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The purpose of this paper is to review the utilization of game theory in the entrepreneurship literature. Game theory can potentially be employed to assess strategies incentivizing productive entrepreneurial activities and subsequent economic development. Therefore, the author reviews entrepreneurship articles and explores the application of game-theoretic models and concepts in the literature.

First, the author provides an overview of the entrepreneurship ecosystem concept, highlighting key challenges in its study. The author also briefly highlights successful applications of game theory in the innovation literature. Second, the author systematically reviews and synthesizes entrepreneurship research employing game-theoretic models and concepts. The author's objective is to provide a state-of-the-art overview of the use of game theory in entrepreneurship.

Broadly, the author categorizes entrepreneurship-game theory articles into three groups based on their scope and purpose: entrepreneurial policy applications, inter-firm applications and entrepreneurship theory applications. Entrepreneurial policy applications include entrepreneurs and the government or policy as the main players in a game. Inter-firm applications encompass games between entrepreneurs and other private entities. Entrepreneurship theory applications include articles that utilize game theory to advance the author's understanding of entrepreneurial behavior and/or mechanisms in the market.

To the best of the author's knowledge, no previous paper has reviewed the use of game-theoretic approaches and models in entrepreneurship literature. This study addresses this research gap.

This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application.

Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis.

As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one.

This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted.

This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS.

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.

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).

A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.

The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.

The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

This study aims to develop a research agenda for the advancement of theory application in practical contexts by presenting a case study of the Engagement in Plastic-free Innovation for Change (EPIC) programme delivered by Plastic Oceans Australasia (POA).

EPIC is a behaviour change programme by POA that aims to reduce single-use plastic (SUP) consumption in workplaces. The study evaluates the programme’s impact on employee perceptions and actual behaviour through pre- and post-programme data collection in two Australian workplaces. Data was gathered via online surveys and waste audits and analysed using SPSS statistics and Excel.

The case study highlights the need for theory application in programme evaluation instruments. Theory was not used in the programme evaluation tool, and theory could not be mapped onto the tool retroactively. The data from the present study showed mixed results. Data from Workplaces 1 and 2 indicated that EPIC successfully improved three out of seven employee perceptions of SUP reduction efforts. However, individual workplace data showed that EPIC only improved one out of seven perceptions in Workplace 1 and three out of seven perceptions in Workplace 2. Surprisingly, Workplace 1 observed a decrease in plastic waste after the programme, while Workplace 2 saw an increase. Without the clear integration of theory, it is difficult to pinpoint areas for improvement. It is, however, posited that COVID-19 restrictions on people attending their workplaces and low survey response rates may have contributed to these unexpected results.

The present study highlights key improvements that can be made to evaluations of voluntary behaviour change programmes. Careful evaluation of behaviour change programmes is key to improving programme effectiveness. Practitioners will find the suggested improvements from this study helpful in developing and refining voluntary behaviour change programme evaluations.

This is one of the first studies to evaluate the impacts of a voluntary behaviour change programme aimed at reducing SUPs in the workplace. It also adds to the limited literature on voluntary behaviour change interventions overall and adds to the movement towards better application of theory in behaviour change interventions.

The purpose of this paper is to present a practical data-based framework for the prioritization of investment in manufacturing technologies, methods and tools, and to demonstrate its applicability and practical relevance through two case studies of manufacturing firms of different industrial segments.

The proposed framework is based on network theory applied on technology adoption. For this, the database of Industry 4.0 maturity assessments of SENAI was used to develop data visualization tools named “Technology Networks”. Thus, this study is descriptive research with correlational design. Besides, the framework was applied in two companies and semi-structured interviews were carried out with domain experts.

The technology networks highlight the technological adoption patterns of six industrial segments, by considering the answers of 863 Brazilian companies. In general, less sophisticated technologies were positioned in the center of the networks, which facilitates the visualization of adoption paths. Moreover, the networks presented a well-balanced adoption scenario of Industry 4.0 related technologies and lean manufacturing methods and tools.

Since the database was not built under an experimental design, it is not expected to make statistical inferences about the variables. Furthermore, the decision to use an available database prevented the editing or inclusion of technologies. Besides, it is estimated that the technology networks given have few years for obsolescence due to the fast pace of technological development.

