Alvesson , M. and Kärreman , D. ( 2007 ), “ Constructing mystery: empirical matters in theory development ”, Academy of Management Review , Vol. 32 No. 4 , pp. 1265 - 1281 .
Alvesson , M. and Kärreman , D. ( 2011 ), Qualitative Research and Theory Development: Mystery as Method , Sage , London .
Alvesson , M. and Sandberg , J. ( 2013 ), Constructing Research Questions Doing Interesting Research , Sage , London .
Alvesson , M. and Sandberg , J. ( 2021 ), “ Pre-understanding: an interpretation-enhancer and horizon-expander in research ”, Organization Studies , doi: 10.1177/0170840621994507 .
Alvesson , M. and Sveningsson , S. ( 2011 ), “ Management is the solution: now what was the problem? On the fragile basis for managerialism ”, Scandinavian Journal of Management , Vol. 27 No. 4 , pp. 349 - 361 .
Andersson , R. ( 2019 ), “ Employee communication responsibility: its antecedents and implications for strategic communication management ”, International Journal of Strategic Communication , Vol. 13 No. 1 , pp. 60 - 75 .
Ansell , C. , Boin , A. and Keller , A. ( 2010 ), “ Managing transboundary crises: identifying the building blocks of an effective response system ”, Journal of Contingencies and Crisis Management , Vol. 18 No. 4 , pp. 195 - 207 .
Bechler , C. ( 2004 ), “ Reframing the organizational exigency: taking a new approach in crisis research ”, in Millar , D.P. and Heath , R.L. (Eds), Responding to Crisis: A Rhetorical Approach to Crisis Communication , Lawrence Erlbaum Associates , London , pp. 63 - 74 .
Berger , P.L. and Luckmann , T. ( 1966 ), The Social Construction of Reality: A Treatise in the Sociology of Knowledge , Doubleday , New York .
Boin , A. ( 2019 ), “ The transboundary crisis: why we are unprepared and the road ahead ”, Journal of Contingencies and Crisis Management , Vol. 27 No. 1 , pp. 94 - 99 .
Boin , A. and Lagadec , P. ( 2000 ), “ Preparing for the future: critical challenges in crisis management ”, Journal of Contingencies and Crisis Management , Vol. 8 No. 4 , pp. 185 - 192 .
Brinkmann , S. and Kvale , S. ( 2014 ), Interviews: Learning the Craft of Qualitative Research Interviewing , Sage , London .
Carroll , J.S. ( 2015 ), “ Making sense of ambiguity through dialogue and collaborative action ”, Journal of Contingencies and Crisis Management , Vol. 23 No. 2 , pp. 59 - 65 .
Christianson , M.K. , Farkas , M.T. , Sutcliffe , K.M. and Weick , K.E. ( 2009 ), “ Learning through rare events: significant interruptions at the Baltimore and Ohio Railroad Museum ”, Organization Science , Vol. 20 No. 5 , pp. 846 - 860 .
Coutu , D.L. ( 2003 ), “ Sense and reliability ”, Harvard Business Review , Vol. 81 No. 4 , pp. 84 - 90 .
Dahlman , S. and Heide , M. ( 2021 ), Strategic Internal Communication: A Practitioner's Guide to Implementing Cutting-Edge Methods for Improved Workplace Culture , Routledge , New York, NY .
David , G. ( 2011 ), “ Internal communication – essential component of crisis communication ”, Journal of Media Research , Vol. 2 No. 10 , pp. 72 - 71 .
de Ridder , J.A. ( 2004 ), “ Organisational communication and supportive employees ”, Human Resource Management Journal , Vol. 14 No. 3 , pp. 20 - 30 .
Deetz , S.A. ( 1992 ), Democracy in an Age of Corporate Colonization: Development in Communication and the Politics of Everyday Life , State University of New York Press , Albany .
Dewey , J. ( 1922/2002 ), Human Nature and Conduct , Dover , Mieola, New York .
Fairhurst , G.T. , Smith , W.K. , Banghart , S.G. , Lewis , M.W. , Putnam , L.L. , Raisch , S. and Schad , J. ( 2016 ), “ Diverging and converging:integrative insights on a paradox meta-perspective ”, The Academy of Management Annals , Vol. 10 No. 1 , pp. 173 - 182 .
Flyvbjerg , B. ( 2006 ), “ Five misunderstandings about case-study research ”, Qualitative Inquiry , Vol. 12 No. 2 , pp. 219 - 245 .
Frandsen , F. and Johansen , W. ( 2011 ), “ The study of internal crisis communication: towards an integrative framework ”, Corporate Communications: An International Journal , Vol. 16 No. 4 , pp. 347 - 361 .
Frandsen , F. and Johansen , W. ( 2017 ), Organizational Crisis Communication , Sage , London .
Gergen , K.J. ( 1999 ), An Invitation to Social Construction , Sage , London .
Gilpin , D.R. and Murphy , P.J. ( 2008 ), Crisis Communication in a Complex World , Oxford University Press , Oxford .
Gray , D.M. , Smart , K.L. and Bennett , M.M. ( 2017 ), “ Examining espoused and enacted values in AACSB assurance of learning ”, The Journal of Education for Business , Vol. 92 No. 5 , pp. 255 - 261 .
Grint , K. ( 2005 ), “ Problems, problems, problems: the social construction of ‘leadership’ ”, Human Relations , Vol. 58 No. 11 , pp. 1467 - 1494 .
Hamel , G. and Zanini , M. ( 2020 ), Humanocracy: Creating Organizations as Amazing as the People inside Them , Harvard Business Review Press , Boston, Massachusetts .
Heide , M. and Simonsson , C. ( 2011 ), “ Putting coworkers in the limelight: new challenges for communication professionals ”, International Journal of Strategic Communication , Vol. 5 No. 4 , pp. 201 - 220 .
Heide , M. and Simonsson , C. ( 2014 ), “ Developing internal crisis communication: new roles and practices of communication professionals ”, Corporate Communications: An International Journal , Vol. 19 No. 2 , pp. 128 - 146 .
Heide , M. and Simonsson , C. ( 2015 ), “ Struggling with internal crisis communication: a balancing act between paradoxical tensions ”, Public Relations Inquiry , Vol. 4 No. 2 , pp. 223 - 255 .
Heide , M. and Simonsson , C. ( 2018 ), “ Coworkership and engaged communicators: a critical reflection on employee engagement ”, in Johnston , K. and Taylor , M. (Eds), The Handbook of Communication Engagement , Wiley-Blackwell , Malden, Massachusetts , pp. 205 - 220 .
Heide , M. and Simonsson , C. ( 2019 ), Internal Crisis Communication: Crisis Awareness, Leadership and Coworkership , Routledge , New York, NY .
Heide , M. and Simonsson , C. ( 2020 ), “ Internal crisis communication: on current and future research ”, in Frandsen , F. and Johansen , W. (Eds), Handbooks of Communication Science , De Gruyter Mouton , Berlin , pp. 259 - 278 .
Heide , M. , Simonsson , C. , von Platen , S. and Falkheimer , J. ( 2018 ), “ Expanding the scope of strategic communication: towards a holistic understanding of organizational complexity ”, International Journal of Strategic Communication , Vol. 12 No. 4 , pp. 452 - 468 .
Kersten , A. ( 2005 ), “ Crisis as usual: organizational dysfunction and public relations ”, Public Relations Review , Vol. 31 No. 4 , pp. 544 - 549 .
Klikauer , T. ( 2013 ), Managerialism: A Critique of an Ideology , Palgrave Macmillan , Basingstoke .
Lewis , M.W. ( 2000 ), “ Exploring paradox: toward a more comprehensive guide ”, Academy of Management Review No. 4 , pp. 760 - 776 .
Madsen Thøis , V. and Verhoeven , J.W.M. ( 2019 ), “ The big idea of employees as strategic communicators in public relation ”, Advances in public relations and communication management , Vol. 4 , pp. 143 - 162 .
Maitlis , S. and Sonenshein , S. ( 2010 ), “ Sensemaking in crisis and change: inspiration and insights from Weick (1988) ”, Journal of Management Studies , Vol. 47 No. 3 , pp. 551 - 580 .
Mazzei , A. ( 2010 ), “ Promoting active communication behaviours through internal communication ”, Corporate Communications: An International Journal , Vol. 15 No. 3 , pp. 221 - 234 .
Mazzei , A. , Kim , J.-N. and Dell'Oro , C. ( 2012 ), “ Strategic value of employee relationships and communicative actions: overcoming corporate crisis with quality internal communication ”, International Journal of Strategic Communication , Vol. 6 No. 1 , pp. 31 - 44 .
Mazzei , A. and Ravazzani , S. ( 2011 ), “ Manager-employee communication during a crisis: the missing link ”, Corporate Communications: An International Journal , Vol. 16 No. 3 , pp. 243 - 254 .
