Sustainable supply chain management and green technologies: a bibliometric review of literature

Mapping sustainable supply chain innovation: A comprehensive bibliometric analysis

This comprehensive bibliometric study investigates Sustainable Supply Chain Innovation (SSCI) research, examining its evolution, identifying key contributors, and unveiling emerging trends. Analyzing 1158 English-language SSCI articles using the robust Scopus dataset exposes noteworthy journals, authors, institutions, and global contributions. The findings suggest a consistent increase in research output since 1999, with a notable surge in the past decade. Network analysis and density-based spatial clustering identified six SSCI research clusters: Sustainability and Responsibility in Business, Navigating Innovation and Disruption, Sustainable Business Strategies, Environmental Sustainability and Innovation, Sustainable Food Systems and Environmental Impact, and Sustainable Business Dynamics. These clusters highlight the diverse nature of the evolving Sustainability and Supply Chain Management (SCM) field, contributing to a thorough understanding of the SSCI research landscape and emphasizing interconnections between sustainability and SCM themes, potentially leading to more comprehensive theoretical models. Furthermore, this understanding aids businesses in anticipating emerging trends and implementing optimal practices in SSCI. Moreover, recognizing active institutions and global contributors provides practical insights for fostering strategic collaborations.

Gender Variation, subject dispersion and citation impact in dental research: A bibliometric analysis of publications from 2009-2021

Aim

This study probed into the gender variation, subject dispersion and citation impact of dental research with an objective to highlight the increasing influence of female authors in the field of dentistry.

Methodology

The research employed bibliometric techniques to highlight gender variation in dental research as demonstrated in the Saudi Dental Journal (SDJ), over a period of thirteen years, 2009-2021. The examination comprised SDJ publications indexed in PubMed, with citation data extracted from Google Scholar on July 2023. The analysis encompassed the progressive growth of papers, authors, and citations, the gender distribution of authors, the co-authorship structure, subject dispersion, and collaboration patterns based on affiliation, both national and international.

Results

625 SDJ publications were identified in the PubMed database from 2009 to 2021. There was a notable rise in the volume of papers and the number of authors per year. The citation impact analysis revealed that these 625 papers achieved an average of 26 citations each. Remarkably, the representation of female authors increased from 0.47 to 2.23 authors per paper during the study period. The authorship pattern largely showed single-authorship, closely followed by a three-author pattern. Almost half the papers (48%) were a collaboration between male and female authors, with female authors solely producing about 15% of papers.The topological classification of papers revealed that the most considerable number of papers were on Periodontics, with the least number focusing on Oral and Maxillofacial Surgery. Female authors prominently contributed to Pediatric Dentistry and Periodontics papers.

Conclusion

The study illustrates an encouraging trend of heightened female authorship in dental research over the years. Their increasing engagement not only enriches the academic diversity but also impacts the progressive evolution of dental science, contributing to a more inclusive and balanced society.

Bibliometric Evaluation of the 100 Top-Cited Articles on Anesthesiology

This review is a bibliometric analysis based on anesthesiology, which is a medical specialty that deals with a patient's complete preoperative, intraoperative, and postoperative care. The objective of the review attempts to analyze the bibliometric characteristics of the 100 most top-cited articles on anesthesiology. The meta-data of the study were collected from the Core Collection of Web of Science database. A title search option was employed, and "Anesthesia" and "Anesthesiology" were typed in two different search boxes separated with the Boolean operator ''OR''. Further, the data were sorted by highest citation order; later, "article" was selected from the filter of document type, and all other types of documents were excluded. Finally, downloaded the bibliographic details of the 100 top-cited articles. VOSviewer Software (version 1.6.10 by van Eck and Waltman) was used for bibliometric network analysis for co-authors and keywords. Pearson chi-square test was used for statistical analysis. The 100 top-cited articles were published between the years of 1971 and 2018. These articles gained a maximum of 1006 to a minimum of 276 citations with an average of 384.57 cites/article. Open accessed articles gained a slightly higher ratio of citations, while more than half of the articles were published in the two leading journals of "Anesthesiology" and "Anesthesia and Analgesia". There was no statistically significant difference in both citation analysis among open and closed access journals and Anesthesia vs Non-Anesthesia journals. Thirty-six articles were published in journals not specifically related to Anesthesia. Most of the top-cited articles were contributed by the United States, whereas Surgery and General Anesthesia were the two most occurred keywords.  We conclude that all the top-cited articles in anesthesiology were contributed by authors who belonged to the developed nations and the United States outclassed the rest of the world. This bibliometric analysis would be valuable to practitioners, academics, researchers, and students to understand the dynamics of progress in the field of anesthesiology.

