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This study aims to explore the impact of the adoption of wearable technology products for Indian health insurance firms. It identifies the key dynamic capabilities that health insurance firms should build to manage big data generated by wearable technology so as to attain a competitive advantage.

A qualitative exploratory study using in-depth personal interviews with 53 Indian health insurance experts was conducted with a semi-structured questionnaire. The data were coded using holistic and pattern codes and then analyzed using the content analysis technique. The findings were based on the thematic and relational intensity analysis of the codes.

An empirical model was established where all the propositions were strongly established except for the moderate relationship between wearable technology adoption and product innovation. The study established the nature of the interaction of variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption and also ascertained its influence on firm performance and competitive advantage.

From a dynamic capabilities perspective, this study deliberates on wearable technology adoption in the health insurance context. It also explicates the relationship between the variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption on firm performance.

This study is one of the first studies to add the context of wearable technology and health insurance to the existing body of knowledge on dynamic capabilities and sustainable competitive advantage for the service sector. It would help existing and prospective players in adopting or setting up appropriate business models.

The purpose of this study was to identify the major quality features of wearable technology embedded products that have the greatest impact on consumer satisfaction using the Kano model, an organized approach to specify consumer requirements and expectation through a preference classification technique.

Using a quantitative research method, an online survey was conducted with a convenience sample of US consumers aged between 19 years old and over. A total of otal 471 useable data were obtained and used for the data analysis.

This study identified that the 11 quality features of wearables belong to one-dimensional quality category among the five Kano categories, although the impact of each quality feature's performance on consumer satisfaction varies. The results also showed that the performance level of durability, long battery life, usability, product safety, comfortability and reasonable price has the greatest impact on consumer satisfaction of wearables.

This study has implications for future research by integrating the Kano model with other design and product development related theoretical models when designing, developing and evaluating various wearable products.

This study quantified the key quality features of wearables using the Kano model, which can be a great measurement tool for future researchers to adopt in their studies. The findings of this study help designers, developers and producers of wearables to prioritize the quality features during the product design, development and manufacturing process.

This research was to understand people's perceptions and trends in wearable robots and the research questions were as follows: (1) investigating key terms related to wearable robots that were frequently used by and exposed to people and (2) analyzing relationships among those key terms.

Textom, a big data collection and analysis software system, was used to collect data using the keyword – wearable robot.

The frequency-inverse document frequency, term frequency and central analyses were investigated, and the major key terms related to wearable robots and their connectivity were identified. After performing network analysis and convergence of iterated correlations analyses using UCINET and NetDraw programs, the major key term categories were identified.

It is important to understand how people think and perceive about wearable robots before developing wearable robots. The results of the research are expected to be helpful to better understand how people perceive and what key terms are mainly discussed by people in both countries and ultimately help when developing wearable robots with better market targeting approach methods.

The aim of this study was to develop a smart wearable mask designed for the prevention of respiratory infectious diseases by understanding consumer's preferences in designs and functions of the smart wearable masks.

To develop a smart mask design, a survey was conducted on Chinese consumers in their 20–40s and analyzed their mask wearing behaviors, preferences and caring aspects of masks. The collected data were analyzed to identify the demographic characteristics of the subjects surveyed by using the SPSS program, and technical statistical analysis was conducted. To identify differences in demographic characteristics, an independent samples t-test, one-way analysis of variance and Scheffe's ad hoc test were conducted.

Based on the research results, design guidelines for wearable masks were defined, and four wearable mask designs were developed and presented in 2D and 3D images based on the design guidelines. There were significant differences among people with different backgrounds.

It is significant that this research presents smart wearable mask design guidelines and designs through supplementation and improvement of existing mask. It is expected that this research provides basic empirical data for mask designs through the planning of smart wearable mask designs and surveys assessing consumer perceptions, attitudes and satisfaction.

