Structural Equation Modeling as a Basis for Theory Development within Logistics and Supply Chain Management Research

Structural Equation Model (SEM) is a statistical technique with simultaneous processing involves measurement errors, indicator variables, and latent variables. SEM is used to test hypotheses that state the relationships between latent variables when latent variables have been assessed through each of the indicator variables. Multiple Group SEM is a basic model analysis that uses more than one sample. This analysis aims to determine whether the components or models of measurement and structural models are invariant for the two sample groups. In this study, the data generated by some requirements. First, the data generated with sample size n = 250. The first generated data is homogeneous data where the measurement model is the same as the structural model in group 1 and group 2, while the second data is non-homogeneous data where the measurement model and the structural model in group 1 and group 2 is not the same. The data was analyzed using the help of the lavaan package available in R to obtain SEM estimation results and Goodness of Fit Model from some data that was formed. From the results of the merger of the two groups, it shows that the invariant of the two models with the largest df (63) which is Fit Mean model states the simplest model. However, the smallest df (48) with Fit.configural model states the most complex model.
 Keywords: SEM, Multiple Group, R Program

PurposeThe purpose of this paper is to describe and discuss a few principal and crucial steps of “antecedents” and “postcedents” in relation to structural equation modeling (SEM) in social science research.Design/methodology/approachThis paper is based upon a conceptual and reflective discussion of SEM in social sciences research.FindingsSEM is not science per se, but just a tool that is used to extract meaning from the previous steps of research processes in terms of a research phenomenon in focus.Research limitations/implicationsBased upon the steps of validity and reliability discussed in relation to predetermined measurement and structural models in SEM, application implications are provided.Practical implicationsLike many statistical tools, SEM can be misused to manipulate findings and fit of measurement and structural models. Researchers may become more interested in the tool than in the subject matter.Originality/valueUsing SEM does not elevate research to be science or make the researcher a scientist! An area of concern for further debate is whether the widespread and variable applications of SEM in social science (in particular, marketing) research are all science.

The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.

Structural equation modeling (SEM) consists of two parts, the measurement model and the structural model. In this paper, we develop a SEM model that the measurement model is linear and the structural model is nonlinear. Structural model explain about relationship between latent variables. Truncated spline is used for modeling the nonlinear relationship between the latent variables. Trought weighted least squares (WLS) method, It is found that estimation of parameter truncated spline in nonlinear SEM for two exogen and an endogen latent variables is resulted by optimizingminγ{‖Σ−12(ωη−Γξκγ)‖2}and it gave an estimatorγ^=(Γ′ξkΣ−1Γξk)−1Γ′ξkΣ−1ωηThe estimator influenced by the location and some knot points κ1 and κ2.

The use of structural equation modeling has gained increased interest in recent years in the social and behavioral sciences. This article reviews the basic tenets of structural modeling in relation to issues in research and practice involving clinical assessment and compares this approach with more traditional psychometric approaches to the validation of assessment instruments with children. Arguments for and against the inclusion of nonexperimental variables in causal studies aimed at establishing construct validity are also discussed. An illustrative example of the application of structural equation modeling in clinical assessment research is provided, and a comparison is made between this approach and traditional psychometric procedures. Implications and suggestions for the use of structural modeling are discussed for both the practitioner and the clinical researcher.

The past decade has seen a tremendous growth in the use of structural equation modeling (SEM) to address research questions in 2 subfields of behavioral science: cancer prevention and control (e.g., determinants of cancer screening adherence) and behavioral oncology (e.g., determinants of psychosocial adjustment among cancer patients or survivors). The application of SEM in these areas can have far-reaching implications for lowering cancer morbidity and mortality and for improving the psychological well-being and quality of life of those afflicted with, and recovering from, cancer. This article reviews the progress made in the application of SEM to cancer prevention and control and behavioral oncology research, highlighting 2 central uses: theory development and evaluation and scale construction. Directions for the future application of SEM to address research questions concerning cancer prevention and control and behavioral oncology are also discussed.

Probit structural equation regression model: general depressive symptoms predicted post-myocardial infarction mortality after controlling for somatic symptoms of depression

This study aims to examine the influence of knowledge about organic products, perception of product quality, and health awareness on attitudes towards organic products among consumers of organic products in Jakarta. A quantitative research approach was employed to objectively measure the relationships among the variables. Primary data were collected through an online questionnaire distributed via Google Forms to organic product consumers, with a total of 128 valid responses obtained using accidental nonprobability sampling. Data were analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) technique, which allows for simultaneous evaluation of the measurement and structural models. The measurement model was assessed through tests of convergent validity, discriminant validity, and reliability, while the structural model was evaluated using path coefficients, R-Square values, T-statistics, and p-values obtained through bootstrapping. The results indicate that the model demonstrates strong predictive power, with the endogenous construct explaining a substantial proportion of variance. The findings reveal that health awareness and perception of product quality have a positive and statistically significant effect on attitudes towards organic products, whereas knowledge about organic products does not exhibit a significant direct influence. These results suggest that affective and perceptual factors play a more dominant role than cognitive factors in shaping consumer attitudes toward organic products. The study provides important theoretical and managerial insights for developing effective marketing strategies in the organic product market.

