet am an expert buyer of products/services via the Internet
[18]
Appendix B. Item loadings and cross loadings #
Construct
Item
1
2
3
4
5
6
7
8
1
Perceived expertise in order fulfillment
PEOF1 PEOF2 PEOF3
0.86 0.86 0.85
0.17 0.24 0.20
0.33 0.36 0.46
0.34 0.31 0.38
0.52 0.50 0.66
0.44 0.38 0.39
0.39 0.38 0.54
0.13 0.19 0.19
2
Distrust in vendor behavior
DIST1 DIST2 DIST3 DIST4
0.16 0.20 0.23 0.20
0.78 0.87 0.86 0.81
0.15 0.25 0.17 0.29
0.22 0.30 0.33 0.35
0.32 0.37 0.38 0.46
0.27 0.35 0.38 0.33
0.26 0.25 0.24 0.31
0.04 0.16 0.13 0.11
3
repurchase intention
ORI1 ORI2 ORI3
0.31 0.43 0.34
0.19 0.24 0.21
0.78 0.89 0.71
0.33 0.40 0.20
0.34 0.48 0.34
0.13 0.33 0.22
0.38 0.47 0.31
0.14 0.20 0.11
4
Satisfaction
SAT1 SAT2 SAT3
0.41 0.32 0.35
0.31 0.33 0.34
0.38 0.35 0.31
0.89 0.91 0.86
0.44 0.38 0.38
0.47 0.48 0.35
0.40 0.36 0.37
0.25 0.25 0.27
5
Trust
TRU1 TRU2 TRU3 TRU4 TRU5 TRU6 TRU7 TRU8
0.60 0.59 0.57 0.54 0.35 0.56 0.57 0.55
0.46 0.33 0.40 0.29 0.35 0.38 0.40 0.47
0.46 0.46 0.37 0.44 0.24 0.39 0.38 0.46
0.47 0.33 0.32 0.39 0.32 0.33 0.38 0.46
0.85 0.83 0.83 0.78 0.72 0.84 0.87 0.88
0.59 0.45 0.43 0.42 0.31 0.39 0.46 0.53
0.50 0.45 0.47 0.50 0.33 0.38 0.47 0.47
0.18 0.12 0.19 0.14 0.17 0.11 0.14 0.18
6
Perceived reputation
PR1 PR2 PR3 PR4 PR5 PR6
0.27 0.38 0.34 0.43 0.35 0.41
0.25 0.36 0.19 0.39 0.29 0.35
0.22 0.23 0.17 0.29 0.15 0.29
0.36 0.39 0.25 0.43 0.39 0.45
0.39 0.48 0.33 0.46 0.41 0.47
0.70 0.82 0.72 0.84 0.83 0.75
0.29 0.41 0.31 0.39 0.28 0.32
0.30 0.15 0.21 0.22 0.16 0.15
Y. Zhang et al. / Information & Management 48 (2011) 192–200
199
Appendix B (Continued ) #
Construct
Item
1
2
3
4
5
6
7
8
7
Perceived website usability
PWU1 PWU2 PWU3 PWU4 PWU5 PWU6 PWU7 PWU8
0.46 0.43 0.43 0.41 0.34 0.47 0.34 0.43
0.20 0.24 0.29 0.22 0.30 0.27 0.27 0.27
0.46 0.36 0.41 0.34 0.31 0.43 0.42 0.43
0.37 0.37 0.41 0.32 0.33 0.33 0.30 0.31
0.41 0.50 0.48 0.44 0.41 0.46 0.37 0.44
0.25 0.46 0.46 0.40 0.36 0.29 0.28 0.28
0.79 0.81 0.85 0.78 0.75 0.87 0.82 0.78
0.18 0.18 0.21 0.24 0.29 0.27 0.20 0.23
8
Customer expertise in using internet
CEUI1 CEUI2 CEUI3 CEUI4
0.16 0.19 0.21 0.14
0.10 0.12 0.11 0.16
0.19 0.20 0.14 0.13
0.27 0.26 0.30 0.18
0.14 0.17 0.19 0.16
0.22 0.23 0.29 0.14
0.25 0.26 0.28 0.17
0.91 0.93 0.87 0.79
Appendix C. Common method bias analysis
Construct
Item
Substantive factor loading (R1)
R12
Perceived expertise in order fulfillment
PEOF1 PEOF2 PEOF3
0.686 0.947 0.930
0.471 0.897 0.865
0.181 0.098 0.077
0.033 0.010 0.006
Relationship quality
RQ1 RQ2
0.982 0.736
0.964 0.542
0.182 0.164
0.033 0.027
Online repurchase intention
ORI1 ORI2 ORI3
0.824 0.816 0.769
0.679 0.666 0.591
0.064 0.096 0.048
0.004 0.009 0.002
Perceived website usability
PWU1 PWU2 PWU3 PWU4 PWU5 PWU6 PWU7 PWU8
0.825 0.685 0.717 0.750 0.739 0.963 0.981 0.786
0.681 0.469 0.514 0.563 0.546 0.927 0.962 0.618
0.049 0.141 0.144 0.041 0.021 0.107 0.177 0.01
0.002 0.020 0.021 0.002 0.000 0.011 0.031 0.000
