Combined Forecasting: Effect of Amplitude of Data variation and Context Framing on the Anchoring Point in Forecasting
Keywords:
Judgmental forecasting, heuristic, anchoring point, controlled experiment, gain/loss framingAbstract
There is no doubt about the importance of quantitative forecasting models in management. However, in uncertain scenarios, established mathematical models should be adjusted, since variables and parameters might have changed compared to the original models. By considering this fact, human judgments are required in forecasting. However, it is known that decision-makers are bounded rationally, hence, they employ heuristics to simplify certain decision-making. Our study aims to investigate how the amplitude of variation of historical demand and the gain/loss framing of business context could drive decision-makers away from the forecasting anchoring point. Methodologically, we employed controlled experiment and analyzed the data using ordinary least square regression. Our results demonstrated that amplitude of variation could negatively influence the deviation around the anchoring point and no statistical effect of the business context framing was noted. Managerially, we contributed by reminding the managers about the possible bias in their judgment and methods to avoid it. The detailed discussion could be found in the manuscript.Downloads
References
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120.
Barrow, D. K. (2016). Forecasting intraday call arrivals using the seasonal moving average method. Journal of Business Research, 69(12), 6088–6096.
Bendoly, E. (2016). Fit, bias, and enacted sensemaking in data visualization: frameworks for continuous development in operations and supply chain management analytics. Journal of Business Logistics, 37(1), 6–17.
Bendoly, E., Donohue, K., & Schultz, K. (2006). Behavior in operations management: Assessing recent findings and revisiting old assumptions. Journal of Operations Management, 24(6), 737–752.
Berger, A. R., & Janoff-Bulman, R. (2006). Costs and satisfaction in close relationships: The role of loss-gain framing. Personal Relationships, 13(1), 53-68.
Billah, B., King, M. L., Snyder, R. D., & Koehler, A. B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239–247.
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.
Bunn, D., & Wright, G. (1991). Interaction of judgemental and statistical forecasting methods: issues & analysis. Management Science, 37(5), 501–518.
Carbone, R., Andersen, A., Corriveau, Y., & Corson, P. P. (1983). Comparing for different time series methods the value of technical expertise individualized analysis, and judgmental adjustment. Management Science, 29(5), 559–566.
Carter, C. R., Kaufmann, L., & Michel, A. (2007). Behavioral supply management: a taxonomy of judgment and decision-making biases. International Journal of Physical Distribution & Logistics Management, 37(8), 631–669.
Cerullo, M. J., & Avila, A. (1975). Sales forecasting practices: A survey. Managerial Planning, 24(2), 33.
Charness, G., Gneezy, U., & Imas, A. (2013). Experimental methods: Eliciting risk preferences. Journal of Economic Behavior & Organization, 87, 43–51.
Cheema-Fox, A., LaPerla, B. R., Serafeim, G., & Wang, H. S. (2020). Corporate resilience and response during COVID-19. Harvard Business School Accounting & Management Unit Working Paper, 20–108.
Contractor, F. J. (2021). The world economy will need even more globalization in the post-pandemic 2021 decade. Journal of International Business Studies, 1–16.
De Baets, S., & Harvey, N. (2018). Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support. International Journal of Forecasting, 34(2), 163–180.
De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473.
De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684–687.
Dennis, S. A., Goodson, B. M., & Pearson, C. A. (2019). Online Worker Fraud and Evolving Threats to the Integrity of MTurk Data: A Discussion of Virtual Private Servers and the Limitations of IP-Based Screening Procedures. Behavioral Research in Accounting.
Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426–436.
Ding, X., Hartog, J., & Sun, Y. (2010). Can we measure individual risk attitudes in a survey?
Donthu, N., & Gilliland, D. (1996). The infomercial shopper. Journal of Advertising Research, 36(2), 69–77.
Edmundson, B., Lawrence, M., & O’Connor, M. (1988). The use of non‐time series information in sales forecasting: A case study. Journal of Forecasting, 7(3), 201–211.
Feng, T., Keller, L. R., & Zheng, X. (2011). Decision making in the newsvendor problem: A cross-national laboratory study. Omega, 39(1), 41-50.
Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3–23.
Fischhoff, B. (1988). Judgmental aspects of forecasting: Needs and possible trends. International Journal of Forecasting, 4(3), 331–339.
Gardner Jr, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28.
Georgoff, D. M., & Murdick, R. G. (1986). Manager’s guide to forecasting. Harvard Business Review.
Gino, F., & Pisano, G. (2008). Toward a Theory of Behavioral Operations. Manufacturing Service Operations Management, 10(4), 676–691.
Griffin, M., Babin, B. J., & Attaway, J. S. (1996). Anticipation of injurious consumption outcomes and its impact on consumer attributions of blame. Journal of the Academy of Marketing Science, 24(4), 314–327.
Hada, M., Grewal, R., & Lilien, G. L. (2013). Purchasing Managers’ Perceived Bias In Supplier-Selected Referrals. Journal of Supply Chain Management, 49(4), 81–95.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Harvey, A. C. (1984). A unified view of statistical forecasting procedures. Journal of Forecasting, 3(3), 245–275.
Harvey, N., & Reimers, S. (2013). Trend damping: Under-adjustment, experimental artifact, or adaptation to features of the natural environment? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(2), 589.
Heizer, J., Render, B., & Munson, C. (2021). Operations management: sustainability and supply chain management. MTM.
Highhouse, S. (2009). Designing Experiments That Generalize. Organizational Research Methods, 12(3), 554–566.
Huang, C.-Y. (2012). To model, or not to model: Forecasting for customer prioritization. International Journal of Forecasting, 28(2), 497–506.
Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454.
