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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.

Over the past two decades, the hub location domain has witnessed remarkable growth, yet no prior study reviewed and synthesised problem formulation and solution methodologies to address real-life challenges.

The current study conducts a comprehensive bibliometric literature review to develop a thematic framework that describes and presents hub location problems. The work employs cluster, bibliometric, and social network analyses to delve into the essential themes.

Key themes include cooperation, coopetition, sustainability, reshoring, and dynamic demand, contributing to the complex challenges in today’s hub location problems. As the first work in this field, the study serves as a valuable single-source reference, providing scholars and industry practitioners with key insights into the evolution of hub location research, prominent research clusters, influential authors, leading countries, and crucial keywords.

Findings have significant implications since they highlight the current state of hub location research and set the stage for future endeavours. Specifically, by identifying prominent research clusters, scholars can explore promising directions to push the boundaries of knowledge in this area.

This work is a valuable resource for scholars in this domain and offers practical insights for industry practitioners seeking to understand the hub location problems. Overall, the study’s holistic approach provides a solid foundation for advancing future research work in the hub location field.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

This paper explores the effect of individual information technology culture archetypes on the perceived ease of use and perceived usefulness of e-banking customers.

A multi-stage approach was used. First, a cluster analysis was performed (based on a survey of 360 Algerian bank customers). Second, a multiple regression analysis was assessed to test the hypotheses.

The cluster analysis reveals five IT cultural groups for e-banking customers: dangerous, dodgers, compliant dodgers, disenchanted and addicted customers. A mapping of these archetypes is then proposed and tested. The multiple regression analysis shows that the dangerous IT culture archetype exhibit the highest level of perceived ease of use and perceived usefulness beliefs when the dodgers show the lowest one.

This study is limited in that it adopts a relatively small convenience sampling in Northwest Algeria. Furthermore, enriching the model with other antecedents could be of use. However, it clarifies the issue of whether the same IT culture archetypes can be found in different contexts and show that the IT cultural archetypes list is not exhaustive.

The study contributes to the existing knowledge on e-banking adoption in developing countries and provides Algerian banks with some crucial elements.

This paper is one of the first to investigate the impact of IT culture archetypes on e-banking adoption. It (1) identified five IT culture archetypes, (2) proposed a mapping of these archetypes, (3) reinforces the use of the spinning top model and (4) goes further as it applies it in a new context (developing country) and industry (banking).

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.

The aim of this study was to empirically examine how much traditional attributes and green attributes characterize products within design-oriented firms. Further, we explored how these attributes relate to the perceived level of innovation of the firms.

An exploratory research was carried out in 86 Italian manufacturing companies that are members of the Industrial Design Association. Using the questionnaire method, the entrepreneurs’ perceptions have been analyzed. Data have been treated with hierarchical cluster analysis.

The analysis shows that environmental sustainability is the least important attribute of a design product and four clusters of highly design-oriented firms differ by design-product attributes. Further, the least green firms are also the least innovative in terms of incremental and general innovation.

The small size of the sample and the provenance of firms from a single country imply limited generalizability, and further research on the topic is recommended.

Design-driven innovation based on traditional design attributes provides many competitive advantages to firms. However, given the growing concern about environmental challenges, investing in green attributes in design products allows for remaining competitive and more effective in innovation.

This study, for the first time, reveals the heterogeneity among design-oriented firms, particularly regarding the presence and assortment of traditional design attributes, as well as the incorporation of environmentally friendly attributes in their products. Moreover, the study uncovers the relationship between varying levels of green attributes in the offerings and the perception of the firm’s innovativeness.

The relationship between entrepreneurship and ethics explores dimensions that entrepreneurs should consider to face ethical dilemmas. One of the controversial points in this relationship is the human complexity and the balance between ethics and profit that characterize the decisions. The external pressure and the scarcity of the resources create entrepreneurs' ethical challenges impacting on strategic and governance decisions from which value should be obtained. Therefore, the nexus between entrepreneurship and ethics should be investigated to understand possible ways to leverage human values in the entrepreneurial actions. To this aim a bibliometric analysis has been carried out and Citespace, VOSviewer and Bibliometrix software have been used. Data have been extracted from Web of Science database in the timespan 1986–2023 generating 583 documents. The analysis shows the current literature published on the relationship between entrepreneurship and ethics by highlighting the main authors, (co)citations, countries, and journals that published papers on the topic. The findings from the four research questions defined shown that the top author publishing on the topic is Prof. Dr Fassin Yves. The most cited scholar is Prof. Spence Laura J. It was also found that the Journal of Business Ethics has the most publications on the topic. The top countries to publish articles on the topic are USA and UK. Five clusters have been found by grouping the main actors, countries and relevant research themes. The cluster on social entrepreneurship research is the main representative the topic. Limitations and future research have been discussed.

This is amongst the first works to develop a technographic segmentation of smartphone users attending music festivals based on attitudes, motivations and usage patterns. We also aim to describe festivalgoers’ characteristics.

The data were collected from 522 festivalgoers who attended the Rainforest World Music Festival (RWMF) in Malaysia. A two-stage cluster analysis of Ward’s method and k-means was applied to develop technographic segmentation during the festival. Using discriminant analysis, we confirmed that each festivalgoer’s characteristics differ amongst groups.

Four technographic segments were developed: alarm hitters, technological tickers, plug pullers and fuse blowers. The results confirmed that festivalgoers had distinct characteristics and preferences based on smartphone use.

We extend previous research on the technographic segmentation of smartphones and festivalgoers. We highlighted the limitations of cluster analysis in terms of stability to produce a suitable number of segments and to include other festivals. The generalisability of the results may be constrained by the time gap between data collection and publication.

Our results can help marketing managers understand the needs of segments by selecting appropriate advertisements and promotional tools that appeal directly to the desired target segments.

This study will help local communities increase their revenue and job opportunities. The culture of music festivals for the next generation can be sustained and promoted by local and international festival lovers.

This study is the first to present festivalgoers' use of the technographic segmentation term in music festivals.

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).

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.