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.

A systematic review of modeling approaches in green supply chain optimization

Over the past decade, the significance of optimizing green supply chain management (GSCM) has gained unprecedented attention from both scholars and industry professionals. This surge in interest has led researchers to employ diverse modeling approaches in the pursuit of enhancing green supply chain networks. In this systematic review, we analyze 159 recent GSCM optimization papers published from 2017 to 2022 and identify the recent trends in mathematical modeling, multi-objective optimization, and the modeling/solver tools utilized. We find that the primary green focus is on minimizing carbon emissions (n = 44), reflecting the increasing concern for environmental sustainability. Among the modeling approaches employed, mixed-integer linear programming has emerged as the most popular choice (n = 51), followed by game theory-based modeling (n = 30). When it comes to multiobjective optimization, the ε-constraint approach is the most widely used. Evolutionary algorithms have emerged as the dominant meta-heuristic optimization approach. Additionally, the widely utilized solver in this domain is CPLEX with the most popular modeling/solver combination being GAMS/CPLEX. Moreover, the Journal of Cleaner Production was the leading outlet for research in this domain (n = 35). In addition to these findings, this study also discusses some other research trends and future research directions. Finally, we discuss the theoretical, managerial, and policy implications of this study. By providing GSCM researchers and practitioners with the latest trends in GSCM optimization approaches, this study contributes to the further advancement of the field.

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

This paper presents a novel decentralized decision support system to optimally design a general global closed-loop supply chain. This is done through an original risk-based robust mixed-integer linear programming that is formulated based on an initial uncertain bi-level programming. Addressing the decision-maker's (DM's) attitude toward risk, a scenario-based conditional value-at-risk is used to deal with demand and return uncertainty. Also, the Karush-Kuhn-Tucker (KKT) conditions are employed to transform the model into its single-level counterpart. The results obtained from solving a numerical example through the proposed framework are compared with those of the corresponding centralized system, which is formulated through deterministic multi-objective programming and solved by the Lp-metric method. The results show that the use of the proposed framework improves the robustness of profit, income, and cost by about 28%, 34%, and 36% on average. However, a more conservative DM faces a larger cost of robustness than an optimistic DM while experiencing a more significant improvement in the system responsiveness. Using the proposed framework, the manager can measure the advantages, disadvantages, and consequences of their decisions before their actual implementation. This is because the model is capable of establishing fundamental trade-offs among risk, cost, profit, income, robustness, and responsiveness according to the DM's attitude toward risk.

Mapping and visualizing of research output on waste management and green technology: A bibliometric review of literature

The transition to a circular economy (CE) and environmental protection highly depends on waste management (WM) and green technology (GT). The purpose of this study is to examine the past two decades of WM and GT research to identify the most significant advancements and potential future research areas. Bibliometrics content analysis and text mining were utilized to resolve the subsequent issues: Has WM and GT research developed over time in the CE industry? Does WM and GT research have a clearly defined purpose? How do you foresee the future of WM and GT research in the context of CE evolving? Consequently, 1149 journal articles from the Scopus database were used to create and evaluate bibliometric networks. Therefore, five significant CE-related issues requiring additional research were identified: The first category is bio-based WM, followed by CE transition, GT, ecological impacts, municipal solid WM and lifecycle assessment, and finally, bio-based WM. Future research topics and a tool for the CE transition may be impacted by the investigation of inclusive WM systems, GT practices and their defining highlight patterns (which aim to minimalize waste generation). Future research goals include reducing waste and implementing WM into the CE framework.

