Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-14T03:11:37.094Z Has data issue: false hasContentIssue false
  • English
  • Français

When Are Models of Technology in Psychology Most Useful?

Published online by Cambridge University Press:  22 November 2017

Richard N. Landers  and
Tara S. Behrend
Richard N. Landers*
Affiliation:
Old Dominion University
Tara S. Behrend
Affiliation:
George Washington University
*
Correspondence concerning this article should be addressed to Richard N. Landers, Department of Psychology, 250 Mills Godwin Building, Old Dominion University, Norfolk, VA 23529. E-mail: rnlanders@odu.edu
Get access Rights & Permissions [Opens in a new window]

Extract

In industrial-organizational (I-O) psychology, much like in the organizational sciences more broadly (Hambrick, 2007), we have a bit of an addiction to theoretical models. It is commonly assumed that developing new theory is the most valuable way to solve pressing research problems and to drive our field forward (Mathieu, 2016). However, this assumption is untested, and there is growing awareness among organizational scientists that this hardline approach, which is unusual among both the natural sciences and other social sciences, may even be damaging the reputation and influence of our field (Antonakis, 2017; Ones, Kaiser, Chamorro-Premuzic, & Svensson, 2017). As Hambrick (2007) describes, the requirement for theory first “takes an array of subtle, but significant, tolls on our field” (p. 1348). As we will describe in this article, Morelli, Potosky, Arthur, and Tippins’ (2017) suggestions, if taken at face value, will likely create such tolls by encouraging the creation of new theories of dubious value. To be clear, we agree with Morelli et al. that better theory is needed for technology's impact on I-O psychology broadly and talent assessment in particular. We disagree, however, that creating new technology theories using the approaches that I-O psychology typically employs is likely to accomplish this broader goal. Rather, it will ultimately only isolate research on I-O technologies even further from both mainstream I-O research and technology research. Given that we are already quite isolated, this would be a disastrous path.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 4 , December 2017 , pp. 668 - 675
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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.)

References

Antonakis, J. (2017). On doing better science: From thrill of discovery to policy implications. The Leadership Quarterly, 28, 5–21.Google Scholar
Arthur, W. Jr., Keiser, N., & Doverspike, D. (2017). An information processing-based conceptual framework of the effects of the use of Internet-based testing devices on scores on employment-related assessments and tests. Manuscript submitted for publication.Google Scholar
Arthur, W., & Villado, A. J. (2008). The importance of distinguishing between constructs and method when comparing predictors in personnel selection research and practice. Journal of Applied Psychology, 93, 435–442.CrossRefGoogle ScholarPubMed
Bell, B. S., Tannenbaum, S. I., Ford, J. K., Noe, R. A., & Kraiger, K. (2017). 100 years of training and development research: What we know and where we should go. Journal of Applied Psychology, 102, 305–323.Google Scholar
Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.Google Scholar
Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42 (1), 21–29.Google Scholar
Hambrick, D. C. (2007). The field of management's devotion to theory: Too much of a good thing? Academy of Management Journal, 50, 1346–1352.Google Scholar
Hastings, N. B., & Tracey, M. W. (2005). Does media affect learning? Where are we now? TechTrends, 49 (2), 28–30.CrossRefGoogle Scholar
Kacmar, K. M., & Whitfield, J. M. (2000). An additional rating method for journal articles in the field of management. Organizational Research Methods, 3, 392–406.Google Scholar
Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42, 1042–1629.CrossRefGoogle Scholar
Landers, R. N., & Reddock, C. M. (2017). A meta-analytic investigation of objective learner control in web-based instruction. Journal of Business & Psychology, 32, 455–478.CrossRefGoogle Scholar
Mathieu, J. E. (2016). The problem with [in] management theory. Journal of Organizational Behavior, 37, 1132–1141.Google Scholar
Morelli, N., Potosky, D., Arthur, W. Jr., & Tippins, N. (2017). A call for conceptual models of technology in I-O psychology: An example from technology-based talent assessment. Industrial and Organizational Psychology: Perspectives on Science and Practice, 10 (4), 634–653.CrossRefGoogle Scholar
Ones, D. S., Kaiser, R. B., Chamorro-Premuzic, T., & Svensson, C. (2017). Has industrial-organizational psychology lost its way? The Industrial-Organizational Psychologist. Retrieved from http://www.siop.org/tip/april17/lostio.aspx Google Scholar
Potosky, D. (2008). A conceptual framework for the role of the administration medium in the personnel assessment process. Academy of Management Review, 33, 629–648.Google Scholar
Raudenbush, S. W., Bryk, A. S., & Congdon, R. (2013). HLM 7.01 for Windows [Computer software]. Skokie, IL: Scientific Software International, Inc.Google Scholar
Rosseel, Y. (2012). lavaan: An R package for structure equation modeling. Journal of Statistical Software, 48, 1–36.Google Scholar
Schneider, B. (1987). The people make the place. Personnel Psychology, 40, 437–453.Google Scholar