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The effects of forecast type and performance-based incentives on the quality of management forecasts

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  • Chen, Clara Xiaoling
  • Rennekamp, Kristina M.
  • Zhou, Flora H.

Abstract

Understanding forecasts is important because of their pervasiveness in business decisions such as budgeting, production, and financial reporting. In this study we use an abstract experiment to examine how the preparation of disaggregated forecasts interacts with performance-based incentives to influence the accuracy and optimism of forecasts. We manipulate two factors between subjects at two levels each: forecast type (disaggregated or aggregated) and performance-based incentives (present or absent). Consistent with our predictions, we find that (1) preparing disaggregated forecasts leads to greater improvements in forecast accuracy (compared to preparing aggregated forecasts) in the absence of performance-based incentives than in the presence of performance-based incentives, and (2) preparing disaggregated forecasts leads to greater increases in forecast optimism (compared to preparing aggregated forecasts) in the presence of performance-based incentives than in the absence of performance-based incentives. Our study contributes to our understanding of unintentional biases in the forecasting process. Our results have important practical implications for designers of management control systems who elicit internal forecasts from managers. Finally, our results also have important practical implications for those who either prepare or use external management forecasts.

Suggested Citation

  • Chen, Clara Xiaoling & Rennekamp, Kristina M. & Zhou, Flora H., 2015. "The effects of forecast type and performance-based incentives on the quality of management forecasts," Accounting, Organizations and Society, Elsevier, vol. 46(C), pages 8-18.
  • Handle: RePEc:eee:aosoci:v:46:y:2015:i:c:p:8-18
    DOI: 10.1016/j.aos.2015.03.002
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    Cited by:

    1. Robert M. Gillenkirch & Julia Ortner & Sebastian Robert & Louis Velthuis, 2023. "Designing incentives and performance measurement for advisors: How to make decision-makers listen to advice," Working Papers 2304, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    2. Christian Daumoser & Bernhard Hirsch & Matthias Sohn, 2018. "Honesty in budgeting: a review of morality and control aspects in the budgetary slack literature," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 29(2), pages 115-159, August.
    3. Hales, Jeffrey, 2015. "Discussion of “The effects of forecast type and performance-based incentives on the quality of management forecasts”," Accounting, Organizations and Society, Elsevier, vol. 46(C), pages 19-22.
    4. Megumi Suto & Hitoshi Takehara, 2020. "Corporate social responsibility intensity, management earnings forecast accuracy, and investor trust: Evidence from Japan," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(6), pages 3047-3059, November.
    5. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    6. Kerry A. Humphreys & Ken T. Trotman, 2022. "Judgment and decision making research on CSR reporting in the COVID‐19 pandemic environment," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 739-765, March.
    7. Platt, William, 2015. "A practitioners perspective: 2014 AOS Conference on Accounting Estimates," Accounting, Organizations and Society, Elsevier, vol. 46(C), pages 5-7.

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