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Reputation vs. price: Sequential recommendations based on cue diagnosticity theory

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  • Guo, Wenhao
  • Tian, Jin
  • Li, Minqiang

Abstract

Sequential recommendations have been widely used in e-commerce platforms to effectively capture consumers' dynamic preferences and provide them with preferred products. Traditional models usually use ratings and product attributes for sequential recommendations to satisfy consumers’ more personalized needs. Consumers also rely on reviews from other consumers to form a general impression of the product or retailer before making their purchase decisions. Such impressions can be treated as reputations of the product or retailer. Inspired by cue diagnosticity theory, we divide the attributes related to product purchase into low- and high-scope cues. High-scope cues, including reputations, are not easily changed because they are formed over a long period by numerous consumers, whereas low-scope cues, such as price, can be easily changed by retailers. We propose an innovative Sequential Recommendation model by Integrating Low-scope cues and High-scope cues (SRILH). We design a cue-extraction layer to extract high-scope cues from consumer online reviews and a hierarchical cue-aware attention layer to learn the joint effect of low- and high-scope cues. We evaluate the performance of the proposed model using three real-world datasets, and our experimental results validate its effectiveness and robustness. Our research contributes to sequential recommendations research by uncovering the joint effects of cues on consumer behavior and by providing valuable insights into the dynamics of cue preference formation in recommendation systems. We also extend the empirical literature on cue diagnosticity theory by drawing conclusions from the micro and individual perspectives to shed light on how different cues impact consumer choices. The interpretable visualization results provide managerial insights for retailers and manufacturers to improve their products.

Suggested Citation

  • Guo, Wenhao & Tian, Jin & Li, Minqiang, 2025. "Reputation vs. price: Sequential recommendations based on cue diagnosticity theory," Journal of Retailing and Consumer Services, Elsevier, vol. 83(C).
  • Handle: RePEc:eee:joreco:v:83:y:2025:i:c:s0969698924004533
    DOI: 10.1016/j.jretconser.2024.104157
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    References listed on IDEAS

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    1. Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
    2. Cowan, Kirsten & Guzman, Francisco, 2020. "How CSR reputation, sustainability signals, and country-of-origin sustainability reputation contribute to corporate brand performance: An exploratory study," Journal of Business Research, Elsevier, vol. 117(C), pages 683-693.
    3. (Kay) Byun, Kyung-ah & Ma, Minghui & Kim, Kevin & Kang, Taeghyun, 2021. "Buying a New Product with Inconsistent Product Reviews from Multiple Sources: The Role of Information Diagnosticity and Advertising," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 81-103.
    4. Jha, Subhash & Balaji, M.S. & Peck, Joann & Oakley, Jared & Deitz, George D., 2020. "The Effects of Environmental Haptic Cues on Consumer Perceptions of Retailer Warmth and Competence," Journal of Retailing, Elsevier, vol. 96(4), pages 590-605.
    5. Qiuzhen Wang & Liang Meng & Manlu Liu & Qi Wang & Qingguo Ma, 2016. "How do social-based cues influence consumers’ online purchase decisions? An event-related potential study," Electronic Commerce Research, Springer, vol. 16(1), pages 1-26, March.
    6. Nasim Mousavi & Panagiotis Adamopoulos & Jesse Bockstedt, 2023. "The Decoy Effect and Recommendation Systems," Information Systems Research, INFORMS, vol. 34(4), pages 1533-1553, December.
    7. Kim, Jungkeun & Kim, Jeong Hyun & Kim, Changju & Park, Jooyoung, 2023. "Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    8. Park, Jinhee & Ahn, Hyeongjin & Kim, Dongjae & Park, Eunil, 2024. "GNN-IR: Examining graph neural networks for influencer recommendations in social media marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    9. Qin, Xuelian & Liu, Zhixue & Tian, Lin, 2020. "The strategic analysis of logistics service sharing in an e-commerce platform," Omega, Elsevier, vol. 92(C).
    10. Yolande Piris & Anne-Cécile Gay, 2021. "Customer satisfaction and natural language processing," Post-Print hal-03110702, HAL.
    11. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    12. Fernando, Angeline Gautami & Aw, Eugene Cheng-Xi, 2023. "What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    13. Gallin, Steffie & Portes, Audrey, 2024. "Online shopping: How can algorithm performance expectancy enhance impulse buying?," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    14. Wang, Qiuzhen & Cui, Xiling & Huang, Liqiang & Dai, Yiling, 2016. "Seller reputation or product presentation? An empirical investigation from cue utilization perspective," International Journal of Information Management, Elsevier, vol. 36(3), pages 271-283.
    15. Du, Yingying & Wang, Xingyuan, 2024. "Dynamic or static? The effect of food imagery on menus on perceived food energy and purchase intention," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    16. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.
    17. Shashank, Salabh & Behera, Rajat Kumar, 2024. "Factors influencing recommendations for women's clothing satisfaction: A latent dirichlet allocation approach using online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    18. Chang, Woondeog & Park, Jungkun, 2024. "A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    19. Piris, Yolande & Gay, Anne-Cécile, 2021. "Customer satisfaction and natural language processing," Journal of Business Research, Elsevier, vol. 124(C), pages 264-271.
    20. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    21. Liu, Yang & Shi, Jiale & Huang, Fei & Hou, Jingrui & Zhang, Chengzhi, 2024. "Unveiling consumer preferences in automotive reviews through aspect-based opinion generation," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    22. Wang, Xiaoli & Zhang, Chenxi & Xu, Zeshui, 2024. "A product recommendation model based on online reviews: Improving PageRank algorithm considering attribute weights," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    23. Narwal, Preeti & Nayak, J.K., 2020. "How consumers form product quality perceptions in absence of fixed posted prices: Interaction of product cues with seller reputation and third-party reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    24. Uhm, Jun-Phil & Kim, Sanghoon & Do, Chanwook & Lee, Hyun-Woo, 2022. "How augmented reality (AR) experience affects purchase intention in sport E-commerce: Roles of perceived diagnosticity, psychological distance, and perceived risks," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    25. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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