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This study is exploratory in nature and designed to address poorly documented issues in the literature. The dimensions of regional distribution or spatial organisation of Industry 4.0 (I4.0), including the potential role of clusters, have only recently been addressed, with most available studies focusing on advanced, mainly Western European countries. Although developing fast, the literature on I4.0 in other countries, such as the Central and Eastern European or post-transition economies like Poland, needs to pay more attention to the spatial distribution or geographical and organisational aspects. In response to the identified knowledge gap, this paper aims to identify the role of clusters in the transformation towards I4.0. This explains why clusters may matter for advancing the fourth digital transformation, how advanced in implementing I4.0 solutions are the residents of Polish clusters and how they perceive the advantages of cluster membership for such implementation. Finally, it seeks to formulate policy recommendations based on the evidence gathered.

The methodology used in this study combines quantitative analysis of secondary data from a cluster benchmarking survey with a case study approach. The benchmarking survey, conducted by the polish agency for enterprise development in 2021, gathered responses from 435 cluster members and 41 cluster managers, representing an estimated 57% of the current clusters in Poland. In addition to quantitative analysis, a case study approach was used, incorporating primary sources such as interview with cluster managers and surveys of cluster members, as well as secondary sources like company documents and information from cluster organisation websites. Statistical analysis involved assessing the relationship between technology implementation and the adoption of management systems, as well as exploring potential correlations between technology use and company characteristics such as revenue, export revenue share and number of employees using Pearson correlation coefficient.

In Poland, implementing I4.0 technologies by cluster companies is still modest. The cluster has influenced the use of I4.0 technologies in 23% of surveyed companies. Every second surveyed company declared a positive impact of a cluster on technological advancement. The use of I4.0 technologies is not correlated with the revenue of clustered companies. A rather bleak picture emerges from the results, revealing a need for more interest among cluster members in advancing I4.0 technologies. This may be due to a comfortable situation in which firms still enjoy alternative competitive advantages that do not force them to seek new advanced advantages brought about by I4.0. It also reflects the sober approach and awareness of associated high costs and necessary investments, which are paramount and prevent successful I4.0 implementation.

The limitations inherent in this study reflect the scarcity of the available data. This paper draws on the elementary survey administered centrally and is confined by the type of questions asked. The empirical section focuses on an important, though only one selected sector of the economy – the automotive industry. Nevertheless, the diagnosis of the Polish cluster’s role in advancing I4.0 should complement the existing literature.

The exploratory study concludes with policy recommendations and sets the stage for more detailed studies. Amidst the research’s limitations, this study pioneers a path for future comprehensive investigations, enabling a deeper understanding of Polish clusters’ maturity in I4.0 adoption. By comparing the authors’ analysis of the Polish Automotive Group (PGM) cluster with existing literature, the authors uncover a distinct disparity between the theoretical prominence of cluster catalysis and the current Polish reality. Future detailed dedicated enquiries will address these constraints and provide a more comprehensive map of Polish clusters’ I4.0 maturity.

This study identifies patterns of I4.0 implementation and diagnoses the role of clusters in the transformation towards I4.0. It investigates how advanced is the adoption of I4.0 solutions among the residents of Polish clusters and how they perceive the advantages of cluster membership for such transformation. Special attention was paid to the analysis of the automotive sector. Comparing the conclusions drawn from the analysis of the Polish PGM cluster in this case study to those from the literature on the subject, it becomes clear that the catalytic role of clusters in the implementation of I4.0 technologies by enterprises, as emphasised in the literature, is not yet fully reflected in the Polish reality.

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.

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.

The purpose of this study is to investigate the obstacles to mechatronics adoption in the construction industry from a Nigerian perspective. It aims to fill the knowledge gap by focusing on the specific challenges faced in developing countries, considering the unique contexts and constraints of the Nigerian construction industry.

The study used a comprehensive literature review to identify 26 obstacles to mechatronics adoption. These obstacles were used to develop a well-structured questionnaire, which was then distributed to construction professionals using Google Forms through purposive and snowball sampling techniques. The rankings obtained from the questionnaire responses were analyzed to determine the most significant obstacles.

The study revealed the top five most significant obstacles to mechatronics adoption in the Nigerian construction industry. These obstacles include high costs of operation and maintenance, resistance to adopting new technologies, a lack of standardized protocols, insufficient maintenance capabilities and a lack of government support. Factor analysis revealed five clusters of obstacles: technological-related factors, economic-related factors, capability-related factors, government-related factors and awareness-related factors.

