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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.

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.

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.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both as a fresh product and as processed food. However, as with many other products, the consumption of fruits and vegetables and, more specifically apples, has been drastically affected by the first lockdown in 2020. In this project, the authors investigate whether the change in consumption habits had long-lasting consequences beyond 2020 and what are the main eating motivations, food-related behavior and socio-demographic affecting the consumption of fruits and vegetables after the pandemic.

The authors ran two online surveys with 1,000 Italian consumers across a year (from October 2021 to December 2022). In the study, participants answered questions about their consumption habits and their eating motives. Out of 1,000 consumers, the authors included in the final analysis only the participants who answered both surveys, leaving a final sample of 651 consumers.

The results show that participants have allocated more budget to fruit and vegetables after the lockdown than before it. Moreover, consumers reported an average increase in the consumption of apples. However, the increase was more pronounced for people aged between 30 and 50 years old and identified as female. After showing the difference across time, a cluster analysis identified three main segments that differ in their eating motives, place of purchase and area of residence.

Overall, the results contribute to a better understanding of how the global pandemic is still affecting people's daily life. Moreover, the findings can be used to guide the marketing and communication strategies of companies in the food sector.

To the best of the authors' knowledge, this is the first study that investigates changes in the consumption of fruits and vegetables, and, more specifically, apples, in Italy more than one year after the beginning of the COVID-19 pandemic. Moreover, the study proposes a classification of consumers based on their habits in a time frame during which the COVID-19 wave was at its bottom which is not currently present in the literature.

This study aims to provide a comprehensive bibliometric analysis of social exclusion research, examining its evolution and identifying emerging trends and influential contributions in the field.

Using bibliometric and thematic analysis of 3,041 Scopus database documents, the study uses tools like VOSviewer for network analysis and Biblioshiny for trend analysis, focusing on publication patterns, author contributions and thematic clusters.

The findings reveal significant growth in social exclusion research since 1979, highlighting key contributions from diverse academic fields. Notable trends include the rise of digital exclusion and environmental justice themes. The study identifies leading authors, institutions and countries contributing to this field, along with highly cited documents that have shaped the discourse on social exclusion.

The study acknowledges its reliance on Scopus data and suggests incorporating other databases for future research. It highlights the need to explore emerging topics and address literature gaps.

This paper presents a unique bibliometric perspective on social exclusion research, underscoring its interdisciplinary nature and evolving focus. The study’s comprehensive approach offers valuable insights into the field’s trajectory, contributing to a deeper understanding of social exclusion phenomena.

In the wake of the COVID-19 pandemic, remote working (RW) has emerged as a viable alternative to working employees in general and knowledge workers in particular. However, previous researchers have worked on the concept, development and facilitation of RW since the 1970s. Therefore, this study aims to review the existing literature on RW to ascertain the evolution of the concept in the business and management domain and provide for requisite arguments to extend the settings for future research agendas.

The authors based this study on a bibliometric analysis of articles (n = 349) retrieved from the Web of Science database published between January 1990 and October 2021. The authors have used a bibliometric toolbox comprising performance analysis, science mapping and network analysis in various software namely, VOSviewer, Gephi and Biblioshiny package in R.

The study’s results accentuated important themes like work–life balance, strengthening digital infrastructure, performance and productivity, hybrid work models and well-being and clustered them under four heads with proposed future research questions.

The study is based on a single database; the authors have used an extensive but not exhaustive list of keywords to retrieve the articles. The analysis employs certain threshold limits while using the science mapping technique.

This study would enable managers and academics to comprehensively understand remote work and offer logical implications to appreciate its nuances.

This study is unique as it recognizes the intellectual structure in the existing literature on RW and traces the advancements and exponential growth post-COVID-19. The authors recapitulated the literature as network analysis of the RW facilitation model comprising the antecedents, outcomes, mediators and moderators.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.