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

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  • Jining Zhou

    (School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
    School of Chinese and Broadcasting, Hengshui University, Hengshui 053000, China
    National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

  • Bo Zhang

    (National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China
    School of Economics and Management, Tianjin University of Technology and Education, Tianjin 300222, China)

  • Runhua Tan

    (National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin 300401, China)

  • Ming-Lang Tseng

    (Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
    Department of Medical Research, China Medical University, Taichung 40402, Taiwan
    Faculty of Economics and Management, University Kebangsaan Malaysia, Bandar Baru Bangi 43600, Malaysia)

  • Yaya Zhang

    (Office of Taocheng District People’s Government, Hengshui 053000, China)

Abstract

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.

Suggested Citation

  • Jining Zhou & Bo Zhang & Runhua Tan & Ming-Lang Tseng & Yaya Zhang, 2020. "Exploring the Systematic Attributes Influencing Gerontechnology Adoption for Elderly Users Using a Meta-Analysis," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2864-:d:341245
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    References listed on IDEAS

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    2. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
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    4. Tianyang Huang & Haitao Liu, 2019. "Acceptability of Robots to Assist the Elderly by Future Designers: A Case of Guangdong Ocean University Industrial Design Students," Sustainability, MDPI, vol. 11(15), pages 1-14, July.
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    Cited by:

    1. Qi Ma & Alan H. S. Chan & Pei-Lee Teh, 2020. "Bridging the Digital Divide for Older Adults via Observational Training: Effects of Model Identity from a Generational Perspective," Sustainability, MDPI, vol. 12(11), pages 1-24, June.

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