research excellence framework 2021

Research Excellence Framework

Securing a world-class, dynamic and responsive research base across the full academic spectrum within UK higher education

The REF is the UK’s system for assessing the quality of research in UK higher education institutions. It first took place in 2014 and 2021 . The next exercise is planned for 2029.

What is the REF’s purpose?

• Inform the allocation of block-grant research funding to HEIs based on research quality
• Provide accountability for public investment in research and produce evidence of the benefits of this investment
• Provide insights into the health of research in HEIs in the UK

The Research Excellence Framework (REF) is the UK’s system for assessing the excellence of research in UK higher education institutions (HEIs). The REF outcomes are used to inform the allocation of around £2 billion per year of public funding for universities’ research. The REF is a process of expert review, carried out by sub-panels focused on subject-based units of assessment (UoAs), under the guidance of overarching main panels and advisory panels. For the purposes of the REF, research is defined as a process of investigation leading to new insights, effectively shared.

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REF 2029 main panel chair recruitment

We are pleased to announce that you can now apply to become a chair for the REF 2029 main panels.   We welcome and encourage applications from diverse groups. There are four main panels and we are seeking to appoint sector leaders in the broad areas of:  Find out more about the role requirements and how…

Update to the HESA Staff record 2024/25 collection for REF volume measure pilot year

As published in the Initial Decisions, for REF 2029 institutions will submit outputs, not staff, to the assessment exercise. The funding bodies have highlighted these changes today to vice chancellors and principals. You can see a copy of the circular letter issued by Research England on their website. Following a working group with the Higher…

Early decisions made on REF 2029 Open Access Policy 

The REF 2029 Open Access Policy will be published later this year. Today we are setting out some early policy decisions relating to implementation dates for journal articles, conference proceedings and longform outputs. These are also detailed on our publications page.   The REF Steering Group considered responses to the REF 2029 Open Access Consultation and…

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Introduction to the ref results, view ref results, view analysis of the ref results, submitted outputs' details, impact case study database, environment database, download the results, download submissions.

How Research England supports research excellence

Research excellence framework.

The Research Excellence Framework (REF) is the UK’s system for assessing the excellence of research in UK higher education providers (HEPs).

The REF outcomes are used to inform the allocation of around £2 billion per year of public funding for universities’ research.

The REF was first carried out in 2014, replacing the previous Research Assessment Exercise. Research England manages the REF on behalf of all the four UK higher education funding bodies:

• Research England
• Scottish Funding Council
• Higher Education Funding Council for Wales
• Department for the Economy, Northern Ireland.

The funding bodies’ shared policy aim for research assessment is to secure the continuation of a world-class, dynamic and responsive research base across the full academic spectrum within UK higher education.

REF objectives

The REF objectives are to:

• provide accountability for public investment in research and produce evidence of the benefits of this investment
• provide benchmarking information and establish reputational yardsticks, for use in the higher education sector and for public information
• inform the selective allocation of funding for research.

Find out more on the REF website.

REF progress update: November 2023

REF progress update: December 2023

Last updated: 8 April 2024

This is the website for UKRI: our seven research councils, Research England and Innovate UK. Let us know if you have feedback or would like to help improve our online products and services .

Oxford’s REF 2021 results show largest volume of world-leading research

The Research Excellence Framework (REF) assesses the quality of research in UK Higher Education Institutions

The UK Funding Bodies have published the outcomes of the recent national research assessment exercise, the Research Excellence Framework (REF) 2021 . The REF 2021 results show Oxford’s submission had the highest volume of world­­­-leading research*.

The REF 2021 results demonstrate once again that Oxford is a research powerhouse, and the impact case studies highlight our effectiveness in putting this research in service to society by making critical contributions to global health, economic prosperity and cultural life. Professor Louise Richardson, Vice-Chancellor of the University of Oxford

The University of Oxford made the largest submission of any Higher Education Institution (HEI) in the UK, submitting over 3,600 researchers (3,405 full time equivalent) into 29 subject areas, over 8,500 research outputs in a range of formats from journal articles to compositions, and 220 case studies about the impact of Oxford research beyond academia.

