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This study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.

324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.

LPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.

This study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

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.

In developing countries like Tanzania, gems and jewellery industry mainly consists of disintegrated and unstable micro and small workshops which operate in a way that misalign value addition processes. This study is aimed to bridge gap by focussing on exploitation of industrial clusters in social normalisation and economic resilience to developing countries. The world economic shocks has been not only individually experienced but also globally shared while disrupted lives across all countries and communities and negatively affected global socio-economic growth.

Furthermore, the explorative design was adopted in this study in order to explore needs of respondents, and with the aim to direct the study towards a descriptive design. The sample frame consists of participants in gems and jewellery activities in Tanzania whereby sample was drawn from Dar es Salaam and Arusha. Semi-structured interview was used to collect quantitative data to establish evidence of Tanzanians’ SSJs linked to global value chains (GVCs).

Results revealed the benefits of exploitation of artisanal industrial clusters to Tanzanians’ SSJs when linked to global value chains (GVCs). Findings of the study demonstrate the importance of artisanal industrial clusters in facilitating Tanzanians’ SSJs to access GVCs. Further, insufficient education, trust and social protection directly affects inclusive GVCs, inferring that the impact of artisanal industrial clusters on inclusive GVCs in social normalisation and economic resilience.

Study findings reveals shortcomings in existing regulatory framework of linking Tanzanians’ SSJs to artisanal industrial clusters, for improvements to better support the inclusiveness in GVCs. Findings of this research invite interventions on institutional capabilities and entrepreneurial competencies to enhance the capabilities of small-scale jewellers (SSJs). Like other studies, this study involved cross-sectional data, limit targeted study population as representative of SSJs in industrial clusters and GVCs in economic crises at limited time.

The study findings makes important practical contributions to the Tanzania’s SSJs by examining mediating role of artisanal industrial clusters hence informing policymakers of mining sector how to improve accessibility on GVCs by focus on offering great institutional capabilities and entrepreneurial competencies. These findings will help SSJs and policy makers to get better understanding of the relationships in exploitation of artisanal industrial clusters when accessing GVCs. Therefore, they can make better decisions on implementing artisanal industrial clusters as well as management accessing GVCs, so that SSJs will attain the best possible performance.

This emphasises the importance of community empowerment in the GVCs process through artisanal industrial clusters. Study findings indicate the influence of industrial relations to social dynamics which are previously inadequately addressed and scantly researched. In actual fact study propose initiatives that ensure local communities benefit socially from the integration of SSJs into GVCs through artisanal industrial clusters. Findings suggest local communities that take into account inter-sectionality of artisanal industrial clusters and inclusive GVCs, by considering how factors like education, trust and social protection status intersect to influence the social inclusiveness of SSJs.

There is limited evidence of linking Tanzanians’ SSJs to GVCs in social normalisation and economic resilience and few researchers have explored this topic. This article leverages exploitation of industrial clusters in normalisation and economic resilience to developing countries such as Tanzania as way of improving shared prosperity, sustainability, inclusive growth, cohesion, value chain upgrading and financial inclusion to SSJs.

The purpose of this study is to ascertain how cluster organisations have been fostering green transformation in Poland.

This paper adopts a multiple case study approach. Data collection methods involved in-depth interviews with cluster organisation managers and researchers to identify support measures for green transformation and to investigate the factors influencing their actions as well as a comprehensive analysis of documents, including cluster organisations‘ strategies.

Cluster organisations manage and participate in actions that create favourable conditions for pursuing low-carbon and circular economy ventures. They not only assist their members in overcoming obstacles related to green transformation but also engage non-members – which can lead to spillovers reaching beyond their borders. Their engagement takes place across all phases of the green transformation process.

For various reasons, the research was designed as qualitative to understand the opinions and experiences of various actors engaged in green transformation within cluster organisations’ ecosystems. The key factor influencing this decision stems from the fact that knowledge of the involvement of cluster organisations in supporting green transformation still needs to be completed and scattered. The limitations of the study include limited access to information and the fact that qualitative research allows for a certain amount of subjectivity, and the results should be generalised carefully. Moreover, the interviews were carried out with a non-random sample of participants. Another limitation of the study is related to biased views, which could have been shared by interviewees acting as representatives of the studied cluster organisations.

Cluster organisations have emerged as drivers of circular transition by promoting sustainable practices such as material recycling, biological recovery and parts harvesting. These initiatives contribute to reducing waste, conserving resources, and minimising the environmental footprint of industries. These organisations can be active agents of transformation, orchestrating collaborative efforts that have a far-reaching impact on industries and economies.

This is one of the first and most comprehensive studies on the role of cluster organisations in Poland in supporting green transformation. This paper identifies and systematises the actions undertaken to provide a clear understanding of the internal processes within cluster organisations.

Prior research examining cultural effects on customer service expectations has primarily used more generic Western cultural theory on an aggregate scale or with only a single variable to draw conclusions on a customer’s underlying reasoning for buying a service. This study aims to focus on culturally distinct clusters within non-Western nations, specifically exploring within-cluster differences in service expectations within the Confucian Asia cluster.

