影响老年人技术接受的因素——基于技术接受模型的元分析

doi:10.16187/j.cnki.issn1001-4918.2025.06.05

摘要/Abstract

摘要： 为探讨老年人技术接受行为意向的影响因素,本研究纳入了85项研究(334个效应量)进行元分析,结果表明:(1)感知技术功能特征、感知技术情感特征和环境特征相关变量与行为意向存在正相关;老年人个人特征中技术自我效能感和行为意向高度正相关,技术焦虑和抵制改变与行为意向相关不显著;(2)智能技术种类显著调节感知有用性和便利条件与行为意向的关系,年龄、性别和文化未发现调节作用。本研究将感知技术情感特征和老年人个人特征拓展到现有的技术接受模型中,为今后关于老年人智能技术的研究、设计和应用提供了理论依据。

关键词: 老年人, 技术接受, 技术情感特征, 个人特征, 元分析

Abstract: To explore the influencing factors of the elderly’s intention to adopt technology, this study conducted a meta-analysis of 85 studies (comprising 334 effect sizes). The results indicated that: (1) Perceived technology functionalcharacteristics, perceived technology emotional characteristics, and perceived environmental characteristics are highly correlated with behavioral intention. Regarding personal characteristics of older adults, technology self-efficacy showed a significantly positive correlation with behavioral intention, while technology anxiety and resistance to change did not demonstrate a significant correlation with behavioral intention; (2) The type of intelligent technology significantly moderates the relationship between perceived usefulness, ease of use, and behavioral intention, while no moderating effects were found for age, gender, and culture. This study extends perceived technology emotional characteristics and personal characteristics of older adults into an existing technology acceptance model, providing a theoretical basis for future research, design, and application regarding intelligent technologies for older adults.

Key words: older adults, technology acceptance, perceived technology emotional features, individual attributes, meta-analysis

中图分类号:

B844

曹贤才, 张浩, 周博霖, 陈宪涛, 崔晨虹, 吴捷. 影响老年人技术接受的因素——基于技术接受模型的元分析[J]. 心理发展与教育, 2025, 41(6): 799-816.

CAO Xiancai, ZHANG Hao, ZHOU Bolin, CHEN Xiantao, CUI Chenghong, WU Jie. A Meta-analysis of the Impact Factors of Technology Acceptance among Older Adults Based on Technology Acceptance Model[J]. Psychological Development and Education, 2025, 41(6): 799-816.

参考文献

Alam, M. Z., & Khanam, L. (2022). Comparison of the young aged and elderly female users’ adoption of mHealth services.Health Care for Women International, 43(10-11), 1259-1283.
^*Alexandrakis, D., Chorianopoulos, K., & Tselios, N. (2020). Olderadults and Web 2.0 storytelling technologies: Probing the technology acceptance model through an age-related perspective. International Journal of Human-Computer Interaction, 36(17), 1623-1635.
^*Alsswey, A., & Al-Samarraie, H. (2020). Elderly users’ acceptance of mHealth user interface (UI) design-based culture: The moderator role of age.Journal on Multimodal User Interfaces, 14(1), 49-59.
^*Alsswey, A., Bin Umar, I. N., & Bervell, B. (2018). Investigating theacceptance of mobile health application user interface cultural-based design to assist Arab elderly users. International Journal of Advanced Computer Science and Applications, 9(8), 144-152.
Andalib Touchaei, S., & Hazarina Hashim, N. (2024). The antecedents of mobile banking adoption among senior citizens in Malaysia. International Journal of Human-Computer Interaction, 40(1),https://doi.org/10.1080/10447318.2022.2161236.
Bandura, A., Freeman, W. H., & Lightsey, R. (1997).Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13(2), 158-166.
Bansal, G., Zahedi, F. M., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online.Decision Support Systems, 49(2), 138-150.
Bao, L. Y., & Younghwan, P. (2021). Astudy on the acceptability for mobile payment platforms by China’s early elder people. Journal of the Korea Convergence Society, 12(11), 53-67.
Bartoš, F., Maier, M., Quintana, D. S., & Wagenmakers, E. J. (2022). Adjusting for publication bias in JASP and R: Selection models, PET-PEESE, and robust Bayesian meta-analysis. Advances in Methods and Practices in Psychological Science, 5(3), 2515 2459221109259.https://doi.org/10.1177/25152459221109259
^*Beran, A. (2021).Shared autonomous vehicles and older adults in the phoenix metropolitan area: A quantitative correlational study (Doctoral dissertation). University of Phoenix.