Professor Louise Richardson, Vice-Chancellor at the University of Oxford, said:

'The REF 2021 results demonstrate once again that Oxford is a research powerhouse, and the impact case studies highlight our effectiveness in putting this research in service to society by making critical contributions to global health, economic prosperity and cultural life.'

'The REF is one of the few opportunities to see the remarkable breadth of our research and to draw together all parts of the collegiate University in a single collaborative effort, and I would like to thank everyone involved for their contribution.'

Professor Patrick Grant, Pro-Vice Chancellor for Research at the University of Oxford said:

We are pleased to have submitted work from researchers at a variety of career stages and from across the Collegiate university, thus giving us the opportunity to showcase the depth and breadth of our research. Our submission also shows how through our collaborations with external partners our research is used to benefit society, across the UK and around the world. Professor Patrick Grant, Pro-Vice Chancellor for Research at the University of Oxford

'The publication of the REF 2021 results marks the culmination of the work done by staff across the University, especially in the past two years or so. Thank you to everyone involved in the submission — both those who, in a variety of ways, led and supported our research and impact activities, and also the many colleagues who coordinated the submission itself.

'We are pleased to have submitted work from researchers at a variety of career stages and from across the Collegiate university, thus giving us the opportunity to showcase the depth and breadth of our research. Our submission also shows how through our collaborations with external partners our research is used to benefit society, across the UK and around the world.'

Highlights of the submission can be found on the Oxford REF 2021 webpages .

*Largest volume of world-leading research is calculated from the sum of (overall %4* x submitted FTE) across all submissions

FURTHER INFORMATION

• Oxford 2021 REF highlights
• 2021 REF outcomes

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Research Excellence Framework (REF)

The 2021 Research Excellence Framework (REF) confirm the outstanding quality and impact of our research in the Arts, Humanities and Social Sciences.

The 2021 Research Excellence Framework (REF) confirmed the outstanding quality and impact of our research in the Arts, Humanities and Social Sciences, and our collegial, supportive and inclusive research culture.

Overall, the University came fourth in the UK for the breadth and quality of our research (based on the Research Professional ranking). Within the submission, our College returned 1,130 staff across 20 Units of Assessment. 86% of our submission was assessed as being world-leading or internationally excellent. Of the 20 Units we led or joint led, two came first in the UK for breadth and quality (Sociology, and Anthropology & Development Studies). Thirteen were ranked in the top five.

We achieved a remarkable 100% for our research environment score for 3 and 4 star research. This part of the assessment measures the ‘vitality and sustainability’ of the Schools and research centres in which research and impact is supported.  

Our impact case studies spanned an extraordinary spectrum of the benefits our research makes to society in Scotland and internationally, helping tackle pressing challenges such as climate change and healthy ageing as well as enriching and preserving cultural heritage.

By working with partners and contributing expertise to public, private and third sector organisations, we have helped improve policy and practice in key areas such as criminal justice, education, and health; developed leadership, skills and capability in a range of businesses including in the creative industries; and fostered critical reflection and informed debate on our history, politics and identity.  

Within CAHSS we celebrate the contribution all of our staff made to the REF –researchers; technical and professional services staff; our postgraduate and early career researchers, and our students - all contribute to our flourishing environment.

Edinburgh Impact

About the Research Excellence Framework (REF)

The Research Excellence Framework (REF) is the UK’s system for assessing the quality of research in UK higher education institutions, evaluating the quality of our research publications and outputs, our research environment, and the societal and economic impact of our research.

https://www.ref.ac.uk/ 

92% of our research is world-leading or internationally excellent.

Our results in REF 2021 demonstrate the world class quality of our research, our approach, and most importantly, our people.

