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Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

Over the past decade, researchers have witnessed an exponential growth in the number of publications on IR. This paper aims to understand the state of the art of the research field and to highlight the areas where further academic research is needed, guiding developments in theory, research, policy and practices.

The authors apply the dynamic literature review method called “Systematic Literature Network Analysis”, which combines systematic literature review and bibliographic network analysis. Furthermore, to overcome some of the limitations connected to the methodology, the authors integrate the literature with a manual content analysis of papers.

IR adoption and practices and their determinants represent the most analyzed aspects of literature. Over time, attention has been paid to more specific issues, such as the relationship between IR and other disclosure mechanisms, IR quality and its assurance, the critical analysis of the IR framework and principles and difficulties in IR adoption. Although the literature on IR can be considered to be in its mature stage, many aspects are still under-researched, so there is plenty of space for future research.

The authors propose the following main issues as subjects to be investigated in future studies: IR is not simply an evolution of sustainability reporting, but an innovative communication tool; the debate on who the recipients of value are (shareholders or stakeholders) and on what the definition of value adopted by IR is still remains an open issue; more attention should be given to the role of IR as a managerial tool, which could support strategy formation and communication, and influence internal processes of performance measurement and evaluation; what the future of IR will be in light of recent EU Corporate Sustainability Reporting Directive and new ISSB's standards is still an open question. From a methodological perspective, little is known about structured approaches in accounting studies. The authors confirm how methodologies, such as that of this paper, may be exploited as a tool to support dynamic analysis for setting the agendas for future studies in the accounting field.

The objective of this study is to examine current business and management research on “Industry 4.0 base technologies” and “business models” to shed light on this vast literature and to point out future research agenda.

The authors conducted a bibliometric analysis of scientific publications based on 482 documents collected from the Scopus database and a co-citation analysis to provide an overview of business model studies related to Industry 4.0 base technologies. After that a qualitative analysis of the articles was also conducted to identify research trends and trajectories.

The results reveal the existence of five research themes: smart products (cluster 1); business model innovation (cluster 2); technological platforms (cluster 3); value creation and appropriation (cluster 4); and digital business models (cluster 5). A qualitative analysis of the articles was also conducted to identify research trends and trajectories.

First, the dataset was collected through Scopus. The authors are aware that other databases, such as Web of Science, can be used to deepen the focus of quantitative bibliometric analysis. Second, the authors based this analysis on the Industry 4.0 base technologies identified by Frank et al. (2019). The authors recognize that Industry 4.0 comprises other technologies beyond IoT, cloud computing, big data and analytics.

Drawing on these analyses, the authors submit a useful baseline for developing Industry 4.0 base technologies and considering their implications for business models.

In this paper, the authors focus their attention on the relationship between technologies underlying the fourth industrial revolution, identified by Frank et al. (2019), and the business model, with a particular focus on the developments that have occurred over the last decade and the authors performed a bibliometric analysis to consider all the burgeoning literature on the topic.

Covid-19 pandemic, war, climate emergency and other recent challenges are inflicting tremendous stress to youth. However, death and tragedy are nowadays considered taboo, as there is generally no standardized nor naturalized discussion on the subject, especially with young people. The current multi-crisis scenario is intensifying the need to incorporate an education on tragedy and resilience in our learning systems. In this context, it is necessary to find suitable teaching resources for this educational challenge that are attractive, entertaining and suitable for children and youth. A resource that meets all these requirements are children’s folk songs (CFSs). Apart from the intrinsic educational potential of music, folk songs have a simplicity and musicality that make them an ideal teaching resource. Considering their oral historical transmission, their survival confirms the attraction that this type of composition causes on children. However, to consider CFSs as an adequate resource to carry out an education for death and tragedy, it is necessary to study whether they present a non-negligible proportion of tragic passages and with enough variety of themes. This paper aims to address the study of the presence of explicit tragic content in Spanish CFSs and thus could be considered a cultural resource with transformative educational potential to develop resilience capabilities on the face of tragedy.

