Search

In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article presents 4 PLS-based approaches: a product indicator approach (Chin, Marcolin, & Newsted, 2003), a 2-stage approach (Chin et al., 2003; Henseler & Fassott, in press), a hybrid approach (Wold, 1982), and an orthogonalizing approach (Little, Bovaird, & Widaman, 2006), and contrasts them using data related to a technology acceptance model. By means of a more extensive Monte Carlo experiment, the different approaches are compared in terms of their point estimate accuracy, their statistical power, and their prediction accuracy. Based on the results of the experiment, the use of the orthogonalizing approach is recommendable under most circumstances. Only if the orthogonalizing approach does not find a significant interaction effect, the 2-stage approach should be additionally used for significance test, because it has a higher statistical power. For prediction accuracy, the orthogonalizing and the product indicator approach provide a significantly and substantially more accurate prediction than the other two approaches. Among these two, the orthogonalizing approach should be used in case of small sample size and few indicators per construct. If the sample size or the number of indicators per construct is medium to large, the product indicator approach should be used. (Contains 8 tables, 5 figures and 11 notes.)

Mobile banking apps have begun to provide non‐core functions to users, for example, mobile shopping, identifying nearby activities, and purchasing tickets as an innovation. Nevertheless, few studies have explored the negative impacts of this change (e.g., information overload and unfamiliarity associated with these new services). By developing and testing a research model with 422 questionnaires collected in China, this study aimed to examine users' psychological and behavioral aspects regarding the non‐core features of mobile banking apps by considering two countervailing constructs: appreciation and fatigue. The results show that the non‐core features of mobile banking apps affect the appreciation emotion positively and affect the fatigue emotion negatively. The different effects of the two opposing constructs on the using behavior are also discussed. This study identifies the partial complementary mediation role of lifestyle fit and function fit with the non‐core features of mobile banking apps. By presenting the necessary condition analysis, this study contributes to identifying the necessary and sufficient effects of both lifestyle fit and function fit on users' using experience evaluation. The findings suggest that, in practice, non‐core features of mobile banking apps can be used as a business strategy to influence users' choice in the mobile context. [ABSTRACT FROM AUTHOR]

The objective of this paper is to gain a better understanding of factors influencing software piracy. A model explaining the contribution of different factors to software piracy intention and its subsequent effect on actual software piracy is constructed based on established theories of human behavior. The model is then tested empirically in a longitudinal study with a survey. Findings show that social factors and beliefs concerning consequences of software piracy have significant effects on software piracy intentions. The data also show that while habits and facilitating conditions were significantly related to actual piracy behavior, intentions did not necessarily lead to the actual act of software piracy. The implications of the findings to research and practice are discussed. Index Terms--Computer crime, empirical study, habit software piracy, softlifting.