essay on language development

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Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language – what words and phrases they unconsciously choose and combine – can help us better understand ourselves and why we behave the way we do.

Linguistics scholars seek to determine what is unique and universal about the language we use, how it is acquired and the ways it changes over time. They consider language as a cultural, social and psychological phenomenon.

“Understanding why and how languages differ tells about the range of what is human,” said Dan Jurafsky , the Jackson Eli Reynolds Professor in Humanities and chair of the Department of Linguistics in the School of Humanities and Sciences at Stanford . “Discovering what’s universal about languages can help us understand the core of our humanity.”

The stories below represent some of the ways linguists have investigated many aspects of language, including its semantics and syntax, phonetics and phonology, and its social, psychological and computational aspects.

Understanding stereotypes

Stanford linguists and psychologists study how language is interpreted by people. Even the slightest differences in language use can correspond with biased beliefs of the speakers, according to research.

One study showed that a relatively harmless sentence, such as “girls are as good as boys at math,” can subtly perpetuate sexist stereotypes. Because of the statement’s grammatical structure, it implies that being good at math is more common or natural for boys than girls, the researchers said.

Language can play a big role in how we and others perceive the world, and linguists work to discover what words and phrases can influence us, unknowingly.

How well-meaning statements can spread stereotypes unintentionally

New Stanford research shows that sentences that frame one gender as the standard for the other can unintentionally perpetuate biases.

Algorithms reveal changes in stereotypes

New Stanford research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

Exploring what an interruption is in conversation

Stanford doctoral candidate Katherine Hilton found that people perceive interruptions in conversation differently, and those perceptions differ depending on the listener’s own conversational style as well as gender.

Cops speak less respectfully to black community members

Professors Jennifer Eberhardt and Dan Jurafsky, along with other Stanford researchers, detected racial disparities in police officers’ speech after analyzing more than 100 hours of body camera footage from Oakland Police.

How other languages inform our own

People speak roughly 7,000 languages worldwide. Although there is a lot in common among languages, each one is unique, both in its structure and in the way it reflects the culture of the people who speak it.

Jurafsky said it’s important to study languages other than our own and how they develop over time because it can help scholars understand what lies at the foundation of humans’ unique way of communicating with one another.

“All this research can help us discover what it means to be human,” Jurafsky said.

Stanford PhD student documents indigenous language of Papua New Guinea

Fifth-year PhD student Kate Lindsey recently returned to the United States after a year of documenting an obscure language indigenous to the South Pacific nation.

Students explore Esperanto across Europe

In a research project spanning eight countries, two Stanford students search for Esperanto, a constructed language, against the backdrop of European populism.

Chris Manning: How computers are learning to understand language​

A computer scientist discusses the evolution of computational linguistics and where it’s headed next.

Stanford research explores novel perspectives on the evolution of Spanish

Using digital tools and literature to explore the evolution of the Spanish language, Stanford researcher Cuauhtémoc García-García reveals a new historical perspective on linguistic changes in Latin America and Spain.

Language as a lens into behavior

Linguists analyze how certain speech patterns correspond to particular behaviors, including how language can impact people’s buying decisions or influence their social media use.

For example, in one research paper, a group of Stanford researchers examined the differences in how Republicans and Democrats express themselves online to better understand how a polarization of beliefs can occur on social media.

“We live in a very polarized time,” Jurafsky said. “Understanding what different groups of people say and why is the first step in determining how we can help bring people together.”

Analyzing the tweets of Republicans and Democrats

New research by Dora Demszky and colleagues examined how Republicans and Democrats express themselves online in an attempt to understand how polarization of beliefs occurs on social media.

Examining bilingual behavior of children at Texas preschool

A Stanford senior studied a group of bilingual children at a Spanish immersion preschool in Texas to understand how they distinguished between their two languages.

Predicting sales of online products from advertising language

Stanford linguist Dan Jurafsky and colleagues have found that products in Japan sell better if their advertising includes polite language and words that invoke cultural traditions or authority.

