Dynamic Capability In Artificial Intelligence Adoption Among Local MSMEs: A Qualitative Study of 8 MSMEs In Manado City

Friska Jutresia Papia; Feiby Sondak

doi:10.34050/91094n72

Authors

Friska Jutresia Papia Politeknik Negeri Manado, Indonesia
Feiby Sondak Politeknik Negeri Manado

DOI:

https://doi.org/10.34050/91094n72

Keywords:

dynamic capability;, artificial intelligence adoption, qualitative study, Manado City, MSMEs

Abstract

The rise of generative Artificial Intelligence (AI) has opened new opportunities for Micro, Small, and Medium Enterprises (MSMEs) to improve the efficiency of promotion, customer service, and business management. However, the readiness of local MSMEs to sense, seize, and adapt to this technology remains poorly understood, particularly among cross-sectoral MSMEs in non-metropolitan cities such as Manado. This study aims to describe the dynamic capability process—sensing, seizing, and reconfiguring—in AI adoption among MSMEs in Manado City. The study employed a descriptive qualitative approach using semi-structured interviews with 8 MSME actors from various sectors (culinary, fashion, café, retail, tourism, beauty, automotive, and home-based bakery). Data were analyzed thematically through the stages of data reduction, data display, and conclusion drawing. The findings reveal a sharp dynamic capability gap across sectors: MSMEs that interact directly with digital-savvy customers (café, fashion, tourism, beauty) show more mature sensing and seizing capability, whereas MSMEs based on routine physical transactions (grocery stores, motorcycle repair shops) remain at a very limited sensing stage. Across all informants, reconfiguring capability remains weak, as AI is so far used only for promotion and content creation rather than core business processes, and adoption is heavily dependent on support from more digitally literate family members. This study contributes to a contextual understanding of the AI adoption gap among regional MSMEs and recommends a differentiated training approach based on sector characteristics.

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