Industry 5.0 era enabling the transition of logistics to smart logistics: A bibliometric and systematic review

PurposeThis paper aims to quantify the trend of variables used for building a theoretical model with the help of analysing bibliometrics data of inventory management (IM) and Total Quality Management (TQM) practices and their effects on firm performance.Design/methodology/approachScopus' research database and the Web of Science (WoS) (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) were used to find relevant articles. The articles, review papers and conference proceedings were screened from 1993 to 2021. The articles were analysed to explain the different types of IM practices, TQM practices and their effect on the firm's performance. Thematic analysis was done using a bibliometric package of “R” (Biblioshiny) and VOSviewer to identify the key trends, approaches and research agenda.FindingsThe research covered 28 years of publications and summarised 497 articles, review papers and conference papers. Researchers concluded that IM highly correlates with the inventory turnover ratio and has no relation to firm performance. Further, TQM positively affects firm performance, but integrating IM and TQM will be a research scope for future study as none of the researchers previously covered this.Research limitations/implicationsDue to the systematic literature review (SLR) and Bibliometric review, the study is limited to the Scopus and WoS (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) databases.Originality/valueBeing the research on a bibliometric and systematic review, relating IM practices with TQM practices would be the novelty of this paper.

Artificial Intelligence and Sustainable Development in Business Management Context – Bibliometric Review

Digital technologies play a pivotal role in driving innovation within organizations and are increasingly essential for generating value in today's evolving business environment. Among these technologies, Business Intelligence (BI) stands out by enhancing organizational control, reducing risks, and offering data-driven insights to support decision-making. For small and medium-sized enterprises (SMEs), BI tools can be especially valuable in systematically addressing control-related challenges. However, current research on the use of BI in SMEs remains limited and tends to focus only on specific areas such as management control, risk management, and auditing. This study introduces a novel, integrated approach to business control systems, combining data and perspectives from the three aforementioned areas. By utilizing BI tools, this method generates interconnected, actionable information that supports the long-term success of SMEs. To date, no bibliometric reviews have specifically explored the application of BI in SME contexts. This research addresses that gap by conducting a bibliometric analysis to highlight the diversity of methodologies used and their relevance to SMEs. The goal is to advance the field, bridge existing gaps in the literature, and encourage innovation in BI applications. Findings show strong SMEs interest in BI, particularly for managing risks, conducting audits, and monitoring organizational transformation.

With the advancement of electric mobility, critical materials that are used in the batteries and electronic equipment of electric vehicles tend to become scarce. This work aims to analyse the state-of-art of the electric vehicle supply chain through bibliometric and systematic reviews, using quantitative and qualitative indicators, to find critical points that represent risks to the supply chain and that should be focused on and to identify trends for further studies. The bibliometric review was carried out with the support of the Bibliometrix software. The systematic review was performed using the PRISMA method. The bibliometric analysis showed the importance of the costs associated with electric vehicles, as well as trends in studies related to sustainability and transparency in the supply chain. Although risk management in the supply chain appears to be relatively little studied when considering the authors’ keyword analysis, the systematic review showed that this process was the most studied topic. Even so, raw materials supply appeared as the topic most focused on, followed by an environmental impact assessment and cost analysis. There were also studies aiming to achieve competitiveness and analyse ecologically correct practices. The battery was the most studied component, but other components must be analysed in search of greater competitiveness in relation to conventional vehicles.

PurposeThe study aims to identify and discuss influential aspects of corporate environmental disclosure (CED) literature, including key streams, themes, authors, keywords, journals, affiliations and countries. This review also constructs agendas for future CED research.Design/methodology/approachUsing a bibliometric review approach, the authors reviewed 560 articles on CED from 215 journals published between 1982 and 2020.FindingsThe authors' insights are three-fold. First, the authors identified three core streams of CED research: “legitimization of environmental hazards via environmental disclosures,” “the role of environmental accounting in achieving corporate environmental sustainability” and “integrating environmental social and governance (ESG) reporting into the global reporting initiatives (GRI) guidelines”. Second, the authors also deployed a thematic map that classifies CED research into four themes: niche themes (e.g. institutional theory and environmental management system), motor themes (e.g. stakeholder engagement), emerging/declining themes (e.g. legitimacy theory) and basic/transversal themes (e.g. voluntary CED, environmental reporting and corporate social responsibility). Third, the authors highlighted important CED authors, keywords, journals, articles, affiliations and countries.Research limitations/implicationsThis study assists researchers, journal editors and consultants in the corporate sector to comprehensively understand various dimensions of CED research and practices and suggests potential emerging research areas. Although this paper appears to have been thoroughly conducted, using authors' keywords to identify themes was a key limitation. Thus, the authors call upon using a more comprehensive data mining technique that uses keywords in abstracts, titles and the whole body of papers and then identifies inclusive trends in CED literature.Originality/valueThe authors contribute to the extant accounting literature by investigating the organizational-level CED, both mandatory and voluntary, using a systematic and bibliometric literature review model to summarize the key research streams, themes, authors, journals, affiliations and countries. By doing so, the authors construct a future research agenda for CED literature.