Language can help the elderly cope with the challenges of aging, says Stanford professor

By examining conversations of elderly Japanese women, linguist Yoshiko Matsumoto uncovers language techniques that help people move past traumatic events and regain a sense of normalcy.

Home — Essay Samples — Science — Language — Essay On The Importance Of Language

Essay on The Importance of Language

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Observation

The littlest linguists: new research on language development.

• Bilingualism
• Developmental Psychology
• Language Development

How do children learn language, and how is language related to other cognitive and social skills? For decades, the specialized field of developmental psycholinguistics has studied how children acquire language—or multiple languages—taking into account biological, neurological, and social factors that influence linguistic developments and, in turn, can play a role in how children learn and socialize. Here’s a look at recent research (2020–2021) on language development published in Psychological Science . 

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment

Dawoon Choi, Laura J. Batterink, Alexis K. Black, Ken A. Paller, and Janet F. Werker (2020)

One of the first challenges faced by infants during language acquisition is identifying word boundaries in continuous speech. This neurological research suggests that even preverbal infants can learn statistical patterns in language, indicating that they may have the ability to segment words within continuous speech.

Using electroencephalogram measures to track infants’ ability to segment words, Choi and colleagues found that 6-month-olds’ neural processing increasingly synchronized with the newly learned words embedded in speech over the learning period in one session in the laboratory. Specifically, patterns of electrical activity in their brains increasingly aligned with sensory regularities associated with word boundaries. This synchronization was comparable to that seen among adults and predicted future ability to discriminate words.

These findings indicate that infants and adults may follow similar learning trajectories when tracking probabilities in speech, with both groups showing a logarithmic (rather than linear) increase in the synchronization of neural processing with frequent words. Moreover, speech segmentation appears to use neural mechanisms that emerge early in life and are maintained throughout adulthood.

Parents Fine-Tune Their Speech to Children’s Vocabulary Knowledge

Ashley Leung, Alexandra Tunkel, and Daniel Yurovsky (2021)

Children can acquire language rapidly, possibly because their caregivers use language in ways that support such development. Specifically, caregivers’ language is often fine-tuned to children’s current linguistic knowledge and vocabulary, providing an optimal level of complexity to support language learning. In their new research, Leung and colleagues add to the body of knowledge involving how caregivers foster children’s language acquisition.

The researchers asked individual parents to play a game with their child (age 2–2.5 years) in which they guided their child to select a target animal from a set. Without prompting, the parents provided more informative references for animals they thought their children did not know. For example, if a parent thought their child did not know the word “leopard,” they might use adjectives (“the spotted, yellow leopard”) or comparisons (“the one like a cat”). This indicates that parents adjust their references to account for their children’s language knowledge and vocabulary—not in a simplifying way but in a way that could increase the children’s vocabulary. Parents also appeared to learn about their children’s knowledge throughout the game and to adjust their references accordingly.

Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication

Elise A. Piazza, Liat Hasenfratz, Uri Hasson, and Casey Lew-Williams (2020)

This research tracked real-time brain activation during infant–adult interactions, providing an innovative measure of social interaction at an early age. When communicating with infants, adults appear to be sensitive to subtle cues that can modify their brain responses and behaviors to improve alignment with, and maximize information transfer to, the infants.

Piazza and colleagues used functional near-infrared spectroscopy—a noninvasive measure of blood oxygenation resulting from neural activity that is minimally affected by movements and thus allows participants to freely interact and move—to measure the brain activation of infants (9–15 months old) and adults while they communicated and played with each other. An adult experimenter either engaged directly with an infant by playing with toys, singing nursery rhymes, and reading a story or performed those same tasks while turned away from the child and toward another adult in the room.

Results indicated that when the adult interacted with the child (but not with the other adult), the activations of many prefrontal cortex (PFC) channels and some parietal channels were intercorrelated, indicating neural coupling of the adult’s and child’s brains. Both infant and adult PFC activation preceded moments of mutual gaze and increased before the infant smiled, with the infant’s PFC response preceding the adult’s. Infant PFC activity also preceded an increase in the pitch variability of the adult’s speech, although no changes occurred in the adult’s PFC, indicating that the adult’s speech influenced the infant but probably did not influence neural coupling between the child and the adult.