92% of our research has been assessed to be ‘world leading or internationally excellent’ by a panel of global experts in the Research Excellence Framework (REF2021).

More on our REF results

The thinking behind the research

Read the stories from our researchers about their work to shape the way ahead to a better world.

Research stories

The impact of research

Discover how our researchers are tackling issues and changing lives through research.

Science Social Science Arts

Research highlights

1st for research quality in Chemistry

REF 2021: Life Sciences

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Life Sciences research

Alternatively, use our A–Z index

Research Excellence Framework 2021

The University of Manchester's position as a research powerhouse has been confirmed in the results of the 2021 Research Excellence Framework (REF).

These comprehensive and independent results confirm Manchester's place as a global powerhouse of research. Professor Dame Nancy Rothwell / former President and Vice-Chancellor of The University of Manchester

Key results

• We have retained fifth place for research power 1 .
• Overall, 93% of the University’s research activity was assessed as ‘world-leading’ (4*) or ‘internationally excellent’ (3*).
• We ranked in 10th place in terms of grade point average 2 (an improvement from 19th in the previous exercise, REF 2014).
• The Times Higher Education places us even higher at eighth on GPA (up from 17th place), as their analysis excludes specialist HE institutions.
• In the top three nationally for nine subjects (Unit of Assessment by grade point average or research power).

The Research Excellence Framework (REF) is the system for assessing the quality of research in UK higher education institutions. Manchester made one of the largest and broadest REF submissions in the UK, entering 2,249 eligible researchers across 31 subject areas.

The evaluation encompasses the quality of research impact, the research environment, research publications and other outputs.

REF results

Overall, 93% of the University’s research activity was assessed as ‘world-leading’ (4*) or ‘internationally excellent’ (3*). The evaluation encompasses the quality of research impact (96% 3* or 4*), the research environment (99% 3* or 4*), research publications and other outputs (90% were 3* or 4*).

We ranked in 10th place in terms of grade point average, an improvement from 19th in the previous exercise, REF 2014. The Times Higher Education places us even higher at eighth on GPA (up from 17th place), as their analysis excludes specialist HE institutions. This result was built upon a significant increase in research assessed as ‘world leading’ (4*) between REF 2014 and REF 2021.

The University came in the top three for the following subjects (Unit of Assessment by grade point average or research power):

• Allied Health Professions, Dentistry, Nursing and Pharmacy
• Business and Management Studies
• Drama, Dance, Performing Arts, Film and Screen Studies
• Development Studies
• Engineering

The University had 19 subjects in the top ten overall by grade point average and 15 when measured by research power.

Research impact

Social responsibility underpins research activity at Manchester, and we combine expertise across disciplines to deliver pioneering solutions to the world’s most urgent problems.

We’re ranked as one of the top ten universities in the world for delivering against the UN’s Sustainable Development Goals ( Times Higher Education Impact Rankings) and our research impact showcase includes examples of the positive impact we’ve made across culture and creativity, economic development and inequalities, health and wellbeing, innovation and commercialisation, and sustainability and climate change.

Professor Dame Nancy Rothwell, former President and Vice-Chancellor of The University of Manchester, said: "These comprehensive and independent results confirm Manchester's place as a global powerhouse of research.

“We create an environment where researchers can thrive and exchange ideas. Most importantly the quality and impact of our research is down to the incredible dedication and creativity of our colleagues who work every day to solve significant world problems, enrich our society and train the next generation of researchers.

“The fact that our REF results are accompanied by examples of the real difference we’ve made in the world, all driven from this city makes me very proud.”

Research environment

The REF exercise also evaluated the University’s work to provide a creative, ambitious and supportive research environment , in which researchers at every career stage can develop and thrive as leaders in their chosen field.

In this category, the University achieved a result of 99% ‘internationally excellent’ or ‘world-leading’, making it one of the best places in the country to build a research career.

