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

Loading...
Thumbnail Image

Date

Journal Title

Journal ISSN

Volume Title

Publisher

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?

Description

Keywords

Citation

1. Von Neuman, J., & Morgenstern, O. (1944). Theory of games and economic behavior. New York: Wiley. 2. Simon, H.A. (1968). Administrative behavior: A study of decision-making processes in administrative organization. New York: Macmillan. 3. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 2 (March), 263-292. 4. Burke, L.A., & Miller, M.K. (1999). Taking the mystery out of intuitive decision-making. Academy of Management Executive, 13 (4), 91-99. 5. Sadler-Smith, E., & Shefy, E. (2004). The intuitive executive: Understanding and applying ‘gut feel’ in decision-making. Academy of Management Executive, 18 (4), 76-91. 6. Simon, H.A. (1987). Making management decisions: The role of intuition and emotion. Academy of Management Executive, 1 (1), 57-64. 7. Shirley, D., & Langan-Fox, J. (1996). Intuition: A review of the literature. Psychological Reports, 79 (2), 563-584. 8. Sinclair, M., & Ashkanasy, N. M. (2005). Intuition: Myth or a decision-making tool. Management Learning, 36 (3), 353-370 9. Dane, E., & Pratt, M. (2007). Exploring intuition and its role in managerial decision-making. Academy of Management Review, 32 (1), 33-54. 10. Ericsson, K.A., & Charness, N. (1994). Expert performance. American Psychologist, 49 (8), 725-747. 11. Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49 (8), 709-724. 12. Nonaka, I.T. (1995). The knowledge creating company. New York: Oxford University Press. 13. Shapiro, S., & Spence, M.T. (1997). Managerial intuition: A conceptual and operational framework. Business horizons, 40 (1), 63-68. 14. Janis, I. (1977). Decision making: A psychological analysis of conflict, choice, and commitment. New York: Free Press. 15. Lieberman, M.D. (2000). Intuition: A social cognitive neuroscience approach. Psychological Bulletin, 126 (1), 109-37. 16. Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58 (9), 697-720. 17. Hogarth, R.M. (2001). Educating Intuition. Chicago: The University of Chicago Press. 18. Noordink, P.J., & Ashkanasy, N.M. (2004). Intuition, emotion, and expertise in financial trading: A study of the subjective experience of decision-making. Conference Proceeding: The 64th Annual Meetings of the Academy of Management. New Orleans, Louisiana, USA. 19. James, W. (1884). What is an Emotion ? Mind, 9 (34), 188- 205. 20. Janet, P. (1926). De l’angoisse à l’extase: Etude sur les croyances et les sentiments. Paris: Librairie Félix Alcan. 21. Scherer, K.R. (1984). On the nature and function of emotion: A component process approach. In K. R. Scherer & P. Ekman (Eds.), Approaches to emotion (pp. 293-317). Hillsdale, NJ: Erlbaum. 22. Ekman, P. (1994). Strong evidence for universal in facial expressions: A reply to Russell’s mistaken critique. Psychological Bulletin, 115 (2), 268-287. 23. Rimé, B., Corsini, S., & Herbette, G. (2002). Emotion, verbal expression, and the social sharing of emotion. In S.R. Fussell (Ed.), The verbal communication of emotions: Interdisciplinary perspectives (pp. 185-208). Mawhaw, NJ: Erlbaum. 24. Frijda, N.H. (1986). The emotions. Cambridge: Cambridge University Press. 25. Lazarus, R. (1999). Stress and Emotion: A New Synthesis. New York: Springer Publishing Company. 26. Scherer, K.R., Schorr, A., & Johnstone, T. (2001). Appraisal processes in emotion: Theory, methods, research. Oxford, New York: Oxford University Press. 27. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 1994; 50: 7–15. 28. Schwarz, N., & Clore, G.L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45 (3), 513-523. 29. Lerner, J.S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgment and choice. Cognition & Emotion, 14 (4), 473-493. 30. Isen, A.M., & Patrick, R. (1983). The effect of positive feelings on risktaking: When the chips are down. Organizational Behavior and Human Performance, 31 (2), 194-202. 31. Isen, A.M., & Means, B. (1983). The influence of positive affect on decision-making strategy. Social Cognition, 2 (1), 18-31. 32. Estrada, C.A., Isen, A.M., & Young, M.J. (1997). Positive affect facilitates integration of information and decreases anchoring in reasoning among physicians. Organizational Behavior and Human Decision Processes, 72 (1), 117-135. 33. Forgas, J.P., & George, J.M. (2001). Affective influences on judgments and behavior in organizations: An information processing perspective. Organizational Behavior and Human Decision Processes, 86 (1), 3-34. 34. Leon S., A. Nikov (2010) “Emotion-oriented eCommerce”. WSEAS Transaction on Systems, 6(9):594-606. 35. Murray PN, How Emotions Influence What We Buy The emotional core of consumer decision-making. Published on February 26, 2013. Inside the Consumer Mind. 36. Giraud M., Bonnefont A. (2000), « Création d’une échelle de mesure de l’impulsivité dans l’achat: impulsivité fonctionnelle et impulsivité dysfonctionnelle », XV ème Journées Nationales des IAE, Bayonne-Biarritz. 37. Crémer C., The Future of E-Commerce: the Brands and Emotion (2011). Fevad. 38. Linden, G., Smith, B., York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 39. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G. (2011). Recommender Systems: An Introduction. Cambridge University Press, New York 40. Mahmood, T., Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. In: C. Cattuto, G. Ruffo, F. Menczer (eds.) Hypertext, pp. 73–82. ACM 41. Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., Molino, P. (2008). Introducing Serendipity in a Content-based Recommender System. In: F. Xhafa, F. Herrera, A. Abraham, M. Koppen, J.M. Benitez (eds.) Proceedings of the Eighth International Conference on Hybrid Intelligent Systems HIS-2008, pp. 168–173. IEEE Computer Society Press, Los Alamitos, California 42. Hostler, R.E., Yoon, V.Y., Guo, Z., Guimaraes, T., & Forgionne, G. (2011). Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Information and Management, 48(8), 336–343 43. Thirumalai, S., & Sinha, K.K. (2011). Customization of the online purchase process in electronic retailing and customer satisfaction: An online field study. Special Issue on Field Research in Operations and Supply Chain Management, 29(5), 477–487 44. Adomavicius, G., Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 45. Massa, P., Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. In: Proceedings of the International Conference on Cooperative Information Systems, CoopIS, pp. 492–508 46. Dabholkar, P.A., Sheng, X. (2012). Consumer participation in using online recommendation agents: Effects on satisfaction, trust, and purchase intentions. The Service Industries Journal, 32(9), 1433–1449 47. Pu, P., & Chen, L. (2007). Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems, 20(6), 542–556 48. Benlian, A., Titah, R., Hess, T. (2012). Differential effects of provider recommendations and consumer reviews in e-commerce transactions: An experimental study. Journal of Management Information Systems, 29(1), 237–272. 49. Sriram, S., P. K. Chintagunta, M. K. Agarwal. (2010). Investigating consumer purchase behavior in related technology product categories. Marketing Science 29(2) 291–314. 50. Murray, K. B., & Häubl, G. (2009). Personalization without interrogation: Towards more effective interactions between consumers and feature-based recommendation agents. Journal of Interactive Marketing, 23(2), 138–146 51. Groh, G., Ehmig, C. (2007). Recommendations in taste related domains: collaborative filtering vs. social filtering. In: GROUP ’07: Proceedings of the 2007 international ACM conference on supporting group work, pp. 127–136. ACM, New York, NY, USA 52. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T. (2004). Evaluating collaborative filtering recommender systems. ACM Transaction on Information Systems 22(1), 5–53 53. