Role of technological dimensions of green supply chain management practices on firm performance

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Analysis of knowledge management process towards minimizing supply chain risks under the green technology: a direct and configurational approach

Adopting green technologies is crucial for a sustainable supply process and organization development. In this construct, the current study analyzes how green technology adoption (GTA) and knowledge management (KM) processes could minimize supply risk in large manufacturing companies. The current study utilizes research techniques based on the resource-based view and contingency theories to minimize supply chain risk. The present study selected green variables (learning, productivity, raw materials, and resource utilization) along with KM processes (acquisition, sharing, and utilization) to investigate these connections to achieve the research objective. The author collected the data (203) from manufacturing firms in Zhengzhou city and used PLS-SEM, fuzzy set qualitative comparative analysis (fsQCA) to examine the study aims. The relationship between the adoption of green technologies and the KM process, which may reduce supply risk, was verified via PLS-SEM. fsQCA is employed as a combined strategy with green technology and the KM dimension to examine supply risk. The study results showed a significant correlation between the KM process's ability to reduce supply risk under the GTA. The results of the fsQCA specify the numerous dimensions of green implementation enablers, and the KM process produced superior results in terms of supply risk minimization. This research contributes to bridging gaps and understanding the interrelationship between green measurement and the supply risk process minimization. The current study provides practical and social justification for enlightening the relationship between GTA and the KM process, aiming to minimize the supply risk. Based on the study outcome and ground information, this study reported limitations and future research direction.

Post-COVID-19's impact on green supply chain management and sustainable E-commerce performance: the moderating role of big data analytics

This study investigates the relationship between COVID-19 and the adoption of green supply chain management practices (GSCM) in the Pakistani e-commerce industry. It also assesses the impact of these practices on ecological sustainability across three dimensions and explores the role of big data analytics (BDA) in enhancing them after the pandemic. The research utilized partial least squares structural equation modeling to evaluate data and test hypotheses. The research sample was composed of 390 managers operating within Pakistan's e-commerce industry. The study's preliminary findings reveal that COVID-19 has positively influenced the adoption of GSCM practices, which are moderated by BDA. Implementing GSCM has a positive impact on the perceived environmental and social resilience of e-commerce enterprises, but no significant effect on their perceived economic resilience. Additionally, GSCM acts as a mediator in the relationship between the impact of COVID-19 and the perceived environmental and social resilience of e-commerce firms, but not for economic resilience. This research focuses on Pakistan's e-commerce industry and investigates how COVID-19 affects the adoption of eco-friendly supply chain practices. It measures the impact of these practices on ecological sustainability using three dimensions. The study also examines how BDA can improve the adoption of GSCM in e-commerce, offering new insights into sustainability during and post-pandemic. The results recommend that the e-commerce industry can use BDA and GSCM practices to improve e-commerce sustainable performance. This is initial research that integrates COVID-19 impact, BDA, GSCM practices, and e-commerce sustainability in a single framework that was overlooked previously.

Digital resilience and new business models in the post-COVID-19 scenario: from B2B perspective in the era of knowledge economy

Purpose

The purpose of this study is to examine how the pandemic impacted on business-to-business (B2B) cooperation and coordination, as well as on firms' financial and operational performance, from the B2B context in the era of knowledge economy.

Design/methodology/approach

With the help of social network theory, coordination theory and existing literature, a theoretical model was developed conceptually. Later, the conceptual model was validated using structural equation modelling technique with consideration of 712 respondents from different firms who are engaged in managing B2B relationships on behalf of their firms.

Findings

The study found that the COVID-19 pandemic has had a considerable moderating impact on the relationship between B2B cooperation and coordination with B2B relationship satisfaction. The study also highlighted that there is a degradation of financial and operational performance of firms due to the impact of COVID-19 pandemic on their B2B relationship management.

Practical implications

There is a challenging and ever-evolving global economy caused by the COVID-19 pandemic. Although it is argued that the pandemic has accelerated the growth of some online firms, it has also had a catastrophic effect, culminating in many firms failing. This study has developed a new business model which helps in improving financial as well as operational performance of the firms in post COVID-19 scenario, especially in the era of knowledge economy.

Originality/value

This is a unique study as this study (1) develops a unique theoretical model with high explanative power, (2) demonstrates how digital reliance and new business model help the firms in post COVID-19 pandemic and (3) adds to the body of literature in the domain of digital reliance, knowledge economy and B2B relationship management.

Top management commitment in greening supply chain operations: post-COVID-19 perspectives from an emerging economy

Purpose

The novel COVID-19 supply chain disruption has globally altered the environmental needs of society. Against this backdrop, this paper aims to examine how top managers are environmentally committed to integrating green supply chain management (GSCM) practices in the operational performance of small- and medium-scale enterprises (SMEs) in Ghana, within the post-pandemic economy.

Design/methodology/approach

The study used a cross-sectional survey to obtain data from 270 SMEs in Ghana, using partial least squares (PLS) structural equation modelling to test seven hypothesized relationships.

