Green Supply Chain Collaboration Research Articles

Big data analytics-artificial intelligence and sustainable performance through green supply chain practices in manufacturing firms of a developing country

Purpose This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI). Design/methodology/approach Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model. Findings This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP. Originality/value This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

Embracing green supply chain collaboration through technologies: the bridging role of advanced manufacturing technology

PurposeThe implementation of green collaboration has evolved from the interfirm level to the supply chain level, which requires more participation in information and manufacturing technologies. Despite many efforts to green supply chain collaboration (GSCC), the research on how to enhance it from a technological perspective remains unclear. Thus, this study aims to address this gap by exploring how to enhance green supplier, internal and customer collaboration through using information technology (IT) and advanced manufacturing technology (AMT), which further accelerates environmental and economic performance.Design/methodology/approachThis study was conducted based on survey data collected from 213 manufacturing firms from different industries. The direct effects of the conceptual model were tested using the method of structural equation modeling (SEM), whereas the bootstrapping method tested the mediation effects of AMT between IT and GSCC.FindingsThe results show that green supplier collaboration (GSC) is significantly associated with environmental performance, while green customer collaboration (GCC) is positively related to economic performance. Green internal collaboration not only enhances green supplier and customer collaboration but also boosts environmental and economic performance. IT directly improves green internal collaboration but is negatively related to GSC and insignificantly related to GCC. However, AMT not only enhances green internal, supplier and customer collaboration but also mediates the relationship between IT and green supplier and customer collaboration.Originality/valueThis study offers comprehensive and novel insights into GSCC from a technological perspective, giving rise to meaningful contributions to green practices for both scholars and practitioners.

The Relationship between Big Data Analytic-Artificial Intelligence and Environmental Performance: A Moderated Mediated Model of Green Supply Chain Collaboration (GSCC) and Top Management Commitment (TMC)

Academics and practitioners have shown growing interests in big data analytics and artificial intelligence (BDA-AI) in recent years. Despite this, research on the application of BDA-AI for green supply chain collaboration (GSCC) and its influence on environmental performance (EP) is still limited. The current research addresses this gap and extends organizational information processing theory by incorporating BDA-AI and exploring top management commitment (TMC) as a moderator. The current study developed a moderated mediation model based on 402 samples of data from Turkish manufacturing firms. The result revealed that the application of BDA-AI has a positive impact on GSCC and EP. The results also indicated that GSCC has a positive impact on EP. Our findings revealed that GSCC mediated the association between BDA-AI and EP. The results also revealed that TMC moderated the positive relationship between BDA-AI and GSCC, such that the strength of the positive relationship is further intensified at higher levels of TMC. The results also show that TMC moderated the positive relationship between BDA-AI and EP, such that the strength of the positive relationship is dampened at lower levels of TMC; significant findings have not been outlined in the extant literature. The current research will assist supply chain and logistics managers and top management in deploying BDA-AI technology to support GSCC and improve EP.

Green supply chain management under uncertainty: a review and content analysis

ABSTRACT This study reviews the literature in the field of green supply chain management (GSCM) under uncertainty published from 2011 to 2020. Following the systematic review process, 198 articles are analyzed and classified according to the content framework. This study contributes to the related literature in the following ways. Firstly, it focuses on the bibliometric analysis of the GSCM under uncertainty, which provides a better understanding of this research field. Second, it identifies the uncertainty factors and highlights the research directions in this domain. The proposed uncertainty factors mainly consist of internal uncertainty, external uncertainty, and hybrid uncertainty. In addition, it shows that the current research covers the following themes: green supply chain network design (GSCND), collaboration, transportation optimization, supplier evaluation and practice and performance. Third, this study draws attention to uncertainty management methods. The fuzzy method, the stochastic method and the grey theory method are the main methods to solve the uncertainties of GSCM at present. Finally, it also identifies the gaps existing in the literature and proposes research agendas that can be expanded in the future.

The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance

Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.

Examining the antecedents and consequences of green product innovation

As an important part of social innovation, green product innovation (GPI) is widely regarded as a beneficial strategy for firms to achieve sustainable success. While the way to effectively leverage GPI has not been fully invested. To address this lack, this study examines the antecedent role of inter-organizational control mechanism by investigating the nature of the interplay between formal control and social control in relation to green supply chain collaboration. In addition, we probe the impact of GPI on firm triple bottom line due to the inconsistent results in existing literature. Based on a sample of 239 senior managers and directors in the Chinese manufacturing industry, we test the hypotheses through moderated structural equations modelling. The results show that formal control and social control should be applied as complements in promoting GPI, while only working on Moreover, enhance the awareness and adoption of GPI stimulates better environmental performance and social performance as a result. The relationship between GPI and financial performance is mediated by both environmental and social performance. Our findings will help B2B participants understand the GPI and potential sustainable, social and economic outcomes, and support them formulate more effective control mechanism strategies.

