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What drives SME formalization in Tanzania? An assessment using the machine learning techniques

Author

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  • Frank Mwombeki

    (Assistant lecturer, College of Business Education (CBE), Tanzania)

Abstract

Identifying the motives for business formalization is important for policy-making and the smooth coordination of entrepreneurs in Tanzania. This paper employs four machine learning (ML) models to investigate the effects of several contingents and institutional factors on SME formalization in Tanzania. Using an updated large dataset of the World bank enterprise survey in 2013, this research relies on 698 firms level data and 37 explanatory variables. The feature importance through SHAP values analysis proves that firm location in a business city, sales revenue, number of full-time employees, firm experience in the sector, and internal and external finance are the most significant factors that positively affect SME's formalization. This paper will be a reference for the actors and stakeholders in business and entrepreneurship to make good policies to drive business formalization in Tanzania. Key Words:SMEs, Formalization, Contingent Factors, Institutional Factors, And Machine Learning Techniques

Suggested Citation

  • Frank Mwombeki, 2023. "What drives SME formalization in Tanzania? An assessment using the machine learning techniques," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 12(1), pages 362-371, January.
    • Handle: RePEc:rbs:ijbrss:v:12:y:2023:i:1:p:362-371
    DOI: 10.20525/ijrbs.v12i1.2273
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