Although an Internet-of-Things-based smart home solution can provide an improved and better approach to healthcare management, yet its end user adoption is very low. With elderly people as the main target, these conservative users pose a serious challenge to the successful implementation of smart home healthcare services. The objective of this research was to develop and test a theoretical framework empirically for determining the core factors that can affect the elderly users' acceptance of smart home services for healthcare. Accordingly, an online survey was conducted with 254 elderly people aged 55 years and above across four Asian countries. Partial least square structural equation modeling was applied to analyze the effect of eight hypothesized predicting constructs. The user perceptions were measured on a conceptual level rather than the actual usage intention toward a specific service. Performance expectancy, effort expectancy, expert advice, and perceived trust have a positive impact on the behavioral intention. The same association is negative for technology anxiety and perceived cost. Facilitating conditions and social influence do not have any effect on the behavioral intention. The model could explain 81.4% of the total variance in the dependent variable i.e., behavioral intention. Effort expectancy is the leading predictor of smart homes for healthcare acceptance among the elderly. Together with expert advice, perceived trust, and perceived cost, these four factors represent the key influence of the elderly peoples' acceptance behavior. This paper provides the groundwork to explore the process of the actual adoption of smart home services for healthcare by the elderly people with potential future research areas.

Physical closure of educational institutes worldwide due to the COVID-19 pandemic has resulted in the emergence of a new era of video-based learning. The current circumstances are unique that have forced students to use digital technologies for their learning purpose. However, the successful usage of such a system relies on the understanding of the adoption factors, for which an integrated model is proposed based on the Technology Acceptance Model and the Task Technology Fit Model. Additionally, this work considers the moderating effects of gender and digital inequality. Data are collected from 232 students all of whom have taken part in a full-semester video-based online learning course in times of the pandemic. A Partial Least Squares method is used for analyzing the data. Results show that video-based learning positively fits into the student’s perception and their actual use of the system. The effect of individual characteristics is more than the technology characteristics on the task-technology-fit chain. The integrated model can predict 64.6% of the variance in the final dependent variable: actual usage of video-based learning. Further, while the moderating effect of gender is found to be significant, that for digital inequality is non-significant. Finally, the theoretical and practical implications are discussed.

A rapid increase in the percentage of elderly people over the past few years has been a cause of serious concern among the research fraternity worldwide. Active research is being carried out to leverage the benefits of information and communication technologies that enable them to live independently and promote a sense of overall well-being. Smart-homes are often employed to assist this group of people. However, there is a serious lack of relevant exploratory research that tries to measure and explain the intention of these people toward using such a service. In this paper, we propose and validate a new comprehensive research model called the elderly smart home technology acceptance model by extending the original technology acceptance model that can explain the elderly intention to use the smart-homes. An online questionnaire survey is conducted for this purpose, the results of which are analyzed using the Partial least squares Structural Equation Modeling approach on data collected from 254 subjects. Subjective norm, compatibility, automation, self-capability, and satisfaction are positively related to the elderly intention in using smart-homes, whereas there is a negative association between affordability, security/privacy, and usage intention. Two other factors, namely universal connectivity and enjoyment, have no effect on the behavioral intention. The present study is a first empirical attempt that tries to explore the adoption of smart-homes among the elderly, as all other previous research has focused only on the technical aspects and implementation issues rather than the actual usage intention.

Technology adoption is accepting, integrating, and using the latest innovative technologies in society. Artificial intelligence (AI) and robotics are changing the face of the industrial and service sectors. There is a need to change the traditional way of teaching by introducing the latest innovative methods. This study aims to measure the intention of adopting AI-based robots in the educational system of Indian universities. This study uses three theories technology acceptance model, the theory of planned behaviour, and the technology readiness index. Thirteen hypotheses are proposed for this study. The teachers and students survey Indian universities. This study also measures the users’ attitudes and the impact on their intention. Nine hypotheses got accepted, and four hypotheses got rejected. This study will benefit the university administration as they will understand the importance of AI-based robots and their applications. These will also be helpful in a way that the students and teachers both are in favours of the adoption process.

Online food delivery applications (OFDA) are an emerging area of an online-to-offline (O2O) service delivery platform. In times of the COVID-19 pandemic the OFDA apps can help the restaurant and catering industry to thrive by allowing the consumers to order food from the safety of their homes. In this aspect evaluating the end-to-end service quality is an important issue starting right from the interaction with the OFDA apps for searching and ordering food to the last mile consumer experience with the delivery quality and food quality. The initial experience with the OFDA apps is captured through three mobile app attributes: visual design, navigational design, and information design. Consumer satisfaction and loyalty are considered to be the dependent variables in this study. Data is collected using an online questionnaire from 315 university students in India who are actual users of different OFDA apps during the pandemic period. Data analysis is done using a Partial Least Squares – Structural Equation Modeling approach. The findings show that satisfaction is the greatest predictor of loyalty, followed by food quality. Among the mobile app attributes information design has the highest impact on both satisfaction and loyalty, followed by navigational and visual design respectively. Based on the results appropriate theoretical and practical implications are discussed and suggested.