1  Research power is calculated by grade point average, multiplied by the number of FTE staff submitted (FTE – full-time equivalent head count) and gives a measure of scale and quality. Grade point average (GPA) measures the overall or average quality of research, which takes no account of the FTE submitted.

2  Grade point average is a measure of the overall or average quality of research calculated by multiplying the percentage of research in each grade by its rating, adding them all together and dividing by 100.

REF 2021 results

View the University’s full set of results by unit of assessment.

REF 2021 submissions

View the University’s full list of 160 submissions.

Research impact showcase

Find out how we're solving the world's most urgent problems.

Library item label woz ere --> Centres Research centres and facilities Cross-faculty centres Faculty centres Externally funded centres All research centres   Translational research and innovation Research facilities Centre criteria Impact Impact Research Excellence Framework 2021 Flagship institutes Our priorities Research features Open Research   Coronavirus: our research and innovation Search for a research story Publications - White Rose Research Online Sheffield Player Culture Our research culture Research culture steering board Listening to our research community Our commitments Enhancing Research Culture funding 2023-24 projects 2022-23 projects 2021-22 projects Expertise Expertise Centres and facilities People Nobel Laureates Facilities Partner with us   Clean energy Research degrees PhD study Find a PhD PhD scholarships About PhD research at Sheffield Support for current researchers Connect Connect Partner with us Contact Donate   Research Excellence Framework 2021

The Research Excellence Framework (REF) confirms our place as a world-leading university. The results show our research and impact excellence across a broad range of disciplines and demonstrate that our research is having a significant positive impact on lives across the globe.

The REF assesses the quality and impact of research taking place in UK universities. The results inform the allocation of around £2 billion per year of public funding for universities’ research.

Research that changes lives

The Research Excellence Framework (REF) 2021 results confirm our research is changing lives and shaping the world we live in.

Our vision is to produce the highest quality research to drive intellectual advances and address global challenges. The REF results demonstrate that we are advancing towards this goal. 

The REF is a retrospective exercise looking back over seven years of incredible research at our University. We’re proud of everything that our researchers, and those that support our research environment have achieved. But we’re also excited for the future and the inspiring discoveries we are yet to make.

Our REF results

92 per cent of our research is rated in the highest two categories in the REF 2021, meaning it is classed as world-leading or internationally excellent. The REF results demonstrate our research and impact excellence across a broad range of disciplines and confirm that our research is having a significant positive impact on lives across the globe.

We submitted to 25 different REF units of assessment comprising: 

• All of our 1,690 independent researchers
• 3,684 outputs (outputs can include peer-reviewed journal articles and books, as well as other types of research output such as designs and compositions)
• 114 impact case studies (impact can include examples of improved technologies, public awareness, medical treatments, government policies or structural changes in society locally, nationally and internationally)

View all REF 2021 results

Pushing the boundaries of innovation and technology

Transforming health, health care and social care

Addressing our food, energy and sustainability challenges

Developing solutions to our social and cultural challenges

Research Excellence Framework results 2021

The Research Excellence Framework (REF) is a UK-wide assessment of the quality of research in universities undertaken by expert review panels. 

How did we do in the latest REF exercise?

• We move into the top 10 (joint 10th) of UK universities for research quality, up from 14th position in REF 2014 (based on the Times Higher Education ranking of the Research Excellence Framework). 
• All of our subject areas are now ranked in the top 30 in the UK, with four of our Departments positioned in the top five: Language and Linguistics (1st), Philosophy (2nd), Sociology (4th), Education (5th). A further eight Departments are in the top 10: Archaeology, Biology, Chemistry, Computer Science, English and Related Literature, Health Sciences (including CHE, CRD and HYMS), History of Art and Psychology.
• Particular recognition was given to the way research is supported at York, through facilities, mentoring and career development, and we rose from 19th to 11th place in this category.

Read our statement on our REF 2021 results to find out more about why REF is so significant.

