EDITORIAL 39-1 2026: Current contributions and upcoming special issues on artificial intelligence and PLS-SEM in management research

Recalibrating Management Research for the Post‐COVID‐19 Scientific Enterprise

Artificial Intelligence (AI) is the in thing, the most buzz word and the most used tool, used by all: industries, organizations and individuals. The use and applications are increasing exponentially every day, increasing both in the depth and diverse directions, with a lot of innovation. AI has become an enabler, a favourite tool, both among the experts as well as among the commoners. It is now embedded to many popular social media platforms, making them more user friendly and much more proactive. It has come as a boon to many. But massive use of AI has raised many a concerns too. It is also said that it seriously hampers the creativity of youngsters, who are now tempted and trapped in the cobweb of AI. A serious challenge it has posed in the field of research and publication. With a growing need and compulsion for the young scholars to publish minimum number of research papers, it is now a common and easy track (and the trap) of using AI (like, ChatGPT) for research paper ‘generation’. As many of us know, articles (or essays) can be written very beautifully and very easily by using this means. Now the buzz thing in research world is not just ‘plagiarism’, but also the need of checking for AI contents in each article. While standard journals were receiving articles earlier in hundreds, now, after the massive use of AI, thousands of articles (in any area/ theme) flood the email of the journal editor. The issue here is to decide where to use AI and how much to use AI, and if we should have some Laxman-rekhaa drawn in the context of use of AI in research. Even we the editors faced the same problem while scrutinizing the papers we received for this volume. We have focussed, as theme for this volume, on the issues, uses and practices in the field of Management Research. This edited volume, “Future of Research in Management and AI”, is a collective endeavour to explore this transformation by integrating views from academia, researchers, and industry experts. It brings together scholarly deliberations and empirical insights on how AI and digital transformation are redesigning key management functions from finance, marketing, human resources, and operations to leadership and sustainability. The chapters presented herein reflect the diversity and depth of thought required to navigate this evolving landscape. It covers many emerging, research worthy areas, like, AI-Driven CRM, HRM best practices using AI, Data Governance using AI, Ethical and Social Dimensions of AI, AI-Driven Digital Transformation, Green Financial Control with AI, Trends and insights in Smart Farming, AI-ML in Finance, Augmented Reality in Retail and the Strategic Role of Artificial Intelligence in India's FinTech Evolution. We sincerely thank all contributing authors for their support and the valuable insights, for making this project a success. We are very much thankful to DeepScience Publications for supporting this meaningful academic footprint, and also guiding us on all technical matters. We hope this volume serves as a valuable reference for students, researchers, academicians, and industry professionals seeking to understand the future contours of management research in the age of Artificial Intelligence and digital transformation.

Purpose Although existing literature has advanced the understanding of information security policy (ISP) management, it has not examined how artificial intelligence (AI) can support ISP activities across management phases. Moreover, no study has yet mapped the empirical domains studied. The purpose of this paper is to systematically map existing ISP management research to assess to what extent AI has been addressed or used. Design/methodology/approach This study follows the five-step scoping review framework proposed by Arksey and O’Malley (2005): identifying the research questions, finding relevant studies, selecting studies, charting the data and reporting the results. Findings The review reveals that very few ISP management papers address or use AI. These few papers focused mostly on operational ISPs and addressed different ISP phases and empirical domains. Most existing work focuses on construction or compliance, while no studies have addressed the technical level. Research methods dominated by experiments, with a notable absence of organizational fieldwork. Research on ethical aspects such as fairness, transparency, accountability and data sensitivity is rare in this area. Research limitations/implications Given the limited research in this area, there are significant opportunities to explore AI in ISP management and to use AI in studying ISP management. The authors suggest a research agenda divided into three-time horizons: short, medium and long term. Originality/value This paper provides the first scoping review of AI in ISP management research, offering a systematic mapping of ISP management phases, ISP levels, research methods and empirical domains. It identifies research gaps, thereby guiding future research.