The framework is a tool that may be used by practicing manufacturing managers and entrepreneurs for taking assertive decisions regarding the adoption of manufacturing technologies, methods and tools. The proposition of using network theory to support decision making on this topic may lead to further studies, developments and adaptations of the framework.

This paper addresses the topics of lean manufacturing and Industry 4.0 in an unprecedented way, by quantifying the adoption of its technologies, methods and tools and presenting it in network visualizations. The main value of this paper is the comprehensive framework that applies the technology networks for supporting decision making regarding technology adoption.

Not that long ago, half of all sales research was demonstrably if not unequivocally “atheoretical” (Williams and Plouffe, 2007). The foundational argument of this paper is that stronger theoretical development and application of theory in sales research is critical for the sales field to retain its relevancy. The purpose of this paper is to underscore that deliberate and cogent application of HWV (2018) is scant in the recent sales literature (over five years after publication in Journal of Marketing) for one or both of two reasons: scholars either do not understand the paper and/or are fearful of (mis)applying it.

More than simply introducing the articles to this special issue of the European Journal of Marketing (EJM), this paper also makes a number of important, overdue contributions. Although Hartmann, Wieland and Vargo’s JM (HWV, 2018) theoretical and conceptual paper has been well-received by the sales community, it has seen limited meaningful integration or application in sales research since its publication. This paper thus clarifies key misunderstandings and misperceptions with HWV (2018) so that sales researchers can more impactfully apply it to future sales research.

This paper identifies and then explains key aspects of service-dominant logic (S-D logic) and commonly misapplied and/or misunderstood aspects of HWV (2018) to guide future sales research. Ultimately, the overarching goal of this special issue of EJM is to focus a “spotlight” on sales theory development, while simultaneously demonstrating – through the five articles the special issue reports – that with purposeful effort, rich theoretical insights can effectively be applied to both “classic” and more current and emergent sales research topics.

Because HWV (2018) draw heavily upon S-D logic, it follows that some aspects of their article have been misinterpreted or misapplied by sales scholars. In particular, the critical concept of “crossing points” (both of the “thick” and “thin” variety) are explicated and detailed further, so as to afford sales researchers with better knowledge and insight on how to apply these key tools within HWV (2018).

The practical implications of this paper primarily revolve around further educating and clarifying for sales researchers “how” to better apply HWV (2018) to sales research, rather than simply citing it in passing. The paper also concludes by providing a summary and introduction to each of the five EJM special issue articles.

The originality and value of this paper and this special issue of the EJM is twofold. First, both this paper and the entire special issue itself emphasize the ongoing importance of advancing sales research through the meaningful and cogent application of theory. Second, the paper demonstrates that purposeful effort can lead to successful applications of HWV (2018) – as exhibited by the five articles in the EJM special issue – such that rich theoretical insights can be woven into both traditional and contemporary sales research topics.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

Expanded awareness and application of recent PLS-SEM reporting practices were again called for by Hair (2022) in his PLS 2022 Keynote Address. This paper aims to analyze and extend the application of PLS-SEM in Industrial Management and Data Systems (IMDS) to focus on trends emerging in the more recent 2016–2022 period.

A review of PLS-SEM applications in information systems studies published in IMDS and MISQ for the period 2012–2022 identifies and comments on a total of 135 articles. Selected emerging advanced analytical PLS-SEM applications are also highlighted to expand awareness of their value in more rigorously evaluating model results.

There is a continually increasing maturity of the information systems field in applying PLS-SEM, particularly for IMDS authors. Model complexity and improved prediction assessment as well as other advanced analytical options are increasingly identified as reasons for applying PLS-SEM.

Findings demonstrate the continued use and acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is the preferred SEM method in many research settings, but particularly when the research objective is prediction to the population, mediation and mediated moderation, formative constructs are specified, constructs must be modeled as higher-order and for competing model comparisons.

This update on PLS-SEM applications and recent methodological developments will help authors to better understand and apply the method, as well as publish their work. Researchers are encouraged to engage in more complete analyses and include enhanced reporting procedures.

Applications of PLS-SEM for prediction, theory testing and confirmation are increasing. Information systems scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for both exploratory and confirmatory research.