Physician Utilization of Computers in Medical Practice: Policy Implications Based on a Structural Model

A structural model is developed to specify the effects of physician perceptions of the impact of computers on their use of a computer-based hospital information system (HIS). The overall model consists of a measurement model, which assumes that the observed variables are generated by a small number of unobserved variables or factors, and a structural equation model that relates exogenous and endogenous variables hypothesized as characterizing physician use of the HIS Parameters of the model are estimated with data from 477 physicians. This approach demonstrates a powerful means of modelling processes involving variables of theoretical interest that cannot be directly observed.

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.

This study aims to determine the description of the quality of educational services to student satisfaction after attending the educational process, and the results of this study are used to increase student satisfaction in the future. The population of this research is the management study program students at the Faculty of Economics, Private Universities in Bandar Lampung in 2014, 2015 and 2016. Determination of the size of the sample uses the Isaac and Michael tables with a 5% confidence level and determines the sample members with stratified random sampling. The data analysis technique used is Structural Equation Modeling (SEM), an analysis that combines factor analysis approaches, structural models (Structural Model), and path analysis (Path Analysis). The exogenous variable education service quality (X) consists of tangibles, reliability, responsiveness, assurance and empathy. Whereas endogenous variables of satisfaction (Y) are cognition, affective and psychomotor. The results showed that 1) a description of the quality of educational services according to students' perceptions as a whole including good categories.variable service quality Tangible, reliability, empathy, assurance is more important than responsiveness, 2) description of student satisfaction after attending the education process according to overall student perceptions including good categories, 3) quality of education services has a significant effect on student satisfaction, 4) policy implications indicate the need for improvement in terms of; a) cleanliness of the lecture room, b) resolve student complaints, c) handle administration quickly, d) pay attention to a controlled period of education, e) hospitality of employees in providing services.

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 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.&#x0D; Keywords: SEM, Multiple Group, R Program

School management effectiveness remains a critical challenge in education systems worldwide. This study aimed to determine a structural model of school management effectiveness by examining the school governance, school-based management practices, and organizational commitment of public school teachers in Region XI. It employed a predictive research design utilizing Structural Equation Modeling approach, using adapted questionnaires for endogenous and exogenous variables. Data analysis included mean computation, correlation, multiple regression, and SEM. Model 1 yielded the best-fit model after modifications based on model-fitting criteria. The indices for CMIN/DF (1.194), p-value (0.166), NFI (0.994), TLI (0.998), CFI (0.999), GFI (0.983), RMSEA (0.020), and p-close (0.999) were all within the standard criteria, confirming the model’s validity. It was confirmed that the hypothesized model, rooted in Contingency Theory, successfully captures the direct influence of exogenous variables and endogenous variables. Policymakers and school leaders may enhance governance frameworks through capacity-building programs for administrators and teachers, focusing on decision-making, transparency, and stakeholder collaboration. Inclusive school-based management practices, such as community involvement in planning and professional development to boost educators’ organizational commitment, are crucial for improving learning environments and educational quality.

Identification of geochemical factors in regression to mineralization endogenous variables using structural equation modeling

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.

Objective: The objective of this study is to investigate the antecedents of service speed on employee performance, with service speed acting as a mediator and Hospital Management Information System (HMIS) as a moderator in Type D hospitals in Central Indonesia. The aim is to improve hospital service delivery by optimizing employee knowledge, responsibility, and technology integration. Theoretical Framework: This study draws on theories of human resource management and information system integration. Knowledge management theory and responsibility-performance linkage are central to understanding how individual competencies influence service outcomes. The moderating role of HMIS is assessed through the lens of organizational performance and digital transformation in healthcare. Method: This research adopted a quantitative descriptive design. Primary data were collected via questionnaires from 250 administrative employees across seven Type D hospitals using a saturated sampling technique. Data were analyzed using Structural Equation Modeling (SEM) with Smart PLS, and validity, reliability, and normality tests were conducted. Results and Discussion: The results showed that employee knowledge positively affects performance, while responsibility has a negative impact. Service speed mediates the influence of both knowledge and responsibility on performance. HMIS enhances overall performance but does not moderate the relationship between service speed and performance. These findings are discussed with reference to the theoretical framework and relevant literature. Research Implications: This study provides practical insights for healthcare management, highlighting the importance of employee training and technology adoption to improve service speed and organizational performance. It aligns with Indonesia’s healthcare regulations and supports the achievement of SDGs 3 and 9. Originality/Value: This study contributes to the literature by integrating human and digital resource perspectives to evaluate performance outcomes in lower-tier healthcare facilities. It provides original findings on how HMIS and service speed interact in the healthcare context.

PurposeThis study's purpose is to propose an integrated model based on expectation-confirmation model (ECM), task-technology fit (TTF) model, and updated DeLone and McLean information system (IS) success model to examine whether quality factors and TTF as antecedents to physician beliefs can affect physicians' continuance intention of the cloud-based hospital information system (HIS) and performance impact.Design/methodology/approachSample data for this study were collected from physicians at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 305 (61.0 percent) usable questionnaires were analyzed using structural equation modeling in this study.FindingsThis study verified that physicians' perceived information quality, system quality, general technical support service quality, and cloud storage service quality all positively caused their PU, confirmation, and perceived TTF in the cloud-based HIS, which together explained their satisfaction with the system, and subsequently led to their continuance intention of the system and performance impact.Originality/valueFirst, IS-related and cloud-related quality factors are simultaneously taken into consideration within this study's research model, and empirical results reveal deep insights into quality evaluation in the field of physicians' cloud-based HIS continuance intention. Next, this study contributes to an understanding of TTF in explaining physicians' cloud-based HIS continuance intention that is difficult to explain with only their utilitarian perception of the system, and places emphasis upon physicians' perception of performance impact greatly driven by their perceived TTF and continuance intention of the system, thus the results can shed light on antecedents and outcome of physicians' cloud-based HIS continuance intention.

Modeling and measuring attributes influencing DevOps implementation in an enterprise using structural equation modeling

Background/Aims: Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed. Methods: This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used. Results: A simulation study shows that our method is computationally feasible, and has good statistical properties. Conclusion: Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.

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

The application of the Bayesian approach emerges as a plausible alternative in multivariate statistical analysis within structural equation models (SEM), being more precise, reliable, and robust for parameter estimation, especially in situations where traditional methods may not be adequate due to sample size restrictions, non-compliance with multivariate normality, or high theoretical complexity. The study aimed to determine the relevance of the proposed ISSM-TAM e-learning model on the academic performance of students in the 2023-II cycle at the National Agrarian University La Molina (UNALM). The methodology was quantitative, basic, explanatory, non-experimental, and cross-sectional. A sample of 767 undergraduate students was collected. A questionnaire with 10 latent constructs, comprising 40 items measured on a 1-5 ordinal Likert scale, was used. Various first and second-generation multivariate statistical techniques were applied: a variance-based structural equation model (PLS-SEM), covariance-based model (CB-SEM), and Bayesian model (B-SEM) with non-informative prior distributions; uniform and reference priors, and a specific informative prior distribution. The obtained data were analyzed exploratorily, descriptively, and inferentially, validating the measurement and structural models with both frequentist and Bayesian approaches. It was found that the Bayesian model with informative priors achieved acceptable values in goodness-of-fit indices compared to the others, and it explained the structural relationships between the main endogenous latent variables of the proposed hybrid model. Received: 5 January 2025 / Accepted: 29 August 2025 / Published: 05 September 2025