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Enhancing the Decision Quality through Learning from the Social Commerce Components

Enhancing the Decision Quality through Learning from the Social Commerce Components

Aihui Chen, Yaobin Lu, Sumeet Gupta
Copyright: © 2017 |Volume: 25 |Issue: 1 |Pages: 26
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781522510772|DOI: 10.4018/JGIM.2017010104
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MLA

Chen, Aihui, et al. "Enhancing the Decision Quality through Learning from the Social Commerce Components." JGIM vol.25, no.1 2017: pp.66-91. http://doi.org/10.4018/JGIM.2017010104

APA

Chen, A., Lu, Y., & Gupta, S. (2017). Enhancing the Decision Quality through Learning from the Social Commerce Components. Journal of Global Information Management (JGIM), 25(1), 66-91. http://doi.org/10.4018/JGIM.2017010104

Chicago

Chen, Aihui, Yaobin Lu, and Sumeet Gupta. "Enhancing the Decision Quality through Learning from the Social Commerce Components," Journal of Global Information Management (JGIM) 25, no.1: 66-91. http://doi.org/10.4018/JGIM.2017010104

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Abstract

The adoption of social networks introduced a new set of components to the e-commerce environment, which are called social commerce components (SCCs) (e.g., forums and communities, rating and reviews and social recommendations). Although various SCCs have transformed customer behaviors and decision patterns, few studies have investigated their roles together in enhancing customers' decision quality. In this study, based on the social learning theory, the authors develop a research model to explore how customers learn from the SCCs to influence their uncertainty in shopping experience, and thus improve their decision quality. The results from 243 actual customers of social commerce site in China suggest that demand uncertainty is one of the most important factors that reduces decision quality, whereas product quality uncertainty has a significant positive influence on decision quality, and seller quality uncertainty has no influence on decision quality. Also, learning from forums and communities, learning from rating and reviews and learning from social recommendations play different roles on forming customers' uncertainty. In addition, product type can moderate most of the above associations. In summary, these findings increase one's understanding of the customers' decision pattern in social commerce context and extend the scope of social learning theory and uncertainty theory. The findings also provide insights for social commerce practitioners in developing strategies for improved implementation of social commerce as well as the design of social commerce sites.

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