Review of Theoretical Models and Limitations of Social Commerce Adoption Literature

Part 1: E-BusinessInternational audienceSocial commerce is emerging as an important platform in e-commerce. It brings people to the comfort zone to buying and selling product that they cannot reach physically. The purpose of this research is to review the empirical research on social commerce published between 2012 to 2019. The paper mainly reviews the theories and models used in this area and limitations acknowledged by studies in social commerce area. The findings indicated that TAM, social support theory and S-O-R model are some of the most frequently used models. Also, use of biased sample, limited factors and cross-sectional studies are some of the most common limitations used across majority of studies

A Meta-analysis of Social Commerce Adoption Research

Social commerce is a subset of e-commerce that utilises social media to facilitate interaction between sellers and consumers. Over the last number of years, the subject of social commerce has attracted significant attention from many researchers as they attempt to understand the factors affecting its adoption by consumers. A review of results from existing studies suggests inconsistent results for many relationships. Hence, this research has conducted a meta-analysis of 65 studies and synthesized the findings from existing studies in order to estimate the cumulative correlation coefficient (β) and significance (p). The investigation found that behavioural intention, trust, perceived usefulness, and social support are frequently examined dependent variables, that are strongly influenced by a number of independent variables. The findings in this study suggests that perceived usefulness, hedonic value, social commerce constructs, subjective norms, informational and emotional support are important for encouraging social commerce adoption. This research highlights various antecedents that have been theoretically examined in different social commerce studies that explore the effect size through meta-analysis