^*Bevilacqua, R., Di Rosa, M., Riccardi, G. R., Pelliccioni, G., Lattanzio, F., Felici, E., … Maranesi, E. (2022). Design and development of a scale for evaluating the acceptance of social robotics for older people: The robot era inventory. Frontiers in Neurorobotics, 16, 883106. https://doi.org/10.3389/fnbot.2022.883106
Carstensen, L. L. (1995). Evidence for a life-span theory of socioemotional selectivity.Current Directions in Psychological Science, 4(5), 151-156.
Card, N. A. (2012).Applied meta-analysis for social science research. New York: Guilford Press
^*Chang, H. J. J., & Min, S. (2022). Evaluation of 3D apparel design spatial visualization training for cognitive function of older adults: cross-cultural comparisons.Fashion and Textiles, 9(1), 33. https://doi.org/10.1186/s40691-022-00292-x
Charness, N., & Boot, W. R. (2022). Agrand challenge for Psychology: Reducing the age-related digital divide. Current Directions in Psychological Science, 31(2), 187-193.
^*Chen, C. K., Tsai, T. H., Lin, Y. C., Lin, C. C., Hsu, S. C., Chung, C. Y., … Wong, A. M. K. (2018). Acceptance of different design exergames in elders.PLoS ONE, 13(7), e0200185. https://doi.org/10.1371/journal.pone.0200185
^*Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM).Ergonomics, 57(5), 635-652.
^*Cimperman, M., Brencic, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model.International Journal of Medical Informatics, 90, 22-31.
^*Cruz, A. M., Portillo, H. P. L., Daum, C., Rutledge, E., King, S., & Liu, L. L. (2022). Technologyacceptance and usability of a mobile App to support the workflow of health care aides Who provide services to older adults: Pilot mixed methods study. JMIR Aging, 5(2), e37521. https://doi.org/10.2196/37521
Davis, F. D. (1989). Perceivedusefulness, Perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
de Veer, A. J. E., Peeters, J. M., Brabers, A. E. M., Schellevis, F. G., Rademakers, J., & Francke, A. L. (2015). Determinants of the intention to use e-Health by community dwelling older people.Bmc Health Services Research, 15, 103. https://doi.org/10.1186/s12913-015-0765-8
^*Dino, M. J. S., & de Guzman, A. B. (2015). Usingpartial least squares (PLS) in predicting behavioral intention for telehealth use among Filipino elderly. Educational Gerontology, 41(1), 53-68.
Erjavec, J., & Manfreda, A. (2022). Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior.Journal of Retailing and Consumer Services, 65, 102867. https://doi.org/10.1016/j.jretconser.2021.102867
Etemad-Sajadi, R., & Dos Santos, G. G. (2019). Senior citizens’ acceptance of connected health technologies in their homes.International Journal of Health Care Quality Assurance, 32(8), 1162-1174.
Fischl, C., Asaba, E., & Nilsson, I. (2017). Exploring potential in participation mediated by digital technology among older adults.Journal of Occupational Science, 24(3), 314-326.
Fisher, K., & Easton, K. (2019). The meaning and value of digital technology adoption for older adults with sight loss: A mixed methods study.Technology and Disability, 30, 177-184.
Gelbrich, K., & Sattler, B. (2014). Anxiety, crowding, and time pressure in public self-service technology acceptance.The Journal of Services Marketing, 28(1), 82-94.
^*Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults.Universal Access in the Information Society, 19(2), 311-330.
^*Günthner, T. (2022). The moderating influence of life events on the acceptance of advanced driver assistance systems in aging societies.Computers in Human Behavior Reports, 7, 100202. https://doi.org/10.1016/j.chbr.2022.100202
Hanif, Y., & Lallie, H. S. (2021). Security factors on the intention to use mobile banking applications in the UK older generation (55+). A mixed-method study using modified UTAUT and MTAM-with perceived cyber security, risk, and trust.Technology in Society, 67, 101693. https://doi.org/10.1016/j.techsoc.2021.101693
Hassenzahl, M. (2005). The Thing and I: Understanding therelationship between user and product. In Blythe, M., & Monk, A. (Eds). Funology 2. Human-Computer Interaction Series (pp.31-42). Springer, Cham.
Hauk, N., Hueffmeier, J., & Krumm, S. (2018). Ready to be asilver surfer? A Meta-analysis on the relationship between chronological age and technology acceptance. Computers in Human Behavior, 84, 304-319.
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses.British Medical Journal, 327(7414), 557. https://doi.org/10.1136/bmj.327.7414.557
Hofstede, G. (1983). Nationalcultures in four dimensions: A research-based theory of cultural differences among nations. International Studies of Management & Organization, 13(1/2), 46-74.
^*Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model.International Journal of Medical Informatics, 101, 75-84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
^*Hsiao, C. H., & Tang, K. Y. (2015). Examining amodel of mobile healthcare technology acceptance by the elderly in Taiwan. Journal of Global Information Technology Management, 18(4), 292-311.
