Knowledge that Transforms

This paper presents a meta-analysis-based technique to estimate the effect of common method variance on the validity of individual theories. The technique explains between-study variance in observed correlations as a function of the susceptibility to common method variance of the methods employed in individual studies. The technique extends to mono-method studies the concept of method variability underpinning the classic multitrait–multimethod technique. The application of the technique is demonstrated by analyzing the effect of common method variance on the observed correlations between perceived usefulness and usage in the technology acceptance model literature. Implications of the technique and the findings for future research are discussed.

In this paper, the authors show that PLS path modeling can be used to assess a hierarchical construct model. They provide guidelines outlining four key steps to construct a hierarchical construct model using PLS path modeling. This approach is illustrated empirically using a reflective, fourth-order latent variable model of online experiential value in the context of online book and CD retailing. Moreover, the guidelines for the use of PLS path modeling to estimate parameters in a hierarchical construct model are extended beyond the scope of the empirical illustration. The findings of the empirical illustration are used to discuss the use of covariance-based SEM versus PLS path modeling. The authors conclude with the limitations of their study and suggestions for future research.

A number of models and theories in information systems research include concepts of a match between two variables or states. The development of measures for this concept can present problems, because decisions must be made about the nature of the comparison. Should indirect measures of the match be employed, then methodological issues arise about how to best handle the measure when testing the model. Difference scores are commonly used to measure a match between variables or states in IS research, but these have implicit assumptions about the theory and data characteristics that are often false. Not unexpectedly, false assumptions can lead to erroneous conclusions about the relationships among the variables that are used to determine a match in a research model. The implicit assumptions restrict the form of the relationships and limit the IS researcher’s ability to understand the possible interplay among theoretical concepts. We suggest some guidelines for the formation and testing of models that measure the match. In addition, we recommend polynomial regression analysis as one means of analyzing the more complex relationships in IS studies. We then use an IS service quality example to illustrate the issues involved in the use of matching variables and make suggestions with regard to using or avoiding difference scores.