Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: a structural equation model with longitudinal data

Sorwar, Golam; Aggar, Christina; Penman, Olivia; Seton, Carolyn; Ward, Anastasia

doi:10.1080/17538157.2022.2069028

Cited by 11 publications

(7 citation statements)

References 58 publications

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“…Prior studies have established that social influence has a positive link with behavioural intention to use a new technology [ 12 , 54 ]. In contrast, [ 55 ] found that social influence has an insignificant impact on behavioural intention to use, due to reduced social interactions. This suggests that when social groups are devoid of interactions, influencing partners and group members to use technology becomes difficult.…”

Section: Research Framework and Hypothesesmentioning

confidence: 99%

Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia

2022

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In recent years, smart home technologies have offered opportunities for elderly people to manage their daily health-related activities. Despite the advancement in smart home technology (SHT), the level of end-user acceptance among elderly people is still low. This study proposes an SHT framework by examining the determinants of elderly behavioural intention (BI) to use smart home technologies by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study uses the quantitative approach to survey about 486 elderly people in Saudi Arabia, and it applies the Partial Least Square Structural Equation Model (PLS-SEM) technique to perform the data analysis. Findings reveal that culture influence and technology awareness are significant factors in determining the BI to use SHT among elderly people. The study also finds that attitude mediates the relationships between performance expectancy, effort expectancy, and behavioural intention. We find that region and education moderate the relationships between culture influence, technology awareness, and behavioural intention. This study theoretically extends the UTAUT theory by including external constructs: culture influence, technology awareness, attitude, education, and region.

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Section: Research Framework and Hypothesesmentioning

confidence: 99%

Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia

2022

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“…In response, Helbing [25] states that we must ensure the ethical use of new digital technologies. Hence, acceptability is a way to mitigate this one-sided approach to adoption and can help to understand the impact of unintended consequences, for example, the erosion trust in technology, privacy [12], or the rate of acceptance of the smart home in older adults [6,26] Technology acceptability is "the degree of primary users' predisposition to carry out daily activities using the intended device" [27]. Philosophically, technology acceptability is a judgment that prescribes the way in which the technology examined ought to be desirable [28].…”

Section: Literature Review 21 Technology Adoption and Acceptabilitymentioning

confidence: 99%

How Is the Internet of Things Industry Responding to the Cybersecurity Challenges of the Smart Home?

Cannizzaro¹,

Procter²

2023

Ubiquitous and Pervasive Computing - New Trends and Opportunities

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In this article, we investigate the privacy and security challenges of the smart home as perceived by the industry, with findings relating to cybersecurity awareness, transparency on legal data use, malicious data use, regulation issues, liability, and market incentives for cybersecurity; we also reveal how the industry has been responding to these challenges. Based on survey findings, we outlined a series of socio-technical challenges to smart home adoption. To understand these findings in more depth, we investigated qualitatively how these challenges were perceived and responded to by organizations in the Internet of Things (IoT) sector. We interviewed seven experts from six organizations involved in the design, development, or review of consumer IoT devices and services including both businesses and NGOs. Thematic analysis focused on two main themes, that is, responses to privacy and responses to security challenges of smart home adoption. Our study revealed that industry stakeholders are looking to address these adoption challenges by providing new technical solutions to mitigate the privacy and security risk of the smart home, producing new standards and influencing regulation, as well as building up communities of learning surrounding common issues. With this knowledge, industry stakeholders can take steps toward increasing smart home acceptability for consumers.

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“…2) [18]. The UTAUT is one of the most widely used theoretical models to predict technology acceptance and adoption [17,19]. The UTAUT model, which had an adjusted R2 of 69% and was superior to the eight individual models, could account for up to 70% of the variance in usage behavior variance [18].…”

Section: Review Of the Literaturementioning

confidence: 99%

Factors Affecting Medical Students' Intentions to Use Rain Classroom: a cross-sectional survey

Low

Tan

2023

Preprint

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Background: Rain Classroom was one of the most popular online learning platforms in Chinese higher education during the pandemic. However, there is little research on user intention under the guidance of technology acceptance and unified theory (UTAUT). Objective: This research aims to discover factors influencing students' intentions to use Rain Classroom. Methods: In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modeling technique was used to confirm the hypothesis. Results: Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioral intentions to use Rain Classroom. Conclusions The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioralintentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.

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Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: a structural equation model with longitudinal data

Cited by 11 publications

References 58 publications

Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia

Factors influencing the elderly’s behavioural intention to use smart home technologies in Saudi Arabia

How Is the Internet of Things Industry Responding to the Cybersecurity Challenges of the Smart Home?

Factors Affecting Medical Students' Intentions to Use Rain Classroom: a cross-sectional survey

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