Men , L.R. ( 2014 ), “ Strategic internal communication: transformational leadership, communication channels, and employee satisfaction ”, Management Communication Quarterly , Vol. 28 No. 2 , pp. 264 - 284 .
Mitroff , I.I. ( 2001 ), “ Crisis leadership ”, Executive Excellence , Vol. 18 No. 8 , p. 19 .
Mitroff , I.I. ( 2004 ), Crisis Leadership: Planning for the Unthinkable , John Wiley and Sons , Hoboken, New Jersey .
Muffet-Willett , S.L. and Kruse , S.D. ( 2009 ), “ Crisis leadership: past research and future directions ”, Journal of Business Continuity and Emergency Planning , Vol. 3 No. 3 , pp. 248 - 258 .
Palys , T. ( 2008 ), “ Purposive sampling ”, in Given , L.M. (Ed.), The Sage Encyclopedia of Qualitative Research Methods , Sage , Thousand Oaks, California , p. 698 .
Patton , M.Q. ( 2002 ), Qualitative Research and Evaluation Methods , Sage , Thousand Oaks, CA .
Peirce , C.S. ( 1978 ), “ Pragmatism and abduction ”, in Hart-Shorne , C. and Weiss , P. (Eds), Collected Papers , Harvard University Press , Cambridge, Massachusetts , pp. 180 - 212 .
Putnam , L.L. ( 1983 ), “ The interpretive perspective: an alternative to functionalism ”, in Putnam , L.L. and Pacanowsky , M.E. (Eds), Communication and Organization: An Interpretive Approach , Sage , Beverly Hills, California , pp. 31 - 54 .
Rittel , H.W.J. and Webber , M.M. ( 1973 ), “ Dilemmas in a general theory of planning ”, Policy Sciences , Vol. 4 No. 2 , pp. 155 - 169 .
Roux-Dufort , C. ( 2007 ), “ Is crisis management (only) a management of exceptions? ”, Journal of Contingencies and Crisis Management , Vol. 15 No. 2 , pp. 105 - 114 .
Smircich , L. and Morgan , G. ( 1982 ), “ Leadership: the management of meaning ”, The Journal of Applied Behavioral Science , Vol. 18 No. 3 , pp. 257 - 273 .
Snoeijers , E.M. and Poels , K. ( 2018 ), “ Factors that influence organisational crisis perception from an internal stakeholder's point of view ”, Public Relations Review , Vol. 44 , pp. 65 - 74 .
Snowden , D.J. and Boone , M.E. ( 2007 ), “ A leader's framework for decision making ”, Harvard Business Review , Vol. 85 No. 11 , pp. 68 - 76 .
Strandberg , J.M. and Vigsø , O. ( 2016 ), “ Internal crisis communication ”, Corporate Communications: An International Journal , Vol. 21 No. 1 , pp. 89 - 102 .
Taylor , M. ( 2010 ), “ Towards a holistic organizational approach to understanding crisis ”, in Coombs , W.T. and Holladay , S.J. (Eds), The Handbook of Crisis Communication , Wiley-Blackwell , Malden, Massachusetts , pp. 698 - 704 .
Tourish , D. ( 2020 ), “ Introduction to the special issue: why the coronavirus crisis is also a crisis of leadership ”, Leadership , Vol. 16 No. 3 , pp. 261 - 272 .
Tourish , D. and Jackson , B. ( 2008 ), “ Communication and leadership: an open invitation to engage' (Guest editorial) ”, Leadership , Vol. 4 No. 3 , pp. 219 - 225 .
Vidal , R. ( 2015 ), “ Managing uncertainty: the engineer, the craftsman and the gardener ”, Journal of Contingencies and Crisis Management , Vol. 23 No. 2 , pp. 106 - 116 .
Weick , K.E. ( 1969 ), The Social Psychology of Organizing , Addison-Wesley , Reading, Massachusetts .
Weick , K.E. ( 1988 ), “ Enacted sensemaking in crisis situations ”, Journal of Management Studies , Vol. 25 No. 4 , pp. 305 - 317 .
Weick , K.E. ( 1993 ), “ The collapse of sensemaking in organizations: the Mann Gulch disaster ”, Administrative Science Quarterly , Vol. 38 No. 4 , pp. 628 - 652 .
Weick , K.E. ( 1995 ), Sensemaking in Organizations , Sage , Thousand Oaks, California .
Weick , K.E. ( 2020 ), “ Sensemaking, organizing, and surpassing: a handoff ”, Journal of Management Studies , Vol. 57 No. 7 , pp. 1420 - 1431 .
Weick , K.E. and Ashford , S.J. ( 2001 ), “ Learning in organizations ”, in Jablin , F.M. and Putnam , L.L. (Eds), The New Handbook of Organizational Communication: Advances in Theory, Research, and Methods , Sage , Thousand Oaks, California , pp. 704 - 731 .
Weick , K.E. and Sutcliffe , K.M. ( 2007 ), Managing the Unexpected: Resilient Performance in an Age of Uncertainty , Wiley , San Francisco, California .
Weick , K.E. , Sutcliffe , K.M. and Obstfeld , D. ( 2005 ), “ Organizing and the process of sensemaking ”, Organization Science , Vol. 16 No. 4 , pp. 409 - 422 .
Wildavsky , A.B. ( 1988 ), Searching for Safety , Transaction Books , New Brunswick, New Jersey .
Young , K. ( 2018 ), “ Enhancing employee communication behaviors for sensemaking and sensegiving in crisis situations: strategic management approach for effective internal crisis communication ”, Journal of Communication Management , Vol. 22 No. 4 , pp. 451 - 475 .
Zaumane , I. ( 2016 ), “ The internal communication crisis and its impact on an organization's performance ”, Journal of Business Management , Vol. 12 , pp. 24 - 33 .
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Learn how you can create a powerful internal communications strategy with these 7 steps.
Are you frustrated with miscommunication and a lack of alignment within your team?
Imagine a world where everyone is on the same page, information flows seamlessly, and employees feel connected to the company’s goals.
Wouldn’t it be great if your internal communications were so effective that it boosted morale, increased productivity, and fostered a sense of community?
This blog post will outline 7 simple steps to create an internal communications strategy that will transform your workplace.
What is internal communications, purpose of internal communications.
Internal communications are held internally amongst the employees of an organization. Internal communication facilitates the effective and successful flow of conversation between teams and individual employees.
It ensures that everyone is informed about company goals, policies, and changes, facilitating collaboration and alignment. Effective internal communication enhances employee engagement and supports the smooth operation of the business.
A strong company culture is not built in one day. It takes time to build the momentum of trust within an organization.
Building a strong company culture through internal communication involves consistently sharing the organization’s values, mission, and vision with your employees. This helps align team members with the company’s goals and fosters a sense of belonging.
By openly communicating achievements, recognizing contributions, and encouraging feedback, internal communication reinforces cultural norms and supports a positive work environment.
Effective internal communication ensures that everyone understands and contributes to the company’s culture, driving engagement and cohesion across the organization.
Creating a healthy work environment through internal communication involves developing transparency, respect, and open dialogue among your employees. By regularly sharing important updates and encouraging feedback, internal communication helps build trust and reduces misunderstandings.
Promoting a culture of recognition and support through internal channels can enhance morale and job satisfaction.
Improving employee retention through internal communication involves keeping employees informed, engaged, and connected to the organization’s goals.
Regular updates, recognition, and transparent communication help build trust and job satisfaction. By addressing concerns, providing feedback, and fostering open dialogue, internal communication creates a supportive environment where your employees feel valued.
This reduces turnover by ensuring your employees are aligned with the company’s mission and are motivated to stay and grow within the organization.
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Evaluating your current situation in internal communication involves assessing how effectively information flows within your organization.
Start by reviewing existing communication channels and tools to identify any gaps or inefficiencies. Gather feedback from employees about their experiences and challenges with current communication methods.
Analyze how well messages are being understood and if they align with your organizational goals. This evaluation helps pinpoint areas for improvement and ensures that communication supports your team collaboration and productivity.
Knowing your audience is fundamental to crafting effective internal communications. It involves more than just understanding basic demographics; it requires a deep dive into the unique characteristics, preferences, and needs of your employees.
Different groups within your organization may have varied communication preferences—some might favor digital platforms and instant messaging, while others might prefer detailed emails or face-to-face interactions. Recognizing these preferences allows you to tailor your messages for maximum engagement.
Accounting for varying levels of technological proficiency ensures that communication tools and content are accessible to all employees. By thoroughly understanding these elements, you can create a more nuanced and effective internal communications strategy that enhances clarity and engagement across your organization.