Findings

The outcome of the analysis revealed that top management environmental commitment has a significantly positive effect on supply chain operational performance. The structural model also revealed that top management environmental commitment has a positive and significant effect on both internal and external GSCM practices. The results further revealed that both internal and external GSCM practices have positive and significant effects on supply chain operational performance. Finally, both internal and external GSCM practices mediate the path between top management environmental commitment and supply chain operational performance.

Research limitations/implications

The study provides a novel framework which contributes to both theoretical studies and managerial decisions on COVID-19 related supply chain management issues. However, the study was limited to the Ghanaian context, thus, further related studies are required in other contexts.

Originality/value

This study provides a novel framework by elucidating the intervening role of GSCM practices in the path between top management environmental commitment and supply chain operations in an emerging post-pandemic world context.

Green supply chain transformation and emission reduction based on machine learning

Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility of upgrading machine learning technology. The investment risk of a supply chain's technology upgrade is either symmetric or asymmetric information. In the case of symmetric information, results show that the machine learning technology upgrade risk does not affect the market equilibrium outcomes of the duopoly model. However, in the case of asymmetric information, technology upgrade risk is vital in determining the quantities and prices of competition equilibrium. To achieve the goal of green supply chain transformation, the government should provide more technology and financial support to traditional supply chains to upgrade their machine learning technology on carbon emissions.

Critical success factors for adopting green supply chain management and clean innovation technology in the small and medium-sized enterprises: A structural equation modeling approach

Organizational sustainability in the form of environmental management and sustainable production is becoming more important for small and medium-sized enterprises (SMEs) throughout the world. This research evaluates the factors affecting the understanding of the CEO's and managers' intention to adopt practices of green supply chain management (GSCM) and clean innovation technology (CIT) in the manufacturing SMEs of Pakistan. This empirical research identifies key determinants influencing the adoption of GSCM practices. Using structural equation modeling (SEM), we selected a sample size of 350 different manufacturing firms in Pakistan. The results of the study revealed that six factors, namely, environmental, government, organization, suppliers, market, and operational factors, significantly influence the intention to adopt GSCM and positively impact sustainable production. The study's findings reveal that market and operational factors are highly significant for adopting GSCM practices at a p-value of 0.05. Environmental and organizational factors are equally significant to adopt GSCM practices at a p-value of 0.10. This research also analyzed CIT as a moderator between environmental, government, organization, customer, supplier, economic, market, and operational factors in the context of Pakistan. Hypotheses H9a, H9b, H9f, and H9g were validated and support the use of CIT to boost enterprise production and consumption. The research findings would help policymakers understand how to implement GSCM practices and guide enterprises to implement GSCM and CIT practices for enhancing enterprise performance and environmental sustainability.

Exploring the technical and behavioral dimensions of green supply chain management: a roadmap toward environmental sustainability

Environmental sustainability issues have become an increasing concern for enterprises and organizations due to new tendencies in climate change. Green supply chain management (GSCM) practices are growing worldwide in this context. Based on socio-technical systems and institutional theory, the present study develops a conceptual model highlighting a mediating effect between two distinct categories of GSCM dimensions, i.e., technical practices and behavioral practices, along with the moderating effect of institutional pressure on organizational performance. Data were collected from 260 Pakistani manufacturers, and the structural equation modeling (SEM) approach was employed to analyze the hypotheses. The classification of technical and behavioral GSCM practices and findings of this research contributes to the literature on GSCM. Empirical results reveal that behavioral practices of GSCM (top management support, supplier, and customer involvement) mediate the relationship between technical GSCM practices (eco-design, green manufacturing, and reverse logistics) and organizational performance (economic, environmental, and social). The results also demonstrate that institutional pressure positively moderates the relationship between technical practices and organizational performance. These findings suggest that organizations in developing countries must focus on the behavioral dimensions of GSCM first for the successful implementation of technical dimensions of GSCM to gain effective environmental, economic, and social performance.

Towards measuring SMEs performance amid the COVID-19 outbreak: exploring the impact of integrated supply chain drivers

Purpose

This study aims to explore supply chain disruptions during the COVID-19 pandemic in the small and medium enterprise (SME) sector in Oman. This study analyzes the impact on selected supply chain drivers – facilities, inventory, transportation and sourcing. It further intends to explore whether the supply chain challenges faced by the SME sector in Oman impact their overall performance.

Design/methodology/approach

This study follows the quantitative technique of structural equation modeling to examine the proposed hypotheses. Data were collected electronically from SME managers/owners/entrepreneurs. All items were adopted and measured using a five-point Likert scale. One hundred and four complete and usable responses were received and considered.

Findings

The data was analyzed using SPSS and PLS statistical software. The model has been supported empirically, and the results showed a significant relationship between supply chain drivers and SMEs’ overall performance in Oman, except for supply chain inventory. The results have demonstrated that the COVID-19 pandemic has affected the SMEs’ supply chain drivers in Oman and, consequently, their overall performance.

Practical implications

The results of this research can drive the development and implementation of a supply chain management strategy. This research will help policymakers induce the performance of SMEs affected by the COVID-19 pandemic. It would further enhance strategic sourcing and supplier performance considering the developed practices associated with the resource-based view.

