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This paper aims to present a summary of the green consumer behaviour (GCB) research conducted during the 2001–2021 period using the bibliometric analysis and to carry out a thematic and content analysis on the three clusters which comprise 57 articles resulting from the co-citation analysis and identify the significant green purchasing factors.

The three-pronged methodology applied to this research analysis includes performance analysis of the literature using biblioshiny and R Studio; network mapping analysis using VOSviewer and Gephi; thematic analysis using word clouds generated with R Software and content analysis of each paper with the aid of within and between-study analyses.

Cluster one acted as a base for the theoretical foundations of GCB which aids in understanding the basic concepts of green marketing, its evolution and the methodologies, whereas cluster two determined the predictors of everyday green behaviour, which helps in gaining knowledge about the everyday sustainable activities the consumers indulge and the factors motivating to do so. Cluster three mainly focused on the psycho-socio demographic determinants of GCB, which assists in segmentation and predicting the purchase behaviour of the various consumer segments.

The significant variables and major gaps in each of the clusters were identified and authors have drawn the implications for future researchers and marketing managers.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Given the growing interest in the topic of knowledge management (KM) in family firms (FFs) and the subsequent increasing number of papers published, this study aims to review the field to identify and analyze the main themes and trends.

This study applies bibliometric techniques to a sample of 146 papers published from 2007 to 2023 and their 8,126 unique cited references. Bibliometric coupling is performed on the sample papers to explore the current intellectual structure of the field of KM in FFs, whereas cocitations analysis is performed to investigate the different literature streams that served as roots for the development of such a field.

Bibliographic coupling reveals that sample papers can be grouped into four clusters, and, through papers content analysis, the author identifies their core themes as knowledge sharing, innovation, knowledge-based dynamic capabilities and intellectual capital. Cocitation analysis of the cited references revealed four main clusters that can be considered the literature streams that served as roots for the development of the field, i.e. knowledge-based view, socioemotional wealth, strategic management and social capital (as a theory and as a resource).

This study contributes to the literature on KM in FFs by extending prior systematic review efforts with bibliometric analyses and combining these results to highlight connections between the main research themes around which scholars have debated (i.e. the clusters identified through bibliometric coupling) and their theoretical foundations (i.e. the clusters identified through cocitation analysis). This study also has practical implications by synthesizing and informing managers about FFs’ advantages and weaknesses in the KM process.

This paper aims to explore and analyse stakeholders’ perceptions of the development priorities and suggests more effective strategies to assist sustainable economic growth in the United Arab Emirates (UAE).

The authors use the World Bank data set, which collects various stakeholders’ opinions on the UAE development. First, the exploratory factor analysis has been applied to detect the main groups of development priorities. Second, the fuzzy cluster analysis has been conducted to detect the groups of stakeholders with different attitudes towards the importance of extracted groups of priorities. Third, clusters have been compared according to demographics, media usage and shared prosperity goals.

The two main groups of development priorities have been extracted by the exploratory factor analysis: economic priorities and sustainability priorities. Four clusters have been detected according to the level of motivation when it comes to the economic and sustainability priorities: Cluster 1 (High economic – High sustainability), Cluster 2 (High economic – Medium sustainability), Cluster 3 (High economic – Low sustainability) and Cluster 4 (Low economic – Low sustainability). Members of the cluster that prefer a high level of economic and sustainability priorities (Cluster 1) also prefer more diversified economic growth providing better employment opportunities and better education and training for young people in the UAE.

Limitations stem from the survey being conducted on a relatively small sample using the data collected by the World Bank; however, this data set allowed a comparison of various stakeholders. Future research should consider a broader sample approach, e.g. exploring and comparing all of the Gulf Cooperation Council (GCC) countries; investigating the opinions of the expatriate managers living in the UAE that are not from GCC countries; and/or including other various groups that are lagging, such as female entrepreneurs.

Several practical implications were identified regarding education and media coverage. Since respondents prioritize the economic development factors over sustainability factors, a media campaign could be developed and executed to increase sustainability awareness. A campaign could target especially male citizens since the analysis indicates that males are more likely to affirm high economic and low sustainability priorities than females. There is no need for further diversification of media campaigns according to age since the analysis did not reveal relevant differences in age groups, implying there is no inter-generational gap between respondents.

