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I-O Psychology and Technology: Why Reinvent the Wheel?

Published online by Cambridge University Press:  22 November 2017

Matt C. Howard*
Affiliation:
The University of South Alabama
Steven D. Travers
Affiliation:
Travers Consulting and The University of South Alabama
Chad J. Marshall
Affiliation:
U.S. Army Aviation and Missile Research, Development, and Engineering Center and The University of South Alabama
Joshua E. Cogswell
Affiliation:
The University of South Alabama
*
Correspondence concerning this article should be addressed to Matt C. Howard, Ph.D., Assistant Professor, Marketing and Quantitative Methods, The University of South Alabama, 337 Mitchell College of Business, Mobile, AL 36695. E-mail: Mhoward@SouthAlabama.edu

Extract

Morelli, Potosky, Arthur, and Tippins (2017) make a timely and appropriate call for authors to create conceptual models of technology in industrial-organizational (I-O) psychology. We agree with their call, but we believe that Morelli et al. overlooked the contributions of related fields that conduct research on technology in the workplace that are already consistent with their call. For this reason, we briefly detail other fields that commonly study the dynamics of technology and its influence on the workplace, followed by a discussion regarding the place of I-O psychology in the broader scheme of technology research. This discussion can aid future authors in conceptualizing appropriate contributions to the study of technology in I-O psychology as well as identifying whether these contributions benefit other fields. Perhaps more importantly, this discussion can help identify where I-O psychology fits in the broader scheme of technology research and which associated fields may be most readily available to aid in the creation of new models—two questions that currently seem unanswered.

Type
Commentaries
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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References

Computers and Electrical Engineering . (2017). Author information pack—description. Retrieved from https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906?generatepdf=true Google Scholar
Computers in Human Behavior . (2017). Author information pack—description. Retrieved from https://www.elsevier.com/journals/computers-in-human-behavior/0747-5632?generatepdf=true Google Scholar
Howard, M. C., & Jayne, B. S. (2015). An analysis of more than 1,400 articles, 900 scales, and 17 years of research: The state of scales in cyberpsychology, behavior, and social networking. Cyberpsychology, Behavior, and Social Networking, 18 (3), 181187.Google Scholar
Ji, W., Zhao, D., Cheng, F., Xu, B., Zhang, Y., & Wang, J. (2012). Automatic recognition vision system guided for apple harvesting robot. Computers & Electrical Engineering, 38 (5), 11861195.Google Scholar
Kozlowski, S. W., Chao, G. T., Grand, J. A., Braun, M. T., & Kuljanin, G. (2013). Advancing multilevel research design: Capturing the dynamics of emergence. Organizational Research Methods, 16 (4), 581615.Google Scholar
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35 (1), 147167.CrossRefGoogle Scholar
MIS Quarterly . (2017). About MIS Quarterly—editorial objective. Retrieved from http://www.misq.org/about/ Google Scholar
Morgeson, F. P., & Hofmann, D. A. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24 (2), 249265.CrossRefGoogle 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.Google Scholar
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors, 56 (3), 476488.Google Scholar
Organizational Behavior and Human Decision Processes . (2017). Author information pack—description. Retrieved from https://www.elsevier.com/journals/organizational-behavior-and-human-decision-processes/0749-5978?generatepdf=true Google Scholar
Plummer, J. P., Schuster, D., & Keebler, J. R. (2017). The effects of gender, flow and video game experience on combat identification training. Ergonomics, 60 (8), 11011111.CrossRefGoogle ScholarPubMed
Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36 (1), 2142.CrossRefGoogle Scholar
Stinchcombe, A. L. (1982). Should sociologists forget their mothers and fathers. The American Sociologist, 17 (1), 211.Google Scholar
Teece, D. J. (1996). Firm organization, industrial structure, and technological innovation. Journal of Economic Behavior & Organization, 31 (2), 193224.Google Scholar
Tuncer, A., & Yildirim, M. (2012). Dynamic path planning of mobile robots with improved genetic algorithm. Computers & Electrical Engineering, 38 (6), 15641572.Google Scholar
Vitak, J., Crouse, J., & LaRose, R. (2011). Personal Internet use at work: Understanding cyberslacking. Computers in Human Behavior, 27 (5), 17511759.Google Scholar