An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter?

Motivated by the lack of knowledge of what factors deterring Internet users from adopting the Internet shopping in developing cultures, this investigation is therefore intended to underline the possible factors responsible for their deterrence. To achieve this objective, a model was proposed by involving factors established in literature as key potential drivers for predicating individuals' behavioral intention to adopt new technologies. The proposed factors were drawn from popular IS/IT adoption theories, namely perceived ease-of-use, perceived usefulness, perceived compatibility, social influence, trust, perceived risk, privacy, security, Internet shopping anxiety, Internet self-efficacy and price. Gender moderating influence was investigated in the conceptualized relationships between proposed factors and behavioral intention. This study was implemented by collecting data through a self-administered questionnaire from a broad diversity of Jordanian Internet users. Analyzing the data which consists of 261 valid datasets was accomplished by using WarpPLS 4.0. The results provide significant statistical evidence in support of all the factors hypothesized to influence behavioral intention to adopt with the exception of three factors: perceived risk, privacy and security. Furthermore, the gender was found to moderate the relationships between five of the proposed factors (perceived ease-of-use, social influence, trust, perceived risk, privacy) and behavioral intention. This study has also revealed some differences between online shoppers (based on prior literature and results) and non-shoppers. Interestingly, the proposed model explains 58% of the total variance in intention to adopt Internet shopping. Findings and limitations are discussed, theoretical contributions and practical implications are outlined, and future research directions are suggested. (C) 2016 Elsevier Ltd. All rights reserved.