Defining clear goals is a pivotal aspect of creating an effective internal communications strategy. Setting specific, measurable, achievable, relevant, and time-bound (SMART) goals provides a focused direction for your communication efforts and ensures alignment with overall organizational objectives.
For example, if your goal is to improve employee engagement, you can set a target to increase participation in internal surveys by 20% within six months. Clear goals help in prioritizing communication initiatives and determining the content and channels that will best support these objectives.
Without clear goals, internal communication efforts can become disjointed or misaligned, leading to wasted resources and missed opportunities.
Crafting clear messages is crucial for effective internal communications, as it ensures that information is conveyed accurately and understood by all employees. Clear messaging involves the clarity of language, simplicity, and relevance of the content.
For instance, using jargon or overly complex terms can lead to misunderstandings and disengagement among employees who might not be familiar with the terminology.
Clear messages should be direct, concise, and tailored to the specific needs of the audience, avoiding ambiguity and ensuring that the intended action or response is straightforward.
Research shows that employees spend up to 30% of their workweek seeking clarification and additional information due to unclear communication.
Crafting clear messages minimizes confusion, enhances understanding, and supports more efficient and effective internal communications.
Choosing the right channels is essential for effective internal communications, as the success of conveying information hinges on selecting platforms that best fit the needs and preferences of your employees.
Each communication channel—be it email, instant messaging, intranet, or face-to-face meetings—has its strengths and ideal use cases.
For example, while email is useful for detailed updates and formal announcements, instant messaging platforms like Slack or Microsoft Teams are more suitable for quick, real-time interactions and collaborative discussions.
Studies show that 60% of employees prefer receiving important updates through their company’s intranet, as it centralizes information and provides easy access to resources.
Conversely, research indicates that 70% of employees favor instant messaging for informal, day-to-day communication due to its immediacy and convenience.
By aligning your communication strategy with the right channels, you enhance message clarity, improve engagement, and ensure that information is delivered in the most effective manner for your audience.
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Developing a communication calendar is crucial for organizing and timing internal communications effectively. By scheduling regular updates, meetings, and announcements, a calendar ensures consistency and prevents overlaps or gaps.
Organizations with structured communication plans see 25% fewer missed deadlines and 30% higher employee engagement.
This proactive approach allows for better resource management and alignment with organizational goals, ensuring that key information reaches the right audience at the optimal time.
Fostering a two-way dialogue is vital for effective internal communications as it promotes open exchanges and feedback between employees and management.
Studies reveal that companies with strong two-way communication experience 20% increase in employee satisfaction and a 15% boost in productivity.
By encouraging employees to share insights and voice concerns, organizations can address issues promptly and build trust.
Interactive tools like town hall meetings and surveys facilitate healthy dialogues, enhancing engagement, collaboration, and overall workplace responsiveness.
An effective internal communication strategy is key to unlocking organizational success.
Setting clear goals, crafting precise messages, and selecting the right channels boosts engagement, productivity, and collaboration
Investing in robust internal communication not only streamlines operations but also builds a cohesive, motivated workforce that is ready to achieve collective goals.
Pratik is a customer experience professional who has worked with startups & conglomerates across various industries & markets for 10 years. He shares latest trends in the areas of CX and Digital Transformation for Customer Service & Contact Center.
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Humanities and Social Sciences Communications volume 11 , Article number: 1115 ( 2024 ) Cite this article
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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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .
Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286
Article PubMed PubMed Central Google Scholar
Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566
Article Google Scholar
Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014
Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120
Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608
Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879
Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006
Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002
Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004
Article MathSciNet Google Scholar
Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052
Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317
Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813
Article PubMed Google Scholar
Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968
Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150
Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955
Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00
Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855
Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005
Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7
Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009
Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83
Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008
Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157
Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043
Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001
Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681
Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380
Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197
Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575
González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314
Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4
Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566
He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732
Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5
Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9
Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4
Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024
Article ADS Google Scholar
Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6
Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934
Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w
Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1
Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013
Article MathSciNet PubMed Google Scholar
Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061
Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359
Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1
Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254
Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176
Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874
Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003
Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006
Article ADS PubMed Google Scholar
Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871
Google Scholar
Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458
Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165
Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0
Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5
Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999
Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317
López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13
Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015
Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005
Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013
Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944
Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496
Article ADS PubMed PubMed Central Google Scholar
Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428
Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020
Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113
Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5
Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049
Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297
Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536
Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569
Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073
Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015
Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005
Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063
Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949
Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5
Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004
Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9
Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653
Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785
Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500
Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012
Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298
Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359
Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3
Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463
Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564
Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522
Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19
Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774
Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681
Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1
Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695
Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793
Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217
Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1
United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220
Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018
Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3
Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540
Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029
Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927
Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y
Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609
Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033
Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915
Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369
Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778
Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060
Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557
Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642
Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w
Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x
Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210
Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896
Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765
Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7
Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004
Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826
Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w
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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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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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Empathy is one of the fundamental factors enhancing the therapeutic effects of physician–patient relationships, but there has been no relevant research in China on the pediatric resident physicians’ capacity for empathy or the influencing factors.
A mixed-methods study was undertaken. The student version of the Jefferson Scale of Empathy was used to assess 181 postgraduate residents at Shanghai Children’s Medical Center and Shanghai Children’s Hospital. Differences in empathy ability among pediatric resident physicians of different genders and specialties were analyzed using independent sample t-tests and Mann–Whitney U tests. A one-way analysis of variance was used to analyze the differences in empathy ability at different educational levels and years of medical residency training. Seven third-year postgraduate pediatric residents from Shanghai Children’s Medical Center participated in semi-structured interviews exploring the influencing factors. We analyzed the interview transcripts using thematic analysis.
The scale was completed by 154 pediatric residents. No statistically significant differences in empathy were found between educational level, postgraduate year, gender, or specialty. The factors influencing empathy in doctor–patient communication included the person who accompanied the child to see the doctor, how the children cooperated with doctors for medical treatment, the volume of pediatric outpatient and emergency visits, and the physician’s ability to withstand pressure. All interviewed resident physicians regarded learning empathy as important but rarely spent extra time learning it.
The evaluation results of resident physicians on changes in empathy after improving clinical abilities vary according to their understanding of empathy, and the work environment has an important impact on pediatricians’ empathy ability. Their empathy score is relatively low, and this requires exploration and intervention.
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There has been a long-standing tension in the physician–patient relationship in pediatric clinics in China [ 1 ]. There are complex reasons for this, but research has found that 80% of doctor–patient disputes result from poor communication, often due to a lack of empathy during interactions [ 2 , 3 ]. The current medical literature defines empathy as the ability to understand the patient’s perspective and feelings, as well as sharing and acting on this understanding during interpersonal interactions [ 4 ]. Studies show that empathy is linked with enhanced patient satisfaction and treatment compliance [ 5 ]. High levels of empathy in healthcare professionals are connected to positive clinical prognoses for patients by reducing mental stress, improving self-awareness, and reducing anxiety and depression [ 6 , 7 ].
Residency training is mandatory for doctors to qualify to practice independently [ 8 ]. In China, standardized residency training began nationwide in 2013; seven government ministries jointly issued the policy document, “Guidance on the Establishment of a Standardized Residency Training System” [ 9 ]. All clinicians, including pediatricians, are required to undergo three-year residency training after graduating from medical school. During these three years, residents study in different departments.
The Chinese Medical Doctor Association recommends six core competencies for medical residents based on the content and standards for standardized residency training (2022 version): professionalism, clinical professionalism, managing patients, communication, teaching, and learning. While professionalism necessarily involves knowledge and skill, the unique characteristic of medical professionalism is empathy [ 10 ], a capacity that is also strongly related to communication. Thus, cultivating empathy is important for medical residents.
The student version of the Jefferson Scale of Empathy (JSE-S) was specifically developed as a self-report scale for the assessment of empathy in medical students [ 11 , 12 ]. Some studies have reported a decline in empathy among medical students [ 13 , 14 , 15 ], while some have noted that students in their final year scored higher for empathy than did first-year medical students [ 16 , 17 ] and others have reported little change in empathy scores across the years [ 18 ]. However, there is little comparable research for China.
Some studies have shown that the work environment can affect the development of empathy [ 19 ], and pediatric departments recorded a high incidence of doctor–patient disputes [ 20 ]. According to the 2019 National Medical Injury Liability Dispute Case Big Data Report, pediatrics is a high-risk area for doctor–patient disputes.
Therefore, this study aimed to analyze whether there are differences in the ability to empathize among pediatric resident physicians of different grades and whether the pediatric medical environment affects that ability. A mixed-methods approach was used: We assessed empathy scores using the JSE-S and then conducted a semi-structured survey to discuss the influencing factors.
Quantitative and qualitative methodologies were used to analyze empathy and influencing factors among pediatric residents, incorporating a survey for the quantitative analysis and interviews for the qualitative assessment.
Data collection: survey.
