Building E-Commerce Satisfaction and Boosting Sales: The Role of Social Commerce Trust and Its Antecedents

ABSTRACT

Consumers are relying increasingly on social commerce for making their purchase decisions, and e-vendors have great interests in applying social commerce features in the traditional e-commerce sites to increase sales. Although the importance of trust has been well recognized in the literature, the previous studies have mainly focused on trust in e-commerce sites and failed to incorporate its multidimensional nature to study consumer behavior. To gain further insights into consumer decision-making, this study aims to develop a social commerce trust-based consumer decision-making framework. Based on the social-technical theory, we conceptualize social commerce trust in a multidimensional view including trust in social media, trust in e-commerce sites, trust in social commerce features, and trust in consumers. Data were collected from an online survey taken by U.S. Amazon consumers. Our results strongly support our new conceptualization of social commerce trust and demonstrate its importance by examining its effects on e-commerce outcomes. Further, trust in consumers and trust in social commerce features have stronger effects than trust in e-commerce sites and trust in social media in the formation of social commerce trust. Our study contributes to the theory by introducing the new conceptualization of social commerce trust and advancing our understanding of how to enhance social commerce trust. Practitioners can gain insights into the implementation of social commerce for building consumer trust and increasing sales.

KEY WORDS AND PHRASES:

• Customer review quality
• e-commerce satisfaction
• online reviews
• online trust
• purchasing behavior
• social commerce trust
• social support

Acknowledgments

This research is supported by an internal research grant from School of Engineering and Information Technology, Murdoch University. We are grateful to the three anonymous reviewers for their constructive comments and suggestions that helped develop this paper.

Notes

¹ In other words, social commerce includes buying and selling on social media or facilitated by social media tools. For example, consumers may see product information posted by their friends on Facebook and decide to purchase the product later.

² Here social media platforms include general platforms such as Facebook and specific social shopping communities (e.g., [75]). These social shopping communities provide features such as recommendations, ratings, and consumer profiles. Consumers can make purchase by clicking links of participating e-commerce sites. Therefore, social shopping is a specific social media context of social commerce.

³ Our study uses social commerce features to refer to those features integrated into e-commerce sites to fuel consumers’ interaction with others, such as posting/responding to product reviews, rating reviews’ helpfulness, and sharing products and their reviews through social media. These features are selected because they enable consumers to interact with one another and exchange product information.

⁴ McKnight and Chervany [70] also proposed the third dimension of trust: intrapersonal, which refers to consumers’ propensity to trust. Because our study does not focus on individuals’ characteristics, intrapersonal dimension of trust is not included in our study.

⁵ Here specific types of social media integrated into e-commerce may vary from site to site and depend on the information to share. For example, at the time of our study, Amazon integrates Facebook, Twitter, and Pinterest to share product description and pictures. In the future, it would be possible that other types of social media such as YouTube would be integrated into e-commerce sites to support product video sharing.

⁶ We thank one reviewer for pointing these issues out to help better understand our conceptualization of social commerce trust. Please note that the trust formation process, among the various factors, is not a part of this study.

⁷ For example, the context of our study is Amazon.com. Therefore, “other consumers” refer to other consumers from Amazon.com. We thank one reviewer for pointing this issue out.

⁸ Therefore, we did not pretest our scales. On the other hand, during our data collection, when the number of complete records reached about 100, we paused the data collection and assessed the measures. Our analysis showed that measures have good convergent and discriminant validity. Then we proceeded until the whole data collection finished. Nevertheless, we admit that it is a limitation of our study.

⁹ We also conducted confirmatory factor analysis. The model had good fit indices, χ²(428) = 1899.51, comparative fit index = .94, Tucker–Lewis index = .93, root mean square error of approximation = .06, standardized root mean square residual = .05. Items also have acceptable loadings, composite reliabilities, and AVEs, and they showed good discriminant validity.

¹⁰ On the other hand, we believe that the competing model can also add value to the literature and form a platform for future research. Our competing model can be retested to assess how the effects of different types of trust vary in other contexts. For example, it is possible that the effect of trust in consumers becomes strengthened relative to that of trust in e-commerce sites in the context of customer-to-customer e-commerce. We thank one reviewer for pointing this issue out.

Additional information

Notes on contributors

Xiaolin Lin

Xiaolin Lin ([email protected]) is an assistant professor of computer information systems in the Department of Computer Information and Decision Management, Paul and Virginia Engler College of Business, West Texas A&M University. He received his Ph.D. in Information Systems from Washington State University. His research focuses on social commerce, healthcare IT, information security, and gender differences in IT behavioral research. His work has appeared in Journal of Business Ethics, Information & Management, Information Systems Frontiers, International Journal of Information Management, and Computers in Human Behavior, among others. He has also presented his work at leading conferences.

Xuequn Wang

Xuequn Wang ([email protected]; corresponding author) is a senior lecturer in Murdoch University, Australia. He received his Ph.D. in Information Systems from Washington State University. His research interests include social media, privacy, e-commerce, and human-computer interaction. His work has appeared in MIS Quarterly, Information Systems Journal, Information & Management, Communications of the ACM, ACM Transactions, and other venues.

Nick Hajli

Nick Hajli ([email protected]) is an associate professor of marketing in Swansea University. He holds a Ph.D. in Management from Birkbeck, University of London. His research has appeared in such journals as Journal of Business Ethics, Journal of Business Research, Industrial Marketing Management, Computers in Human Behavior, Information Technology & People, and Technological Forecasting and Social Change. He is the author of Handbook of Research in Integrating Social Media into Strategic Marketing.