scholarly journals Effect analysis of the driving factors of super-gentrification using structural equation modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248265
Author(s):  
Jiangang Shi ◽  
Kaifeng Duan ◽  
Quanwei Xu ◽  
Jiajia Li
Keyword(s):  
Social Factors ◽  
Structural Equation ◽  
Driving Factors ◽  
Sustainable Urban Development ◽  
Practical Significance ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Economic Factors ◽  
Political Factors ◽  
Spatial Factors

The study of super-gentrification has important practical significance for maintaining social fairness, spatial justice and achieving sustainable urban development. In this article, 23 driving factors influencing super-gentrification are identified by literature research and Delphi method. Then, the 23 driving factors affecting super-gentrification are divided into four dimensions: political, economic, social and spatial dimension. On this basis, hypotheses are proposed and a structural equation model is established. Then, SPSS 25.0 and AMOS 24.0 software are used to test the reliability and validity of the questionnaire data, and the model results are fitted and modified. Finally, the optimization model and path coefficient of super-gentrification driving factors are calculated. The results of the study show that political factors, economic factors, social factors, and spatial factors, all play a positive role in the development of super-gentrification. Social factors are the fundamental factors to promote super-gentrification, political factors, economic factors, and spatial factors also play a key role in the super-gentrification process.

scholarly journals The Identification of Factors Affecting the Use of Pressurized Irrigation Systems by Farmers in Iran

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1532 ◽  
Author(s):  
Abdullah Momvandi ◽  
Maryam Omidi Najafabadi ◽  
Jamal Hosseini ◽  
Farhad Lashgarara
Keyword(s):  
Structural Equation ◽  
Agricultural Sector ◽  
Water Productivity ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Mixed Method Research ◽  
Statistical Research ◽  
Irrigation Systems ◽  
Factors Affecting ◽  
Pressurized Irrigation

Climate change and water scarcity are the most important challenges of the agricultural sector, and pressurized irrigation systems (PISs) are one of the most significant ways to improve agricultural water productivity. The main purpose of this research was to identify the factors affecting the use of PISs by farmers. The statistical research population was a total of 2396 Iranian model farmers. The Cochran formula was used to determine the number of statistical samples. Accordingly, this comprised 331 people. The methodology of the study was mixed method research. The structural equation modeling technique, Mann–Whitney U, and Kruskal–Wallis tests were used to test the hypotheses. The results showed that the personal characteristics, tendency, attitude, self-efficacy, subjective norms, governmental support, environmental tensions, and technological features were the most important factors which influenced the farmers. It was found that all of these variables had a positive and significant relationship with the using of PISs by farmers, and they were able to predict 52% of the behavioral changes (R2) of the farmers. Among these variables, the attitude, with a path coefficient (β) of 0.48, had the highest impact on the using of PISs by the farmers.

scholarly journals Relationship between Social Capital and Job Performance among Faculty Members of a Medical University: The Moderating Role of Entrepreneurship Behavior

2020 ◽  
Author(s):  
Roohollah Kalhor ◽  
Nadia Neysari ◽  
Saeed Shahsavari ◽  
Sima Rafiei
Keyword(s):  
Social Capital ◽  
Job Performance ◽  
Structural Equation ◽  
Faculty Members ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Medical Sciences ◽  
Entrepreneurial Behavior ◽  
Moderating Role

Abstract Background Job performance is an important organizational factor that plays a significant role in the success of organizations. This study aims to investigate the moderating role of entrepreneurial behavior in the relationship between social capital and job performance among faculty members of Qazvin University of Medical Sciences. Methods This is a descriptive-analytical study which has been conducted through a structural equation modeling among all university faculty members working in different faculties of Qazvin University of Medical Sciences in 2017. To evaluate the causal relationships between study variables, Structural Equation Modeling (SEM) on AMOS software, with the significant level of 0.05 was used. Results Findings indicated that entrepreneurial behaviors and social capital could predict job performance. The direct effect of social capital on job performance (path coefficient: 0.17) and its indirect effect with the moderating role of entrepreneurial behavior (path coefficient: 0.39) were confirmed (P< 0.05). Furthermore, Sobel test affirmed the indirect associations between variables (P< 0.05). Conclusions Strengthening social capital and promoting entrepreneurial behavior can lead to higher levels of performance. Building trust among organizational members and designing new incentive methods which use entrepreneurial indicators for performance evaluation can improve social capital. Therefore, managers can contribute to the improvement of job performance through developing entrepreneurial behavior among their employees.

