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The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.

This research paper aimed to study the legal structure of top-performing health governance systems and compare them with the Indonesian health social security system to identify the main differences and provide recommendations for Indonesian and other developing countries’ health policymakers and administrators.

Using formative research with a conceptual approach and statute approach as method in this study. Data was gathered using the document study technique, which studies various documents, especially legal documents related to health law, linked to legal purpose theories. Moreover, the World Health Organization ranking was considered to choose the two countries (France and Singapore) with a high social health security system for comparative analysis. All data collected has been analyzed using a qualitative and theoretical basis. Content analysis was performed by analyzing the legal documents, and the regulatory framework of all three countries was deeply analyzed to draw conclusions and recommendations.

Indonesia has specific laws to implement a social security system in the health sector. However, the lack of the best medical facilities and infrastructure and weak implementation of existing laws were identified as major reasons behind the poor health security system compared to comparative countries. Also, as a developing nation Indonesian Government face budgetary pressures and huge population challenges to meet required standards. Thus, the financing approaches used by Singapore and France may help developing countries meet these challenges effectively. Therefore, there is a dire need to strengthen the social health security system all over the country with amendments to laws and ensure the implementation of prevailing laws and regulations.

Providing understanding related to the social security health system in Indonesia along with a detailed description of the sound social health security system in France and Singapore will further provide an avenue for the researchers to critically analyze this line of study to devise some valuable suggestions further and to draw loopholes in the system.

A comparative approach for legal studies in the health sector is rare. So, this research advanced the social security health system-related literature and legal studies on the health sector by using this comparative approach to develop policy insights and future research directions, which will further help the field to grow.

Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.

A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.

The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.

Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all.

This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL.

Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices.

To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research.

This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

Organisations are increasingly adopting and adapting to technological advancements to stay relevant in the era of intense competition. Simultaneously, employee mental well-being has become a prominent global concern affecting people across various demographics. With this in mind, the present study explores the influence of human resource (HR) analytics, mental health organisational evidence-based management (OEBM) and organisational mental health support on the mental well-being of employees. Additionally, the study examines the moderating effects of manager and peer support on the association between organisational mental health support and the mental well-being of employees.

Data were collected from 418 employees in India and structural equation modelling was performed to analyse the data.

The study found significant positive associations between HR analytics with mental health OEBM, organisational mental health support and mental well-being. Mental health OEBM was also found to be positively related to organisational mental health support and mental well-being. The moderating roles of manager and team support were also found to be significant in the associations between organisational mental health support and well-being.

The study showed that HR analytics is a valuable source of mental health data. This data can facilitate the development of evidence-based management (EBM) strategies to promote the mental well-being of employees.

The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.

This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.

The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.

This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.

This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.

This study aims to investigate the influence of cognitive and affective trust on the revisit intention of medical tourists to a developing country like India, focusing on the role of country image as a moderator.

This study used partial least square-based structural equation modelling to test the hypotheses using data from 297 medical tourists visiting India from abroad for treatment.

The findings of this study confirmed that all the dimensions of cognitive trust, namely, perceived expertise, performance and reputation of the health-care service providers, have a positive and significant impact on the revisit intention of medical tourists to India. In contrast, none of the two dimensions of affective trust have a significant effect on the revisit intention of medical tourists to India. This study also confirmed that country image significantly moderates the relationship between performance dimensions of cognitive trust and medical tourists’ revisit intention to India.

The study’s findings have significant theoretical and managerial implications as they explore the effect of cognitive and affective trust on medical tourists’ the revisit intention to visit an emerging economy, considering the country’s image as a moderator.

本研究旨在调查认知和情感信任对印度等发展中国家医疗游客重游意愿的影响, 重点关注国家形象的调节作用。

本研究采用基于偏最小二乘的结构方程模型, 使用 297 名从国外到印度接受治疗的医疗游客的数据来检验假设。

本研究的结果证实, 认知信任的所有维度, 即医疗服务提供者的专业知识、绩效和声誉, 对印度医疗游客的重游意愿产生积极且显着的影响。相比之下, 情感信任的两个维度都没有对印度医疗游客的重游意愿产生显着影响。这项研究还证实, 国家形象显着调节认知信任绩效维度与医疗游客重访印度意愿之间的关系。

该研究的结果具有重要的理论和管理意义, 因为他们探讨了认知和情感信任对医疗游客重访新兴经济体的意愿的影响, 并考虑到该国作为调节者的形象。

Este estudio tiene como objetivo investigar la influencia de la confianza cognitiva y afectiva en la intención de volver a visitar a un país en desarrollo como la India por parte de turistas médicos, centrándose en el papel de la imagen del país como moderador.

Este estudio empleó un modelo de ecuaciones estructurales parcial basado en mínimos cuadrados para probar las hipótesis utilizando datos de 297 turistas médicos que visitaron la India desde el extranjero para recibir tratamiento.

Los hallazgos de este estudio confirmaron que todas las dimensiones de la confianza cognitiva, es decir, la experiencia percibida, el desempeño y la reputación de los proveedores de servicios de atención médica, tienen un impacto positivo y significativo en la intención de volver a visitar a la India por parte de los turistas médicos. Por el contrario, ninguna de las dos dimensiones de la confianza afectiva tiene un efecto significativo en la intención de volver a visitar la India por parte de los turistas médicos. Este estudio también confirmó que la imagen del país modera significativamente la relación entre las dimensiones de desempeño de la confianza cognitiva y la intención de los turistas médicos de volver a visitar la India.

Los hallazgos del estudio tienen importantes implicaciones teóricas y administrativas, ya que exploran el efecto de la confianza cognitiva y afectiva en la intención de los turistas médicos de visitar una economía emergente, considerando la imagen del país como moderador.

The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.