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The Importance of Micro, Small, and Medium Enterprises Competitiveness through Digital Transformation Ketut , Merta I; Nyoman , Usadha I Dewa; Wayan , Sudiartini Ni
JASF: Journal of Accounting and Strategic Finance Vol. 7 No. 1 (2024): JASF (Journal of Accounting and Strategic Finance) - June 2024
Publisher : Accounting Department, Faculty of Economics and Business, Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasf.v7i1.467

Abstract

The emergence of the Industrial Revolution has led to transformative changes in global industries driven by digital technology and automation. These changes have brought both opportunities and challenges to MSMEs. This research examines how social capital, marketing performance, and human resources affect MSMEs' competitiveness with digital transformation as a mediating variable. This study uses quantitative methodology and a causal associative approach. A stratified random sampling technique will ensure adequate representation from different industries and geographical areas. Data were collected using a questionnaire distributed and filled out by 200 MSME entrepreneurs at three locations in Badung Regency: Kuta, North Kuta, and South Kuta, Bali-Indonesia. The data was analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results of this research show that social capital, marketing performance, and human resources have a significant influence on MSMEs' competitiveness. Digital transformation has an insignificant effect on MSME's competitiveness in Badung Regency, Bali Province. Digital transformation has been proven to mediate the effect of marketing performance on SMEs' competitiveness. Digital transformation cannot mediate the effect of social capital and human resources on SMEs' competitiveness in Badung Regency, Bali Province. The results of this study can serve as a guide for future researchers who will tackle related issues. In addition, more research is required to increase the variety of variables and locations to obtain more meaningful results.