scholarly journals COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

2021 ◽  
pp. 395-405
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Support Vector ◽  
Economic Factors ◽  
Negative Effects ◽  
The Impact

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

Intention-Behavior Gap in the Use of Personal Health Records (PHRs)

2021 ◽  
Vol 13 (1) ◽  
pp. 18-35
Author(s):  
Younsook Yeo ◽  
Changsoo Sohn
Keyword(s):  
Health Information ◽  
Social Factors ◽  
Structural Equation ◽  
Personal Health Records ◽  
Equation Modeling ◽  
Future Research ◽  
Personal Health ◽  
Perceived Behavioral Control ◽  
Health Records ◽  
Actual Use

This paper examined an intention-behavior gap in individuals' personal health records (PHRs) adoption behaviors using Ajzen's theory of planned behavior (TPB) that incorporates social factors. Using structural equation modeling, the authors analyze the health information national trends survey data. The research found that all of the constructs, except for perceived behavioral control (PBC), shape intentions to use PHRs. However, PBC only predicts actual use. Individuals who have higher intentions tend to believe that healthcare providers should be able to share their patients' PHRs with other professionals and that scientists should be able to review de-identified patient PHRs. Individuals who perceive that a need exists for privacy control over their own health information tend to have higher intentions. The moderating social factors between intentions and actual behaviors are healthcare accessibility and being female, while education (positively) and employment (negatively) have significant relationships with actual use of, but not with intentions to use, PHRs. Future research needs to explicate why the moderating effect revolves around gender.

Success Factors of Adoption of Mobile Applications in Rural India

2018 ◽  
pp. 211-238 ◽  
Author(s):  
Debasish Roy
Keyword(s):  
Conceptual Model ◽  
Mobile Applications ◽  
Structural Equation ◽  
Success Factors ◽  
Practical Significance ◽  
Equation Modeling ◽  
Successful Implementation ◽  
Service Characteristics ◽  
Use Of Technology ◽  
Research Findings

The framework for this research is the unified theory of acceptance and use of technology. The increasing rural proliferation of mobile services has created a unique opportunity to deliver to the rural users information and services through innovative mobile applications. This chapter develops a conceptual model of factors that make a rural mobile application successful and that are the barriers to its implementation. The conceptual framework developed has been validated by a questionnaire based field survey using structural equation modeling (AMOS). The chapter explores how the conceptual model is impacted by the service characteristics. The contribution of this research to further the understanding of technology adoption models for rural mobile applications has been discussed. The findings of the study have been corroborated with similar research focusing on adoption of rural mobile applications. The practical significance as to how the research findings help in successful implementation of mobile applications has been presented.

scholarly journals Stand Structure and Abiotic Factors Modulate Karst Forest Biomass in Southwest China

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 443 ◽  
Author(s):  
Lu Liu ◽  
Fuping Zeng ◽  
Tongqing Song ◽  
Kelin Wang ◽  
Hu Du
Keyword(s):  
Carbon Storage ◽  
Structural Equation ◽  
Southwest China ◽  
Structural Equation Models ◽  
Abiotic Factors ◽  
Forest Biomass ◽  
Tree Density ◽  
Forest Carbon ◽  
Driving Factors ◽  
Spatial Factors

