Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

Rapid advances in information technology alongside accelerating population aging make older adults’ acceptance and use of technology a critical topic for enabling participation in an information society. Although technologies such as smart homes, wearables, and telehealth promise safety, independence, and improved access to care, older adults face challenges due to physical, cognitive, experiential, social, and economic factors. Technology acceptance is a major focus of IS research (e.g., TAM, TAM2/3, UTAUT), yet older adults have distinct needs and patterns of acceptance. Existing reviews often qualitatively catalog influencing factors or focus on specific technologies, lacking objectivity, systematization, and a macroscopic view of evolution. This study therefore applies bibliometric methods to systematically map knowledge and trends in older adults’ technology acceptance research from 2013 to 2023 using the Web of Science Core Collection, to provide a comprehensive framework and evidence-based insights for future work. Research questions: RQ1: What are the research dynamics over the past decade? What are the main academic journals and fields? RQ2: How is research productivity distributed across countries, institutions, and authors? RQ3: What are the knowledge base and seminal literature? How have themes progressed? RQ4: What are current hotspots and their evolutionary trajectories? How is research quality distributed?

Prior reviews largely emphasize determinants of acceptance or intention among older adults. Meta-analyses and reviews identified numerous antecedents across technological, psychological, social, personal, cost, behavioral, and environmental categories, and observed a growing emphasis on social and emotional factors. Other works targeted specific technologies, such as wearables for cardiac monitoring, social assistive robots, and mobile nutrition/fitness apps. However, the interdisciplinary nature and complexity of the domain mean qualitative, periodic summaries may lack objectivity and comprehensiveness, offering limited systemic and macroscopic depictions of research trajectories. Given rapid growth in publications over the last decade, a bibliometric approach can uncover structural patterns, dynamics, and evolution with quantitative rigor.

Design: Bibliometric analysis combining quantitative and qualitative approaches using VOSviewer (v1.6.19) and CiteSpace (v6.1.R6) for network construction, visualization, and analysis (co-occurrence, clustering, burst detection, dual-map overlays), supplemented by in-depth literature review. Data source: Web of Science Core Collection (SCIE, SSCI, A&HCI). Search strategy: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR "older adult*") AND TS = ("technology acceptance" OR "user acceptance" OR "consumer acceptance"). Time span: 2013–2023. Document types: Article and Review. Language: English. Search completed on October 27, 2023. Screening: Initial retrieval yielded 764 records. Exclusions: 27 (non-English=4; early access=19; meeting abstract=2; proceeding paper=2). After screening for topic relevance (deviation from topic=237), 500 valid records remained for analysis. PRISMA flow described in the paper. Data standardization: Standardized author and institution names (resolve variants and homonyms), unified journal name variants (and checked for name changes), and cleaned/merged keywords (removed generic terms; merged synonyms; singular/plural unification) to reduce redundancy and improve mapping accuracy. Analytic procedures: Descriptive statistics; trends in publications/citations; disciplinary distribution; dual-map overlay for knowledge flows; journal, country, institution, and author analyses (including collaboration networks); reference co-citation and clustering (with modularity Q and silhouette S); burst detection of references; keyword frequency and centrality; keyword co-occurrence clustering and density; time-zone evolution and burst keywords; strategic diagram (centrality vs. density) for research quality distribution.

