Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
An innovative data mining-driven optimisation modelling approach based on TAM for the design of elderly-centric ICT products
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 24 January 2026

An innovative data mining-driven optimisation modelling approach based on TAM for the design of elderly-centric ICT products

  • Yu Cao1,
  • Xiaogang Yang1,
  • Shijian Luo2 &
  • …
  • Kok-Hoong Wong1 

Scientific Reports , Article number:  (2026) Cite this article

  • 538 Accesses

  • 1 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

To enhance the quality of life and health of the elderly, it is necessary to incorporate their specific needs into the design and manufacturing processes of Information and Communication Technology (ICT) products. However, these needs are often overlooked in product development, leading to poor user experiences and low adoption rates. To address this important issue, this study innovatively proposes a data mining-driven optimisation modelling approach. This approach first extracts key design elements from online reviews of elderly ICT products through text mining techniques such as data purification and Latent Dirichlet Allocation (LDA). These elements are then incorporated as external variables to construct a user needs integrated technology acceptance model (UN-TAM) for the elderly, which is validated using Structural Equation Modelling (SEM). Using the elderly smartwatch as a case study, four design optimisation parameters, functional architecture, human-computer interface, morphological aesthetics, and interaction mode, were extracted from a substantial database based on the elderly user comments. The proposed model establishes a quantitative mapping relationship among “design parameters, user perception, and behavioral intention”. The results indicate that these design parameters significantly influence the product acceptance through three distinct pathways: perceived ease of use (γ = 0.641, p < 0.001), perceived usefulness (γ = 0.601, p < 0.001), and perceived enjoyment (γ = 0.402, p < 0.001). Specifically, functional architecture of the elderly-centric ICT products has the most substantial impact on perceived usefulness while the other three parameters predominantly influence perceived ease of use. The proposed data mining-driven optimisation modelling approach can be effectively used in designing and manufacturing elderly-centric ICT products.

Similar content being viewed by others

An investigation into the acceptance of intelligent care systems: an extended technology acceptance model (TAM)

Article Open access 23 May 2025

A smart community interactive art therapy platform based on multimodal computer graphics and resilient artificial intelligence for home-based elderly care

Article Open access 03 December 2025

Technological monitoring of motor parameters to assess multidimensional frailty of older people in the PRO-HOME project

Article Open access 04 December 2024

Data availability

Data will be made available from the corresponding author (Prof. Xiaogang Yang) or the first author (Yu Cao) upon reasonable request.

References

  1. Cota, T. T., Ishitani, L. & Vieira, N. Mobile game design for the elderly: A study with focus on the motivation to play. Comput. Hum. Behav. 51, 96–105 (2015).

    Google Scholar 

  2. Fang, Y. M. et al. A new smart wearable device design based on the study of the elderly’s mental perception and reading usability. In HCI International 2014 - Posters’ Extended Abstracts (ed. Stephanidis, C.). Vol. 435. 288–293 (Springer International Publishing, 2014).

    Google Scholar 

  3. Ghorayeb, A., Comber, R. & Gooberman-Hill, R. Older adults’ perspectives of smart home technology: Are we developing the technology that older people want? Int. J. Hum. -Comput Stud. 147, 102571 (2021).

    Google Scholar 

  4. Fang, Y., Chau, A. K. C., Wong, A., Fung, H. H. & Woo, J. Information and communicative technology use enhances psychological well-being of older adults: The roles of age, social connectedness, and frailty status. Aging Ment Health. 22, 1516–1524 (2018).

    Google Scholar 

  5. Mshali, H., Lemlouma, T., Moloney, M. & Magoni, D. A survey on health monitoring systems for health smart homes. Int. J. Ind. Ergon. 66, 26–56 (2018).

    Google Scholar 

  6. Huh, J. H. & Kyungryong, S. Design and implementation of the basic technology for solitary senior citizen’s lonely death monitor Ing system using PLC. J. Korea Multimed Soc. 18, 742–752 (2015).