In July 2023, all residents of the Shanghai Children’s Medical Center, affiliated with Shanghai Jiao Tong University School of Medicine, and the Children’s Hospital affiliated with Shanghai Jiao Tong University School of Medicine, were surveyed using an anonymous online questionnaire. Informed consent was obtained from all participants. The survey was available online for one week, and after three days, the residents were sent reminders via WeChat by staff members from the two hospitals.
The JSE-S was used in this study [ 21 ] The scale consists of 20 items, measured using a seven-point Likert scale ranging from 1 = completely disagree to 7 = completely agree but with items 1, 3, 6, 7, 8, 11, 12, 14, 18, and 19 reverse scored. The total score of the scale comprises the total score for all items, with higher scores indicating higher levels of empathy. The scale is subdivided into three dimensions: perspective-taking, compassionate care, and standing in the patient’s shoes [ 12 , 21 ]. The maximum score on the JSE is 140, and the minimum score is 20. Other data collected as part of the JSE survey included sex and years of medical resident training, specialty, and education.
Independent samples t-tests were performed to assess differences in mean JSE scores between sexes. The Mann–Whitney U test was used to compare the differences in mean JSE scores between specialties. A one-way analysis of variance (ANOVA) was performed to compare the differences between the different years of medical residency training and different levels of education. All analyses were performed using the IBM SPSS Statistics Version 25.0. The data are presented as mean ± standard deviation (SD) unless otherwise stated.
Data collection: interviews.
As the third-year postgraduate (PGY3) pediatric residents who entered standardized training for pediatric resident physicians in 2020 had completed their training, in August 2023, PGY3 pediatric residents at the Shanghai Children’s Medical Center were asked to participate in the interviews. Seven consented to participate (Table 1 ).
Two researchers (LPP and WL) conducted individual face-to-face semi-structured interviews. The interviews lasted 50–70 min (60-minute average) and were audio recorded and transcribed verbatim by a professional service. The interview guide (Table 2 ) included three aspects: work environment, residents’ standardized training, and open questions. The open-ended questions explored the most memorable cases of smooth and unsmooth communication with patients.
During the interviews, the research followed the guidelines of the interview outline and interviewees’ actual situations. The order and method of questioning were adjusted according to the context and the value of the questions. The language used by the interviewees was accepted without judgment, and no inducements or interventions were made. To protect the privacy of the respondents, their names have been replaced by numbers.
In accordance with a constructivist approach, the analyses tapped into the sense that the participants made of their experiences of communicating with patients. Inductive thematic analysis [ 22 ] was used to identify themes. The interviews were audio recorded and transcribed verbatim by a professional service (iFLYTEK). WL and LPP read and reread transcripts for immersion and familiarization. Two authors (WL and LPP) iteratively coded the data deemed relevant to the current study using Nvivo14 [ 23 ]. Disagreements were discussed with another author (DL). The next step was to group related codes into potential themes. Subsequently, three authors (LPP, WL, and DL) jointly reviewed the themes to ensure that the codes in each theme were coherent and that the codes in different themes could be clearly distinguished.
Study population characteristics.
In total, 154 residents responded to the survey, a response rate of 85.1% (154/181). The participating pediatric residents included 60 (39.0%) residents from postgraduate year 1 (PGY1), 48 (31.1%) from postgraduate year 2 (PGY2), and 46 (29.9%) from PGY3. A total of 111 participants (72.1%) were women, and 43 (27.9%) were men. A total of 112 (72.7%) participants were pediatric residents, and 42 (27.3%) were pediatric surgery residents. There were 63 (40.9%) undergraduate residents, 69 (44.8%) master’s residents, and 22 (14.3%) doctoral degree residents in this study. The mean JSE-S score for the overall study population was 81.41 ± 5.43.
Based on the independent samples t-test and Mann–Whitney test, we found no differences in pediatrics’ sex (t = 0.878, p = 0.381) or specialty (z=-0.981, p = 0.327).
The education levels of different residents were not significantly different (f = 1.455, p = 0.237) (Table 3 ).
The empathetic recognition mean JSE-S score was 81.41 ± 5.43. Compared to PGY1 (81.33 ± 4.45) and PGY2 (80.75 ± 4.08), PGY3 had a high JSE-S score (82.2 ± 7.48), but there were no significant differences between different years of medical residency training (f = 0.839, p = 0.434) (Table 4 ).
In the perspective-taking scale, the mean JSE-S score was 54.66 ± 6.70, and the one-way ANOVA revealed significant differences between PGYs (f = 3.51, p = 0.032). There were significant differences between PGYs for three items: “Physicians’ understanding of the emotional status of their patients, and that of their families is an important component of the physician–patient relationship” (f = 4.391, p = 0.014); “Physicians should try to stand in their patients’ shoes when providing care to them” (f = 4.697, p = 0.010); and “I believe that empathy is an important therapeutic factor in medical treatment” (f = 250.996, p = 0.000).
The mean JSE-S score on the compassionate care scale was 20.76 ± 5.97. PYG1, PYG2, and PYG3 scored 22.42 ± 4.48, 19.42 ± 6.17, and 20.00 ± 7.00, respectively, indicating significant differences between them (f = 4.053, p = 0.019). Significant differences were found for years of pediatric residency training for “Physicians should not allow themselves to be influenced by strong personal bonds between their patients (f = 40.158, p = 0.000) and their family members” and “I do not enjoy reading non-medical literature or the arts.” (f = 37.236, p = 0.000).
The standing in the patient’s shoes dimension of the JSE-S showed no significant differences between the PGYs.
The influence of pediatric visiting environment on physicians’ empathy ability.
Because children are unable to express their discomfort or illness well, they should be accompanied by parents or grandparents when attending hospital. Doctors, therefore, have to communicate with the parents or grandparents, and their circumstances, including their education level, familiarity with the child, physical health status, communication and understanding skills, and attitude toward doctors, can affect empathy between doctors and patients.
Compared to adult hospitals , the empathy ability of doctors in children’s hospitals may be slightly reduced because we are dealing with parents , not patients themselves , and many of them are brought for treatment by elderly people. Elderly people do not understand the child’s disease or may have difficulty hearing clearly , which can greatly affect communication , let alone empathy. (P1, M) Some elderly people may regard their children’s condition unnecessarily seriously , resulting in us not being able to understand the symptoms of the child properly. (P2, F) Parents tend to have a good understanding of the child’s condition. If grandparents with a low education or if other relatives bring them over , the process of consultation may not be very smooth. (P3, F) The child might be brought over on the first day of treatment by their parents but subsequently by older relatives. Because the child is still running a fever for two or three days , they will be very anxious. When they communicate this to us , their attitude is often poor. (P4, M) If an elderly person brings a child to see a doctor , I often ask the elderly person to call the parents on the spot so I can listen to them. It is better this way. (P7, M)
Some resident physicians said that the language of the patients’ parents significantly impacted their ability to empathize:
Because I am not from Shanghai and grandparents who accompany their children may speak the local dialect , we are unable to communicate. This is challenging for me and many colleagues because most of us cannot understand the Shanghai dialect. (P2, F)
The child’s upbringing and willingness to cooperate with treatment were also identified as important:
Some parents may spoil their children , some children start acting spoiled as soon as they arrive at the clinic , and some even make a scene , which can interfere with the medical treatment. (P2, F)
The volume of pediatric outpatient and emergency visits and the self-regulation ability of physicians facing strong workloads can also affect communication and empathy between doctors and patients:
Outpatient hours may limit our communication with patients. Generally , you need to finish one within 5–10 min. Otherwise , the patient’s visit may be too long , and you may not be able to see all registered patients before leaving work. For example , last summer , our two doctors saw an average of around 130–150 patients a day , while I saw an average of 80–90 patients per day. That was during the pandemic last year , and there will definitely be more this year. (P7, M) The doctor is very tired and has a large number of patients. If the patients are in a hurry , you need to see them within a short period. If our resident physician’s self-regulation ability is not good , it will affect communication. (P5, M)
The three resident physicians interviewed believed that in their first year of participating in standardized resident training, they felt more empathy for patients due to their lack of clinical knowledge. By contrast, after three years of clinical practice and improvements in their clinical knowledge, they viewed the patient’s condition more rationally and from a medical perspective.
Because you have learned systematic knowledge about diseases , you know what the likely outcome will be objectively. Consequently , your empathy regarding the intermediate treatment process and patients may decrease , and you have to think about the treatment from a doctor’s professional perspective. (P2, F) When I first entered standardized training for resident physicians , I lacked clinical experience and was not familiar with the treatment process for many diseases. When I encountered critically ill patients , I felt that they were so pitiful. After three years of training , however , these diseases have become more familiar. I know the treatment processes for each disease and feel that empathy has decreased. (P3, F)
The two residents felt that empathy followed a curved path. Residents who have just entered clinical practice have relatively high empathy. However, as their clinical abilities and understanding of diseases increase, coupled with the busy workload of clinical work, their empathy decreases. However, empathy may improve after becoming a physician.
