IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v33y2017i3p652-661.html
   My bibliography  Save this article

The influence of product involvement and emotion on short-term product demand forecasting

Author

Listed:
  • Belvedere, Valeria
  • Goodwin, Paul

Abstract

Sales forecasters in industries like fast-fashion face challenges posed by short and highly volatile sales time series. Computers can produce statistical forecasts, but these are often adjusted judgmentally to take into account factors such as market intelligence. We explore the effects of two potential influences on these adjustments: the forecaster’s involvement with the product category and their emotional reactions to particular products. Two forecasting experiments were conducted using data from a major Italian leather fashion goods producer. The participants’ judgmental adjustments tended to lower the forecast accuracy, but especially when the participants had strong preferences for particular products. This appeared to result from a false consensus effect. The most accurate forecasts were made when the participants had no knowledge of the product and only received time series information, though a high level of involvement with the product category also led to a greater accuracy.

Suggested Citation

  • Belvedere, Valeria & Goodwin, Paul, 2017. "The influence of product involvement and emotion on short-term product demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 652-661.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:3:p:652-661
    DOI: 10.1016/j.ijforecast.2017.02.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207017300304
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2017.02.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Celsi, Richard L & Olson, Jerry C, 1988. "The Role of Involvement in Attention and Comprehension Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 210-224, September.
    2. Lawrence, Michael & Makridakis, Spyros, 1989. "Factors affecting judgmental forecasts and confidence intervals," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(2), pages 172-187, April.
    3. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
    4. Gregory W. Fischer & Mary Frances Luce & Jianmin Jia, 2000. "Attribute Conflict and Preference Uncertainty: Effects on Judgment Time and Error," Management Science, INFORMS, vol. 46(1), pages 88-103, January.
    5. Andrey Davydenko & Robert Fildes, 2014. "Measuring Forecasting Accuracy: Problems and Recommendations (by the Example of SKU-Level Judgmental Adjustments)," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 43-70, Springer.
    6. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    7. Gary E. Bolton & Axel Ockenfels & Ulrich W. Thonemann, 2012. "Managers and Students as Newsvendors," Management Science, INFORMS, vol. 58(12), pages 2225-2233, December.
    8. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
    9. Andrew D. Gershoff & Ashesh Mukherjee & Anirban Mukhopadhyay, 2007. "Few Ways to Love, but Many Ways to Hate: Attribute Ambiguity and the Positivity Effect in Agent Evaluation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(4), pages 499-505, December.
    10. Estrada, Carlos A. & Isen, Alice M. & Young, Mark J., 1997. "Positive Affect Facilitates Integration of Information and Decreases Anchoring in Reasoning among Physicians," Organizational Behavior and Human Decision Processes, Elsevier, vol. 72(1), pages 117-135, October.
    11. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    12. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    13. Fang, Xin & Zhang, Cheng & Robb, David J. & Blackburn, Joseph D., 2013. "Decision support for lead time and demand variability reduction," Omega, Elsevier, vol. 41(2), pages 390-396.
    14. Lim, Joa Sang & O'Connor, Marcus, 1996. "Judgmental forecasting with time series and causal information," International Journal of Forecasting, Elsevier, vol. 12(1), pages 139-153, March.
    15. Gregory W. Fischer & Jianmin Jia & Mary Frances Luce, 2000. "Attribute Conflict and Preference Uncertainty: The RandMAU Model," Management Science, INFORMS, vol. 46(5), pages 669-684, May.
    16. Dan Lovallo & Carmina Clarke & Colin Camerer, 2012. "Robust analogizing and the outside view: two empirical tests of case‐based decision making," Strategic Management Journal, Wiley Blackwell, vol. 33(5), pages 496-512, May.
    17. Brent Moritz & Enno Siemsen & Mirko Kremer, 2014. "Judgmental Forecasting: Cognitive Reflection and Decision Speed," Production and Operations Management, Production and Operations Management Society, vol. 23(7), pages 1146-1160, July.
