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Notwithstanding the novelty and importance of wearable fitness devices, few studies have focussed on comparing the drivers of adoption and usage of wearable fitness in the context of developing countries. This study aims to explore factors that drive overall acceptance of wearable fitness devices in developing countries (India and the Philippines) and whether the impact of these factors on the intention to adopt (INT) differs by country and gender.

The study extends the existing body of knowledge by developing a model that integrates the impact of various perceived benefits (health, autonomy, social, hedonic, symbolic), health self-efficacy (HEALTHSE) and individual characteristics (technological innovativeness [TI]) on the INT wearable fitness devices and the moderating impact of country and gender. The analysis was carried out using partial least square and data of 343 respondents.

This study finds that the INT wearable fitness devices by consumers in developing countries are positively impacted by hedonic, health and autonomy, HEALTHSE and TI. Symbolic and social factors do not have any significant impact on the overall INT wearable fitness devices. However, there are country and gender-specific differences that are consequential to the development of marketing strategies.

The framework and results are specific to the two countries and limited by convenience sampling. Future research can focus on replication across different countries and extend the model with additional contextual factors such as perceived risks.

To the best knowledge of the authors, this is one of the few studies to examine and compare the drivers of adoption of wearable fitness devices in lesser researched developing countries. Also, it is one of the few studies to compare the moderating impact of country and gender in the context of the INT wearable devices. The study provides a theoretical and methodological foundation for future research, as well as practical implications for global companies developing and promoting wearable fitness devices.

The purpose of this paper is to expand current knowledge about the recent trend of wearable technology to assess both its potential in the work environment and the challenges concerning the utilisation of wearables in the workplace.

After establishing exclusion and inclusion criteria, an independent systematic search of the ACM Digital Library, IEEE Xplore, ScienceDirect and Web of Science databases for relevant studies was performed. Out of a total of 359 articles, 34 met the selection criteria.

This review identifies 23 categories of wearable devices. Further categorisation of the devices based on their utilisation shows they can be used in the work environment for activities including monitoring, augmenting, assisting, delivering and tracking. The review reveals that wearable technology has the potential to increase work efficiency among employees, improve workers’ physical well-being and reduce work-related injuries. However, the review also reveals that technological, social, policy and economic challenges related to the use of wearable devices remain.

Many studies have investigated the benefits of wearable devices for personal use, but information about the use of wearables in the work environment is limited. Further research is required in the fields of technology, social challenges, organisation strategies, policies and economics to enhance the adoption rate of wearable devices in work environments.

Previous studies indicate that occupational stress and injuries are detrimental to employees’ health; this paper analyses the use of wearable devices as an intervention method to monitor or prevent these problems. Introducing a categorisation framework during implementation may help identify which types of device categories are suitable and could be beneficial for specific utilisation purposes, facilitating the adoption of wearable devices in the workplace.

The purpose of this paper is to identify motivational factors for using wearable healthcare devices and examine the process by which these factors are integrated with the technology acceptance model (TAM) and contribute to the adoption of the devices.

An online survey assessed the proposed motivational factors for the adoption of wearable healthcare devices. Confirmatory factor analysis and structural equation modeling were conducted on collected data (n=877) to demonstrate the reliability and validity of the measurement and structural model.

Perceived control and interactivity of wearable healthcare devices as well as users’ innovative tendencies are positively associated with usage intention, while perceived cost has no significant effects on user intention to use the devices. The results also supported the explanatory strength and predictability of TAM.

Although the promising role of wearable devices in healthcare industries has gained much consumer attention, limited empirical investigations have been conducted on explicating how user attitude and usage intention are shaped regarding the devices. This study serves as one of the first attempts to empirically examine the adoption process, with implications for the future usage of wearable technology in the healthcare context.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The purpose of this paper is to investigate the factors associated with consumer’s intention to adopt wearable technology in healthcare, and to examine the moderating effects of product type on consumer’s adoption intention.

An integrated acceptance model was developed based on unified theory of acceptance and use of technology 2 (UTAUT2), protection motivation theory (PMT), and privacy calculus theory. The model was tested with 462 respondents using a survey.

Consumer’s decision to adopt healthcare wearable technology is affected by factors from technology, health, and privacy perspectives. Specially, fitness device users care more about hedonic motivation, functional congruence, social influence, perceived privacy risk, and perceived vulnerability, but medical device users pay more attention to perceived expectancy, self-efficacy, effort expectancy, and perceived severity.