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

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.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

This paper aims to design and control of a novel compact transportation system called the “wearable vehicle”. The wearable vehicle allows for traversing all types of terrains while transporting one's luggage in a comfortable and efficient manner.

The proposed design consists of a lower limb exoskeleton carrying two motorized wheels and two free wheels installed alongside its feet. This paper presents a detailed description of the system with its preliminary design and finite element analysis. Moreover, the system has been optimally designed to decrease wearable vehicle’s total weight, consequently leading to a reduction in motor size. Finally, two controllers have been designed to achieve stable operation of the wearable vehicle while walking. A PD controller with gravity compensation has been designed to ensure that the wearable vehicle tracks human motion, while a PID controller has been designed to ensure that the zero moment point is close to the center of the system’s support polygon.

Experimental tests were carried out to check the wearable vehicle concept. The obtained results prove the feasibility of the proposed wearable vehicle from the design, dynamics and control viewpoints.

This proposed wearable vehicle’s purpose is for traveling faster with less effort than normal walking. When a human comes across a flat open ground, the wearable vehicle can be used as a vehicle. However, when a human enters crowded traffic, an unstructured area or other obstacles like stairs, the vehicle can be switched into walking mode.

The wearable vehicle has seven DOFs exoskeletons, two motorized wheels, two free wheels and a foldable seat. It is used as a vehicle via its motorized and free wheels to travel fast with minimal effort. In addition, the human can switch easily into walking mode, if there is unstructured terrain to be traversed. Furthermore, an illustration of system's mechanisms and main feature parameters are presented to become acquainted with the ultimate benefits of the new system.

The purpose of this study is to investigate the factors, which drive teenagers’ behavioural intention to adopt wearable technologies and their behavioural intention to recommend others.

This study proposes a new adoption model combining two different models including the extended unified theory of acceptance and use of technology and the theory of planned behaviour, which provided relevant contributions for understanding the adoption of wearable technologies. A structural equation modelling approach using analysis of a moment structures 23 software was used to analyse the data collected from 318 respondents.

Findings of this study reveals that performance expectancy (β = 0.28; t = 2.049), facilitating conditions (β = 0.28; t = 1.989), social influence (β = 0.23; t = 3.150) and attitude (β = 0.18; t = 3.246) have a statistically significant impact on behavioural intention. Additionally, behavioural intention (β = 0.15; t = 2.543) and attitude (β = 0.15; t = 3.261) have a statistically significant impact on intention to recommend others. However, effort expectancy, price value, hedonic motivation and habit did not have a significant influence on behavioural intention.

In this study, the understanding of the determinants contributing to teenagers’ behavioural intention to use wearable technologies and driving their intention to recommend others to adopt these devices will provide insights to practitioners and decision makers to customize the features of wearable devices to promote sustainable use of these technologies.

This study is among the first to investigate wearable technologies from behavioural perspectives especially on teenagers in Bangladesh. Hence, the findings of this study will help to comprehensively explain teenagers’ behavioural intention to adopt wearable technologies and their intention to recommend others.

This paper aims to look at the benefits, risks and ethics behind introducing wearable sensors into the workplace. There are expected to be more than three billion wearable sensors worldwide by 2025 (Hayward and Chansin, 2015). The emergence of technology that has the capability to closely monitor employees has provoked widespread ethical debate (Joseph et al., 2015, p. 244).

The author undertook a review of the current wearable devices on the market, the impact of previous technological innovations on workplaces and the possible impact of wearable devices on organisations.

Wearable technology has the potential to increase productivity. Businesses that embrace these devices are likely to become leaders in their industries (Li, 2015, p. 4). However, any move to use wearable devices in the workplace must be undertaken with sensitivity, and it is recommended that employee participation in wearables programmes is initially voluntary. Businesses must also ensure employees understand how the data collected will be used, who has access to the data and how it is stored. Use of a third party to collect and analyse the information is recommended as an extra security and privacy measure.

The work contained in this paper has not been replicated elsewhere.