Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?

Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?

Ned Kock (Department of International Business and Technology Studies, Texas A&M International University, Laredo, TX, USA)
Copyright: © 2013 |Pages: 12
DOI: 10.4018/jec.2013070101
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What if a researcher obtains empirical data by asking questions to gauge the effect of an e-collaboration technology on task performance, but does not obtain data on the extent to which the e-collaboration technology is used? This characterizes what is referred to here as a scenario with one group and one condition, where the researcher is essentially left with only one column of data to be analyzed. When this happens, often researchers do not know how to analyze the data, or analyze the data making incorrect assumptions and using unsuitable techniques. Some of WarpPLS’s features make it particularly useful in this type of scenario, such as its support for small samples and the use of data that does not meet parametric assumptions. The main goal of this paper is to help e-collaboration researchers use WarpPLS to analyze data in this type of scenario, where only one group and one condition are available. Two other scenarios are also discussed – a typical scenario, and a scenario with one group and two before-after technology introduction conditions. While the focus here is on e-collaboration, the recommendations apply to many other fields.
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A Typical E-Collaboration Study Scenario

Let us assume that a researcher introduced an e-collaboration technology into an organization with the goal of facilitating the work of business process improvement teams (Kock, 2005). These are teams that carry out business process redesign projects – they select, analyze and redesign business processes (Kock, 2006).

All teams studied by the researcher use the e-collaboration technology. No controls on how much the teams use the technology are applied by the researcher, characterizing the investigation as a field study with quasi-experimental elements (Shadish et al., 2002). The researcher is interested in the possible effect that the use of the e-collaboration technology has on team performance measures, such as the return on investment of a business process redesign project.

In this scenario, the researcher can measure the degree to which the e-collaboration technology is used, or the degree to which specific features of the e-collaboration technology are used. Either way, the researcher will have one or more variables for which there will be different values for different teams. These values will reflect different degrees of use of the e-collaboration technology as a whole, or of specific features of the technology.

The researcher can next collect team performance measures and build one or more models to be analyzed with WarpPLS (Kock, 2010; 2011; 2011b). A simple model would have two latent variables, one measuring e-collaboration technology use and the other measuring team performance, with e-collaboration technology use pointing at team performance.

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