DETERMINANTS OF NATURAL RESOURCES BASED MICROENTERPRISES PERFORMANCE IN INDIA’S WESTERN HIMALAYAN REGION: A NAÏVE BAYES CLASSIFIER ANALYSIS
Keywords:
Naïve Bayes Classification, Microenteprises, Natural Resources, Performance, Factors.
Abstract
The natural resources-based Microenterprises are the major part of the economy of the western Himalayan region of Uttara hand, India, as the region is predominantly covered with reserved forests. The present study evaluates the performance of Microenterprises and the factors affecting it in the region using the primary data enumerated from 110 microenterprises sampled under four major categories of microenterprises, viz, agro and allied, Animal and allied, handicrafts and handlooms, and miscellaneous. The Naïve Bayes classifier approach has been applied to evaluate the performances (Loss-making, breakeven, profit-making, or high-profit making) of these microenterprises based on their performance determining factors such as ease of raw material availability, level of training received, technological advancement, and the extent of market knowledge, and also on the type of ownership and the employee's number. The Naïve Bayes classification accuracy on the training dataset was 100%, while accuracy on the test dataset ranged from 93% to 100%. The results revealed that agro-based microenterprises have a greater probability (0.67) of making a profit/high profit, while animal product-based microenterprises have a high probability of running into losses. A higher level of Market Knowledge contributes to a high probability (0.89) of making high profits. The higher level of technology and training provides greater chances/probability (0.72, 0.72) of making high profits. Self-help groups (SHGs) have shown a better probability of making profits. The study suggests promoting SHGs in the region, wider dissemination of the market knowledge (marketing strategy), and leveling up the training/technology of the microenterprise.
JEL Classification Codes: O13, L26.
References
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Aragón-Sánchez, A., Barba-Aragón, I., & Sanz-Valle, R. (2010). Effects of training on business results. The International Journal of Human Resource Management, 14(6), 956–980.
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Loader, K., & Johnston, K. (2003). Encouraging SME participation in training: Identifying practical approaches. Journal of European Industrial Training, 27(6), 273–280. https://doi.org/10.1108/03090590310479901
Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), 2142–2155. https://doi.org/10.1016/j.ins.2010.02.004
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Martin, R. U., & Alejandro, M. (2016). The Role of Education and Learning by Experience in the Performance of Microenterprises. Procedia - Social and Behavioral Sciences, 228(June), 523–528. Retrieved from https://doi.org/10.1016/j.sbspro.2016.07.080
Masakure, O., Henson, S., & Cranfield, J. (2009). Performance of microenterprises in Ghana: A resource-based view. Journal of Small Business and Enterprise Development, 16(3), 466–484. https://doi.org/10.1108/14626000910977170
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. C., & Lin, C. C. (2014). e1071: Misc functions of the Department of Statistics (e1071), TU Wien. R package version, 1(3), 9. Retrieved from https://CRAN.R-project.org/package=e1071
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Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Sánchez, A. A., Aragón, I. B., & Valle, R. S. (2003). Effects of training on business results, The International Journal of Human Resource Management, 14(6), 956–980. https://doi.org/10.1080/0958519032000106164
Tambunan, T. (2008). The Role of Government in Technology Transfer to SME Clusters in Indonesia: Micro-level Evidence from the Metalworking Industry Cluster in Tegal (Central Java). Asian Journal of Social Science, 36, 321–349.
Thapa, A. (2015). Determinants of microenterprise performance in Nepal. Small Business Economics, 45(3), 581–594. https://doi.org/10.1007/s11187-015-9654-0
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Webb, G. I., Boughton, J. R., & Wang, Z. (2005). Not so naive Bayes: aggregating one-dependence estimators. Machine learning, 58, 5–24. https://doi.org/10.1007/s10994-005-4258-6
Alom, F., Abdullah, M. A., Moten, A. R., & Azam, S. M. F. (2016). Success factors of overall improvement of microenterprises in Malaysia: an empirical study. Journal of Global Entrepreneurship Research, 6(1), 1–13. https://doi.org/10.1186/s40497-016-0050-2
Andersen, A. D., Marìn, A., & Simensen, E. O. (2018). Innovation in natural resource-based industries: A pathway to development? Introduction to the special issue. Innovation and Development, 8(1), 1–27. https://doi.org/10.1080/2157930X.2018.1439293
Anderson, D. (1982). Small industry in developing countries: A discussion of issues. World Development, 10(11), 913–948. https://doi.org/10.1016/0305-750X(82)90034-1
Anthwal, A., Gupta, N., Sharma, A., Anthwal, S., & Kim, K. H. (2010). Conserving biodiversity through traditional beliefs in sacred groves in Uttarakhand Himalaya, India. Resources, Conservation, and Recycling, 54(11), 962–971. https://doi.org/10.1016/j.resconrec.2010.02.003
Aragón-Sánchez, A., Barba-Aragón, I., & Sanz-Valle, R. (2010). Effects of training on business results. The International Journal of Human Resource Management, 14(6), 956–980.