Departmental results

* The REF exercise is a measure of research quality. The rankings within this listing are based on Grade Point Average (GPA) unless otherwise identified. Some rankings are listed excluding single or specialist institutions who returned in fewer than three Units of Assessment. 

Units of assessment

A full breakdown of the results according to the REF units of assessment is shown in the table below. 1

¹ REF is a subject based exercise, and Units of Assessment do not directly map to departments. Figures here are therefore allocated based on the majority contributing department.

• Four star (4*) : World-leading in terms of originality, significance and rigour.
• Three star (3*) : Internationally excellent in terms of originality, significance and rigour but which falls short of the highest standards of excellence.
• Two star (2*) : Recognised internationally in terms of originality, significance and rigour.
• One star (1*)  Recognised nationally in terms of originality, significance and rigour.
• Unclassified (U/C) : Falls below the standard of nationally recognised work. Or work which does not meet the published definition of research for the purposes of this assessment.

Related links

• Read our statement on our REF results
• Research Excellence Framework website

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REF 2021 - Research Excellence Framework

Our results

Our case studies

What is the REF?

Our research is changing the world, to create a sustainable, healthy and socially just future.

We’re impacting lives and addressing global challenges: more than 90% of our research has internationally excellent impact., our research impact has grown more than any other russell group university., we are doing more world-leading research than ever before..

REF 2021 is the national assessment of the strength of UK research. The results show that we at Exeter have increased the proportion of our research which is world-leading by more than 60% and increased the size of our research community by 70%.

•   More than 99% of our research is of international quality, with 89% internationally excellent and 47% world-leading
•   Our world-leading research impact has grown by 72% since 2014, more than any other Russell Group university
•   94% of our research has internationally excellent impact, and 60% has world-leading impact
•   12 of our subjects are in the Top 10 for world-leading impact in their area
•   10 of our disciplines are in the Top 10 for their overall world-leading performance with 18 in the Top 20
•   We are No1 in the UK for world-leading research in Sport Science
•   We are No1 in the UK for world-leading research in Theology and Religious Studies
•   We are No2 in the UK for world-leading research in our Institute of Arab and Islamic Studies (Area Studies)

Read more of the case studies we submitted to REF 2021 >>

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LSE results for the latest Research Excellence Framework (REF)

The REF 2021 results are a great achievement for the School and reflect the incredibly hard work done by colleagues over a number of years.

Professor Susana Mourato, Pro Director for Research

LSE’s outstanding contribution to social science research has once again been recognised by the 2021 Research Excellence Framework (REF) . LSE is shown as the top university (of multiple submissions) in the UK based on the proportion of ‘world-leading’ (4*) research produced. LSE is also the joint second ranking university in the UK overall, when considering research outputs, research impact and research environment.

58 per cent of LSE’s research was judged to be world-leading (4*) and 35 per cent was deemed to be internationally excellent (3*).

A number of LSE departments did particularly well, with their Units of Assessment coming top overall. These are the departments of Economics, Anthropology, Social Policy, Health Policy and Media and Communications. Academics from the Departments of Gender Studies, Methodology and Psychological and Behavioural Science also contributed to these top scoring Units of Assessments.

Institutional percentages show weighted averages for 4* and 3* and weighted GPA.

View full REF results (REF UK) View LSE's full results (LSE only, Tableau access required)

The Research Excellence Framework (REF) is the system by which the UK’s higher education funding bodies assess the quality of research in publicly funded UK higher education institutions (HEIs).  REF 2021 comprised three elements:

academic outputs, comprising a portfolio based on the FTE of REF-eligible staff submitted;

research impact, submitted as a number of impact case studies (ICSs) in proportion to the total FTE of REF-eligible staff submitted;

research environment, comprising the total number of research degrees awarded between 2014 and 2020, total research income received over the same time period, and an environment statement detailing how the submitting unit(s) supported research and impact over the period.