^*Hsu, C. W., & Peng, C. C. (2022). What drives older adults’ use of mobile registration apps in Taiwan? An investigation using the extended UTAUT model.Informatics for Health & Social Care, 47(3), 258-273.
^*Ibili, E., Ilhanli, N., Zayim, N., & Yardimci, A. (2022). Examination of behavioral intention toward E-learning: A case of University of the Third Age students.Educational Gerontology, 49(1), 60-79.
Jackson, D., & Turner, R. (2017). Power analysis for random-effects meta-analysis.Research Synthesis Methods, 8(3), 290-302.
Jan, J., Alshare, K. A., & Lane, P. L. (2022). Hofstede’s cultural dimensions in technology acceptance models: A meta-analysis.Universal Access in the Information Society, 1-25. https://doi.org/10.1007/s10209-022-00930-7
Jeng, M.-Y., Pai, F.-Y., & Yeh, T.-M. (2022). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model.Behavioral Sciences, 12(4), 114. https://www.mdpi.com/2076-328X/12/4/114
^*Jokisch, M. R., Scheling, L., Doh, M., & Wahl, H. W. (2022). Contrasting Internetadoption in early and advanced old age: Does Internet self-efficacy matter? Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 77(2), 312-320.
Kahneman, D., & Tversky, A. (1979). Prospecttheory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
^*Kamide, H. (2021). Theeffect of social cohesion on Interest, Usefulness, and ease of use of a driving assistance system in older adults. International Journal of Environmental Research and Public Health, 18(21), 11412. https://doi.org/10.3390/ijerph182111412
Kebede, A. S., Ozolins, L.-L., Holst, H., & Galvin, K. (2022). Digitalengagement of older adults: Scoping review. Journal of Medical Internet Research, 24(12), e40192. https://doi.org/10.2196/401 92
^*Khan, T., Khan, K. D., Azhar, M. S., Shah, S. N. A., Uddin, M. M., & Khan, T. H. (2022). Mobile health services and the elderly: Assessing the determinants of technology adoption readiness in Pakistan.Journal of Public Affairs, 22(4), 2685. https://doi.org/10.1002/pa.2685
^*Kim, H.-W., & Kankanhalli, A. (2009). Investigatinguser resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567-582.
^*Kim, M. J., Kim, W. G., Kim, J. M., & Kim, C. (2016). Does knowledge matter to seniors’ usage of mobile devices? Focusing on motivation and attachment.International Journal of Contemporary Hospitality Management, 28(8), 1702-1727.
Kim, U., Chung, T., & Park, E. (2022). Qualitycharacteristics and acceptance intention for healthcare kiosks: Perception of elders from south Korea based on the extended technology acceptance model. International Journal of Environmental Research and Public Health, 19(24), 16485. https://doi.org/10.3390/ijerph192416485
Kong, L., & Woods, O. (2018). Smart eldercare in Singapore: Negotiating agency and apathy at the margins.Journal of Aging Studies, 47, 1-9. https://doi.org/10.1016/j.jaging.2018.08.001
^*Lai, H.-J. (2020). Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology.Interactive Learning Environments, 28(7), 890-901.
^*Lee, M. S. (2019). Effects of personal innovativeness on mobile device adoption by older adults in South Korea: The moderation effect of mobile device use experience.International Journal of Mobile Communications, 17(6), 682-702.
Li, J., Ma, Q., Chan, A. H. S., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model.Applied Ergonomics, 75, 162-169.
^*Li, W. J., Shen, S. W., Yang, J. D., & Tang, Q. H. (2021). Internet-Basedmedical service use and eudaimonic Well-Being of urban older adults: A peer support and technology acceptance model. International Journal of Environmental Research and Public Health, 18(22), 12062. https://doi.org/10.3390/ijerph182212062
Lian, J. W., & Yen, D. C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences.Computers in Human Behavior, 37, 133-143.
^*Lin, C. T., & Chuang, S. S. (2019). Astudy of digital learning for older adults. Journal of Adult Development, 26(2), 149-160.
Lipsey, M. W., & Wilson, D. B. (2001).Practical meta-analysis. Sage publications, Inc.
^*Liu, D. W., Liu, A. Q., & Tu, W. Y. (2020). Theacceptance behavior of new media entertainment among older adults: Living arrangement as a mediator. International Journal of Aging & Human Development, 91(3), 274-298.
Liu, K., Or, C. K., So, M., Cheung, B., Chan, B., Tiwari, A., & Tan, J. (2022). A longitudinal examination of tablet self-management technology acceptance by patients with chronic diseases: Integrating perceived hand function, perceived visual function, and perceived home space adequacy with the TAM and TPB.Applied Ergonomics, 100, 103667. https://doi.org/10.1016/j.apergo.2021.103667
Ma, Q., Chan, A. H. S., & Teh, P.-L. (2021). Insights intoolder adults’ technology acceptance through Meta-Analysis. International Journal of Human-Computer Interaction, 37(11), 1049-1062.
