Call for Papers - SI of the BAR on Navigating the Future: Advances and Challenges in Forecasting

Call for papers

15 July 2024

Navigating the Future: Advances and Challenges in Forecasting

Submission deadline:?28 February 2025

This special issue gathers insights into the evolving forecasting practices in accounting, focusing on technological advancements, ESG factor integration, and the interplay of organizational actors and data sources in today’s dynamic business environment.

Guest editors:

Ferdinand Kunzl, University of Innsbruck, Austria

Mario Schabus, Monash University, Australia

Leona Wiegmann, ESCP Business School, Paris, France

Special issue information:

While forecasting is a key practice in many organisations, it has often been mentioned rather as a side note in budgeting studies. More recent accounting literature has started focusing on forecasting, which has yielded important insights into various aspects of firms’ forecasting practices (e.g., Brüggen, Grabner, & Sedatole, 2021; Chen, Rennekamp, & Zhou, 2015; Ittner & Michels, 2017; Jordan & Messner, 2020; Kroos, Schabus, & Verbeeten, 2021). Relatively recently, the business world has been exposed to a number of innovations and challenges that impact how forecasts are created and used within and beyond organisations. The special issue aims to document and synthesise novel insights to better understand forecasting in today’s business environment.

Preparing and using forecasts involves cooperation among actors from various functions with different perspectives on future developments (e.g., Brüggen & Luft, 2011; Goretzki & Messner, 2016; Jordan & Messner, 2020; Wiegmann, Petrikowski, & Goretzki, 2024). This cooperation is influenced by actors’ roles, targets, incentives, emotions, biases and interaction with non-humans like systems (Goretzki, Strauss, & Wiegmann, 2018). Technological developments (e.g., AI, Big Data, Advanced Analytics) promise greater forecast sophistication and accuracy, but reliance on them can blur responsibilities and accountabilities. Yet, the accounting literature on data-driven forecasting is still emerging (e.g., Chen, Hudgins, & Wright, 2022; Gallo, Labro, & Omartian, 2023; Labro, Lang, & Omartian, 2023).

Researchers have studied various forecast types, like internal predictions for planning, management guidance for stakeholder communication and analyst forecasts, often independently, with notable exceptions (e.g., Brüggen, et al., 2021; Call, Hribar, Skinner, & Volant, 2024; Cotter, Tuna, & Wysocki, 2006; Hutton, Lee, & Shu, 2012; Ittner & Michels, 2017). There are opportunities to explore their interplay with management control and organisational practices (e.g., Becker, 2014; Frow, Marginson, & Ogden, 2010; Henttu-Aho & J?rvinen, 2013; Sivabalan, Booth, Malmi, & Brown, 2009). This is particularly relevant given technological advancements, the use of diverse data sources (e.g., lower-level employees, crowdsourced forecasts, social media (e.g., Fehrenbacher, Ghio, & Weisner, 2023; Huang, Li, & Markov, 2020; Jame, Johnston, Markov, & Wolfe, 2016)) and challenges like climate change/ESG requirements and economic, political or societal uncertainties (e.g., Bernardi & Stark, 2018).

We highlight two promising themes while remaining open for other innovative contributions:

1. Trends and developments:

• The influence of Big Data and Advanced Analytics (including machine learning and AI) on forecast creation, forecast accuracy, and management control practices (e.g., planning, budgeting, performance evaluation).
• Integrating environmental, social, and governance (ESG) factors into forecasting.
• Resilience and adaptations of forecasting in response to unforeseen events (e.g., economic downturns, global crises, pandemics).
• The role of technology in internal forecasts and stakeholder communication (e.g., management guidance).

2. Key actors:

• The role of and interaction between key actors in forecasting (e.g., between forecast preparers and users).
• Human/non-human interaction in forecasting and its impact on responsibilities and accountabilities.
• Behavioural factors, cognitive biases, and emotions in constructing and using forecasts.
• The relationship between management forecasts and information intermediaries (e.g., analyst forecasts).
• The use of complementary data sources in forecasting, like lower-level employees, crowdsourced information and social media.

Manuscript submission information:

We invite researchers to submit original research papers that contribute to the advancement of forecasting in accounting. All empirical methods, including, but not limited to, qualitative, quantitative, field, laboratory, meta-analytic, mixed methods, and analytical submissions, are welcome. Submissions should clearly articulate the research question, methodology, scholarly contributions, and implications for practice. They should follow the submission guidelines of?The British Accounting Review.

Please submit your manuscripts here:?https://www2.cloud.editorialmanager.com/ybare/default2.aspx??

choosing article type "VSI: Forecasting in Accounting."

For inquiries or further information, please contact:

Ferdinand Kunzl, University of Innsbruck, AustriaEmail:?[email protected]?

Mario Schabus, Monash University, AustraliaEmail:?[email protected]?