Outputs, impact and environment were weighted 60:25:15 respectively.  All three elements were graded on a scale from 0 (unclassified) to 4* (world leading) and the results were published as quality profiles showing the percentage of outputs, impact and environment considered to meet each of the starred levels.  Submissions were invited to 34 Units of Assessment (UoAs); LSE made 15 submissions to 13 UoAs across the SHAPE subjects.

For REF2021, HEIs were required to submit research outputs by all eligible members of staff.  Each submitted member of staff could submit between one and five outputs, with the total number of outputs per UoA calculated as total FTE of staff multiplied by 2.5.

Staff were eligible for REF2021 where they were on a teaching-and-research or research-only contract of at least 0.2 FTE on 31 July 2020 and had a substantive connection to the submitting HEI.  Research-only staff also had to be classified as independent researchers.  HEIs were also required to identify which eligible staff had significant responsibility for research.  LSE submitted 100% of its staff meeting these definitions, but other HEIs had eligible staff who did not have significant responsibility for research and hence had a submission rate of less than 100%.

View a full glossary of REF terminology .

LSE Impact Case Studies Browse the library of stories about the impact of our research

LSE Research for the World Special REF edition of our online magazine

Lse departments and institutes more information, lse research centres and groups more information.

Research Excellence Framework 2021

A world leading research institution.

Our research and impact

The University of Liverpool has reinforced its place as a world leading research institution in the Research Excellence Framework (REF) 2021. Our sector position for research quality and size has improved across the board, with nine research units achieving a top ten ranking for their outstanding impact. View a summary of results for each unit of assessment and read about some of the ways our research is changing society for the better.

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• Infectious diseases
• Personalised health
• Starting well, living well, ageing well
• Climate futures

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91 % of our research is world leading or internationally excellent

9 of our units are in the top ten for outstanding research impact, 19 th in the uk for our research power.

Our results

Explore the latest results from the Research Excellence Framework 2021 by units of assessment.

Delve into our research case studies and discover the impact of our research.

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REF 2021: Social sciences on the up as arts and humanities shrink

Main panel c records biggest increase in number of submissions and participating staff.

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There has been a big increase in the amount of social sciences research being submitted to the Research Excellence Framework – but there is mounting evidence of the shrinking of arts and humanities departments.

There were 658 submissions to main panel C – social sciences – for the 2021 exercise, by far the largest of any of the four disciplinary areas examined by the REF, and up 44 on the 2014 exercise.

In contrast, the number of submissions to main panel D, which covers the arts and humanities, fell by 28 to 554. A string of departmental closures in this area has been reported across the UK sector in recent years.

Submissions to main panel A (medicine, health and life sciences) held steady at 312, and while there was a reduction in the number of submissions to main panel B (physical sciences, engineering and mathematics), likely reflecting the merging of four engineering units of assessment into one this time around. In total, there were 354 submissions to main panel B.

UK-wide research quality ratings hit new high in expanded assessment Who's up, who’s down? See how your institution performed in REF 2021 Output v impact: where is your institution strongest?

The size of the social sciences in the UK is also reflected in the number of full-time equivalent staff submitted to the REF, with 23,451 now included in main panel C, up 9,038 on 2014. Main panels A and B also reported significant increases – up 6,375 to 19,983, and 5,046 to 18,393, respectively. While main panel D also reported an increase – most likely a reflection of the requirement to submit all research-active staff this time – the rise was much smaller, 3,612, taking the total to 14,305.

All the main panels reported a rise in mean grade point average (GPA). Main panel B led the way on 3.33 (up 0.26), ahead of main panel A, on 3.29 – although its increase of 0.14 was the smallest of the four.

Mean GPA stood at 3.2 for main panel D (up 0.25) and at 3.14 for main panel C (up 0.24).

