Assessment of Open Innovation Practices Among Micro and Small Enterprises: An Analysis With Machine Learning and Regression

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Author(s): Koppala Venugopal (Aditya Institute of Technology and Management, India), Santosh Neelam Ranganath (Dr. B.R. Ambedkar University, Srikakulam, India), Mulugeta Negash Wodage (University of Gondar, Ethiopia), Nadiminti Madhu (Gayatri College of Science and Management, Srikakulam, India)and Deekonda Pranaya (Aditya Institute of Technology and Management, India)
Copyright: 2026
Pages: 36
Source title: Exploring Human Resource Innovations in E-Economy
Source Author(s)/Editor(s): Agnieszka Wojcik-Czerniawska (SGH Warsaw School of Economics, Poland)
DOI: 10.4018/979-8-3373-4307-5.ch003

Keywords: Business & Management / Business Science Reference / E-Business / Human Resources Management

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Abstract

This study explores open innovation practices among micro and small enterprises (MSEs) in Srikakulam through predictive analysis using machine learning and regression techniques. Key innovation drivers were identified using feature selection methods such as Information Gain, ANOVA, and Chi-square. Components like customer co-creation, digital platforms, and venture capital access emerged as significant predictors of innovation success. Regression analysis further validated their influence on business growth. The findings offer actionable insights for stakeholders to enhance innovation strategies, foster collaboration, and optimize resource allocation. This data-driven approach strengthens the understanding of innovation dynamics in the MSE sector.

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Assessment of Open Innovation Practices Among Micro and Small Enterprises: An Analysis With Machine Learning and Regression

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