Theory-of-Mind Development in Young Deaf Children With Early Hearing Provisions

Chi-Lin Yu, Christopher M. Stanzione, Henry M. Wellman, and Amy R. Lederberg (2020)

Language and communication are important for social and cognitive development. Although deaf and hard-of-hearing (DHH) children born to deaf parents can communicate with their caregivers using sign language, most DHH children are born to hearing parents who do not have experience with sign language. These children may have difficulty with early communication and experience developmental delays. For instance, the development of theory of mind—the understanding of others’ mental states—is usually delayed in DHH children born to hearing parents.

Yu and colleagues studied how providing DHH children with hearing devices early in life (before 2 years of age) might enrich their early communication experiences and benefit their language development, supporting the typical development of other capabilities—in particular, theory of mind. The researchers show that 3- to 6-year-old DHH children who began using cochlear implants or hearing aids earlier had more advanced language abilities, leading to better theory-of-mind growth, than children who started using hearing provisions later. These findings highlight the relationships among hearing, language, and theory of mind.

The Bilingual Advantage in Children’s Executive Functioning Is Not Related to Language Status: A Meta-Analytic Review

Cassandra J. Lowe, Isu Cho, Samantha F. Goldsmith, and J. Bruce Morton (2021)

Acommon idea is that bilingual children, who grow up speaking two languages fluently, perform better than monolingual children in diverse executive-functioning domains (e.g., attention, working memory, decision making). This meta-analysis calls that idea into question.

Lowe and colleagues synthesized data from studies that compared the performance of monolingual and bilingual participants between the ages of 3 and 17 years in executive-functioning domains (1,194 effect sizes). They found only a small effect of bilingualism on participants’ executive functioning, which was largely explained by factors such as publication bias. After accounting for these factors, bilingualism had no distinguishable effect. The results of this large meta-analysis thus suggest that bilingual and monolingual children tend to perform at the same level in executive-functioning tasks. Bilingualism does not appear to boost performance in executive functions that serve learning, thinking, reasoning, or problem solving.

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Elika Bergelson’s Quest Into Infants’ Language Development 

Elika Bergelson, an associate developmental psychology professor at Harvard University, is known for her work on language acquisition, cognitive development, and word learning in infants. Her key research focuses on how infants learn language from the world around them. 

Teaching: Ethical Research to Help Romania’s Abandoned Children 

An early intervention experiment in Bucharest can introduce students to the importance of responsive caregiving during human development.

Silver Linings in the Demographic Revolution 

Podcast: In her final column as APS President, Alison Gopnik makes the case for more effectively and creatively caring for vulnerable humans at either end of life.

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• Essay on Psychology

Language Development In Children Essay

Type of paper: Essay

Topic: Psychology , Learning , Family , Rhetoric , Development , Environment , Children , Sociology

Published: 11/25/2019

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Language development in children

