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Analysis of customer satisfaction as an intervening variable on the effect of retail service quality on customer loyalty at Uniqlo Indonesia
Published in Indira Rachmawati, Ratih Hendayani, Managing Learning Organization in Industry 4.0, 2020
Path analysis was used to examine the effect of the retail service quality variable on customer loyalty by involving the customer satisfaction intervening variable. According to Ghozali (2013), path analysis is an extension of multiple linear regression analysis, or the use of regression analysis to estimate causal relationships between variables that have been predetermined based on theory. The path analysis model can be seen in Figure 3.
Water Pollution Policy in India
Published in J. Rose, Water and the Environment, 2017
Path modelling was used here to examine the patterns of causal influence. Path analysis, as described by Asher13 is basically a method to estimate the magnitude of the linkages between the underlying causal processes. Path analysis also enables one to measure the indirect as well direct effects on one variable upon another. The necessary assumptions required for using this technique were incorporated in the models.14
“Baby, I Can’t Drive My Car”: How Controllability Mediates the Relationship between Personality and the Acceptance of Autonomous Vehicles?
Published in International Journal of Human–Computer Interaction, 2023
Yaron Sela, Yair Amichai-Hamburger
Data were entered and analyzed using SPSS version 27. First, descriptive statistics were produced using means and standard deviations for all variables. Correlations between socio-demographic variables, and personality traits with AV acceptance aspects, were assessed using Pearson correlations. To examine the indirect effects of sensation seeking and big-five traits on AV acceptance, mediated by desirability of control and driving locus of control, path analysis model was performed. The following indices were used to evaluate the model: chi-squared, which is acceptable when the value is not significant; the goodness of fit index (GFI), the comparative fit index (CFI), and the non-normed fit index (NNFI), (adequate values—above 0.90, excellent fit—above 0.95); and the root mean square error of approximation (RMSEA) (adequate values—less than 0.08, excellent fit—less than 0.06). The standardized root mean square residual (SRMR) was also used to assess model fit with values ranging from less than from 0.08 (adequate value) to 0.05 or less (considered reasonable) (Arbuckle, 2013; Byrne, 2010). The path analysis model was tested using AMOS software. Level of significance (p-value) was 5%.
Work-related contact, work–family conflict, psychological distress and sleep problems experienced by construction professionals: an integrated explanatory model
Published in Construction Management and Economics, 2018
Paul Bowen, Rajen Govender, Peter Edwards, Keith Cattell
Finally, a number of path models were specified and tested. Firstly, path models to examine the direct and indirect determinants of psychological distress and sleep problems were separately specified and tested. Thereafter, an integrated, composite path model predicting both psychological distress and sleep problems was specified and tested. The use of path analysis differentiates this analysis from that of Schieman and Young (2013), who used multiple regression to test separate predictive models for explaining psychological distress and sleep problems, and did not test for a single model accounting for both these outcome variables. The use of path models involving SEM is superior to multiple regression analysis in that they permit the examination of a series of dependence relationships simultaneously (Hair et al. 2014). This is particularly useful in the present context for testing the conceptual model containing multiple equations involving dependence relationships. Moreover, path analysis reveals total, direct and indirect effects between variables (total effect = direct effect + indirect effect).
Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways
Published in Journal of Transportation Safety & Security, 2023
Subasish Das, Eun Sug Park, Sobhan Sarkar
Path analysis is a methodology for analyzing systems of structural equations (Bollen, 2014). Path analysis can be considered as an extension of multiple regression. The modeling framework can examine the chains of influence among the selected variables to determine whether the data are consistent with the model. It is a holistic approach that enables the simultaneous estimation of multiple relationships among crash, speed, and other roadway characteristic variables. Through path analysis, it is possible to estimate not only the effects of variables directly affecting crashes (direct effects) but also the effects of variables indirectly affecting crashes (indirect effects) through an intervening variable (mediator variable) such as speed. Although path analysis possess certain advantages, it has been less explored in the transportation safety analysis. The studies by Gargoum and El-Basyouny (2016) and Park, Fitzpatrick, Das, and Avelar (2021) are among the handful of studies that employed the path analysis approach in the safety analysis. Path analysis models in this study consist of two sub-models: Crash model providing the relationship between crash counts (outcome variable) and operating speed measures (mediator variables) as well as other road geometric variables (independent variables).Speed model providing the relationship between operating speed measures and other roadway characteristic variables (including PSL).