When I went to the outpatient clinic with my supervisor , I felt that my supervisor , who was already a chief physician , had reached a very high level of empathy. I think his empathy ability was much stronger than mine; that is , regardless of the patient’s attitude , he could think from the patient’s perspective. As a resident physician , I still cannot reach the level of empathy that my supervisor possesses. Perhaps I need to acquire some experience in my career to reach the level of empathy that my supervisor possesses , but the process may be a bit complex. (P2, F) As a physician , I think that empathy is a curved process , initially high , but as your clinical abilities improve and work experience increases , empathy may decrease. The attending physician is very busy , and at some point , the value of empathy may be underestimated , but it increases again with age. Perhaps at a certain point or stage , you suddenly feel it is important , and you become very focused on the ability to empathize. (P3, F)
Two interviewees believed that after three years of standardized training for resident physicians, their empathy skills had improved. Three years ago, they only thought about the disease. Today, they are able to think from the perspective of the patient and stand in their shoes.
For example , parents who come to the surgical emergency department are very anxious. As a physician , I can understand their feelings. Some common diseases that you have seen before have a likely trajectory. Although you are also anxious about their diseases , you know how to treat different disease symptoms and have the ability to handle them. I know why parents are anxious , and I can think from their perspective. (P4, F) As you gain an understanding of diseases and as your own abilities and clinical experience improve , your feelings toward the patient change. Because I know how a disease like Mycoplasma pneumonia , for example , develops , when I was in PGY1 , I felt that the child’s cough was very severe , which made the parents very anxious. At the time , I was also quite anxious. Now , however , I know that the course of this disease is long. If parents are very anxious , I will explain this disease to them and comfort them. I have had more contact with patients , and I will consider the problem more from their perspective. (P6, F)
Self-study: The residents believed it important to learn theories relevant to doctor–patient communication and empathy. The interviews revealed that most of them improved their communication skills in clinical practice, and a few residents spent time studying how to communicate with patients. Only one student bought a book about communication, and one student paid attention to the ability to communicate with patients because they had to take an exam on doctor–patient communication.
When I was admitted for training , there was a medical teacher talking about doctor–patient disputes , which was quite scary at the time. I bought relevant books but did not read them. (P1, M) I have not bought any books related to doctor–patient communication , but I think in clinical practice , it is necessary to participate more in the conversation process with superiors , listen more to their conversations , listen more to how they communicate with patients , and then try to learn how to better communicate with patients on my own. (P2, F) This year’s standardized training and graduation assessment for resident physicians added an assessment of doctor–patient communication. I have paid attention to this knowledge , but I have not delved into it. (P3, F)
Training course: It is necessary to set courses to cultivate residents’ empathy ability, such as theoretical training courses, case-sharing groups, and scenario simulations.
I think it’s necessary to set courses for residents to teach us how to communicate , how to express the appropriate level of empathy to patients , etc. (P1, M) I think theoretical teaching in this area is possible , but it cannot be a single output of this teaching mode. Instead , we could hold some doctor–patient communication and sharing meetings , where residents or specialists could share their cases in clinical work and learn from each other . (P3, F) Maybe establish some scenario simulation courses for training. (P5, M)
Due to the fact that resident physicians undergo rotational training in different clinical departments over 3 years, clinical departments, patient situations, work environments, and severity of diseases may vary. By conducting interviews with resident physicians during the training period, the factors that affect the empathy ability of resident physicians can be further explored by allowing them to profoundly impact the departments where communication with patients is not smooth or smooth. The results are shown in Table 5 .
Some studies have shown that empathy scores are associated with ratings of clinical competence [ 24 ]. From the results of the questionnaire survey, the JSE-S scores of PGY1, PGY2, and PGY3 showed no significant differences. From the interview results, seven respondents compared the changes in their empathy skills between the beginning and completion of the standardized resident physician training. Five pediatric resident physicians believed that their empathy skills had decreased with the improvement in their medical skills, while two resident physicians believed that their empathy skills improved after receiving standardized resident physician training. The results of the interviews seem to confirm the results of the questionnaire survey that different physicians have different understandings of the relationship between the improvement of clinical abilities and empathy. These two perspectives may be due to different perspectives on empathy. A resident physician who believes that empathy decreases may believe that the physician’s empathy toward patients is more about the patient’s illness. As their medical abilities improve, they can treat the patient’s illness and believe that it will eventually be cured, so the need for empathy decreases. Some studies have reported that doctors who sympathize with their patients share their suffering, which could lead to emotional fatigue and a lack of objectivity [ 25 ]. However, one resident physician believed empathy had improved by progressing from learning about diseases from books during their medical student stage to the realities of clinical practice, seeing the impact of diseases on patients, families, and even society.
Doctor–patient communication in pediatrics is more complex and difficult than when treating adults, meaning that pediatricians bear higher risks. The probability of medical disputes in pediatrics is much higher than in other departments; pediatricians are often insulted and even physically threatened [ 26 ]. Physician empathy is at the heart of doctor–patient communication and significantly influences patient outcomes [ 27 ]. This study explored the factors that influence empathy between pediatricians and patients. In patient terms, the level of cooperation from the child and the characteristics of the person accompanying the child are factors. As for the doctors, they can be confronted with pressure and the need to communicate effectively in the face of high outpatient volumes, which can affect their expressions of empathy, a finding similar to that of previous studies [ 28 , 29 ].
Further analysis of direct doctor–patient communication and empathy among pediatric resident physicians in different rotating departments showed that communication between doctors and patients was seen to be smoother in the Rheumatology and Immunology, General Surgery, and Special Diagnosis Departments, while difficulties were encountered in Outpatients and Emergency, Hematology and Oncology, Surgical Oncology, and Cardiology. The reasons may be complex, but four principal issues can be identified. First, the duration of communication between doctors and patients and the environment of medical treatment; in the Special Diagnosis Department, for example, patients are able to communicate and interact with doctors for a long time, and the medical environment is very good, whereas Outpatients and Emergency see a rapid turnover and high workload. Second, the level of familiarity between patients and physicians can play a role. In Rheumatology and Immunology Departments, for example, there are often patients with chronic diseases who have been hospitalized for a long time; doctors and patients are very familiar with each other, and some studies have shown empathy is easier to generate when closer interpersonal relationships develop [ 30 ]. Third, different teaching methods may have an impact. Better training on the wards can make residents feel more confident in communicating with patients, whereas Outpatients and Emergency can require residents to face patients alone, generating anxiety or even burnout [ 31 ]. Fourth, disease severity can play a role. In some departments, such as Hematology and Oncology, patients may not have a high hope of recovery but may have high expectations of the treatment. This may not only put a lot of pressure on doctors but also make it difficult to communicate effectively with patients; research has indicated that there is still a gap between the actual and expected disclosure of “bad news” about cancer among healthcare workers, patients, and family members, leading to various disclosure dilemmas [ 32 ].
The mean empathy levels found in this study (81.41 ± 5.43) are lower than those reported [ 33 ] in most similar studies around the world. Similar lower JSE scores have been seen in undergraduate medical students in China; the average JSE score among medical students from Sun Yat-sen University was 84 [ 34 ]. This finding is concerning. The shortage of pediatricians, [ 35 ] low wages, [ 36 ] severe occupational burnout, [ 37 ] and the influence of Asian parental culture [ 38 ] may partly explain our findings. Further investigations are required to determine the factors associated with such low scores so that steps can be taken to address the situation.
Our research shows that resident physicians believe that empathy is important, even though their self-rated empathy scores are less than ideal. Interventions to further investigate the teaching and learning of empathy were discussed [ 39 ]. Many training courses have proven to be beneficial in enhancing the empathy skills of resident physicians. The teaching innovation “How to act-in-role” has been shown to be effective not only in increasing medical students’ self-reported empathy but also in their competence in consultation skills [ 40 ]. The addition of narrative medicine-based education in standardized training improved empathy and may have improved the professional knowledge of residents [ 41 , 42 ] The use of Balint group activities [ 43 ] with residents has shown significant improvements in empathy across all dimensions. Medical schools should design appropriate training courses and implement interventions at all stages (from the admission process to curricula to residency) and levels (explicit and implicit curricula) depending on the empathy levels of their resident physicians.
Our findings suggest that, based on the different understandings of empathy among resident physicians, the clinical empathy level of pediatric resident physicians is not closely related to an improvement in clinical abilities. Rather, the working environment of pediatricians significantly impacts their empathy ability. Empathy is lower among pediatric residents in China when compared to their European counterparts, and further research into the underlying factors associated with such low scores is necessary to plan interventions to cultivate empathy among pediatric residents.