Children acquire language through cognitive processes, which are complex and involve a plethora of steps that include sensory awareness, followed by crying, babbling, gurgling and cooing. They then displays signs of comprehensive words ascribed to signals from adults, imitate others speech and differentiating the various sounds, as affirmed by Rathus (2011). Moreover, after a period of several months, they begin to enounce words in a meaningful way. Many theories have been developed concerning the development of language in children, in tandem with this, variable facets have been brought forth attesting the factors that enhance and discourage early language development in children. Rathus ( 2011) further ratifies that the problems and concerns in language development have been largely affected by the nature (heredity) and nurture (environmental) effect. Rathus (2011) attests that language in children is enhanced when parents and other adults participate fully in the nurturing of the child’s language skills and abilities. The likely situations that can foster early development of language in children may comprise versatile aspects that include; Adult-child interaction and dynamics that involve surrounding babies with adequate learning experience, and avoiding baby safe activities and exposure of the child to abstract symbolic systems. These facets can be achieved through response to child’s expressive language efforts, in a friendly and attuned manner. The use of simplified form of speech, also known as infant directed speech, is characterized by brief and straightforward sentences in syntax and largely focused on nouns, verbs and few modifiers. The speech is also spoken slowly, accompanied with high pitch and distinct pauses (Rathus, 2011). Similarly, the key words are placed at the end of the sentences, articulated in higher voice and emphasized through repetition. Other factors that enhance language development comprise of; reading to the child, use of questions to engage the child in a conversion, gesturing to assist the child to understand whatever he or she is saying and describing the aspects of environment engrossing the infant’s current centre of attention (Rathus, 2011). Consequently, social environment provides the desired input necessary for the cognitive development of the child for better language development. Similarly, a well-established environment and exposure of the child to certain language patterns, context and rules, with frequent reinforcement and encouragement, may foster the child’s language acquisition. Gupta (2009) asserts that better health and a prolific socioeconomic environment may provide the child with the necessary materials viable for language and speech enhancement. Language environment, to which the child is exposed, can also help the child in learning auditory skills necessary for language development (Rathus, 2011). However, some situations may discourage adequate, early language development. Lack of parental conversation and presence can contribute to insufficient opportunities for progressive language acquisition, grammar and vocabulary attainment. In the same light, poor cognitive and intellectual development of the child may hinder the development of quality speech due to the strong relationship between intelligence and speech formation (Gupta, 2009). Poor health especially in the initial years of development may adversely affect the speech development. This highly attributed to hearing problems with limited pronunciation and vocabulary content. Poor social and socio economic environments, can also lead to a delayed or relatively retarded speech, characterized by lack of relevant toys and books that can aid in a faster language development (Gupta, 2009). Concisely, the development of language in children is a vital thing, since it enables them communicate and socialize with people in the society. Moreover, it helps them present their needs to parents and other adults. Heredity factors and environmental facets are the fundamental issues that affect cognitive development of speech in children. Thence, it is particularly crucial to keep and maintain a social, friendly environment that can aid and enhance language development in children. It is also worth noting that frequent exposure children to a better environment, facilitates cognitive development that enables a quicker learning of languages.

Gupta, S. (2009). Early Childhood Care and Education. New Delhi: Asoke K. Ghosh, PHI Learning Private Limited. Rathus, A. S. (2011). Childhood: Voyages in Development (4th Ed.). Belmont, CA: Wadsworth Cengage Learning.

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Theories of Language Development: A Brief Overview of the Foundational Ideas

Language development is a seriously complex topic.

This post will start you on the road to uncovering the most important figures, theories and facts you’ll most likely hear over and over again in your language learning journey.

Deep breaths everyone, as we delve into the fascinating theories of language development.

A Brief Overview of Language Development Theories

1. chomsky’s nativist language theories, 2. b.f. skinner’s behaviorist perspective, 3. piaget’s cognitive development theory, 4. vygotsky’s constructivist learning theory, what these language development theories mean for you.

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

The most prominent figure in language development is Noam Chomsky, who’s been studying this ever since his days at MIT. Then there are those who have offered their take on language development from a psychological perspective. This includes psychologists such as B.F. Skinner, Jean Piaget and Lev Vygotsky.

We’ll be giving you a brief overview of their theories and perspectives. Fair warning to all: There’s a lot of psychology here, so be prepared for a bunch of fancy new terms (we’ll explain them briefly as we go, of course).

Noam Chomsky has been studying and developing his theories since the 1950s.

In his book  “Aspects of the Theory of Syntax” published in 1965, he has pushed forward the fundamental observation that there are deep structures and surface structures in every sentence , no matter what language.

This is the reason why you can form sentences with similar meaning using a theoretically infinite combination of words.

Essentially, deep structures are the thoughts and meanings we want to express and surface structures are the words, sounds and symbols we use to try and express them.

Let’s look at some examples. Take a look at the following sentence:

Language development seems really complicated to me.

I think language development is really complicated .

Both express exactly the same thing using different words and a different word order. The deep structure is the same (the notion that language development is obviously not the simplest thing in the world), though the words used (surface structure) are different.

The use of these words and their structures are refined over the course of time. It changes and evolves on the surface, but the deeper structures remain. This is a part of Chomsky’s transformational-generative grammar theory .

