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

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.

Enhancing students’ English language learning via M-learning: Integrating technology acceptance model and S-O-R model

“…However, this study differs from the study by Yoon and Kim [ 23 ], who suggested that perceived ease of use positively affects intention to use, and this relationship was not discussed further in this study. This study used TAM to examine students' cognitive and behavioral use of English M-learning and verified that the relationships between the main variables in TAM were also strongly positively significant in the context of education and learning research [ 22 , 26 , 112 ]. Particularly, the effect of perceived usefulness on attitude toward using was relatively larger than the effect of perceived ease of use.…”

“…Scholars also point out that some past studies have applied the SOR model to examine individuals' use of information technology to understand users' perceptions and behaviors in using communication technology [ 111 ]. These arguments also suggested that the combination of SOR model and TAM is beneficial in explaining students' attitudes and behaviors when using mobile for M-learning [ 112 ]. When users perceive that the use of technology will make their consumption or learning behaviors more effective, they will be more likely to demonstrate less uncertain and less anxiety about using the new technology and will be more engaged in using it [ 22 , 109 ].…”