Multivariate Analysis with LISREL

Jöreskog, Karl G.; Olsson, Ulf; Wallentin, Fan Yang

doi:10.1007/978-3-319-33153-9

Cited by 175 publications

(175 citation statements)

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“…Calculations are based on the correlation matrices in Web Appendix E-F. The condition number of the SEM is 6.791; thus, multicollinearity is not a serious issue (Jöreskog et al 2016). We also revised the model displayed in Fig.…”

Section: Results Of Structural Equation Modelingmentioning

confidence: 99%

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

Blut

Wang

2019

J. of the Acad. Mark. Sci.

292

197

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The technology readiness (TR) index aims to better understand people's propensity to embrace and use cutting-edge technologies. The initial TR construct considers four dimensions-innovativeness, optimism, insecurity, and discomfort-that collectively explain technology usage. The present meta-analysis advances understanding of TR by reexamining its dimensionality, and investigating mediating mechanisms and moderating influences in the TR-technology usage relationship. Using data from 193 independent samples extracted from 163 articles reported by 69,263 individuals, we find that TR is best conceptualized as a two-dimensional construct differentiating between motivators (innovativeness, optimism) and inhibitors (insecurity, discomfort). We observe strong indirect effects of these dimensions on technology usage through mediators proposed by the quality-value-satisfaction chain and technology acceptance model. The results suggest stronger relationships for motivators than for inhibitors, but also that these TR dimensions exert influence through different mediators. Further, the moderator results suggest that the strength of TR-technology usage relationships depends on the technology type (hedonic/utilitarian), examined firm characteristics (voluntary/mandatory use; firm support), and country context (gross domestic product; human development). Finally, customer age, education, and experience are related to TR. These findings enhance managers' understanding of how TR influences technology usage.

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Section: Results Of Structural Equation Modelingmentioning

confidence: 99%

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

Blut

Wang

2019

J. of the Acad. Mark. Sci.

292

197

View full text Add to dashboard Cite

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“…The researchers made use of the survey method for data collection, and for the analysis of the hypotheses and their inter-relationships, Lisrel (linear structural relations) 9.1 software was used (Jöreskog, Olsson, & Fan, 2016). Measurement and data collection implies an evaluation of the measurement model, which for the study included: 1) the individual item reliabilities, 2) the model's convergent validity, and 3) discriminant validity.…”

Section: Discussionmentioning

confidence: 99%

“…From the analysis of the data, and the measurement of the five constructs and their hypotheses, it was determined that there was a good model fit with the empirical research data, with both convergent validity and discriminant validity being used. In structural equation modelling (SEM), CFA is usually used to access construct validity (Jöreskog et al, 2016). Hair et al (2016) and Byrne et al (1989) also indicated that factor loadings or regression weight estimates of latent to observed variables should have values greater than 0.50, which indicates that all the constructs conform to the construct validity test and validity convergence.…”

Section: Convergent Model Analysismentioning

confidence: 99%

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Determinants of the State Railway of Thailand’s (SRT) Total Quality Management process: SEM analysis

Sivalai

Rojniruttikul

2018

Journal of International Studies

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Dropping from the peak of 88 million passengers back in 1994, Thailand's state railway system today is transporting less than 50% of its maximum. Operations run at a loss, with infrastructure and aged equipment contributing to significant delays and a continuing loss of passengers. In late 2017 however, a new 20-year, $US81.57 billion plan was proposed to save the SRT from its downward spiral. The researchers therefore undertook a study to survey 500 Thai state railway (SRT) civil servants. Each was given a 5-level Likert-type agreement scale questionnaire, containing 51 items concerning their personal characteristics, their attitudes, human resource management processes, the use of the balanced scorecard for improvement, their perspectives on SRT's organizational culture, and the SRT's total quality management program. LISREL 9.1 was used to conduct a confirmatory factor analysis (CFA) and then structural equation model (SEM) was applied to analyze the results. The results have determined that organizational culture had the greatest impact on total quality management, followed by attitudes, human resource management, and finally, the balanced scorecard. It is also interesting to note that the surveyed SRT staff consider organizational change as the most important aspect in improving efficiency, while staff evaluation and reward system were rated as least effective for total quality management.

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“…As PLS-SEM resembles ML-SEM in many ways, it can be explained and illustrated using a slightly adjusted version of the LISREL terminology (Jöreskog, Olsson, and Wallentin 2016) and graphical notation used originally for ML-SEM. As depicted in Figure 1, a typical PLS-SEM model will consist of two parts: the measurement (or outer) and the structural (or inner) models.…”

Section: The Pls-sem Methodologymentioning

confidence: 99%

plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares

Venturini¹,

Mehmetoglu²

2019

J. Stat. Soft.

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We provide a package called plssem that fits partial least squares structural equation models, which is often considered an alternative to the commonly known covariance-based structural equation modeling. plssem is developed in line with the algorithm provided by Wold (1975) and Lohmöller (1989). To demonstrate its features, we present an empirical application on the relationship between perception of self-attractiveness and two specific types of motivations for working out using a real-life data set. In the paper we also show that, in line with other software performing structural equation modeling, plssem can be used for putting in relation single-item observed variables too and not only for latent variable modeling.

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Multivariate Analysis with LISREL

Cited by 175 publications

References 0 publications

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage

Determinants of the State Railway of Thailand’s (SRT) Total Quality Management process: SEM analysis

plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares

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