Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models

Pal, Debajyoti; Triyason, Tuul; Funilkul, Suree; Chutimaskul, Wichian

doi:10.1109/access.2018.2798614

Cited by 41 publications

(27 citation statements)

References 64 publications

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“…If the model fits the data 100%, or in other words, the model explains all of the variations in the endogenous variable, then the R 2 for such variable is equal to 1.00. The R 2 value was used in the earlier study [99] especially in human-robot interaction applications [97,100] as a model-fit measure [101]. According to Cohen [96], R 2 of 0.02, 0.13 and 0.26 are considered as small, medium and large effects respectively in the field of social and behavioural science.…”

Section: Objectivementioning

confidence: 99%

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Ghazali

Ham

Barakova

et al. 2020

Int J of Soc Robotics

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In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots' acceptance. Implications for the development of persuasive robots are discussed.

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Section: Objectivementioning

confidence: 99%

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Ghazali

Ham

Barakova

et al. 2020

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…The designed attribute table is composed of multiple, highly reliable symmetric attributes in the adopted theoretical model. There are overlapping among the various attributes, and the symmetric attributes should be deleted to prevent their reduction effects [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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“…The theoretical models are based on the adopted theories to increase constructs or combine with other theories, including the elderly smart home technology acceptance model [17]; the conceptual extension model based on UTAUT model [18,19]. Pal et al [20] presented the effectiveness of three theoretical models of TAM, rational behavior theory, and planned behavior theory on the adoption of smart homes by the elderly. This study uses the structural equation model to analyze the influencing systematic attributes of adoption.…”

Section: Proposed Modelmentioning

confidence: 99%

Exploring the Systematic Attributes Influencing Gerontechnology Adoption for Elderly Users Using a Meta-Analysis

et al. 2020

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This study aims to explore the key systematic attributes influencing the acceptance of gerontechnology by seniors in response to global aging and rapid technological progress. A meta-analysis was carried out to quantitatively synthesize the results of 25 empirical studies published from 2010 to 2020. After standardized coding and descriptive statistics, as well as tests and analysis of main effects and heterogeneity, publication bias. The following results were obtained: Perceived usefulness and perceived ease of use have a significant positive impact on the user’s attitude and behavioral intention; performance expectancy, effort expectancy, trust, technical performance and subjective norm have a significant positive correlation with the user’s behavioral intention; social influence, facilitating conditions have a positive correlation with the user’s behavioral intention; anxiety has a significant negative correlation to the user’s behavioral intention. The key systematic influencing attributes are classified into three categories: (1) User individual characteristics; (2) product and technical characteristics; and (3) environmental characteristics. This study provides researchers and practitioners with a systematic evidence-based basis to reduce the gap in decision-making for gerontechnology practices.

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Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models

Cited by 41 publications

References 64 publications

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Exploring the Systematic Attributes Influencing Gerontechnology Adoption for Elderly Users Using a Meta-Analysis

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