Descriptive landscape: 500 articles (2013–2023), authored by 1839 researchers, across 792 institutions and 54 countries, published in 217 journals, citing 21,585 references. Total citations 13,829; annual average citations 1156; average citations per item 27.66; h-index 60. Trends: Publications showed a clear upward trajectory (peak 108 in 2022; 6.75× 2013), with citations peaking in 2022 (3466). Annual publication growth fit a quadratic curve (R²=0.9661), suggesting continued acceleration. Disciplines: 85 WoS categories involved; top by volume included Medical Informatics (75; 15.0%), Health Care Sciences & Services (71; 14.2%), Gerontology (61; 12.2%), Public Environmental & Occupational Health (57; 11.4%), Geriatrics & Gerontology (52; 10.4%), Ergonomics (50), Computer Science Cybernetics (45), Environmental Sciences (39), Computer Science Information Systems (32), Psychology Multidisciplinary (32). Knowledge flow (dual-map overlay): Major cited domains are HEALTH, NURSING, MEDICINE and PSYCHOLOGY, EDUCATION, SOCIAL, with trajectories toward PSYCHOLOGY, EDUCATION, HEALTH; a prominent path from PSYCHOLOGY, EDUCATION, SOCIAL (Z=6.81). Potential emerging areas include MATHEMATICS/SYSTEMS, MOLECULAR BIOLOGY/IMMUNOLOGY, NEUROLOGY/SPORTS/OPHTHALMOLOGY, and MEDICINE/MEDICAL/CLINICAL. Journals: Top outlets by volume together published 27.4% of papers; leading titles by output include Computers in Human Behavior (15; 1449 cites; avg 96.6), Journal of Medical Internet Research (15; 316; 21.07), International Journal of Human-Computer Interaction (15; 149; 9.93). International Journal of Medical Informatics, despite 9 papers, accrued 1904 citations (avg 211.56), highlighting high impact. Countries: Top producers (share of 500): China 111 (22.2%; 2834 cites; h=27), USA 104 (20.8%; 3029; h=32), UK 44 (8.8%), Germany 42 (8.4%), Italy 33 (6.6%), Netherlands 32 (6.4%). Netherlands led average citations (49.5), indicating high research quality. Collaboration: UK most internationally collaborative (19 partners); USA led in h-index. Institutions: City University of Hong Kong (14; 963 cites; h=12) and The University of Hong Kong (9) lead output; Tilburg University (7) had very high average citations (130.14), suggesting exceptional impact. Other notable institutions: Florida State University, RWTH Aachen, University of Waterloo, Fontys University of Applied Sciences. Authors: Core authorship threshold (Price’s Law) set at ≥3; 63 core authors identified. Top by output: Chen, Ke (10; 585 cites; h=7), Ziefle, Martina (9), Rogers, Wendy A (8; 344; h=7). Highest average citations: Peek, Sebastiaan T.M. (183.2), Wouters, Eveline (152.67). Prominent contributions span smartphone, wearables, mHealth, social robots, and model development. Knowledge base (co-citation): Robust clustering (Q=0.8129; mean silhouette S=0.9391) indicates clear structure with 18 clusters, broadly grouped into (1) theoretical model deepening, (2) emerging technology applications, and (3) research methods and evaluation. Highlighted clusters include Smart Home Technology, Social Life, Customer Service, Social Robot Engagement, and Fall Detection System. Seminal references: Highly co-cited works include Peek et al. (2014) on aging-in-place acceptance; Li et al. (2019) smart wearables acceptance; Hoque & Sorwar (2017) mHealth (UTAUT extension); Chen & Chan (2014) STAM; Lee & Coughlin (2015) determinants/barriers; Macedo (2017) UTAUT2; Guner & Acarturk (2020) TAM comparison; Yusif et al. (2016) barriers to assistive technologies; Ma et al. (2016) smartphone acceptance; Mitzner et al. (2019) PRISM trial. Earlier bursts: Mitzner et al. (2010), Wagner et al. (2010), Pan & Jordan-Marsh (2010), Heerink et al. (2010) advanced theory and context for older adults. Keywords: Highest frequency—TAM (92), UTAUT (24); high centrality—Health care (0.14), Gender (0.11), Assistive technology (0.10), Virtual reality (0.10). Hotspot clusters: (1) Influencing factors and digital inclusion/digital divide; (2) Human-robot interaction and health monitoring (including dementia/MCI), usability/trust/UX, exergames/VR; (3) Mobile health management via apps, smartphones, wearables and telehealth; (4) Aging-in-place technologies (ambient assisted living, smart homes, eHealth) and qualitative/SEM methods. Burst keywords in recent years include ease, validation, perceived risk, human-robot interaction. Evolution and quality distribution: The field evolved from foundational acceptance factors (usefulness, ease, attitudes, internet/computer use) to quality of life and health management, then to cutting-edge technologies (VR, telehealth, HRI) and model deepening/validation. Strategic diagram shows mature and central themes: Usage Experience and Assisted Living Technology (high centrality, high density). Independent yet mature themes: Smart Devices, Theoretical Models, Mobile Health Applications (high density, lower centrality). Emerging/less mature: Human-Robot Interaction, Characteristics of the Elderly, Research Methods (low density, low centrality). High-centrality but underdeveloped cores with strong cross-links: Digital Healthcare Technology, Psychological Factors, Socio-Cultural Factors.

