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The Role of Forecasting and Budgeting Data Accuracy in Improving Organizational Performance

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  • Ruhul Quddus Majumder

    (Daffodil Institute of IT Chattogram, Bangladesh)

Abstract

This study investigates the impact of forecasting and budgeting accuracy on organizational performance in mid-to-large-sized firms from the manufacturing and service sectors. A quantitative, correlational research design was employed, integrating both primary data—collected via structured questionnaires from financial professionals—and secondary data from archival financial records. Stratified random sampling was used to ensure sectoral and organizational size representation. To ensure rigorous analysis, SPSS software was utilised for descriptive statistics, Pearson correlation, and multiple regression analysis, enabling a robust evaluation of relationships between financial planning accuracy and key performance indicators. Descriptive findings revealed that 72% of firms use time-series or regression-based forecasting, while 64% prefer flexible or rolling budgets. The average forecasting error (MAPE) was 12.4%, and the budgeting variance averaged 9.7%, indicating moderate precision levels. Correlation analysis revealed significant associations between forecasting and budgeting accuracy and financial performance metrics, including ROI, ROA, and operating margin. Regression results confirmed that planning accuracy significantly predicts organisational outcomes. These results support key theories, including Agency, Stakeholder, and Contingency Theory, and emphasise the importance of data-driven, adaptive financial planning. The findings advocate for broader adoption of modern forecasting tools and participative budgeting to enhance strategic decision-making and sustainable performance.

Suggested Citation

  • Ruhul Quddus Majumder, 2025. "The Role of Forecasting and Budgeting Data Accuracy in Improving Organizational Performance," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(7), pages 4963-4976, July.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-7:p:4963-4976
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    References listed on IDEAS

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    1. Bojer, Casper Solheim, 2022. "Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1555-1561.
    2. Huanyong Ji & Shuya Sheng & Jun Wan, 2024. "Corporate Social Responsibility Practices and Financial Performance of New Ventures: The Moderating Role of Government Support," Sustainability, MDPI, vol. 16(3), pages 1-19, February.
    3. Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data," European Journal of Operational Research, Elsevier, vol. 322(2), pages 414-426.
    4. Silvia Jordan & Martin Messner, 2020. "The Use of Forecast Accuracy Indicators to Improve Planning Quality: Insights from a Case Study," European Accounting Review, Taylor & Francis Journals, vol. 29(2), pages 337-359, March.
    5. Bhimani, Alnoor & Sivabalan, Prabhu & Soonawalla, Kazbi, 2018. "A study of the linkages between rolling budget forms, uncertainty and strategy," The British Accounting Review, Elsevier, vol. 50(3), pages 306-323.
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