Unpacking teachers’ intentions to integrate technology: A meta-analysis

The Technology Acceptance Model (TAM) is a key model describing teachers' intentions to use technology. This meta-analysis clarifies some of the contradictory findings surrounding the relations within the TAM for a sample of 45 studies comprising 300 correlations. We evaluate the overall fit of the TAM and its structural parameters, and quantify the between-sample variation through meta-analytic structural equation modeling. The TAM fitted the data well, and all structural parameters were statistically significant. On average, the TAM variables explained 39.2% of the variance in teachers' intentions to use technology. Several sample, measurement, and publication characteristics, including teachers’ experience and the representation of the TAM variables, moderated the relations within the TAM. Overall, the TAM represents a valid model explaining technology acceptance—however, the degree of explanation and the relative importance of predictors vary across samples. Implications for further research, in particular the generalizability of the TAM, are discussed.     

Bayesian Meta-Analytic SEM: A One-Stage Approach to Modeling Between-Studies Heterogeneity in Structural Parameters

Meta-analytic structural equation modeling (MASEM) refers to a set of meta-analysis techniques for combining and comparing structural equation modeling (SEM) results from multiple studies. Existing approaches to MASEM cannot appropriately model between-studies heterogeneity in structural parameters because of missing correlations, lack model fit assessment, and suffer from several theoretical limitations. In this study, we address the major shortcomings of existing approaches by proposing a novel Bayesian multilevel SEM approach. Simulation results showed that the proposed approach performed satisfactorily in terms of parameter estimation and model fit evaluation when the number of studies and the within-study sample size were sufficiently large and when correlations were missing completely at random. An empirical example about the structure of personality based on a subset of data was provided. Results favored the third factor structure over the hierarchical structure. We end the article with discussions and future directions.     

A meta-analysis to explore privacy cognition and information disclosure of internet users

Based on the Elaboration Likelihood Model (ELM), this study identifies the differences between perceived privacy risks and privacy concern. Furthermore, the study analyses how perceived privacy risks and privacy concerns affect the disclosure intention and the actual information disclosure behavior of Internet users. In addition, the study discusses the moderating effects of platform types, from the perspective of privacy elaboration likelihood. By applying meta-analyses and SEM on 104 independent studies with 42,256 samples from existing empirical studies, we attempt to systematically reveal the relationship between privacy cognition and information disclosure. The results show that perceived privacy risks can significantly reduce personal information disclosure intention, as well as actual information disclosure behavior. However, privacy concerns only affect disclosure intention, but do not have a significant effect on actual information disclosure behavior. The study also verified that platform types have moderating effects on the privacy decision making of Internet users. The findings yield important and useful implications, both for research and for practice.