ABSTRACT
With the popularization of online shopping platforms across China and in order to maximize profits, online shopping platforms developed various recommender systems (RS), which played an important role in consumers’ decision-making process. Therefore, the impact of RS on consumers’ decision-making process is worthy of studying. In this paper, the RS of online shopping platforms and two examples of them were introduced. This paper firstly analyzed the ethical challenges related to the RS, including algorithmic opacity, privacy concerns, and bias and behavior manipulation and provided solutions accordingly. Besides, the paper through a method of online questionnaire and data analysis, illustrated RS’ ability to influence consumers’ decision making by providing them with personalized recommendations during online shopping. In addition, with the help of an online questionnaire filled out by more than 128 Chinese consumers across different ages and occupations, the author also analyzed people's attitudes towards the RS. According to the research results, this overall finding implies that recommender systems in contemporary electronic commerce are not merely decision aids for lowering search costs, but may also play a substantial part in economic decision making. A randomised trial strategy was used in this work to support the idea that internet recommendations may influence willingness-to-pay judgements, even when the two are tested on distinct scales.
- Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (Eds.). 2010. Recommender Systems Handbook (1st ed.). Springer Nature. 10.1007/978-0-387-85820-3_1Google Scholar
- Alibaba Group Holding Limited. 2019. Taobao Marketplace Overview. www.alizila.com.Google Scholar
- Zhu, H., Li, X., Zhang, P., Li, G., He, J., & Gai, K. 2018. Learning Tree-based Deep Model for Recommender Systems. KDD 2018. 10.1145/3219819.3219826Google Scholar
- Alibaba Group Holding Limited. 2018. It's a Match! Optimizing Item Recommendations in Ecommerce. Alibaba Tech. https://alibabatech.medium.com/its-a-match-optimizing-item-recommendations-in-ecommerce-27c6ca5e193fGoogle Scholar
- Shan, J., & Wade, M. 2020. From Social Shopping to Entertainment-Centric E-Commerce, What Western Brands Can Learn from China's Retail Giants. TFL. https://www.thefashionlaw.com/china-is-revolutionizing-e-commerce-thanks-to-social-shopping-and-an-emphasis-on-entertainment/Google Scholar
- Brennan, M. 2020. Defining interactive e-commerce. China Channel. https://chinachannel.co/defining-interactive-e-commerce/Google Scholar
- Hariharan, A., & Dardenne, N. (n.d.). Pinduoduo and the Rise of Social E-Commerce. Y Combinator. https://www.ycombinator.com/library/2z-pinduoduo-and-the-rise-of-social-e-commerceGoogle Scholar
- Paudyal, P., & Wong, B.L. W. 2018. Algorithmic Opacity: Making Algorithmic Processes Transparent through Abstraction Hierarchy. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62(1), 192-196. 10.1177/1541931218621046)Google ScholarCross Ref
- Sarter, N. B., & Woods, D. D. 1997. Automation surprises. In Handbook of Human Factors and Ergonomics (2nd ed., pp. 1926-1943). John Wiley and Sons.Google Scholar
- Leskovec, J., Rajaraman, A., & Ullman, J. 2014. Mining of Massive Datasets (3rd ed.). Cambridge University Press.Google Scholar
- Wang, C., Zheng, Y., Jiang, J., & Ren, K. 2018. Toward Privacy-Preserving Personalized Recommendation Services. Engineering, 4, 21-28.Google ScholarCross Ref
- Milano, S., Taddeo, M., & Floridi, L. 2020. Recommender systems and their ethical challenges. AI & SOCIETY volume, 35, 957-967. https://doi.org/10.1007/s00146-020-00950-yGoogle ScholarDigital Library
- [13]Abdollahpouri, H., Burke, R., Mansoury, M., & Mobasher, B. 2019. The Unfairness of Popularity Bias in Recommendation. RMSE workshop at ACM Recsys At: Copenhagen, Denmark.Google Scholar
- Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv: Machine Learning.Google Scholar
- Jeckmans, A., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R., & Tang, Q. 2013. Privacy in Recommender Systems. Social Media Retrieval.Google Scholar
- Sweeney, L. 2002. k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557-570. https://doi.org/10.1142/S0218488502001648.Google ScholarDigital Library
- Hall, Robert, Larry Wasserman, and Alessandro Rinaldo. 2013. “Random Differential Privacy”. Journal of Privacy and Confidentiality 4 (2). https://doi.org/10.29012/jpc.v4i2.621.Google ScholarCross Ref
- Abdollahpouri, H., Burke, R., & Mobasher, B. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. FLAIRS Conference.Google Scholar
- Becker, G. M., Degroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9(3), 226–232. https://doi.org/10.1002/bs.3830090304Google ScholarCross Ref
- Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2013). Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2285042Google Scholar
Index Terms
- Analysis on the Impact of Recommender Systems on Consumer Decision: Making on China's Online Shopping Platforms
Recommendations
Empirical Analysis of the Impact of Recommender Systems on Sales
Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer ...
Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids
Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media such as the World Wide Web for prepurchase information search and online shopping, very little is known about how consumers make ...
Consumer's decision to shop online: The moderating role of positive informational social influence
While much of the current literature tends to focus on the direct effect of social influence on consumer online shopping behavior, our study drew heavily on social influence theory and argued for an alternative theory focusing on the moderating role of ...
Comments