Another important contribution Chomsky made to linguistic studies is the theory of universal grammar . He asserted that the human brain contains a mechanism for language acquisition, meaning that our languages share the same deeper structures despite the largely superficial surface structures .

This is why it’s possible for anyone to learn a foreign language , regardless of the complexity of its grammatical structure or script.

Tackling the issue of language from a different perspective was B.F. Skinner , the behavioral psychologist.

Simply put, the behavioral perspective postulates that everything we do is dictated by our environment and that our behavior is a response to external stimuli through operant conditioning , the process through which behavior changes with positive and negative reinforcement.

B.F. Skinner theorized that language acquisition  is dictated by our environment and the positive or negative reinforcement we receive from it.

Parents, for example, enforce correct usage of a word in children with positive facial or verbal reactions. They play larger roles in our “verbal behavior,”  a concept Skinner describes in his book. Verbal behavior introduces the concept of functions to words , as well as meanings.

For example, a child may know what to call a toilet, but they must also learn what the use of that word will allow them to acquire or express. They’ve heard their parents say this word, but what happens when they say it? Most likely, their parents take them to it.

So in this case, the most basic function of the word is to express a need to use the bathroom. A pretty important thing to be able to express, wouldn’t you say?

Jean Piaget was another prominent psychologist who offered yet another take on language acquisition and development. His focus was on child development and the stages children go through to develop and learn.

He asserted that children would only be able to fully grasp some concepts within specific  developmental stages , due to the fact that certain sections of the brain would only further develop at certain ages.

For example, since the sensorimotor stage develops first during the first two years of a child’s life, children focus on their immediate surroundings, experimenting with the things around them by playing with them, biting them or throwing them.

Throughout this stage, they’ll take things apart, put things back together and explore the concept of things existing in and out of sight. By the end of it all, they’ll be able to visualize things that aren’t there in front of them, which is arguably the most crucial part of this stage when it comes to language and communication.

Next comes the preoperational stage in which children are able to think in slightly more abstract ways. They begin to toy with symbols. They’ll use words in ways that aren’t generally accepted or understood.

For example, they may use the word “pillow” to mean “cloth” purely because of the few shared characteristics between the two objects.

They do this for egocentric communication. Anyone who’s ever tried to communicate with a two-year-old will know that they aren’t all that interested in other perspectives. They’re too busy trying to explore their own mind, so don’t take it personally.

You may have noticed already that these concepts focus less on language and more on cognitive development during childhood and you’d be right.

That being said, it’s still important to know because Piaget did establish that language plays a huge role in cognitive development , chiefly in the way children use language throughout each stage.

During the sensorimotor stage, children experiment with sounds, and language is mostly about the auditory aspects. They don’t care about the meaning, they just like to create sounds.

During the preoperational stage, children use language to express themselves, but they can’t really distinguish conversation from pure expression.

During the concrete operational stage , children state facts and observations. Finally, during the formal operational stage , children are able to use language to express, discuss and debate abstract concepts.

Not completely unrelated is Lev Vygotsky ‘s theory of social development.

It’s referred to as the constructivist perspective  and describes the concept of development through construction of thought and meaning. To understand it completely, you first have to understand his perspective.

It challenges the more widely-held concept of knowledge and proposes that knowledge is a construction of meaning unique to the individual . How a person grew up (their culture) will affect how they think. He emphasizes the importance of others in our development (i.e., social interaction and guided learning).

Vygotsky postulated that language develops similarly, but focused on the development of social speech, private speech and inner speech .

Social speech is the language we use with others while private speech (talking to ourselves) is not meant to communicate with others (this happens around the age of three). Inner speech only really begins to appear around the age of six or seven with private speech being internalized.

It’s a complex idea that goes beyond the scope of this post, but children at this stage begin to internalize language and meaning and, as Vygotsky says, begin “thinking in pure meaning.”

Suffice it to say that our relationship with language becomes increasingly more sophisticated and goes beyond the meaning of the words and into the feelings or ideas the words elicit.