Understanding the driving factors of forest biomass are critical for further understanding the forest carbon cycle and carbon storage management in karst forests. This study aimed to investigate the distribution of forest aboveground biomass (AGB) and the effects of stand structural and abiotic factors on AGB in karst forests in Southwest China. We established a 25 ha plot and sampled all trees (≥1 cm diameter) in a subtropical mixed evergreen–deciduous broadleaf forest. We mapped the forest biomass distribution and applied a variation of partitioning analysis to examine the topographic, stand structural, and spatial factors. Furthermore, we used structural equation models (SEM) to test how these variables directly and/or indirectly affect AGB. The average AGB of the 25 ha plot was 73.92 Mg/ha, but that varied from 3.22 to 198.11 Mg/ha in the 20 m × 20 m quadrats. Topographic, stand structural, and spatial factors together explained 67.7% of the variation in AGB distribution. The structural variables (including tree density and the diameter at breast height (DBH) diversity) and topographic factors (including elevation, VDCN (vertical distance to channel network), convexity, and slope) were the most crucial driving factors of AGB in the karst forests. Structural equation models indicated that elevation, tree density, and DBH diversity directly affected AGB, and elevation also indirectly affected AGB through tree density and DBH diversity. Meanwhile, AGB was indirectly influenced by VDCN, convexity, and slope. The evaluation of stand structural and abiotic drivers of AGB provides better insights into the mechanisms that play a role in carbon storage in karst forests, which may assist in improving forest carbon management.

Determinants of intention to use Islamic banking

2019 ◽  
Vol 13 (1) ◽  
pp. 147-163 ◽  
Author(s):  
Samia Ayyub ◽  
Wang Xuhui ◽  
Muhammad Asif ◽  
Rana Muhammad Ayyub
Keyword(s):  
Consumer Behavior ◽  
Technology Acceptance ◽  
Structural Equation ◽  
Perceived Usefulness ◽  
Practical Significance ◽  
Equation Modeling ◽  
Islamic Banking ◽  
Intention To Use ◽  
Content Type ◽  
Acceptance Model

Purpose This paper aims to explore the determinants of intention to use Islamic banking and compare the consumer behavior of users and non-users of Islamic banking. This study incorporates the theory of planned behavior in Islamic banking perspective with an additional construct from technology acceptance model. Design/methodology/approach The research is quantitative in nature, and survey questionnaire was used to get data from four cities of Pakistan. The study manages to get 300 questionnaires from which only 264 were usable for analysis. The structural equation modeling was used for testing the hypotheses. Findings The result shows that perceived behavior control and perceived usefulness are the most significant predictors of intention to use of Islamic banking among users and non-users. Attitude turns out to be a non-significant factor for non-users of Islamic banking. Subjective norm is also found to be non-significant with intention to use Islamic banking in both groups. Originality/value This study has theoretical as well as practical significance in the subject of consumer behavior in Islamic banking. Theoretically, it attempts to fill the gap caused by the scarcity of research in exploring the consumer behavior towards Islamic banking in Pakistan. This study provides insights into the consumer behavior of users and non-users of Islamic banking and thus presents a comparison. Practically, this study provides guidelines for Islamic banks in introduction, propagation and promotion of Islamic banking products and services to establish Islamic banking as a social norm.

scholarly journals Examining the Role of Social Factors and Mooring Effects as Moderators on Consumers’ Shopping Intentions in Social Commerce Environments

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402095207
Author(s):  
Rao Muhammad Rashid ◽  
Qurat ul Ain Rashid ◽  
Abdul Hameed Pitafi
Keyword(s):  
Social Support ◽  
Relationship Quality ◽  
Social Presence ◽  
Social Factors ◽  
Structural Equation ◽  
Product Information ◽  
Equation Modeling ◽  
Social Commerce ◽  
Theoretical Understanding

Consumers on social commerce platforms can easily access product information, but these platforms have not attracted potential consumers in emerging economies. Studying the social factors (social support, social presence, and relationship quality) and mooring effects (conformity and personal experience) in social commerce environments is essential for understanding consumers’ intentions. This study examines the role of social factors by integrating mooring effects as moderators in the Chinese model, where fear for the reliability of consumers’ comments is a concern. Quantitative data are collected from Chinese cities ( N = 303) and analyzed through partial least squares–structural equation modeling. The findings demonstrate the validity of social factors and enjoyment. Mooring effects positively influence shopping intentions, and system and service quality positively influences relationship quality and shopping intentions. Finally, mooring effects positively moderate the relationship between social presence, social support, and consumers’ intentions. The findings have theoretical understanding and practical implications.

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