In this dissertation, I explore two contexts where people disagree about moral issues that have not received as much attention as they deserve. I first examine ethical disagreements in the rapidly evolving field of artificial intelligence (AI). Organizations are widely adopting AI, and in the advent of ChatGPT, AI has finally received widespread attention from the general public. Most people want AI to be ethical and used for ethical reasons, so scholars have proposed many ethical AI frameworks in the literature. In this theoretical paper, I critique these frameworks and suggest they do not usually consider that people disagree about morality. Thus, they cannot help us understand how and why people would disagree about some of the fundamental issues in AI ethics. Namely, our ability to understand, compromise, and communicate with those we disagree with about the problems surrounding AI. I draw from moral foundations theory to argue that a person will find an AI system (or its uses) ethical if it does not violate any of their valued moral foundations. In my second paper, I empirically examine ethical disagreements in the context of management research. The field of management research aims to contribute to management practice. Yet, many practitioners do not pay attention to the research, and many causes of this “research-practice gap” have been discussed, along with a wide-range of proposed solutions. I argue that one reason for this gap could be due to value and moral divergences between researchers and managers. At our current understanding, we do not know what values we hold as a group of researchers, nor do we know the values of modern-day managers. To establish the phenomenon, I sampled a group of management researchers and a group of managers and found several moral and value divergences. I find that managers, on average, placed more emphasis on the moral foundations of equality, loyalty, authority, and purity, and on the values of hedonism, power-dominance, security-societal, tradition, and humility. Management researchers, on the other hand, placed more emphasis on the values of achievement, self-direction (thought), self-direction (action), benevolence (dependability), universalism (concern), and universalism (tolerance). I found that managers were slightly less liberal on average than the management researchers, but only for social issues and not economic issues. Managers also placed a higher emphasis on both idealism and relativism than did researchers, and there was no difference between the groups for how religious they were. I discuss the implications of these findings and lay a path forward.

Artificial intelligence (AI) is revolutionizing management research by enabling more efficient data analysis, decision-making, and operational workflows. However, its application also raises questions about its transformative role and implications for academic writing in this field. A systematic review of literature published over the past decade was conducted to evaluate the applications, benefits, and challenges of AI in management research. Emphasis was placed on identifying key tools and technologies, along with their impacts on research quality and efficiency. Findings reveal that AI significantly enhances research by automating data handling, improving predictive accuracy, reducing biases, and streamlining academic writing processes. Despite these advancements, challenges such as ethical concerns and the need for human oversight persist. This study highlights the importance of balancing AI implementation with human judgment to ensure ethical practices and effective utilization. It addresses gaps in existing research and emphasizes AI's transformative potential in management studies. AI plays a pivotal role in enhancing the quality and efficiency of management research, but its integration requires careful consideration to maximize its benefits while mitigating potential drawbacks.

In the past few years, the EU has shown a growing commitment to address the rapid transformations brought about by the latest Artificial Intelligence (AI) developments by increasing efforts in AI regulation. Nevertheless, despite the growing body of technical knowledge and progress, the governance of AI-intensive technologies remains dynamic and challenging. A mounting chorus of experts have been sharing their reservations regarding an overemphasis on regulation in Europe. Among their core arguments is the concern that such an approach might hinder innovation within the AI arena. This concern resonates particularly strongly compared to the United States and Asia, where AI-driven innovation appears to be surging ahead, potentially leaving Europe behind. The current contribution is a position paper emphasising the need for balanced AI governance to foster ethical innovation, reliability, and competitiveness of European technology. This paper only explores recent AI regulations and upcoming European laws relevant to the topic to ensure conciseness while underscoring Europe’s role in the global AI landscape. The authors analyse European governance approaches and their impact, especially on SMEs and startups, offering a comparative view of global regulatory efforts. We address the complexities of creating a comprehensive, human-centred AI master’s programme for higher education and the importance of ethical AI education. Finally, we discuss how Europe can seize opportunities to promote ethical and reliable AI progress through education, fostering a balanced approach to regulation and enhancing young professionals’ understanding of ethical and legal aspects.

PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

Advances in Artificial Intelligence (AI) are intensively shaping businesses and the economy as a whole, and AI-related research is exploding in many domains of business and management research. In contrast, AI has received relatively little attention within the domain of entrepreneurship research, while many entrepreneurship scholars agree that AI will likely shape entrepreneurship research in deep, disruptive ways. When summarizing both the existing entrepreneurship literature on AI and potential avenues for future research, the growing relevance of AI for entrepreneurship research manifests itself along two dimensions. First, AI applications in the real world establish a distinct research topic (e.g., whether and how entrepreneurs and entrepreneurial ventures use and develop AI-based technologies, or how AI can function as an external enabler that generates and enhances entrepreneurial outcomes). In other words, AI is changing the research object in entrepreneurship research. The second dimension refers to drawing on AI-based research methods, such as big data techniques or AI-based forecasting methods. Such AI-based methods open several avenues for researchers to gain new, influential insights into entrepreneurs and entrepreneurial ventures that are more difficult to assess using traditional methods. In other words, AI is changing the research methods. Given that, so far, human intelligence could not fully uncover and comprehend the secrets behind the entrepreneurial process that is so deeply embedded in uncertainty and opportunity, AI-supported research methods might achieve new breakthrough discoveries. We conclude that the field needs to embrace AI as a topic and research method more enthusiastically while maintaining the essential research standards and scientific rigor that guarantee the field’s well-being, reputation, and impact.

Purpose This study aimed to investigate whether research papers incorporating AI-related themes or methodologies exhibited statistically significant associations within the management science disciplines amidst the rapid advancement of artificial intelligence (AI). Design/methodology/approach A mixed-method approach was used. Based on the UTD 24 journals in the field of management studies, this article systematically collected over 50,000 top-tier papers from six sub-disciplines of management and identified research literature containing elements related to AI through natural language processing methods. Subsequently, two quantitative analyses were carried out. Firstly, the academic influence was quantified through citation analysis, which was a widely recognized approach for evaluating the impact of research works. Secondly, online attention was examined by leveraging the comprehensive altmetric attention score, a metric that captures the broader engagement and dissemination of academic papers across various online platforms. Findings This study revealed contrasting associations of AI integration in management research. In terms of academic impact, AI-related papers in the management (general/strategy) and accounting sub-disciplines exhibited negative academic associations compared to non-AI counterparts. But this negative effect has been gradually weakening in recent years. Even the study found that after 2019, management (general/strategy) exhibited positive associations with the academic influence. While AI-related papers of the marketing sub-discipline outperformed non-AI papers in citation counts since 2013. Regarding online attention, the AI-related research in management (general/strategy) domain attained stronger online attention. Additionally, it was noteworthy that the female first authors might have achieved better academic or online engagement across some sub-disciplines. Practical implications This research might prove to be an invaluable resource for researchers and journal editors in sub-fields. For researchers, this work would illuminate new pathways for exploring AI-related themes and methodologies within their specific domains. For journal editors, it could offer some inspiration for adjusting their research directions and review standards. For example, editors could encourage more submissions that bridge the gap between theory and practice in AI-related research, or that explore emerging AI applications in under-researched areas. Originality/value The proliferation of AI technologies has spurred increased scholarly attention across various domains of management research, prompting questions about its value-added contributions in social science. Firstly, this study pioneered the cross-disciplinary analysis of AI research in the management research domain, revealing divergent associations. Secondly, NLP-driven vocabulary detection was utilized, ensuring comprehensive topic recognition and optimizing the methodology for future interdisciplinary research assessments. Thirdly, female first authors were demonstrated to have superior academic and online visibility compared to their male counterparts across several management domains, which was somewhat different from the conclusions drawn by previous research.