^*Ma, Y., & Luo, M. (2022). Older people’s intention to use medical apps during the COVID-19 pandemic in China: An application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM).Ageing & Society, 44(7), 1515-1532.
^*Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2.Computers in Human Behavior, 75, 935-948.
^*Man, S. S., Guo, Y. Q., Chan, A. H. S., & Zhuang, H. P. (2022). Acceptance ofonline mapping technology among older adults: Technology acceptance model with facilitating condition, compatibility, and self-satisfaction. Isprs International Journal of Geo-Information, 11(11), 558. https://doi.org/10.3390/ijgi11110558
^*Mariano, J., Marques, S., Ramos, M. R., Gerardo, F., da Cunha, C. L., Girenko, A., … de Vries, H. (2022). Too old for technology? Stereotype threat and technology use by older adults.Behaviour & Information Technology, 41(7), 1503-1514.
^*Martin-Garcia, A. V., Redolat, R., & Pinazo-Hernandis, S. (2022). Factorsinfluencing intention to technological use in older adults. The TAM model application. Research on Aging, 44(7-8), 573-588.
^*Mukherjee, S. (2021).A quantitative study using the UTAUT2 model to evaluate the behavioral intention to use telemedicine via IoT-Enabled devices by older adults in the Chicago Tri-State region (Publication Number 29160885) (Ph.D., University of the Cumberlands).
^*Nagle, S., & Schmidt, L. (2012). Computer acceptance of older adults.Work-a Journal of Prevention Assessment & Rehabilitation, 41, 3541-3548.
Nguyen, Q. N., Ta, A., & Prybutok, V. (2018). Anintegrated model of Voice-User interface continuance intention: The gender effect. International Journal of Human-Computer Interaction, 35(15), 1362-1377.
Norman, D. (2013).The design of everyday things: Revised and expanded edition (Rev. and expanded ed.). Boulder: Basic Books.
^*Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing theelderly users’ adoption of smart-home services. IEEE Access, 6, 51238-51252.
Park, C., Kim, D.-G., Cho, S., & Han, H.-J. (2019). Adoption of multimedia technology for learning and gender difference.Computers in Human Behavior, 92, 288-296.
^*Pate, L. (2020).Factors affecting older adults’ adoption of wearable health-related Internet of things applications (Publication Number 29391568) (Unpublished doctorial dissertation). Capella University.
^*Peral-Peral, B., Villarejo-Ramos, A. F., & Arenas-Gaitan, J. (2020). Self-efficacy and anxiety as determinants of older adults’ use of Internet Banking Services.Universal Access in the Information Society, 19(4), 825-840.
^*Pheeraphuttharangkoon, S. (2015).The adoption, use and diffusion of smartphones among adults over fifty in the uk (Publication Number 10094505) (Unpublished doctorial dissertation). University of Hertfordshire(United Kingdom).
^*Phibbs, C. L. (2021).The impact of motivation, price, and habit on intention to use IoT-Enabled technology: A correlational study (Publication Number 28316037) (Unpublished doctorial dissertation). Capella University.
^*Powell, A., Williams, C. K., Bock, D. B., Doellman, T., & Allen, J. (2012). e-Voting intent: A comparison of young and elderly voters.Government Information Quarterly, 29(3), 361-372.
^*Pywell, J. T. (2021).Understanding the psychosocial drivers of adoption and use of mobile mental health interventions among older adults (Doctorial Dissertation). University of Northumbria at Newcastle, United Kingdom.
^*Quaosar, G., Hoque, M. R., & Bao, Y. K. (2018). Investigatingfactors affecting elderly’s intention to use m-Health services: An empirical study. Telemedicine and E-Health, 24(4), 309-314.
^*Rao, M. G. (2015).Factors affecting health care technology use in Baby Boomers: A quantitative study (Doctoral Dissertation). Northcentral University.
Rogers, Y. (1997). A brief introduction to distributed cognition. Retrieved from https://cspeech.ucd.ie/Fred/docs/introDistributedCognition.pdf
^*Rój, J. (2022). Whatdetermines the acceptance and use of eHealth by older adults in Poland? International Journal of Environmental Research and Public Health, 19(23), 15643. https://doi.org/10.3390/ijerph192315643
^*Sancho-Esper, F., Ostrovskaya, L., Rodriguez-Sanchez, C., & Campayo-Sanchez, F. (2022). Virtual reality in retirement communities: Technology acceptance and tourist destination recommendation.Journal of Vacation Marketing, 29(2), 275-290.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects.Information & Management, 44(1), 90-103.
