Societal challenges associated with caring for the physical and mental health of older adults worldwide have grown at an unprecedented pace, increasing demand for health-care services and technolog...

https://www.tandfonline.com/doi/pdf/10.1080/17538157.2021.1923500

Ethics and acceptance of smart homes for older adults.

The proliferation of technology offers potential solutions for enhancing the well-being of older adults. However, older adults often have low digital literacy and are disengaged from the digital world. With age-appropriate training, older adults are expected to acquire a wide range of technological skills and bridge the digital divide. Through the lens of social cognitive theory, this study aims to investigate the effectiveness of observational training through behavior modeling in enhancing technology acceptance in older adults. The moderating effects of model identity on training outcomes from a generational perspective are examined. An empirical training experiment was completed with 59 community-dwelling older adults. Training outcomes were measured using cognitive knowledge, affective variables, and meta-cognition. The results ascertain the effectiveness of observational training in improving their technological knowledge, self-efficacy, outcome expectations, and sense of social connectedness when using technologies. From a generational perspective, the older adult behavior model is more effective than the young or child behavior models in increasing self-efficacy and willingness to use technology. The model identity further enhances the positive outcomes of training. The results of this study contribute to designing educational interventions to bridge the digital divide.

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Bridging the Digital Divide for Older Adults via Observational Training: Effects of Model Identity from a Generational Perspective

Objective: To explore the Chinese community-dwelling intention of older adults to adopt gerontechnology and its influencing factors.

Design: A mixed-methods sequential explanatory design with an inductive approach was employed. In phase 1, a self-made questionnaire was administered from August 2018 to December 2019. Multifactor logistic regression was used to analyze the adoption intention and factors influencing the use of gerontechnology. In phase 2, participants completed a semistructured interview to explore the adoption intention of a specific form of gerontechnology, Smart Aged Care Platform, from May to July 2020.

Setting: Twelve communities in three districts of Chongqing, China.

Participants: Community-dwelling older adults were included.

Results: A total of 1,180 older adults completed the quantitative study; two-thirds of them (68.7%) showed adoption intention toward gerontechnology. Nineteen participants (10 users and nine nonusers) completed the qualitative study and four themes were explored. Through a summarized understanding of the qualitative and quantitative data, a conceptual model of influencing factors, namely, predictive, enabling, and need factors, was constructed.

Conclusions: This study reveals that most Chinese community-dwelling older adults welcome the emergence of new technologies. However, there was a significant difference in the adoption intention of gerontechnology in Chinese community-dwelling older adults based on their sociodemographic and psychographic characteristics. Our findings extend previous technology acceptance models and theories and contribute to the existing resource base.

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Adoption Intention and Factors Influencing the Use of Gerontechnology in Chinese Community-Dwelling Older Adults: A Mixed-Methods Study.

This study aims to evaluate a comprehensive model that includes predictors of use, types of Internet use, and psychological well-being outcomes in the older population.The total sample comprised 650 older Internet users. Structural equation modeling was used to estimate the effect of predictors (i.e., effort expectancy, performance expectancy, social influence, and ageism) on types of Internet use (i.e., information seeking, communication, instrumental use, and leisure) and the impact of the type of use on the psychological well-being of older adults (i.e., depressive and anxious symptomatology). In addition, the indirect effect of different types of Internet use was evaluated, that is, they were considered as mediator variables.Effort expectancy, social influence, and ageism predicted different types of Internet use. The relationship between effort expectancy and anxious symptomatology had an indirect effect via information seeking, whereas the relationship between ageism and anxious symptomatology showed an indirect effect via leisure.The findings support the importance of differentiating the types of Internet use to understand its impact on psychological well-being. Information seeking is associated with anxious symptomatology and is predicted by effort expectancy. On the contrary, leisure is crucial because of its relationship with anxious symptomatology, and less ageism can promote it. This information is useful for developing effective interventions that promote the well-being of older adults through the Internet, including strategies to cope with some online challenges that could lead to negative consequences, such as information overload. 

OUP accepted manuscript

The Antecedents of Consumer Engagement in CSR Communication in Social Media: A Study on Omani University Students

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.

Yaya Zhang

Papers

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