Search results

This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.

We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.

The effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.

When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.

Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.

Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.

The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

The rapid advancement of technology has transformed the health-care industry and enabled the emergence of m-Health solutions such as health apps. The viability and success of these apps depends on the definition of a monetization model appropriate to their specificities. In this sense, the purpose of this paper is to study the mechanisms of monetization of health apps, to stablish how alternative revenues determine if a health app is to be free or paid.

Probability models are used to identify the factors that explain if a health app is free or paid.

Results show that the presence of alternative monetization mechanisms negatively impacts the likelihood of a health app being paid for. The use of personal data to customize advertising (the monetization of “privacy capital”) or the inclusion of ads on the app are alternative means of monetization with potential to decrease the likelihood of a health app being paid for. The possibility of in-app purchases has a lower negative impact on the probability of a health app being paid for. The choice of platform to commercialize an app is also a strategic decision that influences the likelihood of an app being paid for.

This work stands out for bringing together the two largest platforms present in Portugal and for focusing on the perspective of revenue and monetization of health apps and not on the perspective of downloads.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

This works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.

This research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.

Among the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.

This work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

In the recent post-pandemic era, the globe has been anxious for the sustainable disposal of healthcare waste to protect public health, protect the environment and enhance future preparedness. Developing countries, in particular, have struggled to dispose of healthcare waste (HCW) to eradicate the hazardous effects of medical waste generated during and after the deadly COVID-19 pandemic. Hence the purpose of the research paper is to develop a hybrid decision-making framework to identify various barriers for sustainable disposal of healthcare waste use of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP).

A hybrid framework of Grey-Decision Making Trial and Evaluation Laboratory (G-DEMATEL) and Analytical Network Process (ANP) has been used to rank barriers and sub-barriers in the disposal of healthcare waste.

The study’s findings suggest that lack of segregation practices, absence of green procurement policy, obsolete technologies and resistance to adopting change management are the topmost causal barriers influencing the remaining barriers. Lack of commitment among healthcare administrations, lack of standard performance measures and resistance to adopting change appear to be the topmost crucial barriers.

The study’s finding enables all stakeholders to prioritize the barriers systematically for better performance and save resources during the process. The policymakers can use the results to design a clear regulatory framework.

The literature has highlighted the factors and their association with the disposal of healthcare waste mainly in isolation. The results are validated against the Grey-Analytical Hierarchy Process (G-AHP) to ensure the robustness of the proposed framework. This paper is one of the preliminary attempts to propose a framework of the interrelationships of the factors that have a direct role in survival for management education.

This study delves into the behavioural intentions of educators within medical colleges at Mosul Universities concerning the adoption of Knowledge Management-Driven Metaverse technology (KM-D-MT). Rooted in an adapted Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, the research aims to enrich the understanding of Metaverse adoption factors, exploring correlations among key constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value, hedonic motivation and interaction. Furthermore, the study investigates the mediating roles of knowledge generation and knowledge sharing in the relationship between interaction and behavioural intention.

The research employs a quantitative approach, gathering 278 responses from educators in medical colleges. Structural Equation Modelling-Partial Least Squares (SEM-PLS) is used to analyse the data, rigorously examining the reliability and validity of research instruments. The investigation involves an extensive evaluation of various factors influencing educators’ intentions to adopt KM-D-MT, using a cross-sectional design.

The study reveals significant positive impacts of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value and hedonic motivation on behavioural intention to adopt KM-D-MT. Interaction is identified as a key factor positively influencing knowledge sharing and knowledge generation. Furthermore, knowledge sharing and knowledge generation exhibit positive correlations with behavioural intention. Interaction indirectly impacts behavioural intention through the mediating roles of knowledge generation and knowledge sharing, highlighting the transformative potential of Metaverse technology in reshaping knowledge processes.

The findings of this study hold practical implications for educators, institutions and policymakers. The adoption of KM-D-MT can enhance educational experiences, facilitate global collaboration and contribute to the continuous professional development of educators in medical colleges. Institutions are encouraged to strengthen technological and organisational infrastructure to support effective Metaverse implementation. Furthermore, promoting positive social norms, providing technical support and offering training programs can contribute to overcoming barriers and fostering a conducive environment for Metaverse adoption in medical education.

This research significantly contributes to theoretical perspectives by advancing Metaverse research and addressing the call for extensive studies covering theoretical, conceptual and empirical elements. It extends current UTAUT2 frameworks, exploring correlations in the context of medical education and contributes to knowledge management paradigms. The study’s originality lies in its exploration of Metaverse acceptance in higher education institutions, specifically in medical colleges in Iraq, providing valuable insights for further research and practical applications globally.

This study aims to identify several perspectives that affect the adoption of blockchain technology in India (BCTA) and evaluate their impact. To study the sector’s influence on adoption and the impact of BCTA on the performance of the Indian healthcare supply chain (HSCP) using BCTA as a mediating variable.

In this study, we first developed a conceptual model based on Organizational Information Processing Theory and Technology-Organization-Environment, then formulated hypotheses. Based on this, a questionnaire was developed, and data were gathered from experts in the Indian healthcare industry who were familiar with blockchain technology. AMOS 19 was used to analyze data using structural equation modelling.

All the factors have a significant positive influence on BCTA. Healthcare supply chain factors influenced the adoption most dominantly, followed by technological, environmental, organizational and record-keeping unit factors. Both the public and private sectors of HSCP benefited significantly from BCTA.

This research work is fruitful for healthcare practitioners, top management, academicians and policymakers in assessing BCTA’s impact on the HSCP.

We have attempted to evaluate the possible BCTA impact on HSCP. BCTA as a mediating variable and considering different perspectives for a holistic view of adoption in the Indian context add to this work’s originality.

The primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

The European Union (EU) Medical Device Regulations (MDR) 2017/745 entered into force on May 2021 with changes related to strengthening the clinical evaluation requirements, particularly for high-risk devices. This study aims to investigate the impact of these strengthened requirements on medical device manufacturers by investigating the challenges they encounter while generating an MDR-compliant clinical evaluation report.

A systematic literature review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method of peer-reviewed literature and various government jurisdictional reports and legislation.

The findings from the study understanding what constitutes sufficient clinical evidence poses the biggest challenge to the generation of an MDR-compliant clinical evaluation report. Resulting from the challenges they are facing, manufacturers of certain CE-marked medical devices are planning to remove (and have removed) devices from the EU market upon expiration of their certificate, and in the case of new and innovative devices, some manufacturers are planning to launch in other markets ahead of the EU. These challenges will lead to a potential shortage of certain medical devices in the EU and a delay in access to new devices, thereby negatively impacting patients’ quality of life.

This study provides a unique insight into the challenges currently experienced by medical device manufacturers as they transition to the MDR clinical evaluation requirements and the subsequent impact on the continued availability of medical devices in the EU. A limitation is the lack of literature analysing the regulations and their effects.

This study has both theoretical contributions in that, to the best of the authors’ knowledge, it is the first detailed and systematic review of the new MDR Regulations and has implications for practice as manufacturers and policymakers can leverage it alike to understand the challenges of the new MDR.