Berrone, P., Gertel, H., Giuliodori, R., Bernard, L., & Meiners, E. (2014). Determinants of Performance in Microenterprises: Preliminary Evidence from Argentina. Journal of Small Business Management, 52(3), 477–500. https://doi.org/10.1111/jsbm.12045
Boermans, M. A., Willebrands, D., & Willebrands, D. (2012). Financial Constraints, Risk Taking and Firm Performance: Recent Evidence from Microfinance Clients in Tanzania (November 19, 2012). De Nederlandsche Bank Working Paper No. 358. http://dx.doi.org/10.2139/ssrn.2177842
Bhandari, G., & Reddy, B. V. (2015). Impact of Out-Migration on Agriculture and Women Work Load: An Economic Analysis of Hilly Regions of Uttarakhand India. Indian Journal of Agricultural Economics, 70(3), 1-10.
Cardamone, M., & Rentschler, R. (2018). Indigenous innovators: The role of web marketing for cultural micro-enterprises. International Journal of Nonprofit and Voluntary Sector Marketing, 11(4), 347–360. https://doi.org/10.1002/nvsm.278
Dhiman, P. K., & Rani, A. (2011). Problems and prospects of small scale agro-based industries: An analysis of Patiala district. International journal of multidisciplinary research, 1(4), 129-142.
Deshpandé, R., & Farley, J. U. (1998). Measuring market orientation: generalization and synthesis. Journal of market-focused management, 2, 213-232. https://doi.org/10.1023/A:1009719615327
Directorate of Economics and Statistics Uttarakhand. (2017). State Domestic Product of Uttarakhand (2011-12 to 2016-17th (Disaggregated Data 2011-12 to 2015-16th ) with Base Year, 2011-12), Dept of Planning, Govt of Uttarakhand Retrieved from https://des.uk.gov.in/files/GSDP_BOOK_2016-17.pdf
Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine learning, 29, 103-130. https://doi.org/10.1023/A:1007413511361
Duda, R. O., Hart, P. E., & Stork, D. G. (1973). Pattern classification and scene analysis (Vol. 3, pp. 731-739). New York: Wiley.
Datta, D. B., & Bhattacharyya, S. (2016). An Analysis of Problems and Prospects of Indian Handicraft Sector. Asian Journal of Management, 7(1) 5–16. https://doi.org/10.5958/2321-5763.2016.00002.0
Gandhi, V., Kumar, G., & Marsh, R. (1999). Agroindustry for rural and small farmer development: issues and lessons from India. The International Food and Agribusiness Management Review, 2(3-4), 331–344. https://doi.org/10.1016/S1096-7508(01)00036-2
Ghafoor Khan, R., Ahmed Khan, F., & Aslam Khan, M. (2011). Impact of Training and Development on Organizational Performance. Global Journal of Management and Business Research, 11(7), 63–68. Retrieved from https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CAIQw7AJahcKEwigqorD7Nv_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fglobaljournals.org%2FGJMBR_Volume11%2F8-Impact-of-Training-and-Development-on-Organizational-Performance.pdf&psig=AOvVaw1iSgOgXFQWq3WYyoFBB-KB&ust=1687694077241085&opi=89978449
Gulyani, S., & Talukdar, D. (2010). Inside Informality: The Links between Poverty, Microenterprises, and Living Conditions in Nairobi’s Slums. World Development, 38(12), 1710–1726. https://doi.org/10.1016/j.worlddev.2010.06.013
Industries (Development and Regulation) Act. (1951). Retrieved from https://legislative.gov.in/sites/default/files/A1951-65.pdf
Islam, M. M., Habes, E. M., & Alam, M. M. (2018). The usage and social capital of mobile phones and their effect on the performance of microenterprise: An empirical study. Technological Forecasting and Social Change, 132, 156-164. https://doi.org/10.1016/j.techfore.2018.01.029
Kathuria, S. (1986). Handicrafts Exports: An Indian Case Study. Economic and Political Weekly, 21(40), 1743–1755. Retrieved from https://www.jstor.org/stable/4376184
Kamunge, M. S., Njeru, A., & Tirimba, O. I. (2014). Factors affecting the performance of small and micro enterprises in Limuru Town Market of Kiambu County, Kenya. International journal of scientific and research publications, 4(12), 1-20.
Kalita, M., & Prosad, P. (2016). Impact of Globalisation on Bell Metal Industry of Sarthebari Barpeta (with special reference to local artisans). Apeejay- Journal of Management Sciences and Technology, 4(1), 72-80.
