Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-24T04:59:45.943Z Has data issue: false hasContentIssue false
  • English
  • Français

Technology Is More Than Just Error

Published online by Cambridge University Press:  22 November 2017

Matthew J. Grawitch ,
Steven L. Winton ,
Srikanth P. Mudigonda  and
John P. Buerck
Matthew J. Grawitch*
Affiliation:
Saint Louis University
Steven L. Winton
Affiliation:
Saint Louis University
Srikanth P. Mudigonda
Affiliation:
Saint Louis University
John P. Buerck
Affiliation:
Saint Louis University
*
Correspondence concerning this article should be addressed to Matthew J. Grawitch, PhD, Saint Louis University, 3840 Lindell Blvd., St. Louis, MO 63108. E-mail: grawitch@slu.edu
Get access Rights & Permissions [Opens in a new window]

Extract

Modern technology and technological advances offer a variety of benefits and challenges for assessment, data collection, communication, and other research- and practice-related endeavors. The focal article written by Morelli, Potosky, Arthur, and Tippins (2017) offers a segue into discussions about some of these issues. Although the authors offer some unique insights, we believe their view is incomplete, as it is potentially limited by their focus on testing and assessment. Below, we outline a few key points we hope will advance the conversation. Our commentary is largely grounded in the field of human–computer interaction (HCI), which is an interdisciplinary field that integrates expertise from computer science, psychology (and other behavioral sciences), and many other fields. Whereas psychology tends to place the human user at the forefront of discussions concerning technology, HCI expands beyond just the user's psychology, focusing on the design of interfaces that allow users to interact with computing technology in new ways (Card, Moran, & Newell, 1983).

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 4 , December 2017 , pp. 654 - 659
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

American Psychological Association (2013, September). Communication technology: Implications for work and well-being. Retrieved from http://www.apaexcellence.org/assets/general/2013-work-and-communication-technology-survey-final.pdf?_ga=2.101343489.120189717.1501078723-1800684462.1489763177 Google Scholar
Anderson, M., & Perrin, A. (2017, April). Disabled Americans are less likely to use technology. Retrieved from http://www.pewresearch.org/fact-tank/2017/04/07/disabled-americans-are-less-likely-to-use-technology/ Google Scholar
Bowen, W. G., Lack, K. A., Chingos, M., & Nygren, T. I. (2012, May). Interactive learning online at public universities: Evidence from randomized trials. Retrieved from https://doi.org/10.18665/sr.22464 CrossRefGoogle Scholar
Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of human–computer interaction. Hillsdale, NJ: Erlbaum.Google Scholar
Colvin, K. F., Champaign, J., Liu, A., Zhou, Q., Fredericks, C., & Pritchard, D. E. (2014). Learning in an introductory physics MOOC: All cohorts learn equally, including an on-campus class. The International Review of Research in Open and Distributed Learning, 15 (4), 263–283.CrossRefGoogle Scholar
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19 (2), 213–236.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.CrossRefGoogle Scholar
Orlikowski, W. J., & Hofman, D. (1997). An improvisational model for change management: The case of Groupware Technologies. MIT Center for Coordination Science, Working Paper Series, 38.Google Scholar
Orlikowski, W. J., & Scott, S. V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. Academy of Management Annals, 2 (1), 433–474.CrossRefGoogle Scholar
Orlikowski, W. J., Yates, J., Okamura, K., & Fujimoto, M. (1995). Shaping electronic communication: The metastructuring of technology in the context of use. Organization Sscience, 6 (4), 423–444.CrossRefGoogle Scholar
Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The Media and Technology Usage and Attitudes Scale: An empirical investigation. Computers in Human Behavior, 29, 2501–2511.CrossRefGoogle ScholarPubMed
Stack, S. (2015). Learning outcomes in an online vs, traditional course. International Journal for the Scholarship of Teaching and Learning, 9 (1), Article 5.Google Scholar
Venkatesh, V., Morris, M., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478.CrossRefGoogle Scholar