scholarly journals DOES ENVIRONMENTAL, SOCIAL AND GOVERNANCE PERFORMANCE INFLUENCE ECONOMIC PERFORMANCE?

2020 ◽  
Vol 21 (4) ◽  
pp. 1165-1184 ◽  
Author(s):  
Kemal Cek ◽  
Serife Eyupoglu
Keyword(s):  
Structural Equation Modeling ◽  
Linear Regression ◽  
Longitudinal Data ◽  
Economic Performance ◽  
Significant Relationship ◽  
Environmental Performance ◽  
Regression Models ◽  
Structural Equation ◽  
Equation Modeling ◽  
Company Performance

The purpose of this paper is to evaluate the influence of environmental, social and governance performance on the economic performance of the Standard & Poor’s 500 companies. Structural equation modeling and linear regression have been utilized to measure the overall and individual influence of environmental, social and governance (ESG) performance on economic performance using longitudinal data comprising the years from 2010 to 2015. The overall ESG model had a significant relationship on economic performance. Furthermore, the findings of this study show that social and governance performance significantly affects economic performance in all regression models. However, environmental performance failed to show a significant relationship. The research contributes to the literature by providing insights for investors, managers and employees about the influence of ESG performance on company performance.

Influence research of multi-dimensional tech-innovation behavior on tech-innovation performance

2016 ◽  
Vol 8 (2) ◽  
pp. 148-160 ◽  
Author(s):  
Fu-li Zhou ◽  
Xu Wang ◽  
Yun Lin ◽  
Yan-dong He ◽  
Nan Wu
Keyword(s):  
Economic Performance ◽  
Environmental Performance ◽  
Structural Equation ◽  
Structured Interview ◽  
Innovation Performance ◽  
Three Dimensions ◽  
Equation Modeling ◽  
Innovation Activity ◽  
Content Type ◽  
Innovation Behavior

Purpose The purpose of this study is to investigate the research on the influence of tech-innovation behavior on tech-innovation performance for Chinese manufacturing enterprises. Environmental performance has been taken into consideration with the green manufacturing and sustainability philosophy being a hotspot. Design/methodology/approach To verify the hypotheses and assumptions, a questionnaire was designed and a semi-structured interview was conducted for data collection. In addition, structural equation modeling (SEM) is applied to verify the influence model through assessing the fitting indexes based on the 317 questionnaire responses in the form of Likert-type scale. Findings Tech-innovation behavior and activity from direction, mode and investment behavior dimensions show their different positive influences on tech-innovation performance. This paper has creatively taken environmental tech-innovation performance into consideration, as well as economic performance. This investigation has provided the interpretation for each individual enterprise from three dimensions when conducting tech-innovation activity. Research limitations/implications Tech-innovation behavior, which has been a subject of extended discussion during recent decades, is the effective activity or action for tech-innovation. However, there have not been any studies on the environmental performance influence study, as well as the economic performance from these three dimensions and framework. Practical implications As this paper discusses the tech-innovation performance influence study from three dimensions, individual enterprises can choose the corresponding action for the proper tech-innovation path, especially for small- and medium-sized enterprises. Originality/value This study helps managers recognize tech-innovation activities for better tech-innovation performance based on the empirical study.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

Analysis of Multiple Biomarkers Using Structural Equation Modeling

2020 ◽  
Vol 6 (4) ◽  
pp. 266-278
Author(s):  
Wenhao Cao ◽  
Stephen S. Hecht ◽  
Sharon E. Murphy ◽  
Haitao Chu ◽  
Neal L. Benowitz ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Linear Regression ◽  
Structural Equation ◽  
Equation Modeling ◽  
Toxicant Exposure ◽  
Smoking Intensity ◽  
Sum Score ◽  
Exposure Variable ◽  
Multiple Biomarkers ◽  
The Relationship

Objectives: When examining the relationship between smoking intensity and toxicant exposure biomarkers, to understand the potential risk for smoking-related disease, individual biomarkers may not be strongly associated with smoking intensity because of the inherent variability in biomarkers. Structural equation modeling (SEM) offers a powerful solution by modeling the relationship between smoking intensity and multiple biomarkers through a latent variable. Methods: We used baseline data from a randomized trial (N = 1250) to estimate the relationship between smoking intensity and a latent toxicant exposure variable summarizing 5 volatile organic compound biomarkers. We analyzed 2 variables of smoking intensity: the self-report cigarettes smoked per day and total nicotine equivalents in urine. SEM was compared with linear regression with each biomarker analyzed individually or with the sum score of the 5 biomarkers. Results: SEM models showed strong relationships between smoking intensity and the latent toxicant exposure variable, and the relationship was stronger than its counterparts in linear regression with each biomarker analyzed separately or with the sum score. Conclusions: SEM is a powerful multivariate statistical method for studying multiple biomarkers assessing the same class of harmful constituents. This method could be used to evaluate exposure from different combusted tobacco products.

scholarly journals Expressing Emotions, Resilience and Subjective Well-Being: An Investigation with Structural Equation Modeling

2020 ◽  
Vol 13 (6) ◽  
pp. 48
Author(s):  
Jale Eldeleklioğlu ◽  
Meltem Yıldız
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Expression ◽  
Significant Relationship ◽  
Structural Equation ◽  
Pearson Correlation ◽  
Well Being ◽  
Equation Modeling ◽  
Psychological Resilience ◽  
Subjective Well Being ◽  
The Relationship

The present study examined the relationship between expressing emotions, psychological resilience and subjective well-being. The study was carried out with a total of 217 university students, of whom 94 were males and 123 were females, aged between 19 and 25 years. The data of the study were collected using the Emotional Expression Questionnaire, the Psychological Resilience Scale and the Subjective Well-Being Scale, respectively. The relationship between the variables of the study was analyzed via the methods of Pearson Correlation Coefficient and Structural Equation Modeling, and the mediating role of psychological resilience between emotional expression and subjective well-being was tested. The goodness-of-fit indices obtained from the structural equation modeling indicated that the model generated a good fit. According to the results, there was a significant relationship between “expressing emotions” and “psychological resilience” and between “psychological resilience” and “subjective well-being”. It was found that there was no significant relationship between expressing emotions and subjective well-being and that the variable of expressing emotions affected that of subjective well-being by means of the psychological resilience (tool) variable and the model tested was significant.

scholarly journals Interrelations Between Process and Institutionalization of Organizational and Interorganizational Learning with Performance

2021 ◽  
Vol 18 (2) ◽  
pp. 223-247
Author(s):  
Fabiano Larentis ◽  
Verena Alice Borelli ◽  
Mayara Pires Zanotto ◽  
Eduardo Robini da Silva
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Learning ◽  
Theoretical Model ◽  
Organizational Performance ◽  
Structural Equation ◽  
Corporate Performance ◽  
Equation Modeling ◽  
Company Performance ◽  
Performance Impact ◽  
Interorganizational Learning

This study aims to verify the interrelations between process and institutionalization of organizational learning (OL) and interorganizational learning (IL) with organizational performance. We have proposed and tested a theoretical model applied to 181 companies from 14 cooperation networks of the southern region of Brazil, through a survey and using the Structural Equation Modeling. We have identified that OL process influences OL institutionalization, that in turn influences IL process, IL institutionalization and company performance. IL process influences IL institutionalization and relationship-based corporate performance, as well as company performance impact on relationship-based corporate performance. We have rejected the hypotheses regarding the relations between OL process with IL process, IL process and institutionalization with company performance and IL institutionalization with relationship-based corporate performance. The results have reinforced that although OL and IL are conceptually different, they are complementary.

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