One important weakness of this study is that it was based in one medical school with two specialized children’s hospitals; the limited sample size of the investigation and interviews may mean that the study is not representative of pediatric residents in China. Moreover, the cross-sectional survey precluded us from identifying a causal relationship; thus, a prospective longitudinal study with a larger sample size of pediatric residents is warranted.
The questionnaire data that support the findings of this study are available in the Baidu Netdisk repository, https://pan.baidu.com/s/1hRjCKuIVVry79HwTzxB_bA with the primary accession code e9hp.The interview datasets analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author upon reasonable request.
Wenhui G, Xinqing Z, Shanshan L, et al. Cognitive analysis of medical staff in clinical departments of 45 hospitals in nine provinces on the tense doctor-patient relationship. J Southeast Univ. 2018;20(4):124–129145. https://doi.org/10.3969/j.issn.1671-511X.2018.04.014 . Philosophy and Social Sciences Edition.
Article Google Scholar
Zhang X, Sleeboom-Faulkner M. Tensions between medical professionals and patients in mainland China. Camb Q Healthc Ethics. 2011;20(3):458–65. https://doi.org/10.1017/S0963180111000144 .
Jiang S. Pathways linking patient-centered communication to health improvement: a longitudinal study in China. J Health Commun. 2019;24(2):156–64. https://doi.org/10.1080/10810730.2019.1587110 .
Mercer SW, Maxwell M, Heaney D, Watt GC. The consultation and relational empathy (CARE) measure: development and preliminary validation and reliability of an empathy-based consultation process measure. Fam Pract. 2004;21(6):699–705. https://doi.org/10.1093/fampra/cmh621 .
Kane GC, Gotto JL, Mangione S, West S, Hojat M. Jefferson scale of patient’s perceptions of physician empathy: preliminary psychometric data. Croat Med J. 2007;48(1):81–6.
Google Scholar
Lorié áine, Reinero DA, Phillips M, et al. Culture and nonverbal expressions of empathy in clinical settings: a systematic review. Patient Educ Couns. 2017;100:411–24.
Hemmerdinger JM, Stoddart SDR, Lilford RJ. A systematic review of tests of empathy in medicine. BMC Med Educ. 2007;7:24.
Joyce BL, Scher E, Steenbergh T, Voutt-Goos MJ. Development of an institutional resident curriculum in communication skills. J Grad Med Educ. 2011;3(4):524–8.
Li FY, Wen Y, Lei PG, et al. The present situation and consideration of Residency standardized training in China. China Contin Med Educ. 2019;11:92–4.
Montgomery L, Loue S, Stange KC. Linking the heart and the head: humanism and professionalism in medical education and practice. Fam Med. 2017;49(5):378–83.
Alcorta-Garza AJ, Gonzalez-Guerrero JF, Tavitas-Herrera SE, Rodrigues-Lara FJ, Hojat M. Validity of the Jefferson scale of physician empathy among Mexican medical students. Salud Ment (Mex). 2005;28:57–63.
Hojat M. Empathy in Patient Care: antecedents, Development, Measurement, and outcomes. New York: Springer; 2007.
Hojat M, Shannon SC, DeSantis J, Speicher MR, Bragan L, Calabrese LH. Does empathy decline in the clinical phase of medical education? A nationwide, multi-institutional, cross-sectional study of students at DO-granting medical schools. Acad Med 2020;95(6):911–918. https://doi.org/10.1097/ACM.0000000000003175 , PMID: 31977341.
Hojat M, Vergare MJ, Maxwell K et al. The devil is in the third year: a longitudinal study of erosion of empathy in medical school. Acad Med 2009;84(9):1182–1191. https://doi.org/10.1097/ACM.0b013e3181b17e55 . Erratum in: Acad Med 2009;84(9):1182–1191. PMID: 19707055.
Hojat M. Change in empathy in medical school. Med Educ 2018;52(4):456–457. https://doi.org/10.1111/medu.13497 , PMID: 29574956.
Magalhães E, Salgueira AP, Costa P, Costa MJ. Empathy in senior year and first year medical students: a cross-sectional study. B MC Med Educ 2011;11:52. https://doi.org/10.1186/1472-6920-11-52 , PMID: 21801365.
Ye X, Guo H, Xu Z, et al. Empathy variation of undergraduate medical students after early clinical contact: a cross-sectional study in China. BMJ Open. 2020;10:e035690. https://doi.org/10.1136/bmjopen-2019-035690 .
Assing Hvidt E, Søndergaard J, Wehberg S, Hvidt NC, Andersen CM. A cross-sectional study of student empathy across four medical schools in Denmark-associations between empathy level and age, sex, specialty preferences and motivation. BMC Med Educ 2022;22(1):489. https://doi.org/10.1186/s12909-022-03532-2 , PMID: 35739548.
Yu CC, Tan L, Le MK, et al. The development of empathy in the healthcare setting: a qualitative approach. BMC Med Educ. 2022;22(1):245. https://doi.org/10.1186/s12909-022-03312-y . PMID: 35379249, PMCID: PMC8981670.
Wanqi F. Chen panorama analysis and countermeasures of common nurse patient disputes in pediatrics. Qilu J Nurs. 2011;17(21):117–8. https://doi.org/10.3969/j.issn.1006-7256.2011.21.087 .
Hojat M, Gonnella JS, Nasca TJ, Mangione S, Vergare M, Magee M. Physician empathy: definition, components, measurement, and relationship to gender and specialty. Am J Psychiatry. 2002;159(9):1563–9.
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.
Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using NVivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017;26(5–6):873–81. https://doi.org/10.1111/jocn.13443 .
Hojat M, Gonnella JS, Mangione S et al. Empathy in medical students as related to academic performance, clinical competence and gender. Med Educ 2002;36(6):522–527. https://doi.org/10.1046/j.1365-2923.2002.01234.x , PMID: 12047665.
Zhao JB, Liang SW, Hou YF. Etc the relationship between empathy fatigue and post-traumatic stress disorder among clinical doctors. Guangdong Med. 2017;38(24):3841–4. https://doi.org/10.3969/j.issn.1001-9448.2017.24.036 .
Xu W, Zhang SC. Chinese pediatricians face a crisis: should they stay or leave? Pediatrics. 2014;134(6):1045–7. https://doi.org/10.1542/peds.2014-1377 . Epub 2014 Nov 10. PMID: 25384495.
Zhang X, Li L, Zhang Q, Le LH, Wu Y. Physician empathy in doctor-patient communication: A systematic review. Health Commun . 2023:1–11. doi: 10.1080/10410236.2023.2201735. Epub ahead of print. PMID: 37062918.
Libo J. Analysis and resolution of doctor-patient disputes in grassroots pediatric outpatient clinics. Chin Community Phys. 2020;36(10):184–.
Lingyan G. Research on Building a Good Pediatric Doctor-Patient Relationship from the perspective of Public Management [D]. Suzhou University; 2022. https://doi.org/10.27351/dcnki.gszhu.2022.000265 .
Wuying C, Lianqi L. The influence of context on empathy. Prog Psychol Sci. 2016;24(1):91–100. https://doi.org/10.3724/SP.J.1042.2016.00091 .
Song C, Du XT, Hong YX, Mao JH, Zhang W. Association between social supports and negative emotions among pediatric residents in China: the chain-mediating role of psychological resilience and burnout. Front Public Health 2022;10:962259. https://doi.org/10.3389/fpubh.2022.962259 , PMID: 36755738.
Jiaman S, Lihua L, Linling Y, et al. An analysis of the current situation and difficulties in informing cancer bad news. Chin Med Ethics. 2023;36(05):540–7.
Lases LSS, Arah OA, Busch ORC, Heineman MJ, Lombarts KMJMH. Learning climate positively influences residents’ work-related well-being. Adv Health Sci Educ Theory Pract 2019;24(2):317–330. https://doi.org/10.1007/s10459-018-9868-4 . Epub 2018 Dec 5. PMID: 30519786.
Min C, Zhen H, Mengxian L. Etc a survey and analysis of the effectiveness of cultivating empathy skills among medical students. Med Educ Res Pract. 2023;31(5):583–6. https://doi.org/10.13555/j.cnki.c.m.e.2023.05.014 .
Wei W, Ruiling Z, Jiongfeng Z. etc A literature review on the reasons and countermeasures for the shortage of pediatricians in China. Chizi , 2019 (15): 273.
Xinxin Y. A study on the factors influencing the vocational inclination and turnover behavior of Pediatric doctors in Public Medical institutions [D]. Guangdong: Southern Medical University; 2020.
Lei F, Chao S, Yunxia H. Etc analysis of the current situation and countermeasures of occupational burnout among standardized training students for pediatric resident physicians. Chin J Med Educ Explor. 2023;22(5):796–800. https://doi.org/10.3760/cma.j.cn116021-20220518-01297 .
