Abstract:The main purpose of this paper is to explore how green supply chain management (GSCM) and its evaluative factors have affected green supply chain management practice and performance in industrial zone. This study proposes a structural equation model of the relationships among four factors: internal awareness (IA); suppliers' pressure (SP); customers' awareness (CA); and regulations pressure (RP) and their effect on GSCM practice (PA) and GSCM performance (PE). We used a survey questionnaire to elicit perceptio… Show more
“…The PLS-SEM also uses indicators such as Standardized Root Mean Square Residual (SRMR), Normed Fit Index (NFI), and root mean squared (RMS_theta) to evaluate the appropriateness of the overall model. The SRMR value ranged from 0 to 1, and the SRMR value in the study was less than 0.08, indicating it fit the model (Do, Nguyen, Le, & Ta, 2020;Hu & Bentler, 1998;Tran & Huang, 2022). The NFI value also ranged from 0 to 1; when the NFI value is large, better performance will be obtained.…”
Section: Analysis Of Demographic Variablesmentioning
The study's objective was to explore and measure the impact of factors affecting tourism destination competitiveness in Vietnam. Participants included 192 tourists in Hanoi city and neighboring provinces in the period from October 2021 to April 2022. Based on the 192 valid responses from the questionnaire survey method, the correlations between the variables were analyzed and the hypotheses verified. To study the relationships among the latent variables with reliable tools (SmartPLS 3.0 software), the study applied the partial least squares approach to structural equation modeling (PLS-SEM). The results identified the following factors that affect tourism destination competitiveness in Vietnam: Environmental quality is the most influential factor; Tourism infrastructure and Tourist satisfaction have the second-strongest influence on tourist destination competitiveness; Historical and sociocultural perspectives and Human resources also affect tourist destination competitiveness. The study provides empirical evidence and explains the factors that affect tourist destination competitiveness. Finally, several recommendations are put forward to enhance tourism destination competitiveness in Vietnam.
“…The PLS-SEM also uses indicators such as Standardized Root Mean Square Residual (SRMR), Normed Fit Index (NFI), and root mean squared (RMS_theta) to evaluate the appropriateness of the overall model. The SRMR value ranged from 0 to 1, and the SRMR value in the study was less than 0.08, indicating it fit the model (Do, Nguyen, Le, & Ta, 2020;Hu & Bentler, 1998;Tran & Huang, 2022). The NFI value also ranged from 0 to 1; when the NFI value is large, better performance will be obtained.…”
Section: Analysis Of Demographic Variablesmentioning
The study's objective was to explore and measure the impact of factors affecting tourism destination competitiveness in Vietnam. Participants included 192 tourists in Hanoi city and neighboring provinces in the period from October 2021 to April 2022. Based on the 192 valid responses from the questionnaire survey method, the correlations between the variables were analyzed and the hypotheses verified. To study the relationships among the latent variables with reliable tools (SmartPLS 3.0 software), the study applied the partial least squares approach to structural equation modeling (PLS-SEM). The results identified the following factors that affect tourism destination competitiveness in Vietnam: Environmental quality is the most influential factor; Tourism infrastructure and Tourist satisfaction have the second-strongest influence on tourist destination competitiveness; Historical and sociocultural perspectives and Human resources also affect tourist destination competitiveness. The study provides empirical evidence and explains the factors that affect tourist destination competitiveness. Finally, several recommendations are put forward to enhance tourism destination competitiveness in Vietnam.
