Hostname: page-component-7bb8b95d7b-5mhkq Total loading time: 0 Render date: 2024-09-12T23:41:55.090Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrating Technology Into Models of Response Behavior

Published online by Cambridge University Press:  22 November 2017

Dev K. Dalal  and
Jason G. Randall
Dev K. Dalal*
Affiliation:
University at Albany, State University of New York
Jason G. Randall
Affiliation:
University at Albany, State University of New York
*
Correspondence concerning this article should be addressed to Dev K. Dalal, University at Albany, State University of New York, 1400 E. Washington Ave., SS-399, Albany, NY 12222. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

Morelli, Potosky, Arthur, and Tippins (2017) are correct in calling for more conceptual models explicitly linking technology to industrial-organizational (I-O) psychology. As these authors note, in the absence of models and theories of technology to guide the research and practice of I-O psychology, the field runs the risk of chasing the impacts of specific technological innovations and devices rather than guiding organizations on best practices regarding the use of technology. Building theories and models that directly involve technology and placing them within individual psychological and larger organizational processes provides researchers with a way to stay ahead of the fast pace of technological innovation and anticipate its effects on measurement and prediction. Moreover, there are aspects to the use of technology that I-O psychologists are uniquely qualified to consider, including legal considerations (e.g., accessibility concerns), ethical questions (e.g., access in disadvantaged communities), practical concerns (e.g., user and target reactions), and measurement issues (e.g., construct irrelevant variance). In this commentary, we present two main points of consideration that demonstrate how I-O psychologists might use and create technology to improve assessment. First, we argue that technology can improve the measurement of psychological variables if we critically consider how technology can positively influence various parts of response behavior. Additionally, we encourage future research to consider the effects of technology in I-O psychology more comprehensively by extending the emphasis on psychological processes beyond cognition and behavior to include affect and motivation.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 4 , December 2017 , pp. 680 - 687
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

Arthur, W. Jr., Doverspike, D., Kinney, T. B., & O'Connell, M. (2017). The impact of emerging technologies on selection models and research: Mobile devices and gamification as exemplars. In Farr, J. L. & Tippins, N. T. (Eds.), Handbook of employee selection (2nd ed.) (pp. 967–986). New York: Taylor & Francis/Psychology Press.CrossRefGoogle Scholar
Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York: Free Press.Google Scholar
Balzer, W. K., Doherty, M. E., & O'Connor, R. (1989). Effects of cognitive feedback on performance. Psychological Bulletin, 106, 410–433.CrossRefGoogle Scholar
Borman, W. C. (1978). Exploring upper limits of reliability and validity in job performance ratings. Journal of Applied Psychology, 63, 135–144.Google Scholar
Borman, W. C., Buck, D. E., Hanson, M. A., Motowidlo, S. J., Stark, S., & Drasgow, F. (2001). An examination of the comparative reliability, validity, and accuracy of performance ratings made using computerized adaptive rating scales. Journal of Applied Psychology, 86, 965–973.Google Scholar
Brief, A. P., & Weiss, H. M. (2002). Organizational behavior: Affect in the workplace. Annual Review of Psychology, 53, 279–307.CrossRefGoogle ScholarPubMed
Brown, K. G. (2005). An examination of the structure and nomological network of trainee reactions: A closer look at “smile sheets.” Journal of Applied Psychology, 90 (5), 991–1001.CrossRefGoogle Scholar
Dalal, D. K., & Hakel, M. D. (2016). Experimental comparisons of methods for reducing deliberate distortions to self-report measures of sensitive constructs. Organizational Research Methods, 19, 475–505.Google Scholar
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.Google Scholar
Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32, 575–600.Google Scholar
Goffin, R. D., Gellatly, I. R., Paunonen, S. V., Jackson, D. N., & Meyer, J. P. (1996). Criterion validation of two approaches to performance appraisal: The Behavior Observation Scale and the relative percentile method. Journal of Business and Psychology, 11, 23–33.Google Scholar
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213–236.CrossRefGoogle Scholar
Griffith, R. L., Lee, L. M., Peterson, M. H., & Zickar, M. J. (2011). First dates and little white lies: A trait contract classification theory of applicant faking behavior. Human Performance, 24, 338–357.Google Scholar
Hausknecht, J. P., Day, D. V., & Thomas, S. C. (2004). Applicant reactions to selection procedures: An updated model and meta‐analysis. Personnel Psychology, 57, 639–683.Google Scholar
Johnson, R., & Randall, J. G. (in press). A review of design considerations in e-learning. Invited manuscript in Stone, D. L. & Dulebohn, J. H. (Eds.), Research in Human Resource Management.Google Scholar
Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative/aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74, 657– 690.CrossRefGoogle Scholar
Levy, P. E., & Williams, J. R. (2004). The social context of performance appraisal: A review and framework for the future. Journal of Management, 30, 881–905.CrossRefGoogle Scholar
Lievens, F., Reeve, C. L., & Heggestad, E. D. (2007). An examination of psychometric bias due to retesting on cognitive ability tests in selection settings. Journal of Applied Psychology, 92, 1672– 1682.Google Scholar
Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63, 760–769.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.Google Scholar
Morrison, E. W., & Robinson, S. L. (1997). When employees feel betrayed: A model of how psychological contract violation develops. Academy of Management Review, 22, 226–256.Google Scholar
Randall, J. G., Oswald, F. L., & Beier, M. E. (2014). Mind-wandering, cognition, and performance: A theory-driven meta-analysis of attention regulation. Psychological Bulletin, 140, 1411–1431.CrossRefGoogle ScholarPubMed
Randall, J. G., & Villado, A. J. (2017). Take two: Sources and deterrents of score change in employment retesting. Human Resource Management Review, 27, 536–553.CrossRefGoogle Scholar
Salas, E., & Cannon-Bowers, J. A. (2001). The science of training: A decade of progress. Annual Review of Psychology, 52, 471–499.Google Scholar
Schmit, M. J., & Ryan, A. M. (1997). Applicant withdrawal: The role of test-taking attitudes and racial differences. Personnel Psychology, 50, 855–876.CrossRefGoogle Scholar
Schwarz, N. (1999). Self-reports: How the questions shape the answers. American Psychologist, 54, 93–105.CrossRefGoogle Scholar
Sitzmann, T., Brown, K. G., Casper, W. J., Ely, K., & Zimmerman, R. D. (2008). A review and meta-analysis of the nomological network of trainee reactions. Journal of Applied Psychology, 93, 280–295.CrossRefGoogle ScholarPubMed
Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological Bulletin, 105, 131–142.Google Scholar
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25, 216–231.CrossRefGoogle Scholar
Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive processes to survey methodology. San Francisco, CA: Jossey-Bass.Google Scholar
Villado, A. J., Randall, J. G., & Zimmer, C. U. (2016). The effect of method characteristics on retest score gains and criterion-related validity. Journal of Business and Psychology, 31, 233–248.Google Scholar