An Empirical Investigation of Personalized Recommendation and Reward Effect on Customer Behavior: A Stimulus–Organism–Response (SOR) Model Perspective

Jaeho Jeong, Dongeon Kim, Xinzhe Li, Qinglong Li, Ilyoung Choi () and Jaekyeong Kim
Additional contact information
Jaeho Jeong: Department of Business Administration, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Dongeon Kim: Department of Big Data Analytics, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Xinzhe Li: Department of Big Data Analytics, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Qinglong Li: Department of Big Data Analytics, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Ilyoung Choi: Department of Big Data Analytics, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
Jaekyeong Kim: Department of Big Data Analytics, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea

Sustainability, 2022, vol. 14, issue 22, 1-19

Abstract: With the continuous growth in the Home Meal Replacement (HMR) market, the significance of recommender systems has been raised for effectively recommending customized HMR products to each customer. The extant literature has mainly focused on enhancing the performance of recommender systems based on offline evaluations of customers’ past purchase records. However, since the existing offline evaluation methods evaluate the consistency of products on the recommendation list with ones purchased by customers from the test dataset, they are incapable of encompassing components such as serendipity and novelty that are also crucial in recommendation. Moreover, the existing offline evaluation methods cannot measure rewards such as discount coupons that may play a vital role in strengthening customers’ desire for purchase and thereby stimulating their purchase with a provision of a recommendation list. In this study, we used an SOR model to verify the effect of personalized recommendation stimulus on a customer’s response in an actual online environment. The results indicate that the customers’ response rate was higher with a provision of personalized recommendations than that of bestseller recommendations, and higher when being offered with cash discounts than earning redeemable points. Meanwhile, the response rate to the recommendation with higher volumes of rewards was not as high as expected, while the point pressure mechanism did not work either.

Keywords: personalized recommendation service; reward effect; SOR model; customer behavior; e-commerce platform (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/22/15369/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/22/15369/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:22:p:15369-:d:977208

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().