The effects of supply chain collaboration on green innovation performance:An interpretive structural modeling analysis

In previous research, interrelationships between green innovation and supply chain collaboration (SCC) are under-explored. This study explores the effects of SCC on green innovation performance and considers supply chain collaborative drivers as a crucial part of green innovation strategy. We construct a conceptual model containing a set of 17 drivers on the implementation of green innovation and analyze the contextual relationships between these drivers using an interpretive structural modeling (ISM) method to identify the driving and dependence powers in green innovation within the context of SCC. The model is validated on a case study involving an automobile firm in southwestern China. The study reveals that the relationships between supply chain partners have a very high influence on green innovation performance. SCC should be deemed as a core component in green innovation strategies. In addition, environmental regulations, top management commitment and social recognition are among high driving and dependence powers. We have confirmed the significance of SCC enablers for green innovation and identified several specific attributes through research. To further improve the value of this study, theoretical and managerial implications are discussed.

Governance Mechanisms for Green Supply Chain Partnership

The literature has recognized the value of green supply chain management in achieving the goals of environmental management. Yet developing and fostering sustainability partnerships among supply chain organizations remains challenging. Bounded rationality and opportunistic behaviors are likely to hinder joint sustainability collaboration and performance. The literature has called for a better understanding of the governance of green supply chain collaboration. This study applies transactional cost economics as a conceptual framework to investigate the relationships among transaction features, governance mechanisms, and environmental performance. Using the data collected from 969 plants in 17 countries, the statistical analysis compares and validates the effectiveness of three alternative governance mechanisms: contractual governance, problem-solving cooperation governance, and information-sharing governance. The statistical results reveal significant performance differences in how firms apply alternative governance mechanisms to mitigate opportunism, manage adaptation problems, and improve green supply chain collaboration and performance. Overall, this study makes research contributions by confirming the mediation effects of governance mechanisms on green supply chain practices. For green supply chains to be a viable practice, firms should apply governance mechanisms in proper alignment with the nature of the collaborative and environmental conditions.

The mediation role of social capital in relationship between buyer-supplier relationship with green supply chain collaboration

The main aim of this study is to understand how buyer-supplier relationship and social capital influence green supply chain collaboration in manufacturing companies. Also, the mediating role of social capital between buyer-supplier relationship and green supply chain collaboration was examined. Because of that, a sample of 70 from logistic and middle levels' managers were chosen for distributing questionnaires. Collected data were analysed by partial least square method of structural equation modelling. The results indicated that there are positive relationships among buyer-supplier relationship (asset specificity, transaction frequency and competitive environment), social capital and green supply chain collaboration. Also, the mediating role of social capital are confirmed. But two hypotheses which are related to volume uncertainty are rejected. These findings provide considerable insights to managers and marketers of green supply chains that seek to respond to a growing interest of environmental performance of supply chain.

The mediation role of social capital in relationship between buyer-supplier relationship with green supply chain collaboration

The main aim of this study is to understand how buyer-supplier relationship and social capital influence green supply chain collaboration in manufacturing companies. Also, the mediating role of social capital between buyer-supplier relationship and green supply chain collaboration was examined. Because of that, a sample of 70 from logistic and middle levels' managers were chosen for distributing questionnaires. Collected data were analysed by partial least square method of structural equation modelling. The results indicated that there are positive relationships among buyer-supplier relationship (asset specificity, transaction frequency and competitive environment), social capital and green supply chain collaboration. Also, the mediating role of social capital are confirmed. But two hypotheses which are related to volume uncertainty are rejected. These findings provide considerable insights to managers and marketers of green supply chains that seek to respond to a growing interest of environmental performance of supply chain.

Enabling sustainable energy futures: factors influencing green supply chain collaboration

To explore the relationship between sustainability strategies and future energy needs, supply chains need to reduce their CO2 emissions through developing their green credentials and improving performance. Knowledge management (KM) is an enabler to support collaboration efforts. The SCM and KM areas have largely focused on improving organisational performance. While the latter has yielded successful outcomes in different sectors, there is still a scarcity of research on identifying influential factors highlighting those aspects which may enable green supply chain collaboration (GrSCC), thus leading to sustainable energy futures and carbon-efficient production. This paper examines the role of KM in facilitating GrSCC. Through the identification of key factors extrapolated from the literature, a model for implementing GrSCC using a futures-based perspective is proposed. This paper inductively demonstrates the relationship between identified GrSCC factors through fuzzy cognitive mapping technique. Findings support a futures-based perspective that enhances understanding and refines forward-looking strategies for GrSCC.