In the same way that these theories have aided children and teachers in refining their learning and teaching techniques, you can use this knowledge to fine-tune your language-learning methods.

With a better understanding of these theories and their roots, you can ask yourself, “Is the approach I’m using right now working for me?”

If we consider Chomsky’s ideas of universal grammar , we can say that all languages adhere to certain grammatical parameters, like word order. Our job is then to figure out those parameters by hearing example sentences and formulating the rules of the second language. 

Or should you try tackling grammar from another angle? For example, you might do that by spending time in a different environment where that foreign language is abundant as the constructivist perspective might suggest.

Of course, if you can’t go to another country, you can immerse yourself in the language at home. There are many programs that can help you do so, like FluentU —which uses authentic videos like movie clips and music videos to teach languages in context. The program also helps you study through the use of interactive subtitles and flashcards, as well as quizzes, transcripts, a contextual dictionary and other useful learner features.

What about those of you who are trying to find ways to teach your children a second language ? Perhaps taking a look at Piaget’s developmental stages could help you figure out where your child’s mind is focused and how best to introduce a new language to them.

With so many theories out there concerning language development, see what works for you and which theories or perspectives you’d like to explore.

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Language Development

• Reference work entry
• First Online: 01 January 2021
• pp 4469–4483
• Cite this reference work entry

• Catherine S Tamis-LeMonda 3 ,
• Lulu Song 4 &
• Katelyn K Fletcher 3  

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Language learning ; Linguistic development ; Verbal communication

Language development is the process by which children come to understand and produce language to communicate with others, and entails the different components of phonology (sounds of a language), semantics (word meanings), grammar (rules on combining words into sentences), and pragmatics (the norms around communication).

Introduction

Language lies at the heart of humanity. The thousands of words and countless nuanced grammatical rules of language enable people to achieve a depth and breadth of social understanding and communication that is uniquely human. Without language, people would be left to rely on imprecise facial expressions and body movements to guess others’ intentions, states, and emotions. People would be unable to pass along complex information and fully exploit the expertise of others. Language underpins the transmission of knowledge, traditions, expectations, and values across generations...

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Tamis-LeMonda, C.S., Song, L., Fletcher, K.K. (2021). Language Development. In: Shackelford, T.K., Weekes-Shackelford, V.A. (eds) Encyclopedia of Evolutionary Psychological Science. Springer, Cham. https://doi.org/10.1007/978-3-319-19650-3_2410

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These personal essays by first and second language researchers and practitioners reflect on issues, events, and people in their lives that helped them carve out their career paths or clarify an important dimension of their missions as educators. Their narratives depict the ways in which professionals from diverse backgrounds and work settings have grappled with issues in language education that concern all of us: the sources and development of beliefs about language and education, the constructing of a professional identity in the face of ethical and ideological dilemmas, and the constraints and inspirations of teaching and learning environments. They have come together as a collective to engage in a courageous new form of academic discourse, one with the potential to change the field. Many of the authors write their stories of having begun their work with voices positioned at the margins. Now, as established professionals, they feel strong enough collectively to risk the telling and, through their telling, to encourage other voices. This volume is intended to provide graduate students, teachers, and researchers in language education with insights into the struggles that characterize the professional development of language educators. Both readers and contributors should use the stories to view their own professional lives from fresh perspectives -- and be inspired to reflect in new ways on the ideological, ethical, and philosophical underpinnings of their professional personae.

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Language Development Analysis Essay

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Language Development

Though what has led to language and speech difficulties in Autism is yet to be established, many medical practitioners opine that the problem comes as a result of a situation which happened during, after, or before birth period that interfered with brain growth. This problem normally hinders children’s capability to interact with and predict the world. Indeed, the most critical period of language and speech growth is the initial three years of human age.

Actually, this is a crucial period when brain is thought to be growing. Indeed, speech skills normally grow best in areas that are enriched with persistent exposure of interaction with other people. Besides that, communication signs usually happen when children learn that crying actually can cause special attention toward them from parents and people around.

Certainly, it is a time when children become aware of vital sounds or voices from people around them. During the course of the process, children eventually begin to develop sounds that convey whatever they need. At age of six months, children can generate repetitive syllables like “ma” “ma”. Definitely, such jargons improve as age increases.

