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A complex human system, whose description is only possible in a fuzzy way, was put into correspondence with management in the form of fuzzy instructions, which expressed unprecisely formulated actions and led to more than one way of their understanding and realization. We call such management fuzzy. The self‐organization processes in the human systems under fuzzy management are considered — building coalitions, forming a hierarchy, etc. The principle of Gause‐Arrow is presented, according to which in every human system under fuzzy management there is at least one “leader” who possesses the highest ability of generating variety of alternatives in response of fuzzy instructions. The following statement is proved: For each coalition formed under a stable fuzzy management there is a group choice rule which is socially satisfactory in the sense of Arrow.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

To consider aspects of a theory of systemic construction by discussing two concepts which will assist in our understanding of the surrounding world which it is considered is made of both systemic and non‐systemic entities.

Considers how these entities (metasystem, network, transitron etc.) can be conceived and defined. Systemic frames notions are presented and examples of systems given. Discusses the historic use of the word “system” and systemic thinking and its varieties.

Discovered that on the basis of these concepts, an understanding of the surrounding world can be achieved which is not homogeneous but made of both systemic and non‐systemic entities. These can change when certain systemic properties are reached as well as in their specific degrees in their limitations and paradoxes.

Introduces an original approach to the life support system by proposing concepts that are discussed and defined and that will provide cyberneticians and systemists with a revised view of systemic thinking.

This study seeks to identify the main determinants of the optimal capital structure by reexamining the interpretation of the conventional set of explanatory variables used as proxies for the costs and benefits of debt in the context of the dynamic tradeoff theory.

The authors isolate the variation in leverage due to different targets from that caused by deviations by aggregating the data across a dimension identifying firms with similar targets – credit rating category.

Contrary to theoretical priors, large and profitable rated firms have lower targets. The authors show that size and profitability proxy for non-financial risk and that, for rated firms, non-financial risk is positively correlated to the optimal leverage. The benefits of a better rating outweigh the costs of foregone tax shields for firms with relatively low non-financial risk. The authors find support for that theory in institutional trading – institutional investors do not punish highly rated firms when credit downgrades occur.

This paper contributes to the capital structure literature by developing a new approach based on data aggregation. This study is the first, to the authors’ knowledge, to find a positive effect of the firm's non-financial risk on target leverage among rated firms. The authors argue that the benefit of a better credit rating is an increasing function of the rating itself. The authors also contribute to the literature on the impact of credit ratings on the capital structure choices of the firm.

The goal of this article is to provide an overview of healthcare entrepreneurship, both in terms of its current trends and future directions.

The article engages in a systematic review of extant research on healthcare entrepreneurship using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) as the review protocol and bibliometrics or scientometrics analysis as the review method.

Healthcare entrepreneurship research has fared reasonably well in terms of publication productivity and impact, with diverse contributions coming from authors, institutions and countries, as well as a range of monetary and non-monetary support from funders and journals. The (eight) major themes of healthcare entrepreneurship research revolve around innovation and leadership, disruption and technology, entrepreneurship models, education and empowerment, systems and services, orientations and opportunities, choices and freedom and policy and impact.

The article establishes healthcare entrepreneurship as a promising field of academic research and professional practice that leverages the power of entrepreneurship to advance the state of healthcare.

The article offers a seminal state of the art of healthcare entrepreneurship research.

According to the resource-based theory, a firm’s unique resources and capabilities are the key to its competitive advantage. This paper aims to investigate the effect of top management team (TMT) stability, an important intangible resource of the firm, on the maturity mismatch between investment and financing of companies. Additionally, we explore the moderating effects of state ownership and institutional ownership in this context.

This study conducts an empirical analysis based on the ordinary least squares (OLS) model with a sample of Chinese companies listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2022.

The results show that TMT stability significantly mitigates the degree of maturity mismatch. Both state ownership and institutional ownership weaken the negative effect of TMT stability on maturity mismatch. Besides, alleviating financing constraints is a crucial pathway through which TMT stability influences maturity mismatch.

The findings help firms to effectively retain TMT talents and reduce the occurrence of maturity mismatch.

This paper not only helps to expand the research on the economic effects of TMT stability but also provides new ideas on how to alleviate the maturity mismatch of companies.

This study explores the variables that drive the impact of artificial intelligence (AI) on the competitiveness of a tourism firm. The relationship between the variables is established using the modified total interpretive structural modelling (m-TISM) methodology. The factors are identified through literature review and expert opinion. This study investigates the hierarchical relationship between these variables.

The modified total interpretive structural modelling (m-TISM) method is used to develop a hierarchical interrelationship among variables that display direct and indirect impact. The competitiveness of a tourism firm is measured by investigating the effect of variables on the firm's financial performance.

The study identifies ten key factors essential for analysing the impact of AI on a firm's competitiveness. The m-TISM methodology gave us the hierarchical relationship between the factors and their interpretation. A theoretical TISM model has been constructed based on the hierarchy and relationship of the elements. The elements that fall in Level V are “AI Skilled Workforce”, “Infrastructure” and “Policies and Regulations”. Level IV includes the elements “AI Readiness”, “AI-Enabled Technologies” and “Digital Platforms”. Elements that fall under Level III are “Productivity” and “AI Innovation”. Level II and Level I comprise “Tourist Satisfaction” and “Financial Performance”, respectively. The levels indicate the elements' hierarchical level, with Level I the highest and Level V the lowest.

