Advances in Consumer Research
Issue 11 : 65-71
Original Article
A Business Intelligence Framework for Sustainable Growth and Innovation in SMEs
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1
Assistant Professor, Department of BCOM BBA, Dayananda Sagar College of Arts Science and Commerce, Bangaluru, Karnataka
2
Santosh Ramdas Shivane Research Scholar, HR Digital Transformation, AI & Workforce Analytics Specialist, School of Management Studies, Commerce and Management, Kavayitri Bahinabai Chaudhari North Maharashtra University, Umavinagar, Jalgaon – 425001, Maharashtra, India
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Assistant professor, Department of Commerce and Management,K S Degree College, Bangaluru, Karnataka
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Assistant Professor, Department of Commerce and Management,MES College for Arts, Commerce and Science, Bangaluru, Karnataka
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Assistant professor, M.Com (Accounting and Taxation) MBA (HR),MKPM RV Institute of Legal Studies, Jayanagar, Bangaluru, Karnataka
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Assistant Professor, Department of Commerce and Management,Global Institute of Management Sciences, Bangaluru, Karnataka Email ID:
Abstract

Small and medium-sized enterprises (SMEs) face limited resources and growing competitive pressure, making data-driven decision-making essential for sustainable growth and innovation. This study proposes business intelligence (BI) framework comprising five layers: strategic alignment, data foundation, analytics and insight, decision and action integration, and a learning, innovation and sustainability loop. It also examines six factors influencing BI adoption: data quality, technology integration, financial resources, employee skills, ease of use, and data governance. Primary data were collected from 100 SME respondents through a structured questionnaire using convenience sampling. Mean, standard deviation and the Friedman test were applied. SMEs should therefore begin with affordable, scalable tools, invest in data quality and governance, and build staff capability gradually. Leadership commitment and a culture that values evidence are equally important. The study used convenience sampling and a modest sample, so its findings are indicative rather than conclusive. Future research could use larger, probability-based samples, compare sectors, and test the framework through case studies. The study recommends affordable, scalable BI solutions supported by reliable data and sound governance.

 

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