scholarly journals Analysis of Influencing Factors of Teaching Effect Based on Structural Equation Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Xu
Keyword(s):  
Structural Equation Model ◽  
Influencing Factors ◽  
Latent Variables ◽  
Structural Equation ◽  
Measurement Model ◽  
Equation Model ◽  
Student Response ◽  
Design Parameters ◽  
Analysis Tool ◽  
Causal Connection

Structural equation model is a multivariate statistical analysis method. It can not only test some unpredictable abstract ideas, but also design parameters for the causal connection model between independent variables and dependent variables. Among them, the analysis of various latent variables is based on the verification factor analysis technology. The research first collects various relevant data, derives the latent variables and measurement variables, then composes the measurement model, and then verifies the adaptability of the measurement model structure mode through actual data collection. When such suitable factors are determined, a causal model based on path analysis technology for latent variables, that is, a structural model, can be used for parameter design. This article uses the structural equation model as an analysis tool, starting from the three aspects of teachers, cases, and students to evaluate the application effect of the case teaching method in the classroom teaching of intellectual property law course and construct a structural equation model of the influencing factors of the case teaching effect. To understand students’ satisfaction with case teaching effects and related influencing factors, the research results show that the effect of case teaching is jointly affected by the level of teachers, case selection, and student response. Among them, teacher factors have the greatest impact on case teaching effects, followed by case factors, and students have the least.

scholarly journals The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits

2018 ◽  
Vol 8 (7) ◽  
pp. 1153 ◽  
Author(s):  
José Díaz-Reza ◽  
Jorge García-Alcaraz ◽  
Liliana Avelar-Sosa ◽  
José Mendoza-Fong ◽  
Juan Sáenz Diez-Muro ◽  
...  
Keyword(s):  
Least Squares ◽  
Structural Equation Model ◽  
Preventive Maintenance ◽  
Latent Variables ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Implementation Strategies ◽  
Equation Model ◽  
Total Productive Maintenance

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

Big Data Analytics in the Agribusiness Supply Chain Management

2021 ◽  
Vol 9 (3) ◽  
pp. 32-42
Author(s):  
Marisol Valencia-Cárdenas ◽  
Jorge Anibal Restrepo-Morales ◽  
Francisco Javier Día-Serna
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation Model ◽  
Inventory Management ◽  
Latent Variables ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Equation Model ◽  
Chain Management

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

Constructing Structural Equation Model Rule-Based Fuzzy System with Genetic Algorithm

2016 ◽  
Vol 7 (2) ◽  
pp. 69-88 ◽  
Author(s):  
EnDer Su ◽  
Thomas W. Knowles ◽  
Yu-Gin Fen
Keyword(s):  
Genetic Algorithm ◽  
Risk Factor ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Structural Equation ◽  
The United States ◽  
Equation Model ◽  
Lower State ◽  
Inference System ◽  
United States Dollar

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.

scholarly journals Evaluation on the Effect of Service-Learning in E-Business Professional Curriculum

2019 ◽  
Vol 14 (08) ◽  
pp. 115
Author(s):  
Jie Ji ◽  
Jianqiang Gu
Keyword(s):  
Student Engagement ◽  
Service Learning ◽  
Structural Equation Model ◽  
Influencing Factors ◽  
Direct Effect ◽  
Structural Equation ◽  
Equation Model ◽  
Students Satisfaction ◽  
Relevant Factors ◽  
Business Professional

Based on structural equation model, this paper evaluated the effect of Service-Learning in E-business professional curriculum, and built a structural equation model of Service-Learning and its influencing factors from three aspects: project resource, teaching organization and student engagement，in order to understand the students satisfaction to the effect of Service-Learning and the relevant factors. The results show that, the effect of Service-Learning is influenced by the three aspects mentioned above. Among which, teaching organization and student engagement have a direct effect on the effect of Service-Learning, however, the influence of the project resource mainly displays the indirect influence.

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