Online Recommendation Systems: Factors Influencing Use in E-Commerce

Abstract

The increasing use of artificial intelligence (AI) to understand purchasing behavior has led to the development of recommendation systems in e-commerce platforms used as an influential element in the purchase decision process. This paper intends to ascertain what factors affect consumers’ adoption and use of online purchases recommendation systems. In order to achieve this objective, the Unified Theory of Adoption and Use of Technology (UTAUT 2) is extended with two variables that act as an inhibiting or positive influence on intention to use: technology fear and trust. The structural model was assessed using partial least squares (PLS) with an adequate global adjustment on a sample of 448 users of online recommendation systems. Among the results, it’s highlighted the importance of the inhibiting role of technology fear and the importance that users attach to the level of perceived trust in the recommendation system are highlighted. The performance expectancy and hedonic motivations have the greatest influence on intention to use these systems. Based on the results, this work provides a relevant recommendation to companies for the design of their e-commerce platforms and the implementation of online purchase recommendation systems.

Assessing Antecedents of Behavioral Intention to Use Mobile Technologies in E-Commerce

“…Marinkovic and Kalinic [31], Singh et al [36], Do et al [75], and Humbani and Wiese [76] reported positive correlations between perceived utility and consumer satisfaction in the process of adoption of e-commerce and mobile payments. Although several studies [64,70,72] have reported a strong correlation between perceived utility and m-commerce use, they did not focus on customer satisfaction.…”

“…In empirical research, most TAMbased studies use the following antecedent variables for the two variables (PEU and PU): confidence, innovation, mobility, enjoyment, and social influence [30][31][32][66][67][68][69][70][71]. Some studies used customer satisfaction as a variable in TAM to determine the influence on consumer adoption of m-commerce [29][30][31][35][36][37][38][39][40][41][42][43][44][45][72][73][74][75][76][77][78]; exploring consumer behavioral intent and customer satisfaction. Our study used a simplified TAM model, in which consumer satisfaction influences behavioral intention.…”

“…Depending on the domain in which the TAM model is applied, these two main variables are influenced by domain-specific external variables. Our research used the external variables (antecedents) of trust, convenience, customization, innovativeness, rapidity, accessibility, mobility, and enjoyment; variables identified and used in multiple other studies [10,11,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][66][67][68][69][70][71][72][73][74][75][76][77][78]. Our theoretical model defines PEU and PU as latent variables influenced by external variables.…”

“…Based on a series of studies that used customer satisfaction as a variable in the TAM [29][30][31][35][36][37][38][39][40][41][42][43][44][45][71][72][73][74][75][76][77], we included in our research an exogenous variable, consumer satisfaction, measured in parallel with the extent of use of m-commerce. Stated satisfaction and extent of use are exogenous observable variables that characterize endogenous latent variables: consumer satisfaction and actual usage.…”

“…Conceptual structural model. Source: own construction based on[10,11,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][66][67][68][69][70][71][72][73][74][75][76][77][78].…”