An analysis of lyrics of 2,558 Spanish CFSs is presented, using a manual content analysis as well as a computerized content analysis with the aim of identifying the tragic component of these songs and, thereby, assessing their pedagogical potential as a transformative educational resource.

The results obtained show a considerable presence of death and tragedy (19.78%) and a variety of tragedy dimensions. CFSs have been transmitted orally not only as a ludic resource, but also to prepare children for life (and death). The results show the complementarity of both analyses to avoid subjectivity while considering the underlying meanings of the songs.

This task had previously not been approached in an automated manner in the literature, nor there had been a similar study with a sample of this magnitude. The outcomes obtained show the considerable presence of tragedy in Spanish CFSs and emphasize the interest of this currently undervalued didactic resource.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

This article aims to conduct a bibliometric study using structural and relational approaches amongst the extracted documents and investigates the connections between business incubation and entrepreneurship-related papers to describe the field comprehensively.

A total of 259 articles have been retrieved from Scopus database in order to conduct the bibliometric analysis. Performance analysis and science mapping techniques of bibliometrics have been used along with data visualisation software, i.e. VOSviewer and RStudio. The network collaboration and intellectual structures, i.e. bibliographic coupling, co-occurrence analysis, word cloud and trending topics, have been presented to identify the field’s latest trends, themes and development.

The findings highlight annual publication trends, including the most frequently cited articles, the most productive authors, countries and highly influential journals that contribute the most to the said field. The intellectual structures have been developed to identify research themes and trends by running co-occurrence analysis and bibliographic coupling. The findings of this study emphasize the value of technology transfer, mentorship programmes, entrepreneurship education and an emphasis on innovation and creativity through entrepreneurial universities and academia. These findings provide policymakers and administrative officials with crucial guidance for fortifying the pillars of entrepreneurship and education for the comprehensive development of the economy. Further, this article attempts to identify the most influential and relevant publications as well as the newest trends in the area of business incubation in combination with entrepreneurship.

The article contributes not only to broaden the scope of knowledge on the said research discipline but also to comprehend how the field has evolved over a period of time. This study also attracts the interest of scholars/academicians, leading to the significant production of scholarly documents in business incubation and entrepreneurship.

The field of entrepreneurship and business incubation is one of the important pillars for the growth and development of the economy. This piece contributes to this arena by focusing on the areas that must be taken care of by developing the entrepreneurial ecosystem and fostering the progress of startups. The fundamentals of this research highlight the importance of mentorship programs, entrepreneurship education, technology transfer and a focus on innovation and creativity through entrepreneurial education and efforts by universities/academia, giving an important direction to the policymakers and administration for strengthening the pillar of entrepreneurship and education for the holistic development of the economy.

Business incubation is an emerging field of academic research connected to startups, venture formation and entrepreneurship ecosystems, making it a potential scholarly discipline. This study presents a thorough bibliometric analysis over the last three decades, offering comprehensive details on the most significant developments in the field of business incubation. Moreover, the various analytical methods applied to this study make it more attractive.

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.

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

The objective of this study was to conduct a bibliometric analysis of the existing literature on organizational deviance to assess how far this concept has progressed since its introduction in the domain of organizational behavior.

This study employs bibliometric methodologies (citation analysis, co-citation analysis and co-occurrence of author keywords) using VOSviewer. The Scopus database was used, as it is the largest database of scholarly literature.

The findings indicate the character and direction of organizational research over the past two decades. Organizational deviance due to psychological contract breach, organizational deviance in the context of organizational cynicism and organizational deviance in the context of psychological capital are the three major themes in the literature on organizational deviance. In addition, the study highlights the most significant authors, journals, institutions and nations in the field of value co-creation research as well as potential future research areas in this area.

The use of a single database and the inability to contextualize the citation structure of papers revealed by the review are limitations of this study.

This study examines the structure of the literature on organizational deviance and charts the field's evolution over time.