Jackman, M., & Greenidge, K. (2010). Modelling and forecasting tourist flows to Barbados using structural time series models. Tourism and Hospitality Research, 10(1), 1–13.
Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93(5), 1449–1475.
Kahneman, D., & Tversky, A. (1979). On the interpretation of intuitive probability: A reply to Jonathan Cohen. Cognition, 7(4), 409-411.
Katsikopoulos, K. V, & Gigerenzer, G. (2013). Behavioral Operations Management: A Blind Spot And A Research Program. Journal of Supply Chain Management, 49(1), 3–7.
Kaufmann, L., & Carter, C. R. (2006). International supply relationships and non-financial performance - A comparison of US and German practices. Journal of Operations Management, 24(5), 653–675.
Kaufmann, L., Carter, C. R., & Buhrmann, C. (2010). Debiasing the supplier selection decision: a taxonomy and conceptualization. International Journal of Physical Distribution & Logistics Management, 40(10), 792–821.
Kaufmann, L., Kreft, S., Ehrgott, M., & Reimann, F. (2012). Rationality in supplier selection decisions: The effect of the buyer’s national task environment. Journal of Purchasing and Supply Management, 18(2), 76–91.
Kline, R. B. (2015). Principles and practice of structural equation modeling. New York: Guilford publications.
Kremer, M., Moritz, B., & Siemsen, E. (2011). Demand Forecasting Behavior: System Neglect and Change Detection. Management Science, 57(10), 1827–1843.
Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518.
Lawrence, M., & O’Connor, M. (1992). Exploring judgemental forecasting. International Journal of Forecasting, 8(1), 15–26.
Lawson, R. W. (1981). Traffic usage forecasting: is it an art or a science. Telephony, 2, 19–24.
Lee, S., & Fambro, D. B. (1999). Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record, 1678(1), 179–188.
Lee, Y.-S., & Tong, L.-I. (2011). Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowledge-Based Systems, 24(1), 66–72.
Lee, Y. S., Seo, Y. W., & Siemsen, E. (2018). Running Behavioral Operations Experiments Using Amazon’s Mechanical Turk. Production and Operations Management.
Lonati, S., Quiroga, B. F., Zehnder, C., & Antonakis, J. (2018). On doing relevant and rigorous experiments: Review and recommendations. Journal of Operations Management, 64, 19–40.
Marins, C., Baptista, S., & Espina, R. (2021). Ótibos COVID Brasil. https://www.uol.com.br/vivabem/noticias/redacao/2021/07/07/covid-19-coronavirus-casos-mortes-07-de-julho.htm.
NHS. (2020). Mental wellbeing while staying at home. NHS Website. Recuperado de https://
www.nhs.uk/every-mind-matters/coronavirus/mental-wellbeing-while-staying-at-home/.
Nichols, B. S., Stolze, H., & Kirchoff, J. F. (2019). Spillover effects of supply chain news on consumers’ perceptions of product quality: An examination within the triple bottom line. Journal of Operations Management, 65(6), 536–559.
O’Connor, M., Remus, W., & Griggs, K. (1993). Judgemental forecasting in times of change. International Journal of Forecasting, 9(2), 163–172.
Oliveira, E. M. de, & Oliveira, F. L. C. de (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776-788.
Önkal, D., Gönül, M. S., & Lawrence, M. (2008). Judgmental adjustments of previously adjusted forecasts. Decision Sciences, 39(2), 213–238.
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419.
Perera, H. N., Hurley, J., Fahimnia, B., & Reisi, M. (2019). The human factor in supply chain forecasting: A systematic review. European Journal of Operational Research, 274(2), 574–600.
Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection of forecasting models. Journal of Operations Management, 60, 34–46. https://doi.org/10.1016/J.JOM.2018.05.005
Remus, W. (1984). An empirical investigation of the impact of graphical and tabular data presentations on decision making. Management Science, 30(5), 533–542.
Rendon-Sanchez, J. F., & de Menezes, L. M. (2019). Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. European Journal of Operational Research, 275(3), 916–924.
Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).
Rungtusanatham, M., Wallin, C., & Eckerd, S. (2011). The vignette in a scenario-based role-playing experiment. Journal of Supply Chain Management, 47(3), 9–16.
Salehzadeh, R., Tabaeeian, R. A., & Esteki, F. (2020). Exploring the consequences of judgmental and quantitative forecasting on firms’ competitive performance in supply chains. Benchmarking: An International Journal.
Simon, H. A. (1959). Theories of Decision-Making in Economics and Behavioral Science. The American Economic Review, 49(3), 253–283.
Singh, S., Roy, M. D., Sinha, C. P. T. M. K., Parveen, C. P. T. M. S., Sharma, C. P. T. G., & Joshi, C. P. T. G. (2020). Impact of COVID-19 and lockdown on mental health of children and adolescents: A narrative review with recommendations. Psychiatry Research, 113429.
Sitkin, S. B., & Weingart, L. R. (1995). Determinants of risky decision-making behavior: A test of the mediating role of risk perceptions and propensity. Academy of Management Journal, 38(6), 1573-1592.
Song, H., Li, G., Witt, S. F., & Athanasopoulos, G. (2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27(3), 855–869.
Sweeney, D. J., Williams, T. A., & Anderson, D. R. (2013). Estatística Aplicada à Administração e Economia (3 ed.). Cengage Learning.
Theocharis, Z., & Harvey, N. (2016). Order effects in judgmental forecasting. International Journal of Forecasting, 32(1), 44–60.
Tratar, L. F., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162–173.
Turner, D. S. (1990). The role of judgement in macroeconomic forecasting. Journal of Forecasting, 9(4), 315–345.
Zhao, X., Zhao, X., & Wu, Y. (2013). Opportunities for research in behavioral operations management. International Journal of Production Economics, 142(1), 1–2.