    Google Scholar 

  7. Kanimozhi, R., Padmavathi, V. & Ramesh, P. S. Perceived digital threats influencing smartphone use among the aging population. Sci. Rep. 15, 27813 (2025).

    Google Scholar 

  8. Castilla, D. et al. Teaching digital literacy skills to the elderly using a social network with linear navigation: A case study in a rural area. Int. J. Hum. -Comput Stud. 118, 24–37 (2018).

    Google Scholar 

  9. Hsieh, M. H., Ho, C. H. & Lee, I. C. Effects of smartphone numeric keypad designs on performance and satisfaction of elderly users. Int. J. Ind. Ergon. 87, 103236 (2022).

    Google Scholar 

  10. De Angeli, A., Jovanović, M., McNeill, A. & Coventry, L. Desires for active ageing technology. Int. J. Hum. -Comput Stud. 138, 102412 (2020).

    Google Scholar 

  11. Iancu, I. & Iancu, B. Designing mobile technology for elderly. A theoretical overview. Technol. Forecast. Soc. Change. 155, 119977 (2020).

    Google Scholar 

  12. Guner, H. & Acarturk, C. The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults. Univers. Access. Inf. Soc. 19, 311–330 (2020).

    Google Scholar 

  13. Davis, F. D. Perceived usefulness perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989).

  14. Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. User acceptance of computer technology: A comparison of two theoretical models. Manag Sci. 35, 982–1003 (1989).

    Google Scholar 

  15. Marangunić, N. & Granić, A. Technology acceptance model: A literature review from 1986 to 2013. Univers. Access. Inf. Soc. 14, 81–95 (2015).

    Google Scholar 

  16. Macedo, I. M. Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput. Hum. Behav. 75, 935–948 (2017).

    Google Scholar 

  17. Peek, S. T. M. et al. Factors influencing acceptance of technology for aging in place: A systematic review. Int. J. Med. Inf. 83, 235–248 (2014).

    Google Scholar 

  18. Gündüz, N., Zaim, S. & Erzurumlu, Y. Investigating impact of health belief and trust on technology acceptance in smartwatch usage: Turkish senior adults case. Int. J. Pharm. Healthc. Mark. https://doi.org/10.1108/IJPHM-11-2022-0102 (2024).

    Google Scholar 

  19. Shin, H. R. et al. Comprehensive senior technology acceptance model of daily living assistive technology for older adults with frailty: Cross-sectional study. J. Med. Internet Res. 25, e41935 (2023).

    Google Scholar 

  20. Wang, Y. et al. The willingness to continue using wearable devices among the elderly: SEM and FsQCA analysis. BMC Med. Inf. Decis. Mak. 23, 218 (2023).

    Google Scholar 

  21. Wei, W., Gong, X., Li, J., Tian, K. & Xing, K. A study on community older people’s willingness to use smart home—An extended technology acceptance model with intergenerational relationships. Front. Public Health 11 (2023).

  22. Renaud, K. & van Biljon, J. Predicting technology acceptance and adoption by the elderly. Proceedings of the 2008 Annual Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries Rid. Wave Technology (2008).

  23. Cao, X. et al. Factors influencing older adults’ acceptance of voice assistants. Front. Psychol. 15 (2024).

  24. Harris, M. T. & Rogers, W. A. Developing a healthcare technology acceptance model (H-TAM) for older adults with hypertension. Ageing Soc. 43, 814–834 (2023).

    Google Scholar 

  25. Rodeschini, G. Gerotechnology: A new kind of care for aging? An analysis of the relationship between older people and technology. Nurs. Health Sci. 13, 521–528 (2011).

    Google Scholar 

  26. Dodd, C., Athauda, R. & Adam, M. Designing User Interfaces for the Elderly: A Systematic Literature Review. (2017).

  27. Chen, K. & Chan, A. H. Use or non-use of gerontechnology—A qualitative study. Int. J. Environ. Res. Public. Health. 10, 4645–4666 (2013).