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Full REF 2021 results by subject

Clinical medicine Public health, health services and primary care Allied health professions, dentistry, nursing and pharmacy Psychology, psychiatry and neuroscience Biological sciences Agriculture, food and veterinary sciences

Earth systems and environmental sciences Chemistry Physics Mathematical sciences Computer science and informatics Engineering

Architecture, built environment and planning Geography and environmental studies Archaeology Economics and econometrics Business and management studies Law Politics and international studies Social work and social policy Sociology Anthropology and development studies Education Sport and exercise sciences, leisure and tourism

Area studies Modern languages and linguistics English language and literature History Classics Philosophy Theology and religious studies Art and design: history, practice and theory Music, drama, dance, performing arts, film and screen studies Communication, cultural and media studies, library and information management

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Study reveals impact of research assessments on UK legal publishing

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A comprehensive new study has unveiled how the UK's Research Assessment Exercises (RAE) and Research Excellence Framework (REF) have significantly influenced legal academic publishing over the past 30 years. Conducted by esteemed legal scholars Professor Johanna Gibson of Queen Mary University of London and Professor Phillip Johnson of Cardiff University, and Queen Mary alumnus, the research provides an unprecedented analysis of over 30,000 research outputs written between 1990 and 2021.

The full study is published in The Modern Law Review , titled "Thirty Years of Legal Research: An Empirical Analysis of Outputs Submitted to RAE and REF (1990–2021)."

The study found that authorship of textbooks and practitioner texts once seen as prestigious have long lost their sheen. And despite a higher proportion of monographs generally correlating to success in exercises, there appears to have been a strategic shift by academics and institutions to prioritize articles over monographs.

Submissions have become heavily concentrated in a select group of prestigious journals, notably The Modern Law Review , Legal Studies , the Oxford Journal of Legal Studies and the Journal of Law & Society . These four journals alone accounted for over 10% of all journal submissions since 2001, and over 50% of journal submissions come from less than 7% of all journal titles in the assessments.

The pressure to publish in top-tier generalist journals has steered academics towards research favored by these publications, potentially narrowing the scope and diversity of legal scholarship and damaging specialist legal scholarship.

The competitive landscape and emphasis on publishing in top-tier journals present significant challenges for early-career academics, who may face barriers to entry and increased pressure to conform to established research norms.

The findings raise concerns about the potential suppression of innovative and interdisciplinary research , as scholars may avoid unconventional topics or formats that are less likely to score highly in assessment exercises.

Professor Johanna Gibson, Herchel Smith Professor of Intellectual Property Law commented, "Our analysis reveals that the structures put in place to assess research quality are profoundly shaping not just where, but also what and how legal academics publish. While aiming to promote excellence, these mechanisms may inadvertently be limiting the richness and diversity of legal scholarship."

Professor Phillip Johnson, Professor of Law added, "There is a very strong link between academic publishing behavior and the research exercises, and I think it is important that institutions implement strategies to achieve a balance between rigorous assessment and a diverse, innovative research environment which encourages all forms of legal scholarship."

Recommendations include:

• Re-evaluating assessment criteria: The study suggests a need for research exercises to positively encourage the dissemination of research in a wider range of publication outlets thereby encouraging more diverse and exploratory scholarship.
• Supporting publication diversity: Institutions should consider initiatives that support and recognize the importance of long form scholarship and the critical contribution to scholarship made by specialist journals.
• Mentorship and resources for early-career academics: Enhanced support systems can help emerging scholars navigate the complex publishing landscape and so bring out their novel insights.

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• Published: 31 August 2024

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

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: 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”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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School of Art and Design, Shaanxi University of Science and Technology, Xi’an, China

Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

Chengliang Wang

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Conceptualization, XS, YW, CW; methodology, XS, ZL, CG, CW; software, XS, CG, YW; writing-original draft preparation, XS, CW; writing-review and editing, XS, CG, ZH, CW; supervision, ZL, ZH, CW; project administration, ZL, ZH, CW; funding acquisition, XS, CG. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

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Correspondence to Chengliang Wang .

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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Received : 20 June 2024

Accepted : 21 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1057/s41599-024-03658-2

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