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Author

Ramkrishna Mohan Kambli

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Review Open access Aug 2026

Technology-Driven Entrepreneurship and Skill Development in India: A Conceptual Framework for MSME Growth

India’s Micro, Small and Medium Enterprise (MSME) sector is undergoing rapid technology-enabled transformation through digital payments, open digital commerce, artificial intelligence (AI), and government-supported skill-development initiatives. However, technology access alone does not ensure improved entrepreneurial performance; complementary digital, financial and managerial capabilities are required to convert digital infrastructure into productive use. This paper examines the convergence of technology adoption and skill development in Indian entrepreneurship using an integrative review of government publications, industry evidence and peer-reviewed research published or updated primarily during 2020–2026. The review synthesises evidence on four technology enablers—digital payments, open digital commerce, AI-enabled tools, and digital public infrastructure—and two complementary skill pathways: digital/financial literacy and managerial/entrepreneurial capability. Current evidence shows substantial scale: UPI processed 24,161.69 crore transactions worth ₹314.23 lakh crore in FY2025–26, while more than 1.16 lakh retail sellers were live on ONDC across 630+ cities and towns by December 2025. PMKVY had trained or oriented 1.64 crore candidates by March 2026. Building on these findings, the paper proposes a conceptual framework linking technology enablers and skill pathways to five entrepreneurial outcomes: operational efficiency, productivity, cost reduction, market reach and profitability. The framework is explicitly presented as a set of research propositions for future empirical testing rather than as an empirically validated causal model. The paper concludes with implications for entrepreneurs, training providers and policymakers.

Ramkrishna Mohan Kambli, Vinayak M. Kambli · 0 citations
Open access Aug 2026

Machine Learning-Based Predictive Maintenance and Fault Diagnosis for Intelligent Mechanical Systems

This study proposes a machine learning-based predictive maintenance framework for machine failure prediction and fault diagnosis using the AI4I 2020 Predictive Maintenance Dataset, and identified torque, torque–speed ratio, and tool wear as the most influential predictors of machine failure.

Abhishek Sharma, Sujesh Kumar, Ramkrishna Mohan Kambli et al. · 0 citations

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