The findings address RQ1 by evidencing a rapidly expanding, highly interdisciplinary field with strong growth in publications and citations, concentrated in medical/health and HCI-related domains and leading journals with substantial impact. For RQ2, outputs are led by China and the USA, with developed countries overall contributing the majority and the Netherlands exhibiting high citation impact; collaborative networks show strong ties among major producers and extensive UK international collaboration. Institutionally, City University of Hong Kong and The University of Hong Kong are prominent, while Tilburg University demonstrates exceptional impact; key authors (e.g., Chen, Rogers, Peek, Wouters, Ziefle) shape the field’s trajectory. For RQ3, co-citation structures reveal a coherent knowledge base spanning theoretical model development (e.g., STAM, UTAUT variations), emerging technology applications (smart homes, wearables, robots), and methodological innovations (mixed methods, SEM, neural networks). Seminal works depict a transition from model refinement and factor analysis to empirical studies of personal factors and novel technologies, integrating contextual and psychosocial variables (trust, privacy, risk) relevant to older adults’ realities. For RQ4, keyword clusters and bursts show hotspots in factors influencing adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, with a shift toward user experience and real-world applications. Strategic mapping clarifies thematic maturity and interdependencies, signaling where integration and development are needed. Collectively, the results underscore the importance of tailoring acceptance models to aging-related capabilities and contexts, designing usable, trustworthy solutions, and evaluating long-term use and outcomes. They validate the field’s significance for health, independence, and social participation, guiding designers, clinicians, and policymakers toward evidence-based strategies.

This bibliometric study maps the knowledge structure and evolution of older adults’ technology acceptance (2013–2023), providing a comprehensive overview of dynamics, contributors, foundational literature, hotspots, and thematic maturity. Key contributions include: (1) documenting rapid growth and strong interdisciplinarity with leading influence from medical/health and HCI outlets; (2) identifying leading countries (China, USA), high-impact producers (Netherlands), collaborative patterns (UK), and influential institutions/authors; (3) clarifying a knowledge base anchored in model deepening, emerging technology applications, and methodological advancement, with seminal works charting a shift from theoretical/factor analyses to empirical studies targeting individual factors and new technologies; (4) highlighting hotspots in adoption determinants, human-robot interaction, mobile health, and aging-in-place, along with thematic maturity and integration opportunities. Future research directions: (a) refine and validate older adult–specific acceptance models that incorporate demographic, cultural, and capability variations, and evaluate fit to specific technologies (e.g., wearables, mHealth), leveraging interdisciplinary collaboration; (b) investigate long-term use trajectories and impacts on quality of life, social participation, and mental health through longitudinal and mixed-methods designs; (c) foreground user experience evaluation and age-appropriate design to enhance usability, trust, and sustained engagement, integrating iterative feedback from older adults. These agendas will strengthen theory, inform design and policy, and support effective, equitable technology integration for aging populations.

The dataset is limited to the Web of Science Core Collection and English-language publications, potentially omitting relevant studies indexed elsewhere (e.g., PubMed, Scopus, Google Scholar) or in other languages, which may constrain global representativeness. The search strategy did not fully capture the rapidly expanding AI-in-eldercare literature (e.g., trust, privacy, ethics), potentially underrepresenting this emerging area. Future work should expand to multiple databases, include multilingual sources, and explicitly target AI-related acceptance to provide a more comprehensive landscape.