A Technopreneurship FrameworkBased on Accounting Information Systems and Machine Learning To Improve the Competitive Advantage of MSMEs
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Abstract
Micro, small and medium sized enterprises (MSMEs) play a strategic economic role but face challenges due to limited resources, digital skills, disorganized data and tactical decision-making. The literature regarding technology adoption, entrepreneurial orientation, sustainability and competitive advantage is still fragmented. This is despite the fact that technologies like predictive analytics offer a number of benefits in artificial intelligence (AI) and machine learning (ML). This study proposes an Accounting Information Systems (AIS) and ML-based technopreneurship framework to enhance MSMEs' Sustainable Competitive Advantage. Following PRISMA guidelines using Scopus, a systematic literature review screened 2,578 records, resulting in 69 eligible articles from January 2022-May 2026. Findings reveals three primary issue clusters for MSMEs, these are capacities and resources, digitalisation paradox and Institutional and Environmental Factors. In the conceptual framework offered, the internal and external antecedents are placed as enabling conditions. AI is treated as the data foundation, ML-based predictive analytics is treated as the analytical competence, and AI-ML-based technopreneurship is treated as the primary mechanism for value creation. We propose Business Model Innovation, Organisational Agility, Digital Absorptive Capacity, and Dynamic Competencies as mediating ways through which the competencies can contribute to Sustainable Competitive Advantage and Sustainable Performance. This study contributes to the literature in three ways: theoretical (by incorporating the Technology Organization Environment framework, Resource-Based View, and Dynamic Capabilities View), methodological (by combining a PRISMA-guided SLR with the development of a partial eDSR-oriented framework), and practical (by providing a structured roadmap for the data-driven transformation of MSMEs).
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