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The purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.

The organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed “TISM methodology” and “MICMAC analysis” to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.

This study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.

The study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.

The key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.

In this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

The COVID-19 pandemic has affected around 216 countries and territories worldwide and more than 2000 cities in India, alone. The smart cities mission (SCM) in India started in 2015 and 100 smart cities were selected to be initiated with a total project cost of INR 2031.72 billion. Smart city strategies play an important role in implementing the measures adopted by the government such as the issuance of social distancing regulations and other COVID-19 mitigation strategies. However, there is no research reported on the role of smart cities strategies in managing the COVID-19 outbreak in developing countries.

This paper aims to address the research gap in smart cities, technology and healthcare management through a review of the literature and primary data collected using semi-structured interviews.

Each city is unique and has different challenges, the study revealed six key findings on how smart cities in India managed the COVID-19 outbreak. They used: Integrated Command and Control Centres, Artificial Intelligence and Innovative Application-based Solutions, Smart Waste Management Solutions, Smart Healthcare Management, Smart Data Management and Smart Surveillance.

This paper contributes to informing policymakers of key lessons learnt from the management of COVID-19 in developing countries like India from a smart cities’ perspective. This paper draws on the six Cs for the implications directed to leaders and decision-makers to rethink and act on COVID-19. The six Cs are: Crisis management leadership, Credible communication, Collaboration, Creative governance, Capturing knowledge and Capacity building.

This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.

The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.

Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.

An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

Wearable health devices (WHDs) have demonstrated significant potential in assisting elderly adults with proactive health management by utilizing sensors to record and monitor various aspects of their health, including physical activity, heart rate, etc. However, limited research has systematically explored older adults’ continued usage intention toward WHD. By utilizing the extended unified theory of acceptance and use of technology (UTAUT2), this paper aims to probe the precursors of elderly adults’ continuance intention to use WHD from an enabler–inhibitor perspective.

The research model was developed based on UTAUT2 and examined utilizing the partial least squares technique (PLS). The research data were collected through in-person meetings with older people (n = 272) in four cities in China.

Results reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic values and perceived complexity are the positive predictors of elderly adults’ continuance intention to use WHDs. Technology-related anxiety and usage cost negatively influence the formation of older people’s continuance intention.

This work is an original empirical investigation that draws on several theories as guiding frameworks. It adds to the existing literature on the usage of wearable technologies and offers insights into how the elderly’s intentions to continue using WHDs can be developed. This study broadens the scope of the UTAUT2 application and presents an alternative theoretical framework that can be utilized in future research on the usage behavior of wearable devices by individuals.

The scope of this study is to explore and summarize the pool of dimensions, models and measurement techniques of service quality used in healthcare services and to propose a comprehensive conceptual model for practitioners and researchers.

This research employs a comprehensive review of available literature by using multiple keywords on different electronic repositories using the recommendations of the PRISMA approach for the selection of articles. A critical analysis of available studies helped in compiling a list of core service quality dimensions in healthcare services.

This paper presents a comprehensive account of different dimensions and their measurement items used by various researchers to assess service quality in healthcare systems. Most of the researchers have used SERVQUAL model either in its original or modified form while the others have proposed and used totally different dimensions to assess the service quality in healthcare. Many dimensions are just an existing dimension of SERVQUAL that has undergone a name change while others are completely new. The dimensions used by many researchers have items drawn from more than one dimension of SERVQUAL model. The availability of so many dimensions and models adds to the confusion that researchers and practicing managers experience when determining the appropriate model to be used in their work. To mitigate this confusion, there is a need to develop a comprehensive model; the current work is an attempt to meet this need. Through our analysis, we identify four major service quality dimensions: clinical quality, infrastructural quality, relationship and managerial quality and propose a model named CIRMQUAL.

After exploring all available models in the domain of healthcare, this research presents the best possible areas to enhance the quality of healthcare services. It also enhances the research insights for academicians and working professionals by developing and proposing a comprehensive model for measuring healthcare service quality. The proposed model covers almost all of the service quality dimensions used by other researchers and will make the choice of dimensions/model easy for the future researchers/practitioners interested in measuring and improving the quality of services offered by their healthcare units. Such a comprehensive model has not been developed by any researcher thus far.

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.

The objective of this study is to address this central question: “What role do ICTs play in reducing poverty?”

First, in this study, we defined poverty in terms of its roots within health, economic development and education. Then, we conducted a systematic review of the information and communication technologies (ICTs) literature. From our analysis, we proposed a series of subsidiary questions and in-depth answers about the impact of ICTs on alleviating health-related, economic and educational causes of poverty.

This study observed positive effects of ICTs on healthcare, economic and educational dynamics and concluded that the development of more advanced infrastructure and greater access to such technology can amplify that impact.

This article explains how applications of ICT across sectors can substantially enhance quality of life and give people an opportunity to take control of their health-related, economic and educational futures. This study uniquely affords an integrative analysis of research and new thought about how to integrate key ICTs for more effective initiatives and investments to reduce poverty.

Blockchain technology has been recognized as a potential solution to the challenges in managing healthcare information. Its adoption in the healthcare industry has garnered the attention of healthcare institutions and governments. Given the significant role of subsidies in promoting technology adoption, this study applies evolutionary game theory to examine the impact of government subsidies on the adoption of blockchain technology by healthcare institutions.

First, the authors analyze the interests of government administration departments and healthcare institutions separately in regards to blockchain adoption. Subsequently, the authors develop the payoff matrix of both participants and construct the evolutionary game model. And then, the authors calculate the replication dynamic equations and analyze the decision evolution of both participants through the replication dynamic equations and numerical experiments.

The numerical experiments demonstrate that government subsidies are effective in encouraging healthcare institutions to adopt blockchain technology. The study also reveals the necessary amount of subsidy required to guide healthcare institutions towards adoption. Additionally, the validity of the evolutionary game model in analyzing the interaction between governments and healthcare institutions is confirmed by the results.

Blockchain adoption in the healthcare industry differs from other emerging technologies, as there is the potential for it to reduce revenue for healthcare institutions. This study contributes to the analysis of theoretical models for promoting blockchain in the healthcare industry through subsidies. Additionally, it demonstrates the potential of evolutionary game theory in analyzing the adoption of blockchain technology, and the interaction between governments and healthcare institutions.