This paper contributes to the literature by comparing the perceived importance of various development goals in the UAE, such as development priorities and shared prosperity indicators. The fuzzy cluster analysis has been used as a novel approach to detect the relevant groups of stakeholders in the UAE and their developmental priorities. The issue of media usage and demographic characteristics in this context has also been discussed.

The purpose of this study is to provide a comprehensive analysis on the economic relationship between trade openness and economic growth and to identify current developments, potential research area and future directions. The emphasis is on the identification of annual growth of publications, country-wise distribution, publication pattern, intellectual structure and cluster analysis of scientific production in this field.

This study used evaluative techniques, text mining approach and performance analysis to identify possible patterns and correlation and to measure the impact of authors/citations/scientific production. Further, this study used the bibliometric mapping to represent the structural features of scientific production. This study emphasized on identification of the research hotspots based on occurrence of indexed keywords, productive researchers and journals during 2000–2022. Further, cluster analysis is performed using VOS viewer to analyze the current dynamics and future direction of the association between trade openness and economic growth (Eck and Waltman, 2011). Also, co-citation analysis is used in this study to identify the relations among authors or journals or documents using citation data, whereas the bibliographic coupling/mapping is intended to analyze the citing documents. Similarly, co-word analysis is used to study the article keywords that are mainly used to assess the conceptual structure of a concerning subject.

Economic growth is a function of trade openness, and it is important to analyze the relationship between trade openness and economic growth. Trade openness tends to become more liberalized over time, to contribute more to economic growth. Empirical evidence suggested that there exists a strong association between trade openness and economic growth. Further, keyword timeline analysis illustrated that the linkage between trade openness and economic growth is current area of interest among researchers. As per bibliometric analysis, China, Pakistan and Malaysia are the three most prolific countries in the terms of published articles on this theme. However, the most influential publications based on h-index and citation on trade openness–economic growth relationship is produced by Turkey. Based on cluster analysis, this study suggests that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development.

There are some limitations of this study. The first limitation is the authors have used Scopus database, leaving the possibility for future research to use Web of Science, Google Scholar or other similar sources. The second limitation is that the authors have used search terms “trade openness “and “economic growth,” although research could be performed using synonyms or even relevant terms in other languages.

Cluster analysis suggested that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development. Therefore, this study identified the potential research area in this research domain.

To confirm the originality of this study, to the best of the authors’ knowledge, this is the first study to combine bibliometric analysis and cluster analysis on trade openness–economic growth relationship. This study makes a comparison with phenomena/processes/events in contemporary economic and social reality in the field of trade openness and economic growth relationship.

This study aims to provide a comprehensive analysis of the current privacy concerns in the tourism industry by uncovering the key factors leading to such concerns (i.e. smart public services, cyber security issues, consumer behaviour and governance). Using papers from multiple sources, the relationship between technology advancements and tourist’s privacy concerns has been established.

This study adopted a machine learning-based systematic literature review approach to find out the clusters. The study analysed 68 papers using the topic modelling approach. A four-cluster solution was considered to be most representative of the extant literature identified using bibliographic coupling. Finally, content analysis of the selected literature has been performed.

This study identified four factors majorly leading to privacy concerns amid increasing technological advancements. Moreover, these factors were found to have a dyadic relationship with technological advancements. To everyone’s amazement, sustainable tourism was also found to have led to privacy concerns among tourists along with a lack of governance and cyber security issues. Furthermore, cluster-wise future research directions are provided based on the content analysis.

This study contributes to the literature by systematically reviewing and identifying the four dimensions leading to privacy concerns. To the best of the authors’ knowledge, the study done is the only attempt to synthesize the extant literature on tourists’ privacy concerns using an unbiased scientific approach.

本研究对旅游业当前的隐私问题进行了全面分析, 以揭示导致此类担忧的关键因素（即智能公共服务、网络安全问题、消费者行为和治理）。 通过考察来自多方渠道的现有文献, 本研究确立了技术进步与游客隐私问题之间的关系。

本研究采用基于机器学习的系统文献综述方法来找出聚类。 该研究使用主题建模方法分析了 68 篇文章。四聚类解决方案是现有文献中最具代表的 文献分类方法。最后, 本研究对所选文献进行了内容分析。

这项研究确定了在技术进步不断增加的情况下主要导致隐私问题的四个因素。 此外, 这些因素被发现与技术进步有二元关系。 本研究还发现可持续旅游业引发了游客对隐私的担忧以及缺乏治理和网络安全问题。 此外, 通过基于内容的分析, 本研究提供了未来的聚类研究方向。