Purpose This study aims to explore the impact of artificial intelligence (AI) on the management of innovation processes within organizations. The research focuses on how AI tools affect decision-making, resource allocation, leadership and performance control in innovation contexts. Design/methodology/approach A systematic qualitative literature review was conducted, analyzing 77 academic articles published between 2019 and 2024. The Planning, Organizing, Leading and Controlling (POLC) framework guided the analysis, enabling a structured examination of AI’s influence on management functions in innovation processes. Findings AI can significantly enhance planning and organizing functions by enabling data-driven decision-making, automating tasks and optimizing steps of the innovation process. It can also improve quality assessment and risk identification. However, its role in leadership remains underdeveloped, particularly in fostering creativity, collaboration and human-centered leadership, highlighting the need for more empirical research on AI’s integration with human leadership skills. Research limitations/implications The study is limited by its focus on English language publications, a specific publication period and narrowly defined search terms. Practical implications Organizations should assess how AI can be tailored to their innovation strategies, particularly in enhancing planning and operational efficiency. Choosing appropriate tools for both operative and strategic management is essential, but their use does not remove the role of human judgement and intuition. Social implications AI’s integration into innovation management raises ethical and cultural considerations, especially regarding leadership and performance control. Originality/value This study advances innovation and management research by applying the POLC framework to analyze how AI transforms traditional management practices. It offers practical insights, highlights gaps in AI-supported leadership and calls for more empirical research to enhance organizational competitiveness.

The accelerated diffusion of artificial intelligence (AI) tools in management education research presents both strategic opportunities and ethical challenges for business schools and higher education institutions. Even as AI applications promise enhanced analytical efficiency and research productivity, concerns regarding academic integrity, critical thinking development, and data confidentiality complicate their integration. Despite these tensions, empirical evidence explaining and predicting satisfaction with AI tools among management education researchers remains limited. Existing studies have focused largely on conceptual frameworks, ethical implications, often relying on student samples or cross-sectional studies. Consequently, little is known about the specific determinants of AI satisfaction among researchers. To address this gap, the present study investigates the determinants of user satisfaction with AI technologies within a management education context and develops a predictive framework to inform institutional decision-making. The study, based on a sample of 260 respondents, examined 9 key constructs. The Random Forest (RF) model was trained by running the bagging procedure on a dataset, and its performance was validated using out-of-bag error estimation to assess predictive accuracy. Structural Equation Modelling (SEM) findings reveal that perceived ease of use significantly enhances perceived usefulness, which in turn drives satisfaction with AI tools. Ethical concern attitudes are negatively associated with perceived usefulness, underscoring the managerial and pedagogical trade-offs inherent in AI adoption within business schools. The RF model complements the explanatory analysis by demonstrating strong predictive performance (R² = 0.73) and identifying perceived ease of use, access to AI technologies, and perceived usefulness as the most influential predictors of satisfaction. The convergence of theory-driven and machine learning results enhances the robustness and practical relevance of the findings. By integrating explanatory and predictive modelling, this study contributes to management education literature on digital transformation and responsible innovation. The findings offer actionable insights for business school leaders, curriculum designers, and policymakers seeking to support ethically-grounded, strategically-aligned AI integration in management research and education.

Artificial intelligence (AI) has become a transformative technology in medical diagnostics, enabling enhanced analysis of complex clinical data and supporting precise, efficient decision-making across diverse disease areas. This study explores the multi-disease application of AI in diagnosing cancer, cardiovascular diseases, neurological disorders, and infectious diseases, focusing on its role in improving diagnostic accuracy, speeding diagnostic processes, and facilitating early disease detection. By employing machine learning, deep learning, and neural network models, this study critically examines the performance of specific models – such as recurrent neural networks and support vector machines – in diverse healthcare contexts. Challenges addressed include data privacy, annotated dataset needs, overfitting risks, and ethical concerns such as AI bias and transparency, all of which are fundamental to ensuring patient safety and health equity. In addition, this study integrates security considerations, such as fault detection in cryptographic architectures, providing insights into the resilience of AI systems in healthcare. Future research directions, including the potential of AI in real-time patient monitoring, personalized medicine, and multispectral imaging, are proposed to expand AI’s utility in diagnostics. A comparative evaluation with traditional clinical diagnostics underscores AI’s validation potential, emphasizing its need for robust regulatory frameworks, particularly concerning global health standards (e.g., TRIPOD-AI and CONSORT-AI) and data privacy regulations such as Health Insurance Portability and Accountability Act and General Data Protection Regulation. Ultimately, AI-driven diagnostic systems show strong promise to revolutionize medical practice and improve patient outcomes, contingent on addressing the technical, ethical, and regulatory challenges involved. This research supports AI’s growing role in healthcare, providing a foundational understanding of both its current contributions and future potential across disease-specific applications.

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