Claramita M, Dalen JV, Van Der Vleuten CP. Doctors in a southeast Asian country communicate sub-optimally regardless of patients’ educational background. Patient Educ Couns. 2011;85(3):e169–74. https://doi.org/10.1016/j.pec.2011.02.002 . Epub 2011 Mar 21. PMID: 21420821.
Lim BT, Moriarty H, Huthwaite M, Gray L, Pullon S, Gallagher P. How well do medical students rate and communicate clinical empathy? Med Teach 2013;35(2):e946-e951. https://doi.org/10.3109/0142159X.2012.715783 . Epub 2012 Sep 3. PMID: 22938688.
Lim BT, Moriarty H, Huthwaite M. Being-in-role: A teaching innovation to enhance empathic communication skills in medical students. Med Teach 2011;33(12):e663-e669. https://doi.org/10.3109/0142159X.2011.611193 , PMID: 22225448.
Zhao J, Xiantao O, Li Q et al. Role of narrative medicine-based education in cultivating empathy in residents. BMC Med Educ 2023;23(1):124. https://doi.org/10.1186/s12909-023-04096-5 , PMID: 36810009.
Ziółkowska-Rudowicz E, Kładna A. Empathy-building of physicians. Part IV–development of skills enhancing capacity for empathy. Pol Merkur Lekarski. 2010;29(174):400–4. Polish. PMID: 21298994.
Haiyan G, Qinmei Z. Yongfei Z, etc the Impact of Bahrain Group activities on Empathy and Communication skills in standardized training of traditional Chinese medicine residents. J Traditional Chin Med Manage. 2019;27(12):103–5.
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This work was financed by Postgraduate Medical Education Project in 2022 (BYH20220412); The 2022 Science and Technology Innovation Project (Humanities and Social Sciences) Project of Shanghai Jiao Tong University School of Medicine (WK2217); Fujian Medical University Education Reform Project: Application Research on the Intelligent Teaching Platform for Clinical Teachers under the Background of “New Medical Science” (J22021).
Pingping Li and Ling Weng contributed equally to this work and should be considered co-first authors.
Department of Pediatric Clinical Medicine School, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
Pingping Li & Lu Dong
Department of Science and Education, Fujian Maternity and Child Health Hospital, Fujian, 350000, China
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L.P.P. conceptualized the idea of this study. L.P.P. and W.L. contributed to design of the project and survey preparation and dissemination. L.P.P. contributed to investigate. D.L. contributed to writing-review and agreed to be accountable for all aspects of the work. All authors reviewed the manuscript.
Correspondence to Lu Dong .
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Ethical approval for this study was obtained from the institutional research ethics committee of Shanghai Children’s Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine(NO: SCMCTRB-K2023147-1). All participants received written explanations about the study in advance and signed a written consent form to participate.
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Li, P., Weng, L. & Dong, L. Empathy ability and influencing factors among pediatric residents in China: a mixed-methods study. BMC Med Educ 24 , 955 (2024). https://doi.org/10.1186/s12909-024-05858-5
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Dementia DataHub website fills a critical epidemiological data gap
CHICAGO, September 3, 2024 – In the first comprehensive count of all Medicare beneficiaries of all ages with documented diagnosis of some form of dementia, a new study published today in JAMA Network Open estimated that millions of Americans in Medicare had diagnostic evidence of dementia. The study, conducted by researchers at NORC at the University of Chicago and George Washington University’s Milken Institute School of Public Health, estimated that in 2019, 5.4 million Medicare beneficiaries had diagnoses indicating likely or highly likely dementia, and an additional 2.6 million people had diagnoses indicating possible dementia.
The study, “Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare,” developed and applied a new case definition of claims-based dementia identification informed by a systematic review of previous definitions used across the research community. Dementia causes progressive deterioration in memory, language, and bodily function and ultimately results in death. The study used a new diagnostic and drug code case definition based on a systematic review of previous research.
“This new case definition for identifying dementia is novel in that it was driven by researcher consensus and data analysis,” said Kan Gianattasio , the first author of the study and a research scientist in Health Care Evaluation at NORC at the University of Chicago. “It is an important first step toward our goal of developing a refined national surveillance system that can be used by public health researchers and practitioners, policymakers, and medical professionals for dementia monitoring and research purposes.”
The project team used the case definition developed in the paper to create the Dementia DataHub , a public website that provides detailed geographic and demographic data on different types of diagnosed dementia in Medicare at the national, state, and county level. The Dementia DataHub currently includes estimates for beneficiaries enrolled in Medicare in 2020.
According to the study:
The authors caution that their research includes only people who received documentation for a diagnosis or prescription drug through Medicare; prior research has indicated that as many as up to 61 percent of dementia patients in the United States are undiagnosed.
According to the Dementia DataHub:
Research regarding the reasons for differences in diagnosis rates by demographics and geography is ongoing.
The Dementia DataHub draws data from Medicare, which provides nearly universal health coverage for people ages 65 and older, and coverage for selected groups younger than 65 such as those who qualify because of disability. NORC and its collaborating organizations analyzed Medicare fee-for-service claims and Medicare Advantage encounter data to measure the scope and outcomes of Alzheimer’s and related dementias (ADRD). Among Medicare beneficiaries enrolled in 2020, over 8.1 million were coded as having some form of diagnosed dementia.
“We developed the Dementia DataHub to provide the research community and the public with data visualizations and tools to explore the epidemiology of diagnosed dementia in the United States,” said David Rein , program area director in NORC’s Public Health Analytics Program Area and principal investigator of the grant that supports the project. “The DataHub provides the first counts and statistics on diagnosed dementia prevalence, incidence, all-cause Medicare payments, mortality, and COVID-19 infection, at the national, state, and county levels, and uses the internet to facilitate the use of these data. We hope our work can help others better understand regional and demographic variation in diagnosed dementia and use the information to reduce the impact of dementia on individuals and families nationwide.”
The Dementia DataHub is a joint effort led by NORC at the University of Chicago, with intellectual and technical support from George Washington University’s Milken Institute School of Public Health and KPMG LLP. The DataHub is funded by the National Institute on Aging, part of the National Institutes of Health, through Grant R01-AG-075730. The content of both the paper and the DataHub is solely the responsibility of the project team and does not necessarily represent the official views of the National Institutes of Health.
“We needed a nationwide dementia surveillance system to really understand the scope of the dementia epidemic, direct critical support to those in need, address disparities, and create informed policy solutions,” said Dr. Melinda Power, the director of George Washington University’s Institute for Brain Health and Dementia, and co-investigator on the project. “The Dementia DataHub fills that need.”
Multidisciplinary experts from across NORC contributed to building a website that presents our findings via visually appealing and easily accessible interactive maps and dashboards, allowing users to delve deep while safeguarding individual privacy. The DataHub also contains a summary of the website’s data that will eventually be updated to include the drivers and determinants of geographic variation in dementia outcomes.
“The prevalence of Alzheimer’s and related dementias is rising fast, and the impact is not equitable. Policymakers and advocates need the best data possible to track and fight back against this disease. The Dementia Datahub provides all of us with actionable insights that can enable us to fight back against Alzheimer’s with greater efficiency, effectiveness, and precision. Together with our National Alzheimer’s Disease Index, this information enables us to better execute on our mission to end the disease—for everyone, everywhere,” said Russ Paulsen, chief operating officer of UsAgainstAlzheimer’s, a leading Alzheimer’s not-for-profit working on prevention, early detection and diagnosis and equal access to treatments for ADRD. UsAgainstAlzheimer’s has been briefed about the Dementia DataHub but was not involved with project.
The portal’s public use files provide the granular data researchers and others need to better investigate and understand ADRD and its scope and to more effectively plan public health services as the nation prepares for a potential surge in cases. In the future, the Dementia DataHub will include additional indicators, data sources, and more.
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Dementia DataHub
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Contact: For more information, please contact Eric Young at NORC at young-eric@norc.org or (703) 217-6814 (cell).
About George Washington University’s Milken Institute School of Public Health The George Washington University Milken Institute School of Public Health is proud to be a global leader in public health education and research. As the only school of public health in Washington, DC, we work to spearhead initiatives and programs that tackle many of the world’s most pressing public health challenges, work to improve community health policy, and assess the quality of care provided nationally and around the world. GWSPH faculty, researchers and students maximize their long-standing relationships with the world’s most influential health organizations to advance learning and research for the benefit of all. Together, we have developed groundbreaking models for national and international health care reform.
About KPMG LLP. KPMG LLP. is one of the largest professional services firms in the US, with about 40,000 professionals providing audit, tax, and advisory services. With over 90 offices nationwide, we serve clients in all 50 states. The KPMG team on this work brings extensive experience in healthcare data analytics, modeling, and visualization.
