IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v106y2020icp315-322.html
   My bibliography  Save this article

What drives decision makers to follow or ignore forecasting tools - A game based analysis

Author

Listed:
  • Lackes, Richard
  • Siepermann, Markus
  • Vetter, Georg

Abstract

Using forecasts is a prerequisite for good decision making but often decision makers ignore the forecasts' outcomes and rely on their personal assessment. This usually leads to worse decisions. But forecasts can also be defective. Thus, it is crucial that decision makers do not rely blindly on forecasts but scrutinize critically the results. The question is under which circumstances decision makers follow or ignore forecasts. Therefore, we conducted a laboratory experiment where decision makers have the choice between two alternatives. The forecast provided gives an advice. But it is manipulated so that it is only partly reliable. Results show that participants do not act optimally. Many decision makers follow the manipulated forecast if for example their performance is dominated by the forecast or if they perceive competition. Hence, gamifying such research may impact the results that can be to some extent reinforced. However, the general meaning is not distorted.

Suggested Citation

  • Lackes, Richard & Siepermann, Markus & Vetter, Georg, 2020. "What drives decision makers to follow or ignore forecasting tools - A game based analysis," Journal of Business Research, Elsevier, vol. 106(C), pages 315-322.
    • Handle: RePEc:eee:jbrese:v:106:y:2020:i:c:p:315-322
    DOI: 10.1016/j.jbusres.2019.02.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296319301262
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2019.02.036?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. Andrew W. Lo & Dmitry V. Repin & Brett N. Steenbarger, 2005. "Fear and Greed in Financial Markets: A Clinical Study of Day-Traders," American Economic Review, American Economic Association, vol. 95(2), pages 352-359, May.
    2. Caterina Lucarelli & Pierpaolo Uberti & Gianni Brighetti, 2015. "Misclassifications in financial risk tolerance," Journal of Risk Research, Taylor & Francis Journals, vol. 18(4), pages 467-482, April.
    3. Moon, Mark A. & Mentzer, John T. & Smith, Carlo D., 2003. "Conducting a sales forecasting audit," International Journal of Forecasting, Elsevier, vol. 19(1), pages 5-25.
    4. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    5. Kai Huotari & Juho Hamari, 2017. "A definition for gamification: anchoring gamification in the service marketing literature," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(1), pages 21-31, February.
    6. Marie Juanchich & Miroslav Sirota, 2016. "How to improve people's interpretation of probabilities of precipitation," Journal of Risk Research, Taylor & Francis Journals, vol. 19(3), pages 388-404, March.
    7. Huang, Wei, 2016. "The use of management forecasts to dampen analysts' expectations by Chinese listed firms," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 263-272.
    8. 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.
    9. Kathy Rupar, 2017. "Significance of Forecast Precision: The Importance of Investors’ Expectations," Contemporary Accounting Research, John Wiley & Sons, vol. 34(2), pages 849-870, June.
    10. Gerrit Sarens & Giuseppe D’Onza, 2017. "The perception of financial analysts on risk, risk management, and internal control disclosure: Evidence from Belgium and Italy," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 14(2), pages 118-138, May.
    11. Lisa M. Gaynor & Andrea S. Kelton, 2014. "The effects of analyst forecasts and earnings trends on perceptions of management forecast credibility," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(1), pages 189-210, March.
    12. Robert E. O'Connor & Brent Yarnal & Kirstin Dow & Christine L. Jocoy & Gregory J. Carbone, 2005. "Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1265-1275, October.
    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. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    2. Shams, Riad & Chatterjee, Sheshadri & Chaudhuri, Ranjan, 2024. "Developing brand identity and sales strategy in the digital era: Moderating role of consumer belief in brand," Journal of Business Research, Elsevier, vol. 179(C).
    3. Liu, Hsiu-Wen, 2024. "Mining spatial-temporal patterns from customer data to improve forecasting of customer flow across multiple sites," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

    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. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    2. Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
    3. 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.
    4. 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.
    5. Andrew W. Lo & Dmitry V. Repin & Brett N. Steenbarger, 2005. "Fear and Greed in Financial Markets: A Clinical Study of Day-Traders," American Economic Review, American Economic Association, vol. 95(2), pages 352-359, May.
    6. 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.
    7. 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.
    8. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    9. Rainer Alt & Hans-Dieter Zimmermann, 2019. "Electronic Markets on platform competition," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 143-149, June.
    10. 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.
    11. Wilhelm, William J. & Weber, Peter & Douglas, Kacey & Siepermann, Markus & Abuhamdieh, Ayman, 2021. "Moral reasoning and anti-immigrant bias: Experimental evidence from university students in Germany and the United States," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    12. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
    13. Kromidha, Endrit & Li, Matthew C., 2019. "Determinants of leadership in online social trading: A signaling theory perspective," Journal of Business Research, Elsevier, vol. 97(C), pages 184-197.
    14. Parjanen, Satu & Hyypiä, Mirva, 2019. "Innotin game supporting collective creativity in innovation activities," Journal of Business Research, Elsevier, vol. 96(C), pages 26-34.
    15. Hatice Doğan-Südaş & Ali Kara & Emre Karaca, 2023. "Effects of Gamified Mobile Apps on Purchase Intentions and Word-of-Mouth Engagement: Implications for Sustainability Behavior," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    16. Yochi Cohen-Charash & Charles A Scherbaum & John D Kammeyer-Mueller & Barry M Staw, 2013. "Mood and the Market: Can Press Reports of Investors' Mood Predict Stock Prices?," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
    17. Syrjälä, Henna & Kauppinen-Räisänen, Hannele & Luomala, Harri T. & Joelsson, Tapani N. & Könnölä, Kaisa & Mäkilä, Tuomas, 2020. "Gamified package: Consumer insights into multidimensional brand engagement," Journal of Business Research, Elsevier, vol. 119(C), pages 423-434.
    18. Kevin Keasey & Philip Moon & Darren Duxbury, 2000. "Performance measurement and the use of league tables: some experimental evidence of dysfunctional consequences," Accounting and Business Research, Taylor & Francis Journals, vol. 30(4), pages 275-286.
    19. Volk, Stefan & Thöni, Christian & Ruigrok, Winfried, 2012. "Temporal stability and psychological foundations of cooperation preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 81(2), pages 664-676.
    20. Frank Ebbers & Jan Zibuschka & Christian Zimmermann & Oliver Hinz, 2021. "User preferences for privacy features in digital assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 411-426, June.

    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:jbrese:v:106:y:2020:i:c:p:315-322. 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/jbusres .

    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.