Findings from this study have the potential to inform decision-making, drive policy changes and guide future research efforts aimed at promoting the widespread adoption of mechatronics technologies, ultimately leading to the transformation and improvement of the construction industry as a whole.

This study contributes to the field of mechatronics adoption in the construction industry by addressing the gap in research specific to developing countries such as Nigeria. By identifying and analyzing the obstacles from a Nigerian perspective, the study offers unique insights and original findings.

This study investigates the implications and significance of immersive technologies (viz. metaverse, virtual reality, and extended reality) in organizational learning and development.

Employing a strategy map (using Biblioshiny) and network analysis (using VOSViewer), the study analyzed a total of 189 Scopus-indexed documents, spanning the period 1997 to 2023.

The findings revealed four thematic clusters, knowledge management, training, e-learning, and technology. Each cluster provided insights into the various facets of development and learning in respect to these technologies. The strategy map identified key themes, revealing core ideas, emerging trends, and foundational concepts within the domain.

Organizations can utilize this study to incorporate metaverse, virtual reality, and extended reality in organizational development, optimizing training and boosting employee experience and productivity.

While most literature delves into the technical side of immersive technologies, our study uncovers their impact on development and learning in organizations, addressing a critical research gap and offering a guiding framework.

This study aims to undertake a comprehensive analysis of innovation models, tracing their evolution from Innovation 1.0 to Innovation 4.0 and introducing the concept of Innovation 5.0. It explores the intersection between innovation models and the principles of sustainability, resilience and human-centeredness, providing insights into their implications for Industry 5.0, and their potential to foster a resilient ecosystem amidst challenges and multiple crisis.

To achieve this objective, the authors used a systematic literature review approach, considering academic articles on Innovation 4.0, Industry 5.0 (specifically in the context of innovation) and helix models of innovation. The authors conducted thematic analysis and content analysis, followed by keyword co-occurrence analysis, enabling us to systematically synthesize and interpret the relevant literature.

The results conclude that Innovation 5.0 is a new paradigm for innovation that fosters broader societal engagement, and emphasizes sustainability, resilience and human-centeredness. Innovation 5.0 is evolving, but it has the potential to transform the way we produce, consume and live. Using insights from the sextuple helix model, this research leverages media and ICT as sixth helix vital role of knowledge sharing, digital transformation, innovation ecosystem and next industrial revolution in this process.

This study contributes to the ongoing discourse on exploring Innovation 5.0 through the sextuple helix model, offering a fresh perspective on innovation models and their collaborative potential. Its contribution lies in providing practical insights into the transition to Innovation 5.0, emphasizing the need for sustainability, regulatory support and awareness while also offering clear recommendations for future research.

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 study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

Cities are host to many of the world’s knowledge intensive research and innovation clusters. As such, they are likely to be attractive locations for emerging market multinational enterprises (MNEs) seeking to engage in knowledge seeking “springboard” type firm-level catch-up strategies. The purpose of this study is to therefore explore whether city-based research-intensive clusters containing deep pools of location bounded (i.e. “sticky”) knowledge are a stronger driver for greenfield research and development (R&D)-related FDI projects for Chinese MNEs than they are for developed market MNEs.

The authors use logistic modelling on 97,163 worldwide greenfield FDI projects to explore the relative likelihoods of Chinese MNEs engaging in R&D-related greenfield (i.e. “strategic asset seeking”) FDI projects as well as how city type (global or research-intensive cluster city) moderates this relationship for Chinese MNEs.

The authors find that Chinese MNEs are more likely to engage in overseas R&D FDI projects (compared with other types of project) than DMNEs and that research-intensive city clusters hold a stronger attraction for Chinese MNEs than developed market MNEs.

The authors discuss how the research contributes to the debate on emerging market MNE catch-up theory, as well as that on sub-national city location choice, by highlighting the growing importance of sub-national geography to understanding strategic asset seeking related greenfield FDI.

Sub-national city location choice is an important driver of strategic asset seeking FDI for Chinese MNEs, one that both national and local city level policymakers should pay attention to.

Chinese FDI via aggressive mergers and acquisitions to acquire key technologies has been restricted in recent years. Policymakers must consider whether they may also wish to restrict Chinese greenfield FDI in R&D-related projects, which now exhibit a pronounced upward trend.

The authors highlight the growing importance of sub-national geography to understanding strategic asset seeking related greenfield FDI in Chinese MNEs (and how it plays, more generally, a central role in their strategies).