Methodology of an interpretive structural map construction for social commerce success

Abstract

The factors influencing consumer purchase decisions in electronic commerce platforms and the interrelationships of each element are prevalent in the domain literature. However, a comprehensive analysis of the complex interrelationships among the success factors remains unexplored, especially in a social commerce context. To address the gap, this work evaluates the relationship structure and determines the critical factors using interpretive structural modelling (ISM). On the other hand, the Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) is introduced to analyse the interaction of the factors and recognise the most relevant among them. In demonstrating the ISM-MICMAC analysis, this work performed a case study evaluating 13 factors of social commerce success for food products derived from a previous study. The findings of this work suggest that timeliness, data privacy policy, and Internet connectivity drive most other factors. Thus, focusing the resources on augmenting these factors consequently improves other factors. These findings suggest that sellers must streamline their overall service chain to maintain timeliness in their transactions, safeguard consumers’ data privacy, and uphold consumer communication efficiency to maximise Internet connectivity. These insights provide useful information to help decision-makers in the food industry allocate resources and encourage more consumers for social commerce. Several managerial insights were discussed.

Addressing Challenges: Adopting Blockchain Technology in the Pharmaceutical Industry for Enhanced Sustainability

“…MICMAC analysis aims to evaluate and classify variables based on influence and dependency, placing them into four groups: autonomous, dependent, linkage, and independent [72]. Its visual representations and flexibility make it an effective method for strategic decision making across diverse domains, optimizing resource allocation, and facilitating comparative analyses [73,74]. Characteristics associated with autonomous clusters exhibit weak influence and dependency.…”

Constructing an Interpretive Structural Model to Unravel the Interconnected Drivers of Teaching Quality in Higher Education

“…The use of the ISM has been demonstrated in various areas of applications such as big-data analytics (Gupta & Goyal, 2021), online shopping (Basar et al, 2021;Guerrero et al, 2023), social commerce (Pijo et al, 2023), research productivity (Ocampo et al, 2022), additive manufacturing (Sonar et al, 2020), health-care systems (Kumar & Sharma, 2018), the mining industry (Khaba & Bhar, 2018), and university technology transfer (Quiñones et al, 2020), among others. The integrated ISM-MICMAC has also been applied in the education sector, brought about by the need to understand complex systems arising in teaching and learning, not to mention the greater scope of managing educational processes.…”