Schmidt, F. L., Oh, I.-S., & Hayes, T. L. (2009). Fixed- versus random-effects models in meta-analysis: Model properties and an empirical comparison of differences in results.British Journal of Mathematical and Statistical Psychology, 62(1), 97-128.
Sims, T., Hogan, C. L., & Carstensen, L. L. (2015). Selectivity as an emotion regulation strategy: Lessons from older adults.Current Opinion in Psychology, 3, 80-84.
Schlomann, A., Wahl, H. W., Zentel, P., Heyl, V., Knapp, L., Opfermann, C., … Rietz, C. (2021). Potential andpitfalls of digital voice assistants in older adults with and without intellectual disabilities: Relevance of participatory design elements and ecologically valid field studies. Frontiers in Psychology, 12, 684012. https://doi.org/10.3389/fpsyg.2021.684012
^*Soh, P. Y., Heng, H. B., Selvachandran, G., Anh, L. Q., Chau, H. T. M., Son, L. H., … Varatharajan, R. (2020). Perception, acceptance and willingness of older adults in Malaysia towards online shopping: A study using the UTAUT and IRT models.Journal of Ambient Intelligence and Humanized Computing,1-13. https://doi.org/10.1007/s12652-020-01718-4
^* Song, Y., Yang, Y. P., & Cheng, P. Y. (2022). Theinvestigation of adoption of voice-user interface (VUI) in smart home systems among Chinese older adults. Sensors, 22(4), 1614. https://doi.org/10.3390/s22041614
^*Sorwar, G., Aggar, C., Penman, O., Seton, C., & Ward, A. (2022). Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: A structural equation model with longitudinal data.Informatics for Health & Social Care, 48(1), 80-94.
Spruit, A., Goos, L., Weenink, N., Rodenburg, R., Niemeyer, H., Stams, G. J., & Colonnesi, C. (2020). The relation between attachment and depression in children and adolescents: A multilevel meta-analysis.Clinical child and family psychology review, 23, 54-69.
^*Suta, P., Limdumrongnukoon, N., Chalardkitsirikul, P., Suntiyotin, T., Mongkolnam, P., & Chan, J. H. (2018). Analysis offactors affecting multimedia delivery for elderly people. Engineering Journal-Thailand, 22(1), 49-64.
^*Talukder, M. S., Chiong, R., Corbitt, B., & Bao, Y. K. (2020). Criticalfactors influencing the intention to adopt m-Government services by the elderly. Journal of Global Information Management, 28(4), 74-94.
^*Talukder, M. S., Sorwar, G., Bao, Y. K., Ahmed, J. U., & Palash, M. A. S. (2020). Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach.Technological Forecasting and Social Change, 150, 119793. https://doi.org/10.1016/j.techfore.2019.119793
Teh, P.-L., Ahmed, P. K., Chan, A. H. S., Cheong, S.-N., & Yap, W.-J. (2015). Useful oreasy-to-use? Knowing what older people like about near field communication technology. International Publishing (pp. 273-281). Springer, Cham.
^*Teh, P. L., Lim, W. M., Ahmed, P. K., Chan, A. H. S., Loo, J. M. Y., Cheong, S. N., & Yap, W. J. (2017). Does power posing affect gerontechnology adoption among older adults?Behaviour & Information Technology, 36(1), 33-42.
^*Tsai, T. H., Chang, H. T., Chen, Y. J., & Chang, Y. S. (2017). Determinants of user acceptance of a specific social platform for older adults: An empiricalexamination of user interface characteristics and behavioral intention. PLoS ONE, 12(8), e0180102. https://doi.org/10.1371/journal.pone.0180102
^*Tsai, T. H., Lin, W. Y., Chang, Y. S., Chang, P. C., & Lee, M. Y. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults.PLoS ONE, 15(1), e0227270. https://doi.org/10.1371/journal.pone.0227270
^*Tural, E., Lu, D. N., & Cole, D. A. (2020). Factors predicting older Adults’ attitudes toward and intentions to use stair mobility assistive designs at home.Preventive Medicine Reports, 18, 101082. https://doi.org/10.1016/j.pmedr.2020.101082
Valente, T. W., & Rogers, E. M. (1995). Theorigins and development of the diffusion of innovations paradigm as an example of scientific growth. Science Communication, 16(3), 242-273.
van Elburg, F. R. T., Klaver, N. S., Nieboer, A. P., & Askari, M. (2022). Gender differences regarding intention to use mHealth applications in the Dutch elderly population: A cross-sectional study.Bmc Geriatrics, 22(1), 449. https://doi.org/10.1186/s12877-022-03130-3
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions.Decision Sciences, 39, 273-315.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). Useracceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumeracceptance and use of information technology: Extending the Unified Theory of Acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Viechtbauer, W. (2007). Publication bias in meta-analysis: Prevention, assessment and adjustments.Psychometrika, 72(2), 269-271.