Perceived reputation
PR1 PR2 PR3 PR4 PR5 PR6
0.739 0.769 0.801 0.777 0.952 0.640
0.546 0.591 0.642 0.604 0.906 0.410
0.042 0.074 0.084 0.086 0.155 0.117
0.002 0.005 0.007 0.007 0.024 0.014
Distrust in vendor behavior
DIST1 DIST2 DIST3 DIST4
0.835 0.896 0.876 0.718
0.697 0.803 0.767 0.516
0.051 0.023 0.015 0.095
0.003 0.001 0.000 0.009
0.813
0.671
0.001
0.011
Average
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Yulin Fang is an Assistant Professor in the Department of Information Systems, City University of Hong Kong. He earned his Ph.D. at Richard Ivey School of Business, University of Western Ontario. His current research is focused on knowledge management, virtual teams, and open source software projects. He has published papers in major journals such as the Strategic Management Journal, Journal of Management Information Systems, Journal of Management Studies, Organizational Research Methods, Journal of the Association for Information Systems, European Journal of Information Systems, Journal of the American Society for Information Science and Technology, Information & Management, among others. He has won the 2009 Senior Scholars Best IS Publication Award, and was the Samsung Best Paper Award Finalist and the Carolyn Dexter Award Finalist at the 2008 Academy of Management Conference.
Kwok-Kee Wei is Chair Professor in the Department of Information Systems at the City University of Hong Kong. He obtained his Ph.D. from the University of York and B.S. from Nanyang University. His research focuses on human–computer interaction, innovation adoption and management, electronic commerce, and knowledge management. He has published widely in the information systems field with articles appearing in many international journals.
Dr. Elaine Ramsey is a Senior Lecturer in the department of Business, Retail and Financial Services, University of Ulster (UU). She received her Ph.D. in SME E-Marketing from UU in 2005. Her main research interests relate to the adoption and diffusion of IT among SMEs, and the inherent issues relative to ecommerce deployment. She has published in the European Journal of Information Systems, Journal of Business Research, Journal of Marketing Management, International Journal of Innovation Management, Service Industries Journal, and others.
Patrick McCole is a Lecturer in Management at Queen’s University Management School, Queen’s University Belfast. He received his BA (Hons) and PhD degrees from the University of Ulster. His main research interests include the role of trust in e-commerce, new media and customer-connectivity, as well as empirical evidence relating to the Service-Dominant Logic.
Huaping Chen is a Professor in School of Computer Science and Technology at University of Science and Technology of China. His research interests include information strategies, business intelligence and application.