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Investigating the determinants of successful budgeting with SVM and Binary models

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  • Hariharan, Naveen Kunnathuvalappil

Abstract

Learning the determinants of successful project budgeting is crucial. This research attempts to empirically find the determinants of a successful budget. To find this, this work applied three different supervised machine learning algorithms for classification: Support Vector Machine (SVM), Logistic regression, and Probit regression with data from 470 projects. Five features have been selected: coordination, participation, budget control, communication, and motivation. The SVM analysis results showed that SVM could predict successful and failed budgets with fairly good accuracy. The results from Logistic and Probit regression showed that if managers properly focus on coordination, participation, budget control, and communication, the probability of success in project-budget increases.

Suggested Citation

  • Hariharan, Naveen Kunnathuvalappil, 2021. "Investigating the determinants of successful budgeting with SVM and Binary models," OSF Preprints xf7ak, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:xf7ak
    DOI: 10.31219/osf.io/xf7ak
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    References listed on IDEAS

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    1. Thomas Ahrens & Laurence Ferry & Rihab Khalifa, 2018. "The hybridising of financial and service expertise in English local authority budget control," Qualitative Research in Accounting & Management, Emerald Group Publishing Limited, vol. 15(3), pages 341-357, March.
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