This study developed a measurement model of Chinese cultural values and service expectations, consisting of a three and five-factor structure, respectively. Data from a sample of 351 diners were analysed using SmartPLS software. The data was compared with similar studies within the Confucian Asia cluster to understand the culture effect on service expectations and within-cluster variations.

The findings underscore the varying importance of cultural values in shaping customer service expectations, emphasizing their relative, rather than equal, significance. The study provides insights into potential within-group differences in customer service expectations within the same cultural cluster – without losing sight of the fundamental cultural heterogeneity of the Confucian culture.

Managers should leverage the distinct cultural values of their operating country to gain insights into diverse customer groups, predict their behaviours and meet their needs and expectations.

This study offers valuable insights to both service management scholars and practitioners by focusing on culturally distinct clusters of non-Western nations and exploring their effects on variation in service expectations within these clusters.

This paper reviews the literature on the business models of small and medium-sized enterprises (SMEs). It seeks to examine the profile, conceptual and intellectual structure of the literature whilst leveraging the findings to suggest promising future paths to advance our knowledge on business models of SMEs.

The study resorts to a systematic literature review that conducts descriptive, bibliometric (i.e. co-word occurrence analysis and bibliographic coupling of documents analysis) and content analyses to review the literature on business models of SMEs. The research protocol included 301 articles collected in the Web of Science (WoS) database in the descriptive and bibliometric analyses. The bibliometric analysis was performed using the VOSviewer software.

The descriptive analysis portrayed the profile of this research stream. The systematisation of the co-word occurrence analysis describes the four clusters that comprise the conceptual structure of this research field. The content analysis of the bibliographic coupling of documents’ clusters portrays the seven clusters that involve the intellectual structure of this research area.

The integrated and holistic approach adopted in this study provides a detailed overview of the literature on business models of SMEs. We propose an integrative framework for the literature that bridges the main themes that form the conceptual and intellectual structure of this field of research. A comprehensive agenda for future research is suggested and implications for theory, policy and practice are stated.

Recent wireless communication and electronics technology has enabled the development of low‐cost, low‐power, and multi‐functional sensor nodes. However, the fact that sensor nodes are severely energy‐constrained has been an issue and many energy‐efficient routing protocols have been proposed to resolve it. Cluster‐based routing protocol is one of them. To achieve longer lifetime, some cluster‐based routing protocols use information on GPS‐based location of each sensor node. However, because of high cost, not all sensor nodes can be GPS‐enabled. The purpose of this paper is to propose a simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks (WSN).

Instead of using location information of each sensor node, this approach utilizes information of remaining energy of each sensor node and changes in the number of cluster head nodes dependent on the number of sensor nodes alive. Performance results are presented and compared with some related protocols.

The simulations described in the paper show that both residual energy of each sensor node and changing cluster head nodes depending on the number of sensor nodes alive are very critical factors to obtain performance enhancement in terms of lifetime and data transmission. Especially, in some special environment, the proposal has better performance than GPS‐enabled protocol.

The paper is of value in proposing a simple dynamic clustering approach to achieve energy efficiency for WSN.

Purpose – Traditional library catalogs have become inefficient and inconvenient in assisting library users. Readers may spend much time in searching library materials via printed catalogs. Readers need an intelligent and innovative solution to overcome this problem. The purpose of this paper is to illustrate how data mining technology is a good approach to fulfill readers' requirements. Design/methodology/approach – Data mining is considered to be the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This paper analyzes the readers' borrowing records by using the following techniques: data analysis, building data warehouse and data mining. Findings – The mining results show that all readers can be categorized into five clusters, and each cluster has its own characteristics. It was also found that the frequency for graduates and associate researchers to borrow multimedia data is much higher. This phenomenon shows that these readers have a higher preference for accepting digitized publication. Besides, we notice that more readers borrow multimedia data rise in years. This up trend indicates that readers are gradually shifting their preference in reading digital publications. Originality/value – The paper proposes a technique to discover clusters by using ant colony methods.

Cluster analysis was used on three files of citations from social science journals to other journals. The files were a pilot study, a file of criminology data and a very large file covering all social sciences. The criminology data was divided into sections drawn from 1950, 1960, and 1970 sources. The large file was in two sections, one drawn from a ranked list of source journals and the other from a list of journals selected at random. The study looked at the effect of several cluster methods and various ways of normalizing the data to find out which observed effects are true properties of the data. The results indicated that clusters of social science journals generated using citations have a non‐hierarchical structure. The criminology samples from 1960 and 1970 showed little change over ten years in the main clusters, but the two sections of the large file gave results which, although similar in general shape, differed substantially in their details. The overall conclusion is that cluster analysis is an unsuitable approach to the design of secondary services in the social sciences, though it may have some value in automatic retrieval systems. Two problems are the vast amounts of data needed and the difficulty of presenting results comprehensibly, particularly with overlapping clusters.