Teenagers’ behavioural intention towards wearable technologies and intention to recommend others: an empirical study in Bangladesh

Getting Connected to M-Health Technologies through a Meta-Analysis

The demand for mobile e-health technologies (m-health) continues with constant growth, stimulating the technological advancement of such devices. However, the customer needs to perceive the utility of these devices to incorporate them into their daily lives. Hence, this study aims to identify users' perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. Using the relations and constructs proposed in the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) technology acceptance model, the methodological approach utilized a meta-analysis to raise the effect of the main factors on the Behavioral Intention to Use m-health technologies. Furthermore, the model proposed also estimated the moderation effect of gender, age, and timeline variables on the UTAUT2 relations. In total, the meta-analysis utilized 84 different articles, which presented 376 estimations based on a sample of 31,609 respondents. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine users' acceptance of the studied m-health systems.

Predicting the intention to adopt wearable payment devices in China: The use of hybrid SEM-Neural network approach

Wearable payment devices (WPD) are gaining acceptance fast and transforming everyday life and commercial operations in China. Limited research works were conducted on customers’ adoption intentions to obtain a real image of the evolution of WPD in China. This study aims to investigate the effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Trust (PT), and Lifestyle Compatibility (LC) on the intention to adopt WPD among Chinese consumers by expanding unified theory of acceptance and use of technology with two impelling determinants (i.e. PT and LC). Using an online survey, empirical data were collected from 298 respondents in China. In a two-stage data analysis, partial least squares structural equation modelling (PLS-SEM) were employed to analyse the causal effects and associations between independent and dependent variables, whereas artificial neural networks (ANN) were used to evaluate the research model prediction capability. The (PLS-SEM) findings indicated that PE, SI, FC, HM, LC, and PT had substantial positive impacts on adoption intention, whilst EE had no impact on adoption intention among Chinese consumers. The ANN analysis proved the high prediction accuracy of data fitness, with ANN findings highlighting the importance of PT, FC, and PE on the intention to adopt WPD. It was suggested that the study findings assist WPD service providers and the smart wearable device industry practitioners in developing innovative products and implementing efficient marketing strategies to attract the existing and potential WPD users in China.

Exploring the experts’ perceptions of barriers to using internet of things for chronic disease management in Iran

Purpose

Internet of things (IoT) promises advantages in different sectors, especially the health-care sector. Due to its capabilities for chronic disease management, IoT has attracted the attention of researchers. Nowadays, there is research that focuses on the use of IoT for chronic disease management. However, the use of IoT in various contexts faces different barriers. This paper aims to explore Iranian experts’ conceptions of the barriers to using IoT in Iran regarding its application for chronic disease management.

Design/methodology/approach

This study follows a phenomenographic method to investigate Iranian experts’ conceptions of the barriers to using IoT in Iran regarding its application for chronic disease management.

Findings

The results show that there are four categories of description (governance, technical, economic and social barriers) that vary among experts’ conceptions.

Originality/value

The findings of the present work could provide valuable insights for managers and policymakers who want to address IoT barriers.

Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Social implications

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.

Originality/value

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.