Weber, C., Quintus, M., Egloff, B., Luong, G., Riediger, M., & Wrzus, C. (2020). Same old, same old? Age differences in the diversity of daily life.Psychology and Aging, 35, 434-448.
Werner, L., Huang, G., & Pitts, B. J. (2022). Smart speech systems: A focus group study on older adult user and non-user perceptions of speech interfaces.International Journal of Human-Computer Interaction, 39(5), 1149-1161.
^*Wong, C. K. M., Yeung, D. Y., Ho, H. C. Y., Tse, K.-P., & Lam, C.-Y. (2014). Chinese older adults’ internet use for health information.Journal of Applied Gerontology, 33(3), 316-335.
^*Wong, D., Liu, H. F., Meng-Lewis, Y., Sun, Y., & Zhang, Y. (2022). Gamified money: Exploring the effectiveness of gamification in mobile payment adoption among the silver generation in China.Information Technology & People, 35(1), 281-315.
^*Wu, J. J., & Song, S. (2021). Olderadults’ online shopping continuance intentions: Applying the Technology Acceptance Model and the Theory of Planned Behavior. International Journal of Human-Computer Interaction, 37(10), 938-948.
^*Yang, C. C., Li, C. L., Yeh, T. F., & Chang, Y. C. (2022). Assessingolder adults’ intentions to use a smartphone: Using the Meta-Unified Theory of the Acceptance and Use of Technology. International Journal of Environmental Research and Public Health, 19(9), 5403. https://doi.org/10.3390/ijerph19095403
^*Yang, C. C., Liu, C., & Wang, Y. S. (2022). The acceptance and use of smartphones among older adults: Differences in UTAUT determinants before and after training.Library Hi Tech, 41(5), 1357-1375.
^*Yap, Y. Y., Tan, S. H., Tan, S. K., & Choon, S. W. (2022). Integrating the capability approach and technology acceptance model to explain the elderly’s use intention of online grocery shopping.Telematics and Informatics, 72, 101842. https://doi.org/10.1016/j.tele.2022.101842
Yein, N., & Pal, S. (2021). Analysis of the user acceptance of exergaming (fall- preventive measure) - Tailored for Indian elderly using unified theory of acceptance and use of technology (UTAUT2) model.Entertainment Computing, 38, 100419. https://doi.org/10.1016/j.entcom.2021.100419
Yein, N., & Pal, S. (2021). Analysis of the user acceptance of exergaming (fall- preventive measure) - Tailored for Indian elderly using unified theory of acceptance and use of technology (UTAUT2) model.Entertainment Computing, 38, 100419. https://doi.org/https://doi.org/10.1016/j.entcom.2021.100419
Yu, J., de Antonio, A., & Villalba-Mora, E. (2022). Design of anintegrated acceptance framework for older users and eHealth: Influential factor analysis. Journal of Medical Internet Research, 24(1), e31920. https://doi.org/10.2196/31920
Zambianchi, M., Rönnlund, M., & Carelli, M. G. (2019). Attitudestowards and use of information and communication technologies (ICTs) among older adults in Italy and Sweden: The influence of cultural context, Socio-Demographic factors, and time perspective. Journal of Cross-Cultural Gerontology, 34(3), 291-306.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence.Journal of Marketing, 52(3), 2-22.
^*Zhao, S., Kingshuk, Yao, Y., & Ya, N. (2021). Adoption of mobile social media for learning among Chinese older adults in senior citizen colleges.Etr&D-Educational Technology Research and Development, 69(6), 3413-3435.
Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age.International Journal of Information Management, 43, 342-350.
Zhao, Y., Wang, N., Li, Y., Zhou, R., & Li, S. (2020). Do cultural differences affect users’ e-learning adoption? A meta-analysis. British Journal of Educational Technology, 52(1), 20-41.
Zhong, R. T., Ma, M. Y., Zhou, Y. T., Lin, Q. X., Li, L. L., & Zhang, N. J. (2022). User acceptance of smart home voice assistant: A comparison among younger, middle-aged, and older adults.Universal Access in the Information Society, 1-18. https://doi.org/10.1007/s10209-022-00936-1
Zhou, J., Zhang, B., Tan, R., Tseng, M.-L., & Zhang, Y. (2020).Exploring the systematic attributes influencing Gerontechnology adoption for elderly users using a Meta-Analysis. Sustainability, 12(7), 2864. https://doi.org/10.3390/su12072864
Zhou, M., Qu, S. J., Zhao, L. D., Campy, K. S., Wang, S., & Huang, W. (2019). Understanding psychological determinants to promote the adoption of general practitioner by Chinese elderly.Health Policy and Technology, 8(2), 128-136.
