Hostname: page-component-cb9f654ff-c75p9 Total loading time: 0 Render date: 2025-08-25T21:52:25.029Z Has data issue: false hasContentIssue false
  • English
  • Français

Wanted: A Better Psychological Understanding of How Individuals Integrate “Big Data” Into Their Decision Making

Published online by Cambridge University Press:  17 December 2015

Dustin J. Sleesman
Dustin J. Sleesman*
Affiliation:
Department of Business Administration, University of Delaware
*
Correspondence concerning this article should be addressed to Dustin J. Sleesman, Department of Business Administration, University of Delaware, Newark, DE 19716. E-mail: sleesman@udel.edu
Get access Rights & Permissions [Opens in a new window]

Extract

Businesses, governments, universities, hospitals, law enforcement agencies, and other organizations are increasingly collecting and analyzing data to inform decision making. This “big data” movement has benefited from the contributions of a number of academic disciplines, including mathematics, statistics, and computer science. The technical advances involved in big data have grown exponentially in recent years, thus contributing to its growing use by organizations, the experience of which contributes to further refinements and so forth. This cycle of technical advancement is likely to continue into the foreseeable future.

Information

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 4 , December 2015 , pp. 534 - 538
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Ambady, N. (2010). The perils of pondering: Intuition and thin slice judgments. Psychological Inquiry, 21 (4), 271–278. doi:10.1080/1047840x.2010.524882Google Scholar
Arkes, H. R., Shaffer, V. A., & Medow, M. A. (2007). Patients derogate physicians who use a computer-assisted diagnostic aid. Medical Decision Making, 27 (2), 189–202. doi:10.1177/0272989X06297391CrossRefGoogle ScholarPubMed
Curhan, J. R., & Pentland, A. (2007). Thin slices of negotiation: Predicting outcomes from conversational dynamics within the first 5 minutes. Journal of Applied Psychology, 92 (3), 802–811. doi:10.1037/0021-9010.92.3.802CrossRefGoogle ScholarPubMed
Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243 (4899), 1668–1674. doi:10.1126/science.2648573Google Scholar
De Dreu, C. K. W., Nijstad, B. A., & van Knippenberg, D. (2008). Motivated information processing in group judgment and decision making. Personality and Social Psychology Review, 12 (1), 22–49. doi:10.1177/1088868307304092Google Scholar
Erez, A., & Grant, A. M. (2014). Separating data from intuition: Bringing evidence into the management classroom. Academy of Management Learning & Education, 13 (1), 104–119. doi:10.5465/amle.2013.0098Google Scholar
Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26 (4), 331–362. doi:10.1002/job.322Google Scholar
Gore, J., & Sadler-Smith, E. (2011). Unpacking intuition: A process and outcome framework. Review of General Psychology, 15 (4), 304–316. doi:10.1037/a0025069CrossRefGoogle Scholar
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical-statistical controversy. Psychology, Public Policy, and Law, 2 (2), 293–323. doi:10.1037/1076-8971.2.2.293CrossRefGoogle Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8 (4), 491–508.Google Scholar
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108 (4), 814–834. doi:10.1037//0033-295x.108.4.814Google Scholar
Highhouse, S. (2008). Stubborn reliance on intuition and subjectivity in employee selection. Industrial and Organizational Psychology, 1 (3), 333–342. doi:10.1111/j.1754-9434.2008.00058.xGoogle Scholar
Khatri, N., & Ng, H. A. (2000). The role of intuition in strategic decision making. Human Relations, 53 (1), 57–86. Retrieved from http://hum.sagepub.comGoogle Scholar
Naquin, C. E., & Kurtzberg, T. R. (2004). Human reactions to technological failure: How accidents rooted in technology vs. human error influence judgments of organizational accountability. Organizational Behavior and Human Decision Processes, 93 (2), 129–141. doi:10.1016/j.obhdp.2003.12.001Google Scholar
Onkal, D., Goodwin, P., Thomson, M., Gӧnül, S., & Pollock, A. (2009). The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making, 22 (4), 390–409. doi:10.1002/bdm.637Google Scholar
Pfeffer, J. (1981). Power in organizations. Marshfield, MA: Pitman.Google Scholar
Sadler-Smith, E. (2004). Cognitive style and the management of small and medium-sized enterprises. Organization Studies, 25 (2), 155–181. doi:10.1177/0170840604036914Google Scholar
Sieck, W. R., & Arkes, H. R. (2005). The recalcitrance of overconfidence and its contribution to decision aid neglect. Journal of Behavioral Decision Making, 18 (1), 29–53. doi:10.1002/bdm.486Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185 (4157), 1124–1131. doi:10.1126/science.185.4157.1124Google Scholar
Weiner, B. (1986). An attributional theory of motivation and emotion. New York, NY: Springer-Verlag.CrossRefGoogle Scholar
Zsambok, C. E., & Klein, G. (2014). Naturalistic decision making (2nd ed.). New York, NY: Psychology Press.Google Scholar