This study is among the first to investigate healthcare wearable device from behavioral perspective. It also helps to comprehensively understand emerging health information technology (HIT) acceptance from technology, health, and privacy perspectives.

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.

Adopting an interdisciplinary perspective, the purpose of this paper was to posit an industry-wide technological intervention for hotel housekeeper safety and health through the advancement of wearable technology.

Using the task-technology fit (TTF) model and examples of successful safety and health applications of wearable technologies in the health-care and construction management industries, interventions and future research directions are presented to address workplace hazards experienced by hotel housekeepers.

The fit between a variety of hotel housekeeper user requirements, task demands and wearable functions are explored with justification for the use of wearable devices to improve safety and health-related outcomes.

A research agenda is proposed for the adoption and use of wearables in the hospitality industry with the intention to generate meaningful interventions beyond corporate wellness, and the mitigation of employee privacy concerns to enhance wearable adoption.

Given the importance of consumer safety and health assurance in a post-pandemic business environment, hospitality and tourism organizations should place greater emphasis on protecting front line employees who will be essential in regaining economic viability.

Theoretical and practical foci should move beyond a simplistic view of hospitality and tourism worker safety and health that generally centers on wellness initiatives and other baseline strategies, toward a more holistic view benefitting the hospitality industry.

Extant concerns about hotel housekeeper safety and health, in addition to new concerns and threats in a post-pandemic work environment, are largely understudied and worthy of investigation.

Technologies have had a positive impact on the construction industry. Technologies such as BIM, automation, augmented and virtual reality, Internet of Things and robotics have been adopted by construction firms to enhance productivity. However, not much research has been done on the awareness and adoption of wearable technologies for health and safety (H&S) management. This paper investigates the level of awareness and adoption of wearable technologies for H&S management in the Nigerian construction industry.

A quantitative research method was adopted for the study. An electronic questionnaire format was used as an instrument to collect the data. Both descriptive (mean score) and inferential statistics (Kruskal–Wallis test) were used to analyse the data.

The results indicate that organisations rarely use H&S wearable devices for H&S management although professionals within the construction industry are somewhat aware of the common H&S wearable devices. The findings further indicate that all 11 variables were perceived as “rarely adopted”, whereas 2 variables were perceived as “aware”, 3 variables as “slightly aware” and the remaining 6 variables as “somewhat aware”.

Data were collected from only construction professionals working in government agencies, consultancy firms and grade D contracting firms in Lagos and Abuja. For a broader perspective, a study that expands the number of states and categories of construction firms is recommended.

The construction industry in Nigeria can use the recommendations to improve H&S management on site. Moreover, the recommendations can contribute to the development of policies to promote the adoption of wearable technologies in construction sites.

Research on wearable technologies, particularly in the Nigerian construction industry, is at the developing stage. With this article, the authors contribute to the body of knowledge in this area of research.

Wearable health information systems (IS) open up a new era for personal health self-management, and bring about disruptive changes to individual lives. However, prior studies on IS post-adoptive behavior primarily focused on either continuance or discontinuance, neglecting the mutual transformation process between IS usage and rejection behavior. This behavior is expressed as intermittent discontinuance in the current study. By revising and extending the expectation-disconfirmation model, the purpose of this paper is to understand the factors affecting information system intermittent discontinuance in the context of wearable eHealth.

This study proposes a revised expectation-disconfirmation model by incorporating four new theoretical constructs, i.e. neutral disconfirmation, attitudinal ambivalence, neutral satisfaction, and intermittent discontinuance. An online survey was used to validate the research model, and 428 wearable health device users were recruited in this study.

Empirical results demonstrate that neutral disconfirmation exerts positive effects on neutral satisfaction and attitudinal ambivalence, both of which further have positive effects on intermittent discontinuance. In addition, attitudinal ambivalence also has a positive and significant effect on neutral satisfaction.

This study extends the current understanding on IS post-adoption usage behavior by introducing a new concept, i.e., intermittent discontinuance. In particular, post-adoption usage behavior is viewed as a dynamic process within a changing environment in this study. Some unique features specific to intermittent discontinuance are also identified. In this regard, neutral and conflicting constructs further add new values to IS adoption and diffusion literature. In addition, the integration of eHealth and wearable devices provides a fruitful research context for future research, and this study represents one of the first attempts to empirically explore the intermittent usage problems encountered within this emerging phenomenon.

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.