More Personalized, More Useful? Reinvestigating Recommendation Mechanisms in E-Commerce

ABSTRACT

To what extent should firms invest in personalized recommendation mechanisms, and are all personalized recommendations equally welcomed by online consumers? To answer these questions through the lens of resource matching theory, we investigate users’ perceptions of three types of personalized recommendations: one-to-all (nonpersonalized), one-to-many (partially personalized), and one-to-one (most personalized). Using both experimental and configurational analysis approaches, our study posits that online consumers differently experience each type of personalized recommendation and their resource matching sources (familiarity, complexity, external information) in various shopping contexts. Our study abductively formulates several theoretical propositions regarding the usefulness of each personalized recommendation. We show empirical evidence that the most personalized recommendation is not always perceived to be as useful as conventionally believed. In particular, highly personalized recommendation is found to be useful for recommending simple technology products for experienced customers. Ironically, a partially personalized recommendation, one-to-many, is perceived as the most useful mechanism for recommending complicated technology products. Based on our findings, we suggest that e-commerce vendors consider the three resource matching dimensions to avoid collecting more than enough customer data, thus enabling adequately personalized recommendation results on their online digital platforms.

KEYWORDS AND PHRASES:

• Online recommendation
• online personalization
• qualitative comparative analysis
• multivalue QCA
• resource matching theory

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed on the publisher’s website

Notes

1. Yahoo!Buy is featured as a local e-commerce website in Taiwan. The original language used on Yahoo!Buy was Chinese, and we took these snapshots using a translating machine at the experiment time for demonstration purposes.

Additional information

Notes on contributors

Tuan (Kellan) Nguyen

Dr. Tuan (Kellan) Nguyen ([email protected]) is a postdoctoral researcher at the Innovation, Entrepreneurship & Information Systems Department, IÉSEG School of Management, France. He received his Ph.D in management from the Institute of Service Science, College of Technology Management, National Tsing Hua University, Taiwan. His Ph.D. dissertation was awarded as the MOST Doctoral Dissertation Fellowships of the Humanities and Social Sciences by Ministry of Science and Technology, Taiwan. Dr. Ngyuen’s research focuses on IT business value, digital innovation, technology management, and qualitative comparative analysis. He has published in International Journal of Electronic Commerce, Electronic Commerce Research and Applications, and the Proceedings of International Conference on Information Systems.

Pei-Fang Hsu

Pei-Fang Hsu ([email protected]; corresponding author) is an professor and Acting Director at the Institute of Service Science, College of Technology Management, National Tsing Hua University. She received her Ph.D. in Information Systems from the Paul Merage School of Business at the University of California, Irvine. Her research focuses on the adoption and value of information systems, IT user behavior, and IT-enabled service innovations. She has published in Journal of Management Information Systems, International Journal of Electronic Commerce, European Journal of Information Systems, Decision Support Systems, Decision Sciences, Information and Management, and other journals.