IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-01065283.html
   My bibliography  Save this paper

Do we need to believe Data/Tangible or Emotional/Intuition?

Author

Listed:
  • Fanjuan Shi

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon)

  • Jean-Luc Marini

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon)

Abstract

Now Data are clearly prevailing in all domains like a new black gold for companies and the rules in business decision-making are called into question. In this context, we think that Data Analytics combined with collaborative decision processes promotes a rational decision-making. However best practices show that more and more executives and managers, the famous HiPPO (Highest Paid Person's Opinion), now frequently use their intuition for strategic decision-making. Moreover a lot of empirical surveys also show how important is the emotion in the intuitive decision-making processes. We will try to explain how we can interpret differently data coming from big data using the most recent scientific advances in the field of psycho-cognitive sciences, in the goal to improve decision support systems and to take into account emotion in the decision-making processes. Finally we hope this could provide some elements to answer to the question: Do we need to believe Data/Tangible or Emotional/Intuition?

Suggested Citation

  • Fanjuan Shi & Jean-Luc Marini, 2014. "Do we need to believe Data/Tangible or Emotional/Intuition?," Post-Print halshs-01065283, HAL.
    • Handle: RePEc:hal:journl:halshs-01065283
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01065283
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-01065283/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    2. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57946, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 59346, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Forgas, Joseph P. & George, Jennifer M., 2001. "Affective Influences on Judgments and Behavior in Organizations: An Information Processing Perspective," Organizational Behavior and Human Decision Processes, Elsevier, vol. 86(1), pages 3-34, September.
    5. 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.
    6. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    7. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    8. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65605, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Murray, Kyle B. & Häubl, Gerald, 2009. "Personalization without Interrogation: Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 138-146.
    10. Shapiro, Stewart & Spence, Mark T., 1997. "Managerial intuition: A conceptual and operational framework," Business Horizons, Elsevier, vol. 40(1), pages 63-68.
    11. S. Sriram & Pradeep K. Chintagunta & Manoj K. Agarwal, 2010. "Investigating Consumer Purchase Behavior in Related Technology Product Categories," Marketing Science, INFORMS, vol. 29(2), pages 291-314, 03-04.
    Full references (including those not matched with items on IDEAS)

    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. Christian Matt & Thomas Hess, 2016. "Product fit uncertainty and its effects on vendor choice: an experimental study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(1), pages 83-93, February.
    2. Zhang, Hong & Zhao, Ling & Gupta, Sumeet, 2018. "The role of online product recommendations on customer decision making and loyalty in social shopping communities," International Journal of Information Management, Elsevier, vol. 38(1), pages 150-166.
    3. Chen Liang & Zhan (Michael) Shi & T. S. Raghu, 2019. "The Spillover of Spotlight: Platform Recommendation in the Mobile App Market," Information Systems Research, INFORMS, vol. 30(4), pages 1296-1318, December.
    4. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    6. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    7. Shih-Wei Chou & Chia-Shiang Hsu, 2016. "Understanding online repurchase intention: social exchange theory and shopping habit," Information Systems and e-Business Management, Springer, vol. 14(1), pages 19-45, February.
    8. Fursov, Konstantin & Thurner, Thomas & Nefedova, Alena, 2017. "What user-innovators do that others don't: A study of daily practices," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 153-160.
    9. Severin Weiler & Christian Matt & Thomas Hess, 2022. "Immunizing with information – Inoculation messages against conversational agents’ response failures," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 239-258, March.
    10. Thomas Friedrich & Sebastian Schlauderer & Sven Overhage, 2021. "Some things are just better rich: how social commerce feature richness affects consumers’ buying intention via social factors," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 159-180, March.
    11. Tuncer, Ilhami, 2021. "The relationship between IT affordance, flow experience, trust, and social commerce intention: An exploration using the S-O-R paradigm," Technology in Society, Elsevier, vol. 65(C).
    12. Hu, Xi & Huang, Qian & Zhong, Xuepan & Davison, Robert M. & Zhao, Dingtao, 2016. "The influence of peer characteristics and technical features of a social shopping website on a consumer’s purchase intention," International Journal of Information Management, Elsevier, vol. 36(6), pages 1218-1230.
    13. Isamar Troncoso & Lan Luo, 2023. "Look the Part? The Role of Profile Pictures in Online Labor Markets," Marketing Science, INFORMS, vol. 42(6), pages 1080-1100, November.
    14. David Schneider & Johannes Klumpe & Martin Adam & Alexander Benlian, 2020. "Nudging users into digital service solutions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 863-881, December.
    15. Sofia Cardoso & Luis F. Martinez, 2019. "Online payments strategy: how third-party internet seals of approval and payment provider reputation influence the Millennials’ online transactions," Electronic Commerce Research, Springer, vol. 19(1), pages 189-209, March.
    16. Kick, Markus, 2015. "Social Media Research: A Narrative Review," EconStor Preprints 182506, ZBW - Leibniz Information Centre for Economics.
    17. Ni Wayan Masri & Athapol Ruangkanjanases & Shih-Chih Chen, 2020. "The Effects of Product Monetary Value, Product Evaluation Cost, and Customer Enjoyment on Customer Intention to Purchase and Reuse Vendors: Institutional Trust-Based Mechanisms," Sustainability, MDPI, vol. 13(1), pages 1-20, December.
    18. Sullivan, Yulia W. & Kim, Dan J., 2018. "Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments," International Journal of Information Management, Elsevier, vol. 39(C), pages 199-219.
    19. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    20. Zhan (Michael) Shi & T. S. Raghu, 2020. "An Economic Analysis of Product Recommendation in the Presence of Quality and Taste-Match Heterogeneity," Information Systems Research, INFORMS, vol. 31(2), pages 399-411, June.

    More about this item

    Keywords

    decision-making; intuitive decision-making; emotion; e-commerce; recommender systems;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:halshs-01065283. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.