Brown Roger was a reputed psychologist for his linguistics studies. Brown studied linguistics research upon children. In fact, Brown came up with five stages that analyzed children’s language development. Brown’s analysis was founded upon the Mean of Utterance (MLUm’s) that is the amount of morphemes (essential quantity of meaning) a child can generate. For instance, the word “jump” has a single morpheme, meaning an action. On the other hand, “jumped” has two morphemes, meaning past tense and an action.

Stage I comprises of children who are aged from twelve to twenty six months old. In this stage, children comprehend simple phrases and begin to conserve few words to convey basic needs. Children exhibit common mistakes in writing, pronunciation and grammar which frequently obstruct meaning.

Moreover, children comprehend simple and brief speech under visual assistance. Children also respond to simple subject-predicate sentences; and can read and write simple sentences. In this stage, children are capable to link an entity with an act, and an action with object. For example, child can say, “mum move”. Indeed, such simple word has no negation. For sequence of objects, children can identify objects by using conjunctions.

Moreover, children who are aged from twenty two to twenty six months are capable to generate whole “subject–verb-object” sentences. Indeed, children are able to link words, also can use progressive words using –“ing”, for instance, “dad reading”. The children can easily use prepositions such as “on”, “in”, etc. Actually, MLUm’s rating of children at stage I is above 1.0 which shows that children are beginning to use multi-morphemic speech.

The stage II happen to children who are aged between twenty seven to thirty months old in age. Children normally respond and comprehend to simple tasks; and can speak simple sentences and phrases.

Children are able to make choices when given options and exhibit frequent mistakes in writing, pronunciation and grammar which obstruct meaning. Furthermore, children respond to social speech using simple subject-predicate sentences; and can write and read simple sentences using pictures or graphs. Children can comprehend simple speech, though need visual assistance.

Actually, children at this stage are capable to use words such as “wanna” or “gonna”. Indeed, copula normally begins to emerge together with negative sentences such as “can’t” or “don’t”. Moreover, children are able to place negative entities between predicates and negatives. Children are also capable to use interrogative sentences using words like “where” and “what”. Furthermore, children are able to utilize regular plural sentences and irregular past tense, such as “ran”.

In stage III, children who are aged between thirty one to thirty four months old can comfortably adopt auxiliary verbs when using interrogative and declarative questions. Furthermore, children are able to use auxiliary verbs with copula at end of this stage. Children can converse, write and read simple sentences, and can narrate simple stories and take part in school discussion. Besides that, children can speak with possible grammatical errors; comprehend unfamiliar and usual topics through visual support.

Moreover, children can respond to social speech using complex sentences, and can read and write complex sentences using picture and graphs. Children also exhibit fairly common mistakes in writing, grammar and pronunciation which obstruct meaning. Moreover, children can use conjunctions like “but”, “or”, “so” etc. Besides that, children can use “won’t” in negative sentences. Furthermore, children also adopt regular past tense, articles and possessives when forming sentences.

At this stage, children are developing close to indigenous proficiency in English and take part in complex learning tasks with few grammatical mistakes. Children also can exhibit some mistakes in writing, grammar and pronunciation which do not obstruct meaning. Children can comprehend both academic and social speech; though need visual assistance for unusual topics.

Children can respond to academic learning and social speech using complex sentences and developed vocabulary; and can read complex texts through use of pictures and graphs. Moreover, children who are aged between thirty five to forty months old are under this stage. Indeed, children are capable to use double auxiliary verbs in declarative sentences. Actually, children are able to employ such verbs when negating sentences such as “didn’t”, “isn’t” or “doesn’t”.

Besides that, children are capable to use interrogative sentences using words such as “how” and “when”. Both infinitive and inquiry phrases are comfortably used at the end of sentences and also the use of “because” in order to explain a “cause and impact” of an action. This is the stage when children learn several capabilities which led to development of their pragmatic competences.

