The convergence of rapid technological advancement and a globally aging population presents both opportunities and challenges. Emerging technologies offer significant potential to improve the health, independence, and quality of life for older adults, impacting areas such as residential safety (smart homes), independent living (wearables), and healthcare access (telehealth). However, successful technology adoption hinges on user acceptance and usage intentions, which are particularly complex among older adults due to physical, cognitive, and socio-economic factors. While existing research on older adults' technology acceptance is fragmented, this study addresses this gap by employing bibliometric methods to provide a systematic overview of the field's evolution and key themes from 2013 to 2023. The study aims to identify research dynamics, leading contributors, knowledge bases, seminal literature, current hotspots, and future research directions in the field.

Prior literature reviews on older adults' technology acceptance have primarily focused on identifying factors influencing technology use, often categorized into technological, psychological, social, personal, cost, behavioral, and environmental aspects. These reviews, largely qualitative, lack the comprehensive and objective perspective afforded by bibliometric analysis. Existing studies have analyzed determinants of technology adoption, identified shifts toward social and emotional factors, and investigated specific technologies (e.g., wearables, social robots, mHealth apps). However, a systematic, large-scale quantitative analysis of research trends and patterns within the field has been missing. This study uses bibliometrics to address this gap and provide a macroscopic view of the research landscape.

This study employed a bibliometric approach using the Web of Science Core Collection (SCIE, SSCI, A&HCI) to identify English-language articles and reviews published between 2013 and 2023 on older adults’ technology acceptance. The search terms combined keywords related to older adults and technology acceptance. After screening for relevance (resulting in a final sample of 500 articles), the data underwent standardization to unify author names, institutional names, and keywords. Data analysis was conducted using VOSviewer and CiteSpace software. VOSviewer was used for creating visual maps of co-occurrence networks, identifying keywords and clusters. CiteSpace was employed for analysis of citation patterns, identifying influential papers, and mapping knowledge flow among different disciplines and journals. Various bibliometric methods were utilized, including co-occurrence analysis, clustering, and burst detection, to analyze publication trends, country and institutional collaborations, research themes, seminal literature, research hotspots, and quality distribution.

The bibliometric analysis revealed significant growth in publications and citations related to older adults' technology acceptance over the past decade. China and the USA emerged as leading countries in terms of publication volume, while the Netherlands showed a high average citation rate, suggesting high-quality research. Key journals included Computers in Human Behavior, Journal of Medical Internet Research, and International Journal of Human-Computer Interaction. Analysis of co-citation networks identified three major thematic clusters within the field: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature highlighted research focusing on specific technologies (e.g., smartphones, mHealth), theoretical model development (e.g., STAM, UTAUT adaptations), factors influencing technology adoption (e.g., perceived usefulness, ease of use), and research methodologies. Burst detection revealed that research shifted from early theoretical focus toward empirical studies on individual factors and emerging technologies. Current research hotspots included factors influencing technology adoption, human-robot interaction, mobile health, and aging-in-place technologies. A strategic diagram, analyzing keyword centrality and density, showed that 'Usage Experience' and 'Assisted Living Technology' were mature and central themes, while other areas like 'Smart Devices', 'Theoretical Models', and 'Mobile Health Applications' showed potential for future development.

The findings address the research questions by providing a comprehensive overview of the research landscape in older adults’ technology acceptance. The significant growth in publications and the emergence of specific research hotspots underscore the increasing importance of this field. The identification of leading countries, institutions, and authors helps understand the distribution of research strength and expertise globally. The evolution of research themes, from theoretical modeling to empirical studies of emerging technologies, highlights the dynamic nature of the field and its adaptation to technological advancements. The study's insights into the knowledge base, seminal literature, and current hotspots offer valuable guidance for future research directions. The findings underscore the interdisciplinary nature of the field, requiring collaborations across disciplines like gerontology, computer science, and health sciences. The identification of mature and emerging research areas can guide future research investment and collaboration strategies.

This study provides a comprehensive bibliometric analysis of research on older adults' technology acceptance, revealing its growth, key themes, influential contributors, and future directions. The field is marked by increasing interdisciplinary collaboration, and a shift towards empirical investigations of emerging technologies. Future research should focus on refining theoretical models specifically for older adults, exploring long-term technology use and its impact on quality of life, and prioritizing user experience evaluation in technology design. The detailed knowledge mapping provided offers a valuable resource for researchers and practitioners aiming to improve technology acceptance and usage among older adults.

This study's reliance on the Web of Science Core Collection may have resulted in the exclusion of relevant literature from other databases (PubMed, Scopus, Google Scholar). The focus on English-language publications limits the inclusion of studies from non-English speaking countries, potentially affecting the global representativeness of the findings. Furthermore, the study did not comprehensively address the rapidly expanding field of AI applications in elder care, due to limitations in the search terms employed. Future research should consider these limitations by expanding data sources and incorporating a wider range of publications.