Loader, K., & Johnston, K. (2003). Encouraging SME participation in training: Identifying practical approaches. Journal of European Industrial Training, 27(6), 273–280. https://doi.org/10.1108/03090590310479901
Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), 2142–2155. https://doi.org/10.1016/j.ins.2010.02.004
Mamgain, R. P., & Reddy, D. N. (2017). Out-migration from the hill region of Uttarakhand: Magnitude, challenges, and policy options. Rural labor mobility in times of structural transformation: Dynamics and perspectives from Asian economies, 209–235. Retrieved from https://link.springer.com/chapter/10.1007/978-981-10-5628-4_10
Martin, R. U., & Alejandro, M. (2016). The Role of Education and Learning by Experience in the Performance of Microenterprises. Procedia - Social and Behavioral Sciences, 228(June), 523–528. Retrieved from https://doi.org/10.1016/j.sbspro.2016.07.080
Masakure, O., Henson, S., & Cranfield, J. (2009). Performance of microenterprises in Ghana: A resource-based view. Journal of Small Business and Enterprise Development, 16(3), 466–484. https://doi.org/10.1108/14626000910977170
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. C., & Lin, C. C. (2014). e1071: Misc functions of the Department of Statistics (e1071), TU Wien. R package version, 1(3), 9. Retrieved from https://CRAN.R-project.org/package=e1071
Micro, Small, and Medium Enterprises Development (MSMED) Act (2006). MSME development institute, Ministry of MSME, & GOI. (2017). Annual Report 2016-17. Retrieved from https://legislative.gov.in/sites/default/files/A2006-27.pdf
Murty, M. N., & Devi, V. S. (2011). Pattern recognition: An algorithmic approach. Springer Science & Business Media.
Nardi, P. M. (2018). Doing Survey Research (4th ed.). Taylor and Francis.
Paramasivan, C., & Pasupathi, R. (2016). Performance of agro-based industries in India. National Journal of Advanced Research, 2(6), 25-28.
Psaltopoulos, D., Stathopoulou, S., & Skuras, D. (2005). The location of markets perceived entrepreneurial risk, and start-up capital of micro rural firms. Small Business Economics, 25(2), 147–158. https://doi.org/10.1007/s11187-003-6456-6
Parichatnon, S., & Maichum K, P. (2018). Measuring technical efficiency of Thai rubber production using the three-stage data envelopment analysis. Agricultural Economics, 64(5), 227–240. https://doi.org/10.17221/19/2016-AGRICECON.
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org
Rosa, P., Carter, S., & Hamilton, D. (1994). Gender as a Determinant of Small Business Performance: Preliminary Insights from a British Study. National Small Firms Policy and Research Conference, 271–288.
Sohns, F., & Revilla Diez, J. (2018). Explaining micro-entrepreneurship in rural Vietnam—a multilevel analysis. Small Business Economics, 50(1), 219–237. https://doi.org/10.1007/s11187-017-9886-2
Stone, D. L., & Deadrick, D. L. (2015). Challenges and opportunities affecting the future of human resource management. Human Resource Management Review, 25(2), 139–145. https://doi.org/10.1016/j.hrmr.2015.01.003
Shahidullah, A. K. M., & Haque, C. E. (2014). Environmental orientation of small enterprises: Can microcredit-assisted microenterprises be "green"? Sustainability, 6(6), 3232-3251. https://doi.org/10.3390/su6063232
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Sánchez, A. A., Aragón, I. B., & Valle, R. S. (2003). Effects of training on business results, The International Journal of Human Resource Management, 14(6), 956–980. https://doi.org/10.1080/0958519032000106164
Tambunan, T. (2008). The Role of Government in Technology Transfer to SME Clusters in Indonesia: Micro-level Evidence from the Metalworking Industry Cluster in Tegal (Central Java). Asian Journal of Social Science, 36, 321–349.
Thapa, A. (2015). Determinants of microenterprise performance in Nepal. Small Business Economics, 45(3), 581–594. https://doi.org/10.1007/s11187-015-9654-0
U.S. Small Business Administration. (2010). Program for investment in microentrepreneurs act (‘‘PRIME’’). Retrieved from http://www.sba.gov/sites/default/files/files/serv_fa_2010_ primetrack123.pdf
Uematsu, H., & Mishra, A. K. (2011). Use of Direct Marketing Strategies by Farmers and Their Impact on Farm Business Income. Agricultural and Resource Economics Review, 40(1), 1–19. https://doi.org/10.1017/S1068280500004482
Vershinina, N., Markman, G., Han, L., Rodgers, P., Kitching, J., Hashimzade, N., & Barrett, R. (2022). Gendered regulations and SME performance in transition economies. Small Business Economics, 58(2), 1113–1130. https://doi.org/10.1007/s11187-020-00436-7
Webb, G. I., Boughton, J. R., & Wang, Z. (2005). Not so naive Bayes: aggregating one-dependence estimators. Machine learning, 58, 5–24. https://doi.org/10.1007/s10994-005-4258-6
Published
2023-06-24
How to Cite
Bahuguna, I., Kumar, U., Arunachalam, K., Shridhar, V., & Sharma, A. (2023). DETERMINANTS OF NATURAL RESOURCES BASED MICROENTERPRISES PERFORMANCE IN INDIA’S WESTERN HIMALAYAN REGION: A NAÏVE BAYES CLASSIFIER ANALYSIS. International Journal of Small and Medium Enterprises, 6(1), 27-40. https://doi.org/10.46281/ijsmes.v6i1.2032
Section
Original Articles/ Short Communications
Copyright (c) 2023 Ina Bahuguna, Ujjwal Kumar, Kusum Arunachalam, Vijay Shridhar, Archana Sharma
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