A leadership looking glass: How reflected appraisals of leadership shape individuals’ own perceived prototypicality and group identification

This is the final version. Available on open access from Routledge via the DOI in this recordData availability: Data reported in this article are available upon request from the corresponding author.Research on social identity and leadership rarely examines leadership processes from the perspective of leaders themselves. Three studies (experimental, longitudinal, cross-sectional) help fill this gap. Integrating social identity principles with a reflected appraisals perspective, we demonstrate that as individuals come to see themselves as (informal) leaders in a group, it positively affects their own sense of fit to the group prototype. Their own perceived prototypicality, in turn, yields a strengthened attachment to the group (identification). Importantly, we demonstrate this in racial and ethnic minority groups – an understudied context, yet where individuals develop meaningful conceptions of leadership and identification, with implications for their health and commitment to collective action. Altogether, this provides insights on social identity processes, and minority group leadership.University of California, Los AngelesEuropean Research Council (ERC

Extending The Technology Acceptance Model Using Perceived User Resources In Higher Education Web-based Online Learning Courses

The purpose of this research was to examine students\u27 acceptance of the World Wide Web Course Tools (WebCT) online learning system. The Perceived Resources and Technology Acceptance Model (PRATAM) was created based on previous research to address the factors of perceived resources, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use and actual system use. The aim for this research was to investigate the critical determinants and provide the causal relationships regarding students\u27 acceptance behaviors when using WebCT. While institutions are expecting to adopt online learning to reach more students, there are still many challenges for institutions to retain students in their online courses. The literature review conducted in this research indicated that the Technology Acceptance Model (TAM) has successfully explained students\u27 behaviors when they use educational information systems. In addition, the additional perceived resources variable in the PRATAM also showed a significant influence on the other belief and intention variables. The study analyzed a total of 115 students responses in two surveys administered during two WebCT based courses taught at a large southeastern public university. The beliefs, attitudes, intentions, and behavioral constructs of PRATAM showed significant goodness-of-fit indices and coefficient of determination after analyzing the data in both surveys. However, the results indicated several exceptions on PRATAM\u27s constructs and causal relationships. First, the path coefficient between perceived resources to behavioral intention to use in both pre-test and post-test were insignificant. Second, the path coefficient between behavioral intention to use and actual system use in pre-test was insignificant. Third, the path coefficient between perceived resources and perceived usefulness in post-test were insignificant. In addition, the research also suggested an additional link between perceived ease of use and behavioral intention to use at the pre-test data. Overall, this research validated the influences of PRATAM\u27s constructs factors to students\u27 acceptance behaviors toward WebCT. The findings of this research could provide a guideline for future implementations of online learning systems in higher education

Revisiting the Current Issues IN Multilevel Structural Equation Modeling (MSEM): The Application of Sampling Weights and the Test of Measurement Invariance in MSEM

Multilevel structural equation modeling (MSEM) has been widely used throughout the applied social and behavioral sciences. This dissertation revisited current issues in MSEM, including: the application of sampling weights and the test of measurement invariance. The impact of using sampling weights on testing multilevel mediation effects in large-scale, complex survey data was evaluated in Study 1. This study compared design-based, weighted design-based, model-based, and weighted model-based approaches in a noninformative sampling design. First, results showed that the model-based approaches produced unbiased indirect effect estimates and smaller standard errors. Second, ignoring sampling weights led to substantial bias in the design-based approaches. Finally, in the model-based approaches, weighted parameter estimates and standard errors differed moderately from unweighted results. The model-based approaches were thereby suggested for testing multilevel mediation effects in large-scale, complex survey data. In addition, researchers were always encouraged to apply sampling weights in analysis. The advantages of applying sampling weights in model-based approach were less obvious when cluster sizes were large, and particularly when ICC was small. The pursuit of evaluating various goodness-of-fit indices for testing measurement invariance has been a focus over the past decade. Study 2 expanded the investigation in MSEM. ICC and between-group difference accounted for a large proportion of variance in the model fit change. Among five model fit indices investigated in this study (i.e., X^2, CFI, RMSEA, SRMR, and TLI), ΔCFI and ΔSRMR in the level-specific approach had identical results to that of the standard approach. ΔSRMRB appeared to be the most sensitive to noninvariant factor loadings among all criteria. ΔSRMRB performed equally well in examining lack of intercept invariance when between-group difference was large. ΔRMSEA was less sensitive. Fractional changes in ΔCFI and ΔTLI indicated that neither was sensitive regardless of the level-specific approach or the standard approach. ΔX^2 was able to detect noninvariant intercepts when between-group difference was large, whereas only detected noninvariant factor loadings when both ICC and between-group difference were large. In conclusion, level-specific ΔSRMRB was suggested as a major index for examining between-level factor loading and intercept invariance in MSEM. ΔX^2 can be a supplementary index