Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling

By Scott B. MacKenzie. 2001

220 citations recorded by the source corpus.

Subfields: Applied Psychology, Information Systems and Management, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/c4ccaf2f9d789c5df11697de0629ad1813ea8ac5

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In Applied Psychology

In Information Systems and Management

In Organizational Behavior and Human Resource Management

Citations in the corpus

To What Degree are Environmentally Beneficial Choices Reflective of a General Conservation Stance? (2006, methodology): “When researchers use single-item fallible measures, as is commonly done, attenuation of true correlations because of measurement error is inevitable (Mackenzie, 2001).”
Structural equation modelling and regression analysis in tourism research (2012, methodology): “In this context, Mackenzie (2001) argues that SEM can be applied to model the nonindependence effects of group level factors on individual level parameters (both latent variable means and structural relationships), while also taking measurement error into account and permitting a systems of…”
Physical attractiveness of the service worker in the moment of truth and its effects on customer satisfaction (2009, background): “Several authors have called for the use of SEM on experimental data (MacKenzie, 2001), particularly when the data involve multiple latent variables (Cole et al.”
How Much can you Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information (2011, background): “MacKenzie (2001) suggested that Latent Variable Structural Equation Modeling (LVSEM) can improve experimental research because it takes measurement error into account and has the potential to improve experimental research analysis.”
The Effects of Online Incivility and Consumer-to-Consumer Interactional Justice on Complainants, Observers, and Service Providers during Social Media Service Recovery (2018, methodology): “Also, using experimental data with SEM enables the assessment of “entire systems of conceptual relationships that better represent the complex environments,” of reality (MacKenzie 2001, p. 159).”
Gamified Information Presentation and Consumer Adoption of Product Innovations (2017, methodology): “Following MacKenzie (2001), we estimated a structural equation model using the scaled Satorra–Bentler procedure.”
Brand Authenticity: Testing the Antecedents and Outcomes of Brand Management's Passion for its Products (2016, methodology): “, nonhypothesized effects) on manipulation check measures and on the dependent variable(s) (Bagozzi & Yi, 1989; MacKenzie, 2001; Michon & Chebat, 2008). The data were modeled similarly for both experiments, consistent with the method suggested by MacKenzie (2001). These structural models and results”
Specifying Formative Constructs in Information Systems Research (2007, background): “…error (a false positive by declaring a path significant when it is really nonsignificant) and Type II error (a false negative by declaring a path nonsignificant when it is really significant) in conclusions related to the structural model (Jarvis et al. 2003; MacKenzie 2001; MacKenzie et al. 2005).”
Revisiting the smiling service worker and customer satisfaction (2008, background): “Several authors have called for the use of SEM on experimental data (MacKenzie, 2001), particularly when the data involve multiple latent variables (Cole et al.”
Self-identity and internal environmental locus of control: Comparing their influences on green purchase intentions in high-context versus low-context cultures (2020, unknown): “, 2012) or multi-level models (MacKenzie, 2001), and, most importantly, to compare complex theoretical models across different cultures (Steenkamp and Baumgartner, 1998) over traditional multivariate techniques.”