A new study by researchers at the Azrieli Centre for Autism Research (ACAR) has uncovered a promising approach for reducing brain inflammation.
Glial cells, which support and protect neurons, can become overactive during injury and brain inflammation. This overactivity may contribute to chronic neurodegeneration and worsen brain disorders. Understanding how this process, called reactive gliosis, is controlled could help scientists better understand brain diseases and improve treatments.
The study, published in the open-access journal Cell Reports , found that removing a gene linked to autism, CHD8, from certain brain cells called astrocytes, reduces over-reactivity during brain injury and inflammations in experimental mouse models.
Researchers found that adult mice with the CHD8 gene removed from astrocytes experienced less brain inflammation and alleviated symptoms compared to mice with the gene. Removing the gene changed how DNA is packaged and transcribed in astrocytes, leading to changes in the activity of other genes necessary for the growth and communication of these brain cells with others.
The team was eager to explore how their discovery could be applied in real-world treatments. Further work showed they could reduce astrocyte reactivity directly in the brain using CRISPR, a specific gene-editing technology that is revolutionizing biomedical research. These results suggest that targeting the CHD8 gene in astrocytes in the adult brain could be a promising approach for reducing brain inflammation and treating related brain disorders.
This study was led by Platon Megagiannis, a PhD student in the Integrated Program in Neuroscience of McGill University, in the lab of Yang Zhou . Contributing labs include those of Guy Rouleau, Stefano Stifani, and Keith Murai from ACAR; Neville Sanjana from the New York Genome Center; Gene Yeo and Trey Ideker from the University of California San Diego; and Guoping Feng at MIT.
Autism-associated CHD8 controls reactive gliosis and neuroinflammation via remodeling chromatin in astrocytes. Megagiannis, Platon et al. Cell Reports, Volume 43, Issue 8, 114637. DOI: 10.1016/j.celrep.2024.114637
Yang Zhou (left) and Platon Megagiannis.
The Neuro (Montreal Neurological Institute-Hospital) is a bilingual academic healthcare institution. We are a McGill research and teaching institute; delivering high-quality patient care, as part of the Neuroscience Mission of the McGill University Health Centre. We are proud to be a Killam Institution, supported by the Killam Trusts.
The neuro (montreal neurological institute-hospital).
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This study thus aims to address this research gap by examining the impacts of different levels of internal communication, more specifically, corporate symmetrical communication and supportive peer communication, two positive indicators of internal communication revealed in previous literature (e.g., Linke & Zerfass 2011; Men & Bowen 2017), on ...
isolated functions of internal communication, instead of examining it as an integrated system (see Bowman, 2020; Brown & Roloff, 2015; Kang & Sung, 2017). This study thus aims to address this research gap by examining the impacts of different levels of internal communication, more specifically, corporate symmetrical communication and
Despite the importance of internal communication (IC) in organization and management studies, its role or impact on employee outcomes (specifically employee loyalty, commitment and citizenship behaviour) remains understudied. To address the preceding gap, this article systematically reviews the role of IC and its impact on employee outcomes.
This book is grounded in solid scientific research and informed by evolving theories and practice in internal communication. Each chapter includes a practitioner's perspective at the end contributed by an industry leader in internal communication, being a professional interview or a mini-case study.
John Baldoni. [For more, visit the Communication Insight Center.] "Courage, innovation and discipline help drive company performance especially in tough economic times. Effective internal ...
Chapter 1: Evolving Research and Practices in Internal Communication. Linjuan Rita Men, Ph.D., APR, University of Florida. Internal communication, sometimes referred to as employee communication ...
The prevalence of theoretical frameworks across the spectrum of internal communication research highlights the interdisciplinarity and complexity of the field. The fact that a substantial portion of the studies reviewed derives from social psychological or psychological theories—like social capital, social cognitive, and social information ...
The present study examines the roles of internal communication (IC), job engagement (JE), organisation engagement (OE) and job satisfaction (JS) in producing employee loyalty (EL) based on the ...
This study aims to identify and provide an overview of the research in the area through a systematic review of 77 research studies published in 52 journals between the beginning of 1990 and November 2022. The findings reveal the evolution of research on digital internal communication and highlight geographical, theoretical, and methodological ...
The research aims to examine the impact of internal communication and enterprise social networking. This was tested through the formation of eight sub-hypothesis and analysis of data from the survey.
INTRODUCTION. Research suggests that internal communication (IC) is crucial for organisational outcomes (Murray, 2013; Ruck & Men, 2021).Despite its strategic management function, IC is still considered a neglected research field (Klewes et al., 2017; Yaxley & Ruck, 2017).Some scholars explain the limited literature on IC by it being a flourishing but still young discipline (Lee & Yue, 2020 ...
Highlights Internal communication is among the fastest growing specializations in public relations and communication management. The Delphi study in internal communication in Europe reveals its fuzzy picture - it seems to be in adolescence. The study defines internal communication as a management function in charge of communication (within organizations). Internal communication is an ...
Pruyn, and Riel (2001) and DeRidder (2004) internal communication is a factor. contributing to external prestige and when that external image is positive, employees. experience a greater sense of identification with the organization. Organizational. identification is a variable of employee engagement.
Internal communication is an important function in an organization in relation to human resource management. This research examined the employee-perceived internal communication and investigated ...
Finally, the discriminant validity of ICSQ was not tested and should be examined in future studies.,The resulting 32-item instrument, in English, can be used for empirical and practical purposes in improving internal communication.,The study confirms that internal communication is a multidimensional construct and should be measured as such.
We see this approach to internal crisis communication as obsolete, and not in line with the research front in organization studies, management, or communication studies. One of the biggest problems with this approach, at least from a strategic communication perspective, is that communication is reduced to a matter of transmission of information ...
[For more, visit the Communication Insight Center.] "Courage, innovation and discipline help drive company performance especially in tough economic times. Effective internal communications can ...
Strategic Communication . Dr. Linjuan "Rita" Men, assistant professor in the Department of Public Relations at the University of Florida College of Journalism and Communications co-authored a new book titled Excellence in Internal Communication Management, published by Business Expert Press.. The book "integrates theories, research insights, practices, as well as current issues and cases ...
Although an abundance of research has examined the drivers of organizational innovativeness in organization studies, limited research has examined it in communication research. Yet communication is critical to organizational innovation (Leonardi, 2014; Monge et al., 1992). Hence, this research will have important practical implications for ...
Evaluate the effectiveness of your communication strategy. With these six factors in mind, leaders should consider the following three steps: 1. Articulate why your organization exists ...
Internal communication facilitates the effective and successful flow of conversation between teams and individual employees. ... Research shows that employees spend up to 30% of their workweek seeking clarification and additional information due ... Studies reveal that companies with strong two-way communication experience 20% increase in ...
This study examines the trends of internal communication research in public relations with a quantitative content analysis. • Research topics, theories, methodology, and authorship information were analyzed using a total of 223 articles.
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 ...
DALLAS - Sept. 03, 2024 - A computer model developed by UT Southwestern Medical Center researchers significantly enhances the ability of scientists to detect communication between cells, according to a new study published in Nature Methods.The model, called Spacia, could help advance understanding of a wide range of diseases including cancers, autoimmune disorders, infectious diseases, and ...
Effective internal communication may become the key to increase of. employees' motivation and involvement, increase of work efficiency, success of changes and. to build a positive internal and ...
Empathy is one of the fundamental factors enhancing the therapeutic effects of physician-patient relationships, but there has been no relevant research in China on the pediatric resident physicians' capacity for empathy or the influencing factors. A mixed-methods study was undertaken. The student version of the Jefferson Scale of Empathy was used to assess 181 postgraduate residents at ...
THIS VACANCY IS OPEN TO INTERNAL APPLICANTS ONLY. About us. Recently re-founded, the Department of Engineering is rapidly expanding into a world-class research and teaching department. Research currently focuses on robotics, telecommunications and biomedical engineering, but we are looking to establish new research themes.
CHICAGO, September 3, 2024 - In the first comprehensive count of all Medicare beneficiaries of all ages with documented diagnosis of some form of dementia, a new study published today in JAMA Network Open estimated that millions of Americans in Medicare had diagnostic evidence of dementia. The study, conducted by researchers at NORC at the University of Chicago and George Washington ...
A new study by researchers at the Azrieli Centre for Autism Research (ACAR) has uncovered a promising approach for reducing brain inflammation. Glial cells, which support and protect neurons, can become overactive during injury and brain inflammation. This overactivity may contribute to chronic neurodegeneration and worsen brain disorders. Understanding how this process, called reactive ...
In the Conceptual Framework below (see figure-1) the research objectives are integrated with the independent variable (leadership behaviors) and the dependent variables (organizational change management process), change initiatives implementation, and organizational performance.The academic community is identified as the specific group perceiving the leadership behaviors and their impact on ...