    18. Raghunathan, Rajagopal & Irwin, Julie R, 2001. "Walking the Hedonic Product Treadmill: Default Contrast and Mood-Based Assimilation in Judgments of Predicted Happiness with a Target Product," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(3), pages 355-368, December.
    19. Au, Kin-Fan & Choi, Tsan-Ming & Yu, Yong, 2008. "Fashion retail forecasting by evolutionary neural networks," International Journal of Production Economics, Elsevier, vol. 114(2), pages 615-630, August.
    20. O'Connor, Marcus & Remus, William & Griggs, Ken, 1993. "Judgemental forecasting in times of change," International Journal of Forecasting, Elsevier, vol. 9(2), pages 163-172, August.
    21. Remus, William, 1986. "Graduate students as surrogates for managers in experiments on business decision making," Journal of Business Research, Elsevier, vol. 14(1), pages 19-25, February.
    22. De Toni, Alberto & Meneghetti, Antonella, 2000. "The production planning process for a network of firms in the textile-apparel industry," International Journal of Production Economics, Elsevier, vol. 65(1), pages 17-32, April.
    23. Gregory A. Liyanarachchi & Markus J. Milne, 2005. "Comparing the investment decisions of accounting practitioners and students: an empirical study on the adequacy of student surrogates," Accounting Forum, Taylor & Francis Journals, vol. 29(2), pages 121-135, June.
    24. Tyebjee, Tyzoon T., 1987. "Behavioral biases in new product forecasting," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 393-404.
    25. Sébastien Thomassey, 2014. "Sales Forecasting in Apparel and Fashion Industry: A Review," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 9-27, Springer.
    26. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    27. Jain, Kriti & Bearden, J. Neil & Filipowicz, Allan, 2013. "Depression and forecast accuracy: Evidence from the 2010 FIFA World Cup," International Journal of Forecasting, Elsevier, vol. 29(1), pages 69-79.
    28. Zaichkowsky, Judith Lynne, 1985. "Measuring the Involvement Construct," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(3), pages 341-352, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    2. Woodside, Arch G., 2020. "Interventions as experiments: Connecting the dots in forecasting and overcoming pandemics, global warming, corruption, civil rights violations, misogyny, income inequality, and guns," Journal of Business Research, Elsevier, vol. 117(C), pages 212-218.
    3. Mayukh Dass & Masoud Moradi & Fereshteh Zihagh, 2023. "Forecasting purchase rates of new products introduced in existing categories," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 385-408, September.
    4. Miguel Afonso Sellitto & Domingos Rafael Ferla Valladares & Erica Pastore & Arianna Alfieri, 2022. "Comparing Competitive Priorities of Slow Fashion and Fast Fashion Operations of Large Retailers in an Emerging Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 1-19, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    2. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    5. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    6. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    8. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    9. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    10. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    11. Goodwin, Paul & Fildes, Robert & Lawrence, Michael & Nikolopoulos, Konstantinos, 2007. "The process of using a forecasting support system," International Journal of Forecasting, Elsevier, vol. 23(3), pages 391-404.
    12. De Baets, Shari & Harvey, Nigel, 2020. "Using judgment to select and adjust forecasts from statistical models," European Journal of Operational Research, Elsevier, vol. 284(3), pages 882-895.
    13. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    14. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    15. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.
    16. F Caniato & M Kalchschmidt & S Ronchi, 2011. "Integrating quantitative and qualitative forecasting approaches: organizational learning in an action research case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 413-424, March.
    17. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    18. Lin, Vera Shanshan & Goodwin, Paul & Song, Haiyan, 2014. "Accuracy and bias of experts’ adjusted forecasts," Annals of Tourism Research, Elsevier, vol. 48(C), pages 156-174.
    19. Petropoulos, Fotios & Goodwin, Paul & Fildes, Robert, 2017. "Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge," International Journal of Forecasting, Elsevier, vol. 33(1), pages 314-324.
    20. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:33:y:2017:i:3:p:652-661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.