Under this stage, children can write, converse and read English in such a way that resemble indigenous English speakers and take part in school activities actively. Children also can exhibit minimal mistakes in writing, grammar and pronunciation which do not obstruct meaning.

Children can effectively communicate using broad range of topics, and can comprehend both academic and social speech effectively. Furthermore, children can respond to academic learning and social speech using variety of sentences and enriched vocabulary; and can independently write and read technical text. Indeed, these are children who are aged between forty-one to forty-six months old; are able to employ indirect objects in sentences.

Actually, children are capable to use “shouldn’t”, “wasn’t”, wouldn’t” etc. In fact, children are able to use relative clauses to form complex sentences. Certainly, both regular and irregular third persons are easily used when forming sentences. Furthermore, children have enhanced speech and also learn vital concept like third person pronouns which develop their pragmatic capabilities.

Brown’s language analysis offered a comprehensive structure in predicting and knowing the course which growth of expressive language normally takes. Indeed, syntax and morphology are common terms that are used by language psychologists when performing structural psychotherapy of children’s speech.

Actually, this particular language development analysis includes examination of child’s progress in aspect such as “pronunciation clarity” and “speech sound”. Indeed, such analysis looks into phonetic assessment; how children generate “speech sound”. Moreover, phonological assessment is also analyzed in terms of the manner sounds are structured.

Indeed, morphology is a linguistics concept that is a branch of grammar which is committed to study framework of words, basically through adoption of morpheme construct. Actually, morphology is a different concept from syntax. Indeed, syntax is a linguistic term that studies regulation which governs word linkages in order to build sentences. Definitely, this is how syntax and morphology are different. Besides that, morpheme refers to the unit of word meaning.

Nevertheless, this is not associated with syllable count or word count of speech. Indeed, the following is an illustration of how morpheme can be counted. For example, the word “happy” is only one word that has two syllables. In fact, happy only has one unit of meaning. Therefore it has only one morpheme. On the other hand, unhappy is one word though is has three syllables, which is “un”-“ha”-“ppy”.

Actually unhappy has two morphemes; this is “un” and “happy”. Moreover, unhappily is just a single word, that has four syllables, such has un-happy-i-ily. Unhappily is a word that has three morpheme, “un”, “happy” and “ly”. Besides that unhappiest is only one word, that has four syllables but with three morphemes. For example, a sentence like, “Mary encounters the unhappiest girls” is a sentence that has five words, containing eight syllables and nine morphemes: “Mary” “encounter’-“s” “the” “un”-“happi”-“est” “girl”-“s”.

The MLUm (Mean Length of Utterance measured in Morphemes) is a technique that measures competence of children’s morphemes. For example, children aged between fifteen to thirty months have 1.75 morphemes. Actually, it is important to note that as MLUm progress, the level language generated also increases. This is to say that as MLUm develops, children’s competence to use and know grammatical structures also progresses.

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• Published: 31 August 2024

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

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

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

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• Science, technology and society

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

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Introduction.

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

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

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

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

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

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

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

Methodology and materials

Research method.

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

Data source

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

Presentation of the data culling process in detail.

Data standardization

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

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

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

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

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

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

Trends in publications and disciplinary distribution

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

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

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

Knowledge flow analysis

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

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

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

Main research journals analysis

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

Research power (RQ2)

Countries and collaborations analysis.

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

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

Institutions and authors analysis

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

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

Knowledge base and theme progress (RQ3)

Research knowledge base.

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

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

Seminal literature analysis

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

Research thematic progress

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

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

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

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

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

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

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

Core keywords analysis.

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

Research hotspots analysis

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

A Co-occurrence clustering network. B Keyword density.

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

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

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

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

Evolutionary trends analysis

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

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

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

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

Research quality distribution

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

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

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

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

Discussion on distribution power (RQ1)

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

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

Discussion on research power (RQ2)

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

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

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

Discussion on knowledge base and thematic progress (RQ3)

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

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

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

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

Discussion on research hotspots and evolutionary trends (RQ4)

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

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

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

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

Research agenda

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

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

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

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

Conclusions

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

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

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

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

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

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

Limitations

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

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

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

Data availability

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

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

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

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

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