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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.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

While attempting to persuade surgeons to accept their health technology, sales representatives for medical devices face daily challenges in the operating room. Surgeons exhibit cognitive complexity (abstractness vs. concreteness) when accepting any form of health technology. Surgeons choose technologies on behalf of their patients, taking patient priorities and expectations into account. Prior research has focused on cognitive complexity in the context of health technology adoption, but the issue of technology acceptance has not been addressed. The purpose of this study to use the construal level (CL) theory to determine the role of behavioural abstraction levels in the acceptance of surgical health technology.

On the basis of 556 min of seminar-based data and semi-directive interviews, the surgeons’ experiences regarding the acceptance of health technology were analysed. A non-directive observational method was used to permit the spontaneous emergence of CL dimensions in a natural environment. A categorization model was used for data coding, and MAXQDA, in addition to traditional multidimensional scaling and hierarchical cluster analysis, was used to generate results with joint displays.

Effort expectancy, learning curve, performance risk, habit, patient clinical condition, clinical outcome expectancy, technology setting and social influence were construed at a low construal level (LCL). On the other hand, patient paying capacity, technology cost, price value, financial risk and patient performance expectation were construed at a high construal level (HCL). The study also reveals duality-based factors which showed proximity to HCL but intersected at LCL, and vice versa. Duality-based factors such as effort expectancy, surgical technique, trust and perceived risk intersected at HCL, whereas performance expectancy, relative advantage, time expectancy, perceived value, physical risk and peer group influence intersected at LCL.

This is one of the early studies that presented the impact of behavioural abstraction on behavioural intention to accept health technology for surgeries.

The purpose of this paper is to explore the moderating role of Lean Six Sigma (LSS) practices in explaining the relationship between quality management practices (QMPs) and quality performance.

Partial least square-based structural equation modeling (PLS-SEM) was used to empirically examine the moderating effect of LSS practices on QMPs and quality performance in Malaysian medical device manufacturing companies.

Findings revealed that both QMPs and LSS practices have a significant and positive effect on quality performance. Furthermore, LSS practices served as a substitute for moderating the positive relationship between QMPs and quality performance in such a way that the relationship becomes weaker as LSS practices increase.

LSS is acknowledged as the most well-known hybrid methodology; however, due to its relative newness, it has not been studied in great detail. Unlike previous studies, this paper argued that Lean and Six Sigma practices are distinct from its predecessor TQM practices; moreover, both Lean and Six Sigma practices do not need to substitute QM/TQM practices instead of complimenting the QMPs. In addition, this study adds to the growing body of QM literature by empirically examine the effect of LSS practices in moderating the relationship between QMPs and quality performance.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this study is to examine diverse risks and barriers that influence customers' attitude leading to their actual use of wearable devices in India. This study used technological literacy as a moderating variable to understand the relationship between barriers and attitudes toward adoption of wearable device.

A survey questionnaire was developed through focused group discussions with field experts. Data were collected through online as well as offline modes. A Google form was created and its weblink was shared with the respondents using wearable devices. Both online as well as offline modes were used for data collection. Several reminders through telephone and revisits were undertaken to approach the respondents.

The results of this study indicated that psychological risk and financial risk emerged strongest barriers of wearable technologies. This was followed by infrastructure barriers and performance risk. The strength of the relationship between technological anxiety and attitudes was lower but still significant. Surprisingly, privacy risk and social risk were not statistically significant. This study also validated the impact of technological literacy as a moderator between risks and attitudes.

This study contributes to the research by validating numerous risks and barriers in the adoption of wearable devices. This study not only offers a novel perspective on researching diverse barriers but also elucidates the moderating role of technological literacy which has not been covered in extant literature.

This paper aims to understand how the global supply chain in the medical device industry embraces resilience by adopting agility approach following COVID-19.

This study adopts an interpretative approach to examine the qualitative data drawn from interviews and observation under dynamic capability theory. The data collection concerned multiple stakeholders involved in purchasing and supply management in the medical device market: manufacturing suppliers, channel partners, hospital management and end-users. The coding analysis uses an application that helps the researchers categorise the nodes and extend the existing literature.

The findings show that global supply chain leaders leverage the dynamic capability by centralising the business process decision to respond to the shifting demand from the local governments to the national health ministry, shaping the partnership style from the area- to the funnel-based agreement, even though it exposes a risk of product acceptability from the end-users, encouraging the distributor to convert just-in-time approach into holding safety stock to avoid penalty from missing the procurement target even and restructuring the local partners’ debts to manage long-term performance.

This study extends the emerging literature in international business by underpinning dynamic capability theory.

The purpose of this paper was to examine consumer perceptions of aesthetic attributes of textile-based assistive devices (ADs) and the language used by consumers to express those perceptions and concerns. Previous investigations of user feedback for ADs have largely focused on functional attributes rather than aesthetics.

An interpretivist research philosophy was selected to investigate the meaning behind consumer perceptions and to understand their viewpoints on the aesthetic dimensions of ADs. Using product reviews for two ADs sold on Amazon.com as data, the researchers conducted qualitative data analysis through coding and interpretation of meanings behind reviews to determine consumers’ perceptions related to their ADs.

The authors identified consumer concerns linking to aesthetics evidenced as a multisensory integration of visual, tactile and olfactory cues. Consumer-preferred language used to address aesthetic preferences was found to supplement the literature. Aesthetic considerations were found to be impactful on avoiding stigma and encouraging or discouraging continued use of the devices.

Findings may contribute to the development of textile-based ADs with improved aesthetics to enhance user experiences. New ways of using consumer language to interpret user needs may assist in future research and design practice for consumer products.

The use of consumer product reviews as a rich source of user data is discussed in this paper. As previous research on assistive technology has largely focused on functionality, results of this analysis offer insight into consumers’ aesthetic judgments related to ADs and bring a sensory perspective to the research area.