Interpretive Structural Modeling of Uncertainty Reduction Factors in Social Commerce

As a new approach to online transactions, social commerce, like other online business models, suffers from uncertainty. The purpose of this research is to identify factors for reducing uncertainty in social commerce. However, considering the complex interactions of these factors; it is not enough to just identify them. In this research, an interpretive structural model of uncertainty-reducing factors was created. For this purpose, first, the factors extracted from the research literature were sifted by the scree test method, and 26 factors were confirmed. Then, during the implementation process of interpretative structural modeling, a model with 5 levels of importance was created. The final analysis was related to the classification of factors, based on which the identified factors were placed in two categories: independent and linkage, and no factor was included in the category of autonomous and dependent factors. The investigation showed that the independent factors are mostly located in the lower levels of the model, influence the reduction of uncertainty through other factors, while the linkage factors are mostly located in the upper levels of the model, and have a direct effect on the reduction of uncertainty. The leveling, determination of the type of impact, and determination of the complex relationships between uncertainty reduction factors, which were carried out in this research, have not been carried out in any similar research to date. The obtained 5-level model, the factors at each level and the complex relationships between them, will guide the development of successful business models in ecommerce and social commerce.