Hypothesizing the Enablers of the Gig Economy : A TISM and MICMAC Approach - Panackal, Nehajoan; Rautela, Sonica; Sharma, Adya (Prabandhan: Indian Journal of Management, 2024)
Purpose : The present study focused on identifying the enablers that encourage the successful implementation of the gig economy. The sharing economy, sometimes referred to as the gig economy, offers choice, flexibility, autonomy, and several revenue streams. It is thought to represent the workforce of the future. Methodology : In the study, we followed the steps laid down by total interpretive structural modeling (TISM) and fuzzy MICMAC analysis. Seven enablers were identified through extant literature review and expert opinion. The enablers were validated and translated through the Delphi approach into the structural self-interaction matrix, which helped arrive at the proposed model. A study using fuzzy MICMAC verified the type of connections among the recognized facilitators. Findings : The results of the investigation demonstrated that the three tiers of the presumed TISM model of the gig economy’s enablers existed. The discovered seven enablers’ direct and indirect relationships are represented by the model. The findings of the Fuzzy MICMAC analysis showed that entrepreneurial spirit and short time commitment were the dependent variables. Freedom, autonomy, and multiple sources of income were the key drivers of the gig economy. Flexibility, technology, and nature of work were the linkage variables in the study. Practical Implications : Many people could find employment options with flexibility, autonomy, and the possibility of earning from several sources of income, thanks to the gig economy. Academics, legislators, business owners, and gig economy participants should all take note of the study’s important conclusions. It would be easier for people and businesses to embrace this shared economy if they concentrated on the facilitators. Originality : Unlikse prior research, the current study explored the enablers of the gig economy and systematically developed a hypothesized model highlighting crucial aspects of the gig economy using TISM and fuzzy MICMAC analysis.