Leona Wiegmann, ESCP Business School, Paris, FranceEmail:?[email protected]?

We look forward to receiving your innovative contributions to advance the field of forecasting in accounting.

• Final Manuscript Submission Deadline: 28.02.2025
• Editorial Acceptance Deadline: 28.02.2026

References:

Becker, S. D. (2014). When organisations deinstitutionalise control practices: a multiple-case study of budget abandonment.?European Accounting Review, 23, 593-623.

Bernardi, C., & Stark, A. W. (2018). Environmental, social and governance disclosure, integrated reporting, and the accuracy of analyst forecasts.?The British Accounting Review, 50, 16-31.

Brüggen, A., Grabner, I., & Sedatole, K. L. (2021). The folly of forecasting: the effects of a disaggregated demand forecasting system on forecast error, forecast positive bias, and inventory levels.?The Accounting Review, 96, 127-152.

Brüggen, A., & Luft, J. (2011). Capital rationing, competition, and misrepresentation in budget forecasts.?Accounting, Organizations and Society, 36, 399-411.

Call, Andrew C. and Hribar, Paul and Skinner, Douglas J. and Volant, David, Corporate Managers’ Perspectives on Forward-Looking Fuidance: Furvey Evidence (February 11, 2024). Available at SSRN: https://ssrn.com/abstract=4214740 or https://dx.doi.org/10.2139/ssrn.4214740

Chen, C. X., Hudgins, R., & Wright, W. F. (2022). The effect of advice valence on the perceived credibility of data analytics.?Journal of Management Accounting Research, 34, 97-116.

Chen, C. X., Rennekamp, K. M., & Zhou, F. H. (2015). The effects of forecast type and performance-based incentives on the quality of management forecasts.?Accounting, Organizations and Society, 46, 8-18.

Cotter, J., Tuna, I., & Wysocki, P. D. (2006). Expectations management and beatable targets: How do analysts react to explicit earnings guidance??Contemporary Accounting Research, 23, 593-624.

Fehrenbacher, D. D., Ghio, A., & Weisner, M. (2023). Advice utilization from predictive analytics tools: The trend is your friend.?European Accounting Review, 32, 637-662.

Frow, N., Marginson, D., & Ogden, S. (2010). "Continuous" budgeting: reconciling budget flexibility with budgetary control.?Accounting, Organizations and Society, 35, 444–461.

Gallo, L., Labro, E., & Omartian, J. D. (2023). Overreliance on Data in Forecasting. Working Paper.

Goretzki, L., & Messner, M. (2016). Coordination under uncertainty: a sensemaking perspective on cross-functional planning meetings.?Qualitative Research in Accounting & Management, 13, 92–126.

Goretzki, L., Strauss, E., & Wiegmann, L. (2018). Exploring the roles of vernacular accounting systems in the development of “enabling” global accounting and control systems.?Contemporary Accounting Research, 35, 1888-1916.

Henttu-Aho, T., & J?rvinen, J. (2013). A field study of the emerging practice of beyond budgeting in industrial companies: An institutional perspective.?European Accounting Review, 22, 765-785.

Huang, K., Li, M., & Markov, S. (2020). What do employees know? Evidence from a social media platform.?The Accounting Review, 95, 199-226.

Hutton, A. P., Lee, L. F., & Shu, S. Z. (2012). Do managers always know better? The relative accuracy of management and analyst forecasts.?Journal of Accounting Research, 50, 1217-1244.

Ittner, C. D., & Michels, J. (2017). Risk-based forecasting and planning and management earnings forecasts.?Review of Accounting Studies, 22, 1005-1047.

Jame, R., Johnston, R., Markov, S., & Wolfe, M. C. (2016). The value of crowdsourced earnings forecasts.?Journal of Accounting Research, 54, 1077-1110.

Jordan, S., & Messner, M. (2020). The use of forecast accuracy indicators to improve planning quality: insights from a case study.?European Accounting Review, 29, 337-359.

Kroos, P., Schabus, M., & Verbeeten, F. H. M. (2021). The relation between internal forecasting sophistication and accounting misreporting.?Journal of Management Accounting Research, 34, 51-73.

Labro, E., Lang, M., & Omartian, J. D. (2023). Predictive analytics and centralization of authority.?Journal of Accounting and Economics, 75, 101526.

Sivabalan, P., Booth, P., Malmi, T., & Brown, D. A. (2009). An exploratory study of operational reasons to budget.?Accounting & Finance, 49, 849-871.

Wiegmann, L., Petrikowski, L., & Goretzki, L. (2024). Business unit controllers' credibility and the hardening of local forecasts.?Contemporary Accounting Research, 41, 324–354.

Keywords:

forecasting; management control; management forecasts; analyst forecasts; digitalisation; ESG; forecasting stakeholders; novel data sources Learn more about the benefits of publishing in a special issue.