AUTHOR=Liu Yu , Khan Muhammad Bashir , Ashraf Muhammad , Orangzab , Sharif Wareesa , Ahmad Jamil TITLE=Customer’s decision and affective assessment of online product recommendation: A recommendation-product congruity proposition JOURNAL=Frontiers in Psychology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.916520 DOI=10.3389/fpsyg.2022.916520 ISSN=1664-1078 ABSTRACT=

Online product recommendation (OPR) systems have gained prominence in the context of e-commerce over the past years. Despite the increased research on OPR use, less attention has been paid to examining how decision and affective assessment of the OPR are contingent upon the product type. This study proposes and examines a recommendation-product congruity proposition based on cognitive fit and schema congruity theories. The proposition states that when the content (i.e., a stimulus-based schema) of the OPR [either system-generated recommendation (SGR) or a consumer-generated recommendation (CGR)] matches the brain-stored schema initiated by a particular product (either a search product or an experienced product), then a consumer would use a schema-based information assessment strategy and experience favorable decision and affective assessment of the OPR. This then affects consumers’ intentions to purchase and reuse OPR. The proposition is tested via a 2 × 2 between-respondents factorial design of a cross-sectional survey with 482 Amazon customers. The results support the following two matching conditions of the proposition: (1) SGR describing a search product and (2) CGR explaining an experienced product, which might lead customers to perceive lower decision effort, greater decision quality, and higher enjoyment with the OPR that subsequently have a significant impact on their intentions to purchase and reuse OPR. This study expands our understanding of how recommendation-product congruence influences the consumer’s decision and affective assessment behavior and provides practical implications for the identification and presentation of the recommendation type and product type for a better customer decision.