    Google Scholar 

  28. Kuerbis, A., Mulliken, A., Muench, F., Moore, A. & Gardner, D. Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health. Ment Health Addict. Res 2 (2017).

  29. Amado, A., Cortez, P., Rita, P. & Moro, S. Research trends on big data in marketing: A text mining and topic modeling based literature analysis. Eur. Res. Manag Bus. Econ. 24, 1–7 (2018).

    Google Scholar 

  30. Loureiro, S. M. C., Guerreiro, J., Eloy, S., Langaro, D. & Panchapakesan, P. Understanding the use of virtual reality in marketing: A text mining-based review. J. Bus. Res. 100, 514–530 (2019).

    Google Scholar 

  31. Li, L., Yang, X., Liu, S. & Deng, F. AI big model and text mining-driven framework for urban greening policy analysis. Sci. Rep. 15, 29587 (2025).

    Google Scholar 

  32. Pan, X. & Xue, Y. Advancements of artificial intelligence techniques in the realm about library and information subject—A case survey of latent dirichlet allocation method. IEEE Access. 11, 132627–132640 (2023).

    Google Scholar 

  33. Wang, Z., Wang, Y., Zeng, Y., Su, J. & Li, Z. An investigation into the acceptance of intelligent care systems: An extended technology acceptance model (TAM). Sci. Rep. 15, 17912 (2025).

    Google Scholar 

  34. Venkatesh, V. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11, 342–365 (2000).

    Google Scholar 

  35. Bargas-Avila, J. & Hornbæk, K. Foci and blind spots in user experience research. Interactions 16, 24–27 (2012).

    Google Scholar 

  36. Kobayashi, M. et al. Elderly user evaluation of mobile touchscreen interactions. In Human-Computer Interaction – INTERACT 2011 (Eds. Campos, P. et al.). 83–99. https://doi.org/10.1007/978-3-642-23774-4_9 (Springer, 2011).

  37. Morris, J. M. User interface design for older adults. Interact. Comput. 6, 373–393 (1994).

    Google Scholar 

  38. Ramírez-Correa, P., Grandón, E. E., Ramírez-Santana, M. & Belmar Órdenes, L. Explaining the use of social network sites as seen by older adults: the enjoyment component of a hedonic information system. Int. J. Environ. Res. Public. Health 16, 1673 (2019).

  39. Yu, J., Kim, S., Hailu, T. B., Park, J. & Han, H. The effects of virtual reality (VR) and augmented reality (AR) on senior tourists’ experiential quality, perceived advantages, perceived enjoyment, and reuse intention. Curr. Issues Tour. 27, 464–478 (2024).

  40. Braun, M. T. Obstacles to social networking website use among older adults. Comput. Hum. Behav. 29, 673–680 (2013).

    Google Scholar 

  41. Lefever, S., Dal, M. & Matthíasdóttir, Á. Online data collection in academic research: Advantages and limitations. Br. J. Educ. Technol. 38, 574–582 (2007).

    Google Scholar 

  42. Riva, G., Teruzzi, T. & Anolli, L. The use of the internet in psychological research: Comparison of online and offline questionnaires. Cyberpsychology Behav. Impact Internet Multimed Virtual Real. Behav. Soc. 6, 73–80 (2003).

    Google Scholar 

  43. National Health Commission of China. Guidelines for Life Sciences and Medical Research Involving Humans. https://www.nhc.gov.cn/qjjys/c100016/202302/6b6e447b3edc4338856c9a652a85f44b.shtml (2023).

  44. Zhang, X., Tang, Q. & Li, S. Modeling behavioral intention of using health-related WeChat official accounts through ELM and SCT factors using the PLS-SEM approach. Sci. Rep. 15, 27475 (2025).

    Google Scholar 

  45. Nascimento, J. M. R. S., Bispo, L. G. M. & da Silva, J. M. N. Risk factors for work-related musculoskeletal disorders among workers in Brazil: A structural equation model approach. Int. J. Ind. Ergon. 99, 103551 (2024).

  46. Li, J., Ma, Q., Chan, A. H. & Man, S. S. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl. Ergon. 75, 162–169 (2019).