The increasing need to manage natural resources sustainably, driven by population growth, requires the simultaneous use of Participatory Techniques (PT) and landscape planning for structured decision-making. We conducted a bibliometric and systematic review to provide an overview of PT usage, identifying evolution in scientific production. We considered the number of publications and citations, prominent journals, and highly cited articles on scientific papers published in the Web of Science database between 1993 and 2023. A total of 415 articles related to PT were identified. After content evaluation, 19 critical articles were selected that underpin the growing combined use of models and indices with PT, enhancing the robustness and credibility of decision-making processes.

The present article aims to summarize and classify existing research entrepreneurs' well-being through a bibliometric literature review. Its main objectives are: to identify the different theoretical perspectives and research strands that characterize and define literature on entrepreneurs' well-being and highlight the connections between them; as well to look for emerging trends and gaps in its literature by comparing the most recent works with those that represent the field's core. The document is based on bibliometric data: it uses citation techniques to select, analyze, and interpret citation patterns within the literature on entrepreneurs' well-being. The paper identifies six main groups, as well as several specific research flows and common themes that represent academic publications on entrepreneurs' well-being. The research strands on the topic are grouped into six different theoretical perspectives grounded in entrepreneurship related to: culture, education, innovation, sustainable development and small business; psychological well-being; social entrepreneurship and economic development; women and employment; and self-employment; life satisfaction and economic growth, and business administration. Data from the most recent publications were used to verify whether original topics and themes are reflected in contemporary debate and in which fashion. Limitations related to search engines, such as missing keywords were accounted by utilizing three different database as well as expanding keyword number. From a practical perspective, this research is expected to contribute on theory construction, management decision making, and teaching. This study describes the growing development of the literature on entrepreneurs' well-being, and the underlying structure of the different streams of research therein.

The discipline of shopping tourism has seen a surge in published research in recent years, covering a wide range of topics and issues. However, there are surprisingly few reviews and/or bibliometric studies to review and visually map the literature in this field of research. As a result, the present work has employed a comprehensive bibliometric and systematic review concerning shopping tourism-related articles published in academic journals indexed in Scopus and Web of Science databases between 1979 and 2021. Two methodologies were used in this bibliometric analysis. The first is performance analysis, which analyzes the contributions of the components that comprise research in shopping tourism. The second method is scientific mapping, which outlines the connections (i.e., intellectual, social, and conceptual structure) between facets related to shopping tourism. Using Bradford's and Lotka's laws, the research revealed a remarkable increase in shopping tourism sources, authors, and articles, reflecting a substantial evolutionary curve of this research theme. Various affiliations and countries have contributed considerably to shopping tourism research during the period. The analysis of science mapping produced a substantial understanding of shopping tourism's social, intellectual, and conceptual structure. This review article provides several implications for research via perspicuous overviews and insights into shopping tourism, Big Data, and its knowledge structures. It also holds a variety of practical implications for tourism policymakers, destination management organizations (DMOs), and tourism marketers regarding the key themes, new trends, and main contributors to shopping tourism research over four decades of research.

Supply chain management (SCM) concerns itself with the integration of a firm’s internal management processes with the external environment. This could explain why sustainability has been embraced by scholars who study SCM. This bibliometric review was untaken with the explicit goals of updating and extending prior reviews of research on sustainable SCM (S-SCM). The goals of this research were to document the scope and development of S-SCM research; identify influential journals, authors, and documents; analyze the intellectual structure of this field of sustainability inquiry, and highlight emerging topics on the frontier of S-SCM inquiry. By using bibliometric tools, a relatively large and rapidly growing corpus of peer-reviewed research documents concerned with S-SCM were found. Citation analyses of journals, authors, and documents yielded a surprisingly high level of scholarly content for a literature body of such recent vintage. The author co-citation analysis revealed three coherent but closely connected groups of thought that comprise the intellectual structure of this knowledge base. Finally, the analysis identified a constellation of topics concerned with the integration of internal processes (e.g., decision-making, manufacturing, sensitivity analysis, risk assessment, life-cycle assessment) and the organizational environment (e.g., climate change, gas emissions, carbon emissions, greenhouse gases, energy utilization, climate change). The results of this research concluded that SCM practitioners and scholars may have embraced sustainability more than any other field of management.

Purpose The purpose of this paper is to investigate how higher education institutions (HEIs), from different contexts, catalyze the consolidation of climate action in their territories. Design/methodology/approach A bibliometric and systematic review of the literature was run to map the performance of HEIs and their contribution to climate action. Findings The results showed that research around the topics of higher education and climate change has been growing, especially since 2007 and, in the past decade, the research focus is leaning towards learning and educational aspects. Originality/value This study also identified research gaps, especially the need to explore the potential of climate action promoted by the dimension of outreach. Finally, the research contributes to the understanding of the impacts of HEIs on global climate action, guides future efforts and supports future research to fill in the research gaps.

Influential literatures in periprosthetic infection following joint arthroplasty: A bibliometric review

This study uses a bibliometric literature review approach with a sample mapping literature of 10 articles regarding planned behavior theory approaches to purchase decisions; Reference articles from 2007 - 2021 under publisher Elsevier (e.g., LWT, Journal of Retailing and Consumer Services, Journal of Interactive Marketing, Regional Science and Urban Economics, Food Quality and Preference Journal, Energy Policy, Decision Support Systems Journal, and Industrial Marketing Management ). Universally, the results of this literature study state two propositions and hypotheses (e.g., Firstly, the theoretical implication states that there is a robust theoretical correlation between the theory of Planned behavior on purchase decisions; secondly, the correlation of planned behavior theory in aspects of management (e.g., marketing, Technology, and HRM) make a positive contribution as a grounded theory to explain antecedent variables and their correlation to other approaches)

Purpose The growing competitiveness of markets and the increasing complexity of production systems pose new challenges to quality control, demanding more effective and adaptive tools. Statistical quality control (SQC), although established as a strategic resource for reducing variability and preventing defects, it shows limitations when dealing with non-normal data distributions or when faced with data with high dimensionality. In this context, machine learning (ML) techniques have emerged as complementary solutions to enhance the detection of out-of-control and abnormal patterns in control charts. Therefore, this study aims to map the current advances, emerging trends and critical knowledge gaps in the application of ML for SQC. Design/methodology/approach This article presents a combined bibliometric and systematic review of the literature. The bibliometric analysis is conducted on an initial set of 574 articles collected from scientific databases, while the systematic literature review is based on 80 studies published between 2010 and 2024, selected through the PRISMA protocol. The analysis aims to map the main techniques employed, the predominant types of tasks and learning, hyperparameter optimization techniques and data types employed, as well as the objectives proposed by the models. Findings The bibliometric analysis indicates an increasing trend in scientific publications, with research primarily concentrated in the fields of industrial engineering and applied computer science. China stands out as the leading contributor in terms of publication volume and the development of more robust co-authorship networks. Keyword analysis highlights the predominant use of artificial neural networks (ANN), support vector machines (SVM) and dimensionality reduction techniques in statistical process control, as well as the recent emergence of topics aligned with Industry 5.0 and human-centric manufacturing. The systematic literature review reinforces the predominance of ANN and SVM applied to SQC, mainly addressing classification tasks for detecting in-control and out-of-control process states. Most studies rely on simulated data and supervised learning approaches, with scarce use of real-world industrial data and a limited adoption of unsupervised and adaptive learning strategies, highlighting important gaps for future research. Future research should prioritize validation with real industrial datasets, the development of unsupervised and adaptive learning approaches capable of handling dynamic and non-stationary processes, and the integration of explainability, real-time analytics and human-centric principles in ML-based SQC systems. Originality/value This study identifies research gaps and proposes directions for future work, emphasizing the development of more robust, interpretable models suitable for connected industrial systems.

Garcinia brasiliensis fruits and its by-products: Antioxidant activity, health effects and future food industry trends – A bibliometric review

The adoption of artificial intelligence (AI) in the workplace is changing the way organizations function, and profoundly affecting employees. These organizational changes raise crucial questions about the employee's future and well-being. Our study aims to explore the intersection between artificial intelligence and employee well-being through a bibliometric review and a contextual analysis. Carried out in May 2024, our study is divided into two phases. The first phase, dedicated to bibliometric review, was conducted using the PRISMA method, and explored the Scopus and Web of Science databases for the period from 2015 to 2024. A total of 92 articles were selected for quantitative analysis using VOSviewer software. The second phase is based on an in-depth systematic analysis of 25 articles selected from those previously identified. These articles were selected on the basis of their relevance to the research question, and were subjected to in-depth thematic analysis using NVivo software. The bibliometric analysis results reveal a significant increase in publications starting from the year 2020, highlighting advancements in research, primarily in the United States and China. The co-occurrence analysis identifies four main clusters: ethics, work autonomy, employee stress, and mental health, thus illustrating the dynamics created by artificial intelligence in the professional environment. Furthermore, the systematic analysis has brought to light theoretical gaps and under-explored areas, such as the need to conduct empirical studies in non-Western cultural contexts and among diverse target groups, including older adults, individuals of different sexes, people with low education levels, and participants from various sectors, including primary and secondary industries, small manufacturing businesses, call centers, as well as public and private healthcare sectors. Existing literature emphasize the importance for organizations to implement supportive strategies aimed at mitigating the potential adverse effects of AI on employee well-being, while also leveraging its benefits to enhance workplace autonomy and satisfaction and promote AI-enabled innovation through employee creativity and self-efficacy.