Originality/value

The originality of the current study lies in its ability to empirically test two models within the Omani SMEs context while considering the supply chain drivers as a single variable or dividing it into four separate independent variables. This study would provide a preview for scholars for such empirical investigation and serve as a reference for policymakers and practitioners to maintain a management system of crises that may protect the SME supply chain drivers.

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability (AIC), management (AIM), driven decision making (AIDDM), and firm performance. Based on the resource-based view (RBV) and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling (PLS-SEM). As a second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture (IC) positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism (ED) positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice.

Research on the Impact of Enterprise Green Development Behavior: A Meta-Analytic Approach

The environmental situation is not optimistic. Improving the level of enterprise green development behavior can help enterprises to comply with the trend of environmental protection. However, existing studies do not explain the factors influencing enterprise green development behavior. This research collects and screens 33 empirical studies related to enterprise green development behavior from multiple authoritative data platforms, which cover 10 different countries and regions. A quantitative approach is then used to comprehensively explore the influencing factors, deeply dig into their degree of influence, and explore the moderating effect of the moderators. The results show the following: (1) corporate tangible resources, corporate intangible resources, market environment, policy and institutional environment, and public supervision have positive effects on enterprise green development behavior, and there are differences in the degree of influence; (2) corporate intangible resources have the most significant influence on enterprise green development behavior; (3) the size, region, and industry of enterprise can moderate enterprise green development behavior. This research suggests four participants: society, enterprise, market, and government. The research results are intended to provide a basis for researchers to further study enterprise green development behavior for specific industries and promote enterprise green development.

Shaping a View on the Influence of Technologies on Sustainable Tourism

To date, tourism is the fastest growing industry globally, but one of the least developed in terms of environmentally sustainable practices. However, only a small portion of documents elaborate on how the introduction of new technologies can impact a more sustainable development route for tourism. This study’s objective is to provide an overview on literature state-of-the-art related to sustainable tourism and technological innovations, offering insights for further advancing this domain. We employ a bibliometric analysis and a comprehensive review of 139 articles, collected from Web of Science and Scopus databases, for the purpose of: (i) exploring and discussing the most relevant contributions in the publication network: (ii) highlighting key issues and emerging topics; (iii) uncovering open questions for the future. Our findings reveal contradictory views on the risks and benefits of technology adoption. Artificial intelligence, internet of things, circular economy, big data, augmented and virtual reality emerge as major trends. Five work streams are identified and described, leading to a broader perspective on how technology can shape the future of sustainable tourism. Relevant theoretical and managerial implications are derived. Finally, a research agenda is proposed as guidance for future studies addressing the outcomes of digital disruption on sustainable tourism.

Green supply chain management pressures, practices and performance: a critical literature review

Purpose

The concept of green supply chain management (GSCM) is evolving rapidly due to the environmental concerns and gaining popularity in the research community. This study critically analyze the literature of GSCM pressure, practice and performance for manufacturing enterprises based on the results of bibliometric, network and frequency analyses.

Design/methodology/approach

Scopus database is used for literature search. Bibliometric network and frequency analysis are used to critically review the evolution of identified constructs and measures of GSCM pressure, practice and performance.

Findings

This study has identified that the selected topic is in growing stage through the collaborative effort of the worldwide researchers. This study also shows the diffusion of influence in developing countries and there are enormous opportunities of research in these regions. The study finds evaluation of GSCM constructs and measures with time and shift in focus areas by the researchers. The study suggests more focus on the measurement of informative pressures, reverse logistics practices and negative impact on economic performance due to the adoption of GSCM practices. There is a need of simultaneous analysis of GSCM pressures, practices and performance to identify the cascading effect in different regions and industrial sectors and developed a conceptual framework to identify this effect. This study also observes the need of more quantitative measures-based case studies and suggests the use of life cycle assessment for the quantification of environmental performance.

Originality/value

This study for the first time has analyzed a specific topic of GSCM pressure, practice and performance through bibliometric and network analyses. This study critically reviews the constructs and measures of GSCM pressure, practice and performance and identified the future research directions.

Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review

Industry 4.0 (I4.0) technologies have been highlighted in recent literature as enablers of servitisation. Simultaneously, businesses are advised to implement a circular economy (CE) to bring new opportunities. However, it is pertinent to mention that little attention has been given to assess the role of I4.0 in adopting the CE and servitisation in a fully integrated manner. This research fills this gap by developing a conceptual framework through a systematic literature review of 139 studies investigating the relationship between the I4.0, CE, and servitisation. This study identifies the impact of these variables on a firm’s operational and financial performance (revenue stream, growth, and profitability). Our research findings advocate that adopting I4.0 technologies to the business and manufacturing model enables sustainability, energy and resource efficiency while enhancing performance and offering innovative products through smart services. Thus, firms must systematically adopt I4.0 technologies to support a CE model that creates value through servitisation. This study identifies the research gaps that are unexplored for practitioners and future researchers while providing insight into the role of I4.0 in implementing CE in the servitisation business model.