Knowledge that Transforms

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a significant percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory (cf. McKeen et al. 1994, Weill and Olson 1989). In our survey of such studies, the majority failed to either detect or provide an estimate of the effect size. In cases where effect sizes are estimated, the numbers are generally small. These results have led some researchers to question both the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships. The capacity of this approach at recovering true effects in comparison to summated regression is demonstrated in a Monte Carlo study that creates a simulated data set in which the underlying true effects are known. Analysis of a second, empirical data set is included to demonstrate the technique's use within IS theory. In this second analysis, substantial direct and interaction effects of enjoyment on electronic-mail adoption are shown to exist.

Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.

This research investigates how knowledge workers are influenced to adopt the advice that they receive in mediated contexts. The research integrates the Technology Acceptance Model (Davis 1989) with dual-process models of informational influence (e.g., Petty and Cacioppo 1986, Chaiken and Eagly 1976) to build a theoretical model of information adoption. This model highlights the assessment of information usefulness as a mediator of the information adoption process. Importantly, the model draws on the dual-process models to make predictions about the antecedents of informational usefulness under different processing conditions. The model is investigated qualitatively first, using interviews of a sample of 40 consultants, and then quantitatively on another sample of 63 consultants from the same international consulting organization. Data reflect participants' perceptions of actual e-mails they received from colleagues consisting of advice or recommendations. Results support the model, suggesting that the process models used to understand information adoption can be generalized to the field of knowledge management, and that usefulness serves a mediating role between influence processes and information adoption. Organizational knowledge work is becoming increasingly global. This research offers a model for understanding knowledge transfer using computer-mediated communication.