The Knowledge, Skills, Abilities, and Other Characteristics Required for Face-to-Face Versus Computer-Mediated Communication: Similar or Distinct Constructs?
- 1.8k Downloads
This study investigated the convergence of knowledge, skills, abilities, and other characteristics (KSAOs) required for either face-to-face (FtF) or text-based computer-mediated (CM) communication, the latter being frequently mentioned as core twenty-first century competencies.
In a pilot study (n = 150, paired self- and peer reports), data were analyzed to develop a measurement model for the constructs of interest. In the main study, FtF and CM communication KSAOs were assessed via an online panel (n = 450, paired self- and peer reports). Correlated-trait-correlated-method minus one models were used to examine the convergence of FtF and CM communication KSAOs at the latent variable level. Finally, we applied structural equation modeling to examine the influence of communication KSAOs on communication outcomes within (e.g., CM KSAOs on CM outcomes) and across contexts (e.g., CM KSAOs on FtF outcomes).
Self-reported communication KSAOs showed only low to moderate convergence between FtF and CM contexts. Convergence was somewhat higher in peer reports, but still suggested that the contextualized KSAOs are separable. Communication KSAOs contributed significantly to communication outcomes; context-incongruent KSAOs explained less variance in outcomes than context-congruent KSAOs.
The results imply that FtF and CM communication KSAOs are distinct, thus speaking to the consideration of CM KSAOs as twenty-first century competencies and not just a derivative of FtF communication competencies.
This study is the first to examine the convergence of context-specific communication KSAOs within a correlated-trait-correlated-method minus one framework using self- and peer reports.
KeywordsComputer-mediated communication Face-to-face communication Communication competence KSAO Correlated-trait-correlated-method minus one model [CT-C(M-1) model]
We would like to thank Michael Eid for his valuable advice on interpreting the CT-C(M-1) models and Manuel Trumpfheller for his help in collecting the data.
- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.Google Scholar
- De Vries, R. E., Bakker-Pieper, A., & Oostenveld, W. (2010). Leadership = communication? The relations of leaders’ communication styles with leadership styles, knowledge sharing and leadership outcomes. Journal of Business and Psychology, 25(3), 367–380. doi:10.1007/s10869-009-9140-2.CrossRefPubMedGoogle Scholar
- Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575–600.Google Scholar
- Enders, C. (2010). Applied missing data analysis. New York, NY: Guilford.Google Scholar
- Geiser, C., Eid, M., West, S. G., Lischetzke, T., & Nussbeck, F. W. (2012). A comparison of method effects in two confirmatory factor models for structurally different methods. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 409–436. doi:10.1080/10705511.2012.687658.CrossRefGoogle Scholar
- Holtz, B. C., Ployhart, R. E., & Dominguez, A. (2005). Testing the rules of justice: The effects of frame-of-reference and pre-test validity information on personality test responses and test perceptions. International Journal of Selection and Assessment, 13(1), 75–86. doi:10.1111/j.0965-075X.2005.00301.x.CrossRefGoogle Scholar
- Kline, R. B. (2005). Principles and practices of structural equation modeling. New York: Guilford.Google Scholar
- Lenhart, A., Madden, M., & Hitlin, P. (2005). Teens and technology: Youth are leading the transition to a fully wired and mobile nation. Washington DC: Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/files/old-media/Files/Reports/2005/PIP_Teens_Tech_July2005web.pdf.pdf.
- Mesmer-Magnus, J. R., DeChurch, L. A., Jimenez-Rodriguez, M., Wildman, J., & Shuffler, M. (2011). A meta-analytic investigation of virtuality and information sharing in teams. Organizational Behavior and Human Decision Processes, 115(2), 214–225. doi:10.1016/j.obhdp.2011.03.002.CrossRefGoogle Scholar
- R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
- Spitzberg, B. H. (1988). Communication competence: Measures of perceived effectiveness. In C. H. Tardy (Ed.), A handbook for the study of human communication: Methods and instruments for observing, measuring, and assessing communication processes (pp. 67–105). Westport, CT: Ablex Publishing.Google Scholar
- Spitzberg, B. H., & Cupach, W. R. (1984). Interpersonal communication competence. Beverly Hills, CA: Sage.Google Scholar
- West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with non-normal variables: Problems and remedies. In R. Hoyle (Ed.), Structural equation modeling: Issues and applications (pp. 56–75). Newbury Park, CA: Sage.Google Scholar
- West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York: Guilford.Google Scholar