“…In the recent years, more and more companies seek to convert their supply chain activities into environmentally friendly entities, especially after having noticed that there is a link between environmental performance and economic benefits (de Giovanni and Vinzi, 2012;Do et al, 2020;Liu et al, 2020b). This practically means that the environmental objectives and the measurement of the performance of the supply chain is translated into economic and operational objectives, in order to reduce the environmental impact, increase consumer satisfaction and enhance business profits (Eltayeb and Zailani, 2009;Feng et al, 2018;Longoni and Cagliano, 2018;Laari et al, 2018).…”
Section: Literature Review 21 Main Conceptsmentioning
confidence: 99%
“…In the recent years, more and more companies seek to convert their supply chain activities into environmentally friendly entities, especially after having noticed that there is a link between environmental performance and economic benefits (de Giovanni and Vinzi, 2012; Do et al. , 2020; Liu et al.…”
PurposeDuring the previous two decades, “Green Supply Chain Management” (GSCM) has been gaining the attention of researchers and practitioners from various fields (e.g. operations, logistics and supply chain management). Its significance is constantly growing, and various studies are conducted in order to capture its overall organizational contribution. The present study attempts to bring together various organizational aspects that have never been collectively investigated before in the relevant literature. Under that rationale, a robust conceptual framework is developed and empirically tested. This framework includes 17 factors that are classified in three dimensions: (1) drivers of GSCM practices, (2) GSCM practices and (3) firm performance (GSCM outcomes).Design/methodology/approachThe examination of the proposed conceptual framework was performed using a newly developed structured questionnaire that was distributed to a sample of Greek manufacturing organizations. Supply Chain managers and Chief Executive Officers (CEOs) were used as key respondents, due to their knowledge and experience. After the completion of the three-month research period (last quarter of 2019), 292 useable questionnaires were returned. The empirical data were analyzed using the “Structural Equation Modeling” technique. The study is empirical (based on primary data), explanatory (examines cause and effect relationships), deductive (tests research hypotheses) and quantitative (includes the analysis of quantitative data collected with the use of a structured questionnaire).FindingsEmpirical results point out that internal environmental management, green innovative practices and environmental proactivity are GSCM practices with the most significant impact on firm performance. Moreover, the mediating role of GSCM practices in the relationship between GSCM drivers and firm performance is also highlighted. Finally, it was found that GSCM practices can explain 35% of the variance in firm performance and the drivers of GSCM practices can explain 78% of the variance of these practices.Originality/valueThe proposed three-dimensional conceptual framework of this empirical study and its underlining rationale has rarely been adopted in the relevant literature. Moreover, the study investigates which GSCM practices have an impact on firm performance, thus offering value to practitioners of the field. Also, it is one of the few similar studies that have been conducted on a European country.
“…Increasingly stringent international environmental regulations and consumer awareness of environmental protection or sustainable supply chain ( Pahlevan et al, 2021 ) have urged suppliers of final products in the supply chain to implement green production to reduce environmental damage from production activities ( Mohsin et al, 2020 ; Zhu et al, 2013 ; Li, 2008 ; Wang et al, 2017 ; Dai et al, 2015 ; Zhao and Sun, 2019 ; Babaee Tirkolaee et al, 2017 ). Many countries and regions have successively issued relevant energy-saving and environmental protection laws and regulations, prompting enterprises to attach importance to green production ( Hamedirostami et al, 2021 ; Do et al, 2020 ). For example, in 2006, the European Union began to implement the “Directive on the Restriction of the Use of Certain Hazardous Substances in Electrical and Electronic Equipment” ( Jiang et al, 2019 ; So and Xu, 2016 ; Liu et al, 2019 ).…”
The purpose of this paper is to establish a green supply chain differential game model for green technology research and development based on a secondary green supply chain composed of a single manufacturer and a single retailer. It compares the differential game equilibrium solutions under centralized and decentralized decision-making. The green supply chain members are coordinated through the dynamic wholesale price mechanism, and numerical simulation is used as a methodology, to verify and explain the results. The study found that compared to decentralized decision-making, the level of green technology and the total profit of green channels are higher under centralized decision-making. When the coordination parameters are within a certain range, the dynamic wholesale price mechanism can coordinate the behavior of manufacturers and retailers. The result also discovers that under the dynamic wholesale price mechanism, with the increase of investment cost coefficient, or the increase of price sensitivity or the decrease of consumer's environmental awareness, the green technology level, product green degree, price, retailer's profit, and the total profit of green channel is decreased. In contrast, the wholesale price and manufacturer's profits are increased.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.