本研究对现有文献的贡献在于通过系统地文献综述和分析以确定导致隐私问题的四个维度。该研究是基于公正的科学方法综合现有游客隐私问题文献的首度尝试。

Este estudio proporciona un análisis exhaustivo de la actual preocupación por la privacidad en el sector turístico, poniendo al descubierto los factores clave que la generan (los servicios públicos inteligentes, los problemas de ciberseguridad, el comportamiento de los consumidores y la gobernanza). Gracias al uso de artículos de múltiples fuentes, se ha establecido la relación entre los avances tecnológicos y la preocupación de los turistas por la privacidad.

Este estudio adoptó un enfoque de revisión sistemática de la literatura basado en el aprendizaje automático para descubrir los conglomerados. El estudio analizó sesenta y ocho artículos utilizando el enfoque de modelización de temas. Se consideró que una solución de cuatro conglomerados era la más representativa de la literatura existente identificada mediante el acoplamiento bibliográfico. Por último, se realizó un análisis de contenido de la bibliografía seleccionada.

En este estudio se identificaron cuatro factores principales que suscitan inquietud por la privacidad en medio de los crecientes avances tecnológicos. Además, se descubrió que estos factores tienen una relación diádica con dichos avances. Para sorpresa de todos, se halló que el turismo sostenible, junto con la falta de gobernanza y los problemas de ciberseguridad, también suscitan entre los turistas preocupaciones por su privacidad. Finalmente, el análisis de contenido ofrece orientaciones para futuras investigaciones.

Este estudio contribuye a la literatura haciendo una revisión sistemática e identificando las cuatro dimensiones que conducen a la preocupación por la privacidad. Este estudio es el único intento de sintetizar la bibliografía existente sobre la preocupación de los turistas por su privacidad utilizando un enfoque científico imparcial.

The primary goal of this review is to identify emerging themes in the cold supply chain (CSC) and their future research directions, methodologies, and theories.

The review looks at CSC related articles from Scopus database published in the years 2000–2020. Thereafter, bibliometric and co-citation analyses have been conducted to identify emerging themes, methodologies, and theoretical perspectives related to CSC management.

This study revealed a clear research gap in CSC literature with emerging themes relevant to diverse aspects. Primarily, the most prominent authors, methodologies, and theories were identified from bibliometric analysis. Next, we generated clusters to identify the insights of each cluster using co-citation analysis. Consequently, the significance of clusters concerning the number of articles, theoretical frameworks, methodologies, and themes was recognized. Finally, a few future research questions regarding emerging themes have been identified.

The importance of co-citation and bibliometric analyses in studying the evolution of research over a definite time is emphasized in this work. As per emerging themes, implementing digital technologies has increased the efficiency of traditional CSC and transformed it into digital CSC.

As per the authors' knowledge, this work is the first in literature to explore the significance of identifying emerging areas and future research directions in managing CSC through literature review based on bibliometric and co-citation analysis.

This article examines the dominant research topics that guide the literature on women's entrepreneurship in family businesses.

The authors used performance and scientific network mapping analyses from bibliometric techniques. Performance analysis was used to identify the most influential journals, authors, countries, co-citation, multidimensional scaling (MDS), hierarchical cluster (HCA) and document analysis to identify dominant research themes.

The research results show that studies on women's entrepreneurship in family businesses are gathered in three clusters. The studies in the first cluster focused on family succession and women's roles. The themes of the succession process, gender bias, leadership and entrepreneurship in the second cluster are intense. Finally, in the third cluster, the themes of women leaders and identity construction dominate.

First, new conceptualizations of female entrepreneurship from family businesses emerge over time (example: “fementerpreneur”); accepting and using these words takes time. For this reason, the authors may have missed the newly emerged concepts in the field of family businesses in the search strategy. Second, although MDS results are widely used in bibliometric research, other forms of MDS analysis may reveal different groups and clusters. Finally, bibliometric analysis is based more on retrospective and dominant themes in the most cited articles, with a heavy emphasis on the most cited papers. Hence, new articles and contributions can be equally important.

Previous studies have not examined the subject of women's entrepreneurship in family businesses. By addressing this issue and setting the agenda for future research, the authors contribute to the literature on women's entrepreneurship in family businesses.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.