^*Zhou, M., Zhao, L. D., Kong, N., Campy, K. S., Qu, S. J., & Wang, S. (2019). Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model.International Journal of Medical Informatics, 126, 118-127.
^*Zhu, X. W., & Cheng, X. P. (2022). Staying connected: Smartphone acceptance and use level differences of older adults in China.Universal Access in the Information Society, 23, 203-212.
^* Ali, M. (2019). 探索巴基斯坦老年人电子政务采用的因素 (硕士学位论文). 中国科学技术大学, 合肥.
敖玲敏, 吕厚超,黄希庭. (2011). 社会情绪选择理论概述. 心理科学进展, 19(02), 217-223.
^*陈锦辉. (2021). 老年人移动政务平台使用影响因素研究 (硕士学位论文). 华中师范大学,武汉.
^*陈璟浩, 罗淇. (2022). 突发公共卫生事件中老年人防疫信息技术采纳意愿影响因素研究. 农业图书情报学报, 34(04), 30-40.
高冰洁, 张宁. (2020). 老年人在线健康信息行为的研究现状与前沿展望. 图书馆学研究, 43(06), 9-16+77.
何灿群, 朱敏, 张海霞, 侯冠华. (2022). TPB视域下低龄老年人手工艺众创平台研究综述. 包装工程, 43(20), 72-83.
^*贺建平, 黄肖肖. (2020). 城市老年人的智能手机使用与实现幸福感:基于代际支持理论和技术接受模型. 国际新闻界, 42(03), 49-73.
^*胡健. (2022). 互联网医疗时代老年人健康信息交互行为影响因素研究 (硕士学位论文). 黑龙江大学,哈尔滨.
^*黄河. (2019). 基于老年人技术接受模型的智能家电产品设计方法研究 (博士学位论文). 华东理工大学,上海.
黄鲁成, 薛爽, 吴菲菲, 苗红. (2019). 老年人对不同科技产品的接受倾向及其影响因素分析——以北京市为例. 老龄科学研究, 7(06), 68-80.
姜照君. (2022). 社会网络、媒介依赖与老年人主观幸福感——基于新冠肺炎疫情的实证研究. 现代传播(中国传媒大学学报), 44(07), 161-168.
李宏洁, 张艳, 杜灿灿, 赵敬, 李思思, 田雨同, 刘珍. (2022). 积极老龄化理论的国内外研究进展. 中国老年学杂志, 41(05), 1222-1226.
李敏, 王振振, 王立剑. (2017). 居家老年人使用养老科技的影响因素分析——基于北京、南京、咸阳三市的调查. 人口与发展, 23(03), 84-92.
范玉吉, 李紫繁. (2021). 从赋权到限权:老年人智能手机使用研究. 未来传播, 28(5), 29-37.
^*刘满成, 石卫星, 李晓慧. (2014). 为老服务网站用户需求及影响因素分析. 求索, 21(10), 101-105.
刘育猛. (2022). 数字包容视域下的老年人数字鸿沟协同治理:智慧实践与实践智慧. 湖湘论坛, 35(3), 107-119.
^*罗盛, 张锦, 李伟, 井淇, 胡善菊, 董毅, … 冀洪海. (2018). 基于TAM理论的城市社区智能化养老服务项目需求因素分析. 中国卫生统计, 35(03), 372-374+379.
^*聂淼. (2016). 智能居家养老服务用户采纳行为的影响因素研究 (硕士学位论文). 山东财经大学, 济南.
牛湘, 冉光明. (2023). 同伴关系与幼儿问题行为关系的三水平元分析. 心理发展与教育, 39(4), 473-487.
^*石林. (2022). 老年患者对移动医疗服务的使用意愿研究 (硕士学位论文). 河南大学, 郑州.
^*谭宗婷. (2021). 居家老人对老龄科技产品的使用意愿及影响因素研究 (硕士学位论文). 西南交通大学, 成都.
^*王静. (2020). 互联网持续使用行为与老年人主观幸福感的关系研究 (硕士学位论文).合肥工业大学.
^*王钰沂. (2020). 我国老年群体的微信采纳影响因素研究 (硕士学位论文). 华中科技大学, 武汉.
王平, 肖先沛, 苏超萍, 曹珂瑞, 李昕. (2022). 城市老年人数字生活的现状及影响因素的分析——以中部某省会城市为例. 图书馆杂志, 41(06), 108-115.
^*吴越. (2020). 银发人群对在线健康社区的使用意愿研究 (硕士学位论文). 北京外国语大学.
许为. (2017). 再论以用户为中心的设计:新挑战和新机遇. 人类工效学, 23(01), 82-86.
许为. (2019). 四论以用户为中心的设计:以人为中心的人工智能. 应用心理学, 25(04), 291-305.
