Abstract
Forecast accuracy plays a pivotal role in corporate budgeting, shaping financial planning, resource allocation, performance evaluation, and strategic decision-making. Despite advances in data analytics, budgeting processes are persistently vulnerable to errors and cognitive, organizational, and methodological biases. This paper provides a systematic review of the literature on forecast accuracy in corporate budgeting and develops a taxonomy of bias-correction mechanisms. The review synthesizes findings from financial economics, behavioral accounting, and management sciences, highlighting sources of forecast errors, the magnitude of their organizational consequences, and the evolution of corrective practices. The taxonomy proposed categorizes bias-correction strategies into behavioral, statistical, technological, and governance-driven approaches, offering a comprehensive framework for both practitioners and scholars. By consolidating fragmented insights, this study enhances understanding of budgeting as not only a technical forecasting exercise but also a socio-behavioral process embedded in corporate governance and organizational psychology. The review underscores the need for integrated solutions that combine advanced analytics with behavioral and governance reforms to improve budgetary reliability and organizational resilience.
Keywords
Forecast Accuracy, Corporate Budgeting, Bias Correction, Systematic Review, Financial Decision-Making, Behavioral Finance
Citations
IRE Journals:
Omoize Fatimetu Dako, Chizoba Michael Okafor, Blessing Olajumoke Farounbi, Ogochukwu Prisca Onyelucheya "Forecast Accuracy in Corporate Budgeting: A Systematic Review and Bias-Correction Taxonomy" Iconic Research And Engineering Journals Volume 2 Issue 4 2018 Page 127-145
IEEE:
Omoize Fatimetu Dako, Chizoba Michael Okafor, Blessing Olajumoke Farounbi, Ogochukwu Prisca Onyelucheya
"Forecast Accuracy in Corporate Budgeting: A Systematic Review and Bias-Correction Taxonomy" Iconic Research And Engineering Journals, 2(4)
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