    Google Scholar 

  47. Park, H. K., Chung, J. & Ha, J. Acceptance of technology related to healthcare among older Korean adults in rural areas: A mixed-method study. Technol. Soc. 72, 102182 (2023).

    Google Scholar 

  48. Reeder, B. & David, A. Health at hand: A systematic review of smart watch uses for health and wellness. J. Biomed. Inf. 63, 269–276 (2016).

    Google Scholar 

  49. Li, H., Gupta, A., Zhang, J. & Flor, N. Who will use augmented reality? An integrated approach based on text analytics and field survey. Eur. J. Oper. Res. 281, 502–516 (2020).

    Google Scholar 

  50. Meidute-Kavaliauskiene, I., Çiğdem, Ş., Yıldız, B. & Davidavicius, S. The effect of perceptions on service robot usage intention: A survey study in the service sector. Sustainability 13, 9655 (2021).

    Google Scholar 

  51. Turel, O. & Serenko, A. The benefits and dangers of enjoyment with social networking websites. Eur. J. Inf. Syst. 21, 512–528 (2012).

    Google Scholar 

  52. Ma, Z., Gao, Q. & Yang, M. Adoption of wearable devices by older people: Changes in use behaviors and user experiences. Int. J. Human–Computer Interact. 39, 964–987 (2023).

    Google Scholar 

  53. El-Gayar, O. & Elnoshokaty, A. Factors and design features influencing the continued use of wearable devices. J. Healthc. Inf. Res. 7, 359–385 (2023).

    Google Scholar 

  54. Jeng, M. Y., Pai, F. Y. & Yeh, T. M. Antecedents for older adults’ intention to use smart health wearable devices-technology anxiety as a moderator. Behav. Sci. Basel Switz. 12, 114 (2022).

    Google Scholar 

  55. Wang, L., Jiang, Y., Chang, D. & Li, Z. Towards age-friendly interactions: Enhancing usability and experience of smart wearable health devices for elderly users. In Cross-Cultural Design (ed. Rau, P.-L. P.). 416–429 (Springer Nature Switzerland, 2025).

  56. Liu, N. & Yu, R. Identifying design feature factors critical to acceptance and usage behavior of smartphones. Comput. Hum. Behav. 70, 131–142 (2017).

    Google Scholar 

Download references

Funding

This work was supported by a DTP-PhD scholarship program between the University of Nottingham Ningbo China and Zhejiang University.

Author information

Authors and Affiliations

  1. Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, 315000, China

    Yu Cao, Xiaogang Yang & Kok-Hoong Wong

  2. Department of Industrial Design, College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Shijian Luo

Authors
  1. Yu Cao
    View author publications

    Search author on:PubMed Google Scholar

  2. Xiaogang Yang
    View author publications

    Search author on:PubMed Google Scholar

  3. Shijian Luo
    View author publications

    Search author on:PubMed Google Scholar

  4. Kok-Hoong Wong
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Yu Cao: Conceptualization, Investigation, Methodology, Data collection and analysis, Validation, Coding, Modelling, Draft writing. Xiaogang Yang: Conceptualization, Methodology, Writing review & editing, Supervision. Shijian Luo: Supervision, Investigation, Writing review & editing. Kok-Hoong Wong: Writing review & editing, Supervision, final correction. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Xiaogang Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical statement

The authors state that the research was conducted according to ethical standards. The questionnaire and methodology for this study were approved by the Human Research Ethics Committee of the University of Nottingham (Research Ethics Checklist: REQ2025080511).

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, Y., Yang, X., Luo, S. et al. An innovative data mining-driven optimisation modelling approach based on TAM for the design of elderly-centric ICT products. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37657-x

Download citation

  • Received: 19 August 2025

  • Accepted: 23 January 2026

  • Published: 24 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-37657-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Design and manufacturing
  • Design optimisation model
  • Elderly-centric products
  • Technology acceptance model
  • Data mining
  • Latent dirichlet allocation
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics