GENERATIVE AI STRATEGIES FOR BUILDING RESILIENCE IN MSMEs

Digital technolo y has been proven to improve the adaptability of Micro, Small, and Medium Enterprises (MSMEs) in facing various crises, one of which is through the use of Artificial Intelligence (AI), particularly Generative AI, which supports faster decision-making, improved risk mitigation, and business model innovation. Building resilience during crises is a crucial key strategy for business continuity and supports the Sustainable Development Goals (SDGs) agenda, particularly Goal 9 (Industry, Innovation, and Infrastructure) and Goal 13 (Addressing Climate Change). This research uses a Systematic Literature Review (SLR) approach based on the PRISMA method, with Boolean search formulated through the PICO and SPIDER frameworks. A total of 713 initial articles were identified from international databases (Scopus, ScienceDirect, Springer, Wiley, Sage, IEEE, Web of Science, and Google Scholar). After removing 101 duplicate articles, 43 articles were screened, and 37 articles were finally selected for in-depth analysis. The selection process was carried out in two stages: title and abstract screening, followed by full-text review. The theoretical analysis used the Resource-Based View (RBV), Dynamic Capability Theory, and Organizational Resilience Theory. This study found that generative AI can support the resilience of MSMEs by improving market adaptation, operational efficiency, and continuous innovation. The study's findings are expected to enrich the academic literature, provide practical contributions to strengthening MSME resilience, and support digital transformation and global economic sustainability in the face of future uncertainty.

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Publication Details

Journal
Management Research and Business Journal
Published
2025-08-20
DOI
https://doi.org/10.64237/mrb.v3i1.110
Primary Topic
Digital Transformation in Industry
Type
article
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article

GENERATIVE AI STRATEGIES FOR BUILDING RESILIENCE IN MSMEs

Lies Hendrawan
Management Research and Business Journal
Digital Transformation in Industry
article

GENERATIVE AI STRATEGIES FOR BUILDING RESILIENCE IN MSMEs

Lies Hendrawan
article en

Abstract

Digital technolo y has been proven to improve the adaptability of Micro, Small, and Medium Enterprises (MSMEs) in facing various crises, one of which is through the use of Artificial Intelligence (AI), particularly Generative AI, which supports faster decision-making, improved risk mitigation, and business model innovation. Building resilience during crises is a crucial key strategy for business continuity and supports the Sustainable Development Goals (SDGs) agenda, particularly Goal 9 (Industry, Innovation, and Infrastructure) and Goal 13 (Addressing Climate Change). This research uses a Systematic Literature Review (SLR) approach based on the PRISMA method, with Boolean search formulated through the PICO and SPIDER frameworks. A total of 713 initial articles were identified from international databases (Scopus, ScienceDirect, Springer, Wiley, Sage, IEEE, Web of Science, and Google Scholar). After removing 101 duplicate articles, 43 articles were screened, and 37 articles were finally selected for in-depth analysis. The selection process was carried out in two stages: title and abstract screening, followed by full-text review. The theoretical analysis used the Resource-Based View (RBV), Dynamic Capability Theory, and Organizational Resilience Theory. This study found that generative AI can support the resilience of MSMEs by improving market adaptation, operational efficiency, and continuous innovation. The study's findings are expected to enrich the academic literature, provide practical contributions to strengthening MSME resilience, and support digital transformation and global economic sustainability in the face of future uncertainty.

Management Research and Business JournalVol. 3(1)
University of Palangka Raya (ID)
Openalex Percentile: Top 69%
Digital Transformation in Industry
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GENERATIVE AI STRATEGIES FOR BUILDING RESILIENCE IN MSMEs — Lies Hendrawan · Management Research and Business Journal (2025) | TGRS Research Map | TGRS