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S. (2007). Collaborative filtering recommender systems. In: The Adaptive Web, pp. 291–324. Springer Berlin / Heidelberg 54. Balabanovic, M., Shoham, Y. (1997). Content-based, collaborative recommendation. Communication of ACM 40(3), 66–72 55. Symeonidis, P., Nanopoulos, A., Manolopoulos, Y. (2008). Justified recommendations based on content and rating data. In: Workshop on Web Mining and Web Usage Analysis in Conjunction with the International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA 56. He, L., Zhang, J., Zhuo, L., Shen, L. (2008). Construction of user preference profile in a personalized image retrieval. In: Neural Networks and Signal Processing, 2008 International Conference on, 434–439 57. Gorgoglione, M., Panniello, U., & Tuzhilin, A. (2011). The effect of context-aware recommendations on customer purchasing behavior and trust: Proceedings of the fifth ACM conference on Recommender systems: ACM, 85–92 58. Pazzani, M.J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13, 393–408 59. Bridge, D., Goker, M., McGinty, L., Smyth, B. (2006). Case-based recommender systems. The Knowledge Engineering review 20(3), 315–320 60. Jannach, D., Zanker, M., Fuchs, M. (2009). Constraint-based recommendation in tourism: A multi-perspective case study. Information Technology and Tourism 11(2), 139–156 61. Arazy, O., Kumar, N., Shapira, B. (2009). Improving social recommender systems. IT Professional 11(4), 38–44 62. Hess, T., Fuller, M., & Campbell, D. (2009). Designing interfaces with social presence: Using vividness and extraversion to create social recommendation agents. Journal of the Association for Information Systems, 10(12), 889–919 63. Burke, R. (2007). Hybrid web recommender systems. In: The AdaptiveWeb, pp. 377–408. Springer Berlin / Heidelberg 64. Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S. (2009). Personalized recommendation of social software items based on social relations. In: RecSys 09: Proceedings of the third ACM conference on Recommender systems, 53–60. ACM, New York, NY, USA 65. Jessenitschnig, M., Zanker, M. (2009). A generic user modeling component for hybrid recommendation strategies. E-Commerce Technology, IEEE International Conference on 0, 337–344 66. xAmatriain X, Basilico J, (2013). System Architecture for Personalization and Recommendation. Netflix Tech Blog (http://techblog.netflix.com/2013/03/system-architectures-for.html) 67. McNee, S.M., Riedl, J., Konstan, J.A. (2006). Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06: CHI ’06 extended abstracts on Human factors in computing systems, pp. 1097–1101. ACM Press, New York, NY, USA 68. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 1–64 69. Hu, R. and Pu, P. (2009). Potential Acceptance Issues of Personality based Recommender Systems. In: Proceedings of ACM Conference on Recommender Systems (RecSys'09), New York City, NY, USA, Oct. 22-25. 70. Pommeranz, A., Broekens, J.,Wiggers, P., Brinkman,W.P., Jonker, C.M. (2012). Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process. User Model. User Adapt. Interact. 22 71. Cosley, D., Lam, S.K., Albert, I., Konstan, J.A. and Riedl, J. (2003). Is seeing believing? How recommender system interfaces affect users’ opinions. In: Proceedings of the conference on Human Factors in Computing Systems, 585–592 72. Konstan, J., Riedl, J., (2012). Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22(1-2): 101-123. 73. Fleder, D., Hosanger, K. (2009). Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manage. Sci. 55(5), 697–712 74. Murphy-Hill, E., Murphy G.C. (2014). Recommendation Delivery. Recommendation Systems in Software Engineering, Springer: 223-242 75. Hu R., Pu P. (2011). Enhancing recommendation diversity with organization interfaces. In: Proceedings of the 16th International conference on intelligent user interfaces, IUI 11, 347-350, New York, NY, USA, (2011). ACM