许为. (2022). 八论以用户为中心的设计:一个智能社会技术系统新框架及人因工程研究展望. 应用心理学, 28(05), 387-401.
^*杨秋霞. (2021). 基于UTAUT模型的老年人健康管理系统接受度的影响因素研究 (硕士学位论文). 甘肃中医药大学, 兰州.
喻婧, 饶俪琳, 雷旭. (2019). 越老越风险规避吗?——年龄对冲动性预测决策行为的调节效应. 心理科学, 42(06), 1382-1388.
^*张冬妮. (2013). 基于科技接受模型理论的老年居家健康管理系统接受度及其影响因素的调查研究 (硕士学位论文). 南方医科大学, 广州.
^*张金凤, 刘萍萍, 赵伯尧, 韩布新. (2022). 老年人移动支付接受行为的影响因素及其机制研究. 人类工效学, 28(03), 56-61+67.
张力元, 张宝山, 陈璐. (2015). 老年人行为决策:领域现状与挑战. 心理科学进展, 23(5), 858-870.
张亚利, 李森, 俞国良. (2019). 自尊与社交焦虑的关系:基于中国学生群体的元分析. 心理科学进展, 27(06), 1005-1018.
^*张岩. (2020). 老年特质和感知风险对老年人采纳智能家居的影响研究 (硕士学位论文). 大连理工大学.
^*赵一霖, 何建佳. (2022). 后疫情时代老年群体数字化学习行为的影响因素研究. 改革与开放,25(05), 41-54.
^*甄云飞. (2021). 老年人对“虚拟养老院”的使用意愿驱动力研究 (硕士学位论文). 哈尔滨商业大学.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

[1]	赵沐恩, 韩布新, 王大华. 老年人身体健康与依恋长期性变化的联系:六年的追踪研究[J]. 心理发展与教育, 2025, 41(5): 663-674.
[2]	牛更枫, 金斯妤, 史晓涵, 杨文城, 周宗奎. 跨越年龄数字鸿沟:老年人互联网使用行为及其促进和阻碍因素[J]. 心理发展与教育, 2025, 41(5): 740-749.
[3]	王彦姣, 黄家桢, 龙嘉, 王沛. 领域一般和领域特殊能力与计算障碍的内在联系及其启示——基于元分析视角[J]. 心理发展与教育, 2025, 41(4): 473-481.
[4]	李莉, 李倩倩, 阮世芳, 张云运. 师幼互动质量与儿童发展的领域内和跨领域关联——基于国内外45项实证研究的元分析[J]. 心理发展与教育, 2025, 41(3): 332-347.
[5]	艾美杉, 刘玺, 王大华. 失独父母的社区支持现状及其与心理健康的关系[J]. 心理发展与教育, 2024, 40(5): 720-728.
[6]	刘啸莳, 曹中平, 俞俊杰, 莫碧波, 吴芸. 受教育水平能促进农村中老年人的身体健康吗？——安全感和年龄的作用[J]. 心理发展与教育, 2024, 40(5): 729-736.
[7]	余林伟, 李爽, 刘燊, 潘文沛, 徐强, 张林. 老年人模糊表情识别的积极效应及其机制[J]. 心理发展与教育, 2024, 40(2): 196-206.
[8]	谢和平, 王燕青, 王福兴, 周宗奎, 邓素娥, 段朝辉. 记忆的生成绘图效应及其边界条件：一项元分析[J]. 心理发展与教育, 2024, 40(1): 29-43.
[9]	张弘弛, 毛伟宾, 崔慧园, 安姝, 李庆元. 价值对老年人项目记忆和联结记忆的影响[J]. 心理发展与教育, 2023, 39(5): 625-634.
[10]	牛湘, 冉光明. 同伴关系与幼儿问题行为关系的三水平元分析[J]. 心理发展与教育, 2023, 39(4): 473-487.
[11]	程阳春, 黄瑾. 近似数量系统与数学能力的关系:一项元分析[J]. 心理发展与教育, 2023, 39(3): 379-390.
[12]	林瑶, 张宝山, 徐冉, 张何雅婷. 老年人睡眠质量与家人沟通质量的关系及其影响机制[J]. 心理发展与教育, 2023, 39(2): 200-209.
[13]	高峰, 白学军, 章鹏, 曹海波. 中国青少年父母教养方式与自杀意念的元分析[J]. 心理发展与教育, 2023, 39(1): 97-108.
[14]	刘钰, 苏彦捷, 曹春梅, 丁宇, 樊煜. 社区老年人执行功能和加工速度与运动能力的关系[J]. 心理发展与教育, 2022, 38(6): 769-777.
[15]	谢云天, 史滋福, 尹霖, 兰洛. 中国父母教养方式与儿童学业成绩关系的元分析[J]. 心理发展与教育, 2022, 38(3): 366-379.