Dynamic Assessment of Business Performance in Green Supply Chain based on Analytic Hierarchy Process Method

Traditional supply chain management is concerned about benefit maximization, although it can also involve raw materials recycling and energy conservation. According to the evaluation index system of green supply chain performance, the green degree will be composed in three layers; the first layer includes environmental impact degree, resource utilization rate, environmental benefit and reputation. In this study, we invited experts group to put forward the relative importance of the indicators, and then use the analytic hierarchy process to determine the weight of the indicators. Results show that the maximum weight is economic benefit, and the weight of the other four angles is more balanced and degree of attention was significantly lower than economic benefit. Green supply chain collaboration is the core competitiveness of enterprise development, green supply chain collaboration to better meet the user's personalized needs, but also to better enhances the competitiveness of the whole supply chain

Reprint of “Green Supply Chain Collaboration implementation in China: The mediating role of guanxi”

This study aims to understand how buyer–seller relationship, competitive environment and guanxi affect Chinese manufacturers’ decision to implement Green Supply Chain Collaboration (GSCC). We also examine whether guanxi is able to mediate the buyer–seller relationship and GSCC implementation. Data collected from 222 Chinese manufacturing organizations were analyzed using the partial least squares method of structural equation modeling. The result shows that buyer–seller relationship influences Green Supply Chain Collaboration through asset specificity, volume uncertainty, transaction frequency and competitive environment. The results also showed support for our hypotheses that guanxi mediates the effect of asset specificity, volume uncertainty and environmental competition on GSCC.

Green Supply Chain Collaboration implementation in China: The mediating role of guanxi

This study aims to understand how buyer–seller relationship, competitive environment and guanxi affect Chinese manufacturers’ decision to implement Green Supply Chain Collaboration (GSCC). We also examine whether guanxi is able to mediate the buyer–seller relationship and GSCC implementation. Data collected from 222 Chinese manufacturing organizations were analyzed using the partial least squares method of structural equation modeling. The result shows that buyer–seller relationship influences Green Supply Chain Collaboration through asset specificity, volume uncertainty, transaction frequency and competitive environment. The results also showed support for our hypotheses that guanxi mediates the effect of asset specificity, volume uncertainty and environmental competition on GSCC.

Green Supply Chain Collaboration for Fashionable Consumer Electronics Products under Third-Party Power Intervention—A Resource Dependence Perspective

Under third-party power intervention (TPPI), which increases uncertainty in task environments, complex channel power interplays and restructuring are indispensable among green supply chain members as they move toward sustainable collaborative relationships for increased viability and competitive advantage. From the resource dependence perspective, this work presents a novel conceptual model to investigate the influence of political and social power on channel power restructuring and induced green supply chain collaboration in brander-retailer bidirectional green supply chains of fashionable consumer electronics products (FCEPs). An FCEP refers to the consumer electronics product (e.g., personal computers, mobile phones, computer notebooks, and game consoles) with the features of a well-known brand associated, a short product lifecycle, timely and fashionable design fit for market trends, and quick responsiveness to the variations of market demands. The proposed model is tested empirically using questionnaire data obtained from retailers in the FCEP brander-retailer distribution channels. Analytical results reveal that as an extension of political and social power, TPPI positively affects the reciprocal interdependence of dyadic members and reduces power asymmetry, thereby enhancing the collaborative relationship of dyadic members and leading to improved green supply chain performance. Therein, reciprocal interdependence underlying collaborative relationship is the key to reducing the external environmental uncertainties in the TPPI context.

Supply Chain Performance Measurement: The Case Of A State Owned Pharmaceuticals Company (BFM)

Objective - The Purpose of this study is to identify Supply Chain Management and performance measurement in BFM, benchmarked with other Pharmaceutical companies, and to create solutions in improving and developing Supply Chain Management using the Supply Chain Operations Reference model (SCOR). The Supply Chain Operations Reference Framework for supply chain collaboration (SCOR) model developed a set of performance metrics for collaboration, including reliability, flexibility and responsiveness, expenses, and assets/utilization. Methodology/Technique - The methodology of supply chain research in BFM was surveyed by field observation, interviews and research. Interviews were conducted to examine the main issues and a hypothesis was tested by applying SCOR model, which also includes green SCOR model as consideration of environmental management. Findings - This study found that problems arose in the Supply Chain Management of BFM are lead time issues and low performance compared to the score of Pharmaceuticals industry. Environmental management could not be measured using SCOR metrics at Plan Source stage due to limited supplier of BFM. Novelty : SCOR model was developed performance measurement integrated the supply chain scorecard for process control and result control. Type of Paper: Empirical Paper Keywords: Supply Chain Management; Performance Measurement; Indonesia Pharmaceutical Industry; SCOR