Big data analytics capabilities for reinforcing green production and sustainable firm performance: the moderating role of corporate reputation and supply chain innovativeness

The literature review lacks empirical studies on the role of big data analytics (BDA) and green technological innovation capabilities (GTICs) in promoting the sustainable performance of the manufacturing industry. The primary objective of this study is to examine the role of BDA-technology capability, GTIC, and environmental orientation toward green production and sustainable firm performance. Moreover, this research paper investigates the mediating role of green production and green competitive advantage and the moderating role of corporate reputation and supply chain innovativeness. Primary data was collected from Pakistani manufacturing firms through the survey method. Structural equation modeling was applied to measure and verify the relationship of proposed hypotheses. Empirical findings show BDA technology capability, GTIC, and environmental orientation positively contribute to green production. Moreover, green production helps achieve a green competitive advantage, and green competitive advantage positively influences sustainable firm performance. Furthermore, mediating role of green production and green competitive advantage and moderating role of corporate reputation and supply chain innovativeness was also confirmed. This study contributes by developing a comprehensive model showing the relationship between organizational capabilities, BDA technology capability, GTIC, and sustainable firm performance by considering potential mediators and moderators. Thus, this research suggests enhancing green production and sustainable firm performance through adopting BDA technology capability and GTIC by Pakistani manufacturing firms.

The impacts of sustainable industrial revolution (IR) on the profitability of Hungarian food companies

There is growing literature on the concept and objectives of corporate sustainability (CS), but less attention is paid to a comprehensive approach to economic, social and ecological factors and industrial revolution (IR). Specifically, this paper contributes to the academic debate on the relationship between CS and IR in agri-food economics using firm-level data. The study used quantitative pathway models to measure the extent to which technologies affect the development of social, ecological and economic factors in Hungarian food manufacturing companies. The research was conducted using partial least squares path modeling (PLS-PM) and categorical principal component analysis (CATPCA) to calculate the direct and indirect effects of IR technologies on profitability outcomes. This study has shown that the livable and sustainable path hypotheses can be confirmed. Consequently, the food manufacturing businesses whose managers think along the viable and sustainable lines tend to be more profitable. However, the ecological and economic factors strengthened the positive impact of the social dimension on food corporate profitability. Decision-makers should not wait for a pie in the sky for emerging sustainability but consciously embrace the CS issues that only provide a direction for the changes.

A Bibliometric Analysis of End-of-Life Vehicles Related Research: Exploring a Path to Environmental Sustainability

Considering rapid economic development and continuously increasing environmental concerns, end-of-life vehicles (ELVs) have significant socioeconomic value as a crucial waste stream. The research relating to ELVs has rapidly evolved over the last few years. However, existing review studies focus on specific research themes, and thus, fail to present a complete picture. Hence, this research intends to explain the current research scenario relating to ELVs by reviewing the critical published studies of the last 22 years. A total of 1405 research publications were extracted from the Scopus database covering the period from 2000 to 2021. Mainly employing bibliometric analysis techniques, this research analyzes the quantity of literature, researchers, institutions, countries, and research themes to understand the current status and future trends in ELV recycling and management. The results revealed a considerable rise in the number of articles published in the last five years. The key producers of influential ELV research are listed as the United States, China, and the United Kingdom. Globally, Chinese universities have the most ELV-related articles published. Similarly, Serbian researcher Vladimir Simic authored the most ELV-related articles during the research period. This article also identifies various research themes: management and recycling, resource recovery and components, life cycle evaluation, and socioeconomic effects. The results also reveal a strong association between distinct ELV research clusters.