Research On The Influence Of Product Set Granularity On Consumer Adoption Intention Of Online Shopping Platform Recommendation System

Online shopping is in the ascendant,and it is of great significance to promote consumption and expand domestic demand,but recommendation platforms face the fierce competition because of the limited consumer increment,low category penetration rate and emerging scenarios,and it is urgent to optimize recommendation services and user experience.At the same time,product set granularity is an important factor for recommendation platforms to optimize consumer experience and there is currently a lack of relevant research.Based on the cue utilization theory,this study explores the influence of product set granularity on consumers’ adoption intention,proposes trust as the mediating variable of the influence mechanism,and explores the moderating effect of algorithm transparency and brand familiarity.In this paper,the above mechanism is verified by three experiments.Experiment 1 manipulates the degree of product set granularity through the shopping recommendation scenario,measures consumer trust and adoption intention,and verifies the influence of product set granularity on adoption intention and the mediating role of trust.Experiment 2 verifies the moderating effect of algorithm transparency through the experimental scenarios of 2(fine product set gra nularity vs rough product set granularity)× 2(high algorithm transparency vs low algorithm transparency).Experiment 3 verifies the moderating effect of brand familiarity through the experimental scenarios of 2(fine product set granularity vs.rough product set granularity)× 2(high brand familiarity vs.low brand familiarity).This paper finds that coarse product set granularity(compared to fine product set granularity)reduces consumer adoption intention.The influence mechanism uses trust as the mediating variable,algorithmic transparency and brand familiarity as the moderating variables.Finally,this paper gives relevant guidance and suggestions for enterprises and government departments,discusses the limitations and looks forward to future research directions.