Using structural equation modeling and intima-media complex texture features to assess cardiovascular disease risk in the common carotid artery

This research aims to investigate to what extent consumers are sensitive to social and health concerns, and if this can be expected to influence the consumer attitudes towards foods produced by social farms. The study area is located in the Friuli Venezia Giulia region, in North East Italy. In order to investigate the consumers’ attitudes towards social farm foods, the relationships among three latent constructs, i.e., social consciousness, health consciousness and social farm foods, is analysed. The proposed hypotheses are tested via a structural equation model that is calculated with the linear structural relationship method. A two-stage analysis is adopted, estimating, firstly, the measurement model and, secondly, the structural model. The results support the reliability of the latent constructs on the observed variables and the hypotheses of the proposed model. These results indicate that if consumers are aware of social concerns such as those regarding people with special needs, as well as the fact that the quality of life is also related to the quality of the foods they eat, these consumers could be potential buyers of foods produced by social farms.

This study investigated the relationships among managerial leadership, transformational leadership, and performance of school administrators of Northern Iloilo Polytechnic State College (NIPSC) through Structural Equation Modeling (SEM). Covariance-Based SEM (CB-SEM), Confirmatory Factor Analysis (CFA) with its default Maximum Likelihood Estimation (MLE) were used to test the hypothesized model that managerial leadership covary transformational leadership which were both related to the school administrators’ performance. Results revealed that of the eight alternative models, two equivalent models, one model generates every probability distribution that can be generated by another model, Model B3 and Model D3 were generated, got the smallest Alkaike Information Criterion (AIC) and Bayes Information Criterion (BIC) indicating that both models had relatively better fit. Model B3 and Model D3 have the same degrees of freedom but feature a different configuration of paths among the same variables. However, of the two equivalent models, model B3 was rejected due to discriminant validity concerns while model D3 passed both measurement model and structural model, model D3 was confirmed and retained. As contribution to the fields of education, management and leadership, the researcher confirmed and recommends, through CB-SEM using CFA with its default MLE, the Managerial Leadership and Performance as fully mediated by Transformational Leadership Model.

Objective Family doctors fulfill the role of gatekeepers in protecting residents' health with contracted services. Providing these valuable services involves multiple causes, relationships, and indirectly observable variables. This study used structural equation modeling to construct a dynamic model of the work of family doctors to provide a basis for incentives. Methods This study used 2-year follow-up data from a survey of 294 family doctors in Changning District, Shanghai. Data were analyzed using confirmatory factor analysis and structural equation modeling. The measurement model and structural model were defined, identified, verified, integrated, and revised to identify the factors motivating family doctors to provide contracted services. A dynamic path for the family doctor contracted services model was established and eventually modified with six endogenous latent variables: cognition, environmental satisfaction, income satisfaction, support satisfaction, stability, and contracting performance, underpinned by 27 measurement variables. Result The standardized regression coefficient of the effect of cognition on environmental satisfaction was 0.37 (P < 0.05) and the degree of variation interpretation was 0.14. The effect of cognition on income satisfaction was 0.54 (P < 0.05) and the degree of variation interpretation was 0.29. The effect of cognition on stability was 0.40 (P < 0.01), the effect of environmental satisfaction on stability was 0.12 (P < 0.05), and the effect of income satisfaction on stability was 0.22 (P < 0.05), all with a degree of variation interpretation of 0.369. Finally, the effect of stability on contracting performance was 0.51 (P < 0.05) with a degree of variation interpretation of 0.343. Conclusions The degree of family doctors' understanding (cognition) of their own work largely determines their behavioral orientation and service effectiveness. These results raise the possibility of enhancing family doctors' work stability and improving the performance of contracted services by increasing the income of family doctors.

Purpose The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students. Design/methodology/approach This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model. Findings The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth. Research limitations/implications Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data. Practical implications The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs. Originality/value First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

Structural equation modeling (SEM) is a quantitative technique for evaluating the causal relations between and among a number of variables using a combination of statistical methods and assumptions. Although there have been recent developments in expanding SEM to include climatic changes, most applications have been restricted to the simulation of causal processes; this is especially true for the modelling of environmental changes. However, when SEM is applied as an exploratory technique in climate studies, the proposed model can illustrate and examine the relationships among several climatic elements in the environment. To investigate the relationship between temperature and precipitation, a combination of SEM, partial least squares (PLS) and GIS methods were employed in this study. A measurement model, a structural model and a spatial model for examining the relationships between temperature and precipitation were proposed. A SEM-PLS-GIS model consisting of three measurement sub-models was created on the basis of the concentration values of 14 climatic elements; the data were the monthly and seasonal values for the period between 1975 and 2012, and were obtained from 140 stations. The results of the new SEM-PLS-GIS model showed that seven temperature factors affected, directly or indirectly, the precipitation with minimum temperatures were being the most effective of all. It can be concluded that employing SEM-PLS-GIS model for the purpose of describing causal patterns of climate variations especially the variations of precipitation can be of great value.

Resilience is an essential trait in the academic and non-academic lives of students that has been associated with positive psychological and non-psychological outcomes. Given the importance and role of resilience, the main goal of this study was to create a structural model of resilience based on parental support and emphasizing the mediating role of hope and coping. The research design was correlational in nature and used structural equation modeling (SEM). The population of this study included high school students in Tehran city in 2019–2020 school year, and the sample consisted of 560 students selected by cluster sampling method. By applying Spearman correlation analysis and structural equation modeling, a significant relationship between hope, coping, resilience, and parental support was found. Both the SEM measurement and structural models provided a good fit. The significant findings of the present study include the direct and significant effect of parental support on resilience. The indirect and significant effect of parental support on resilience through the mediating role of hope and coping was also confirmed. Overall, the results of this study show that the effect of parental support on resilience can be improved by promoting hope and coping styles.