Tourism and AI scholars can analyse the given variables by including the transitive links and incorporate new variables depending upon future research. The m-TISM model constructed from literature review and expert opinion can act as a theoretical base for future studies to be conducted by researchers.

Management/Practitioners can focus on the available characteristics and capitalise on them while working on the factors lacking in their organisation to enhance their competitiveness. Entrepreneurs starting their own business can utilise the elements in understanding the ecosystem of strengthening a firm's competitiveness. They can work to improve on the aspects which are crucial and trigger the impact on competitiveness. The government and management can devise policies and strategies that encompass the essential factors that positively impact the competitiveness of the firms. The approach can then be looked at with a holistic approach to cater to the other related components of the tourism industry.

This study is the first of its kind to use the modified TISM methodology to understand the impact of AI on the competitiveness of tourism firms.

This paper aims to identify barriers toward the adoption of blockchain (BC) technology in Indian health-care industry and also examines the significant issues of BC applications in health-care industry.

The barriers of the study are identified by two phases including the review of literature and semistructured interviews with hospital staff and administration operating in India. The experts (N = 15) are being taken from top-level management, IT experts and patients from the hospitals. The study implemented integrated total interpretative structural modeling-FUZZY-Cross-impact matrix multiplication applied to classification (TISM-FUZZY-MICMAC) methods for identifying the interrelationship among the barriers.

A total of 15 barriers have been determined in the Indian health-care industry through discussion with the selected experts. TISM is applied to develop multilevel structure for BC barriers. Further, FUZZY-MICMAC has been used to compute driving and dependent barriers. The findings suggest that low awareness related to legal issues and low support from high level of management have maximum driving power.

The present study applies multicriterion approach to identify the limited barriers in BC adoption in health care. Future studies may develop the relationship and mark down the steps for implementation of BC in health-care setting of a developing economy. Empirical study can be conducted to verify the results along with selected case studies.

The present study identifies the BC adoption barriers in health-care industry. The study examines the pertinent issues in context to major support required, bottlenecks in adoption, key benefits of adoption planning and activities. The technology adoption practices are expected to provide applications such as distributed, secured medical and clinical data and patient centric systems that will enhance the efficiency of the health-care industry.

The study is among few primary studies that identify and analyze the BC adoption in health-care industry.

The purpose of this paper is to review the relevant literature on the causes of and regulatory reactions to the financial crisis of the last decade, popularly known as the “Global Financial Crisis (GFC)” or the “Housing Crisis” in the USA.

This review primarily focuses on the four main causes of the crisis, namely, excessive household leverage, securitization, corporate governance and credit ratings. The main reaction vis-à-vis recovery measures taken by most governments were quantitative easing (QE), bailouts and more stringent regulations of banks, though the discussion mainly focuses on QE.

In this paper, the authors summarize the literature on the causes and regulatory reactions to the GFC and propose future avenues of research for various topics.

Research on the GFC spans multiple disciplines as well as multiple facets of financial economics. A review paper such as this should help future researchers in generating ideas and gathering information for their research. Given that no review uncovers all worthy papers, the authors apologize in advance to the authors of any papers that the authors have inadvertently not reviewed in this paper.

Health-care knowledge is dispersed among different departments in a health care organization, which makes it difficult at times to provide quality care services to patients. Therefore, this study aims to identify the main challenges in adopting health information technology (HIT).

This study surveyed 148 stakeholders in 4 key categories [patients, health-care providers, United Arab Emirates (UAE) citizens and foresight experts] to identify the challenges they face in adopting health care technologies. Responses were analyzed using exploratory (EFA) and confirmatory factor analysis (CFA).

EFA revealed four key latent factors predicting resistance to HIT adoption, namely, organizational strategy (ORGS); technical barriers; readiness for big data and the internet of things (IoT); and orientation (ORI). ORGS accounted for the greatest amount of variance. CFA indicated that readiness for big data and the IoT was only moderately correlated with HIT adoption, but the other three factors were strongly correlated. Specific items relating to cost, the effectiveness and usability of the technology and the organization were strongly correlated with HIT adoption. These results indicate that, in addition to financial considerations, effective HIT adoption requires ensuring that technologies will be easy to implement to ensure their long-term use.

The results indicate that readiness for big data and the IoT-related infrastructure poses a challenge to HIT adoption in the UAE context. Respondents believed that the infrastructure of big data can be helpful in more efficiently storing and sharing health-care information. On the technological side, respondents felt that they may experience a steep learning curve. Regarding ORI, stakeholders expected many more such initiatives from health-care providers to make it more knowledge-specific and proactive.

This study has implications for knowledge management in the health -care sector for information technologies. The HIT can help firms in creating a knowledge eco-system, which is not possible in a dispersed knowledge environment. The utilization of the knowledge base that emerged from the practices and data can help the health care sector to set new standards of information flow and other clinical services such as monitoring the self-health condition. The HIT can further influence the actions of the pharmaceutical and medical device industry.

This paper highlights the challenges in HIT adoption and the most prominent factors. The conceptual model was empirically tested after the collection of primary data from the UAE using stakeholder theory.