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Research in marketing strategy

• Review Paper
• Published: 18 August 2018
• Volume 47 , pages 4–29, ( 2019 )

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• Neil A. Morgan 1 ,
• Kimberly A. Whitler 2 ,
• Hui Feng 3 &
• Simos Chari 4  

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Marketing strategy is a construct that lies at the conceptual heart of the field of strategic marketing and is central to the practice of marketing. It is also the area within which many of the most pressing current challenges identified by marketers and CMOs arise. We develop a new conceptualization of the domain and sub-domains of marketing strategy and use this lens to assess the current state of marketing strategy research by examining the papers in the six most influential marketing journals over the period 1999 through 2017. We uncover important challenges to marketing strategy research—not least the increasingly limited number and focus of studies, and the declining use of both theory and primary research designs. However, we also uncover numerous opportunities for developing important and highly relevant new marketing strategy knowledge—the number and importance of unanswered marketing strategy questions and opportunities to impact practice has arguably never been greater. To guide such research, we develop a new research agenda that provides opportunities for researchers to develop new theory, establish clear relevance, and contribute to improving practice.

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We follow Varadarjan’s (2010) distinction, using “strategic marketing” as the term describing the general field of study and “marketing strategy” as the construct that is central in the field of strategic marketing—just as analogically “strategic management” is a field of study in which “corporate strategy” is a central construct.

Following the strategic management literature (e.g., Mintzberg 1994 ; Pascale 1984 ), marketing strategy has also been viewed from an “emergent” strategy perspective (e.g. Hutt et al. 1988 ; Menon et al. 1999 ). Conceptually this is captured as realized (but not pre-planned) tactics and actions in Figure 1 .

These may be at the product/brand, SBU, or firm level.

These strategic marketing but “non-strategy” coding areas are not mutually exclusive. For example, many papers in this non-strategy category cover both inputs/outputs and environment (e.g., Kumar et al. 2016 ; Lee et al. 2014 ; Palmatier et al. 2013 ; Zhou et al. 2005 ), or specific tactics, input/output, and environment (e.g., Bharadwaj et al. 2011 ; Palmatier et al. 2007 ; Rubera and Kirca 2012 ).

The relative drop in marketing strategy studies published in JM may be a function of the recent growth of interest in the shareholder perspective (Katsikeas et al. 2016 ) and studies linking marketing-related resources and capabilities directly with stock market performance indicators. Such studies typically treat marketing strategy as an unobserved intervening construct.

Since this concerns integrated marketing program design and execution, marketing mix studies contribute to knowledge of strategy implementation–content when all four major marketing program areas are either directly modeled or are controlled for in studies focusing on one or more specific marketing program components.

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Kelley School of Business, Indiana University, 1309 E. Tenth St., Bloomington, IN, 47405-1701, USA

Neil A. Morgan

Darden School of Business, University of Virginia, 100 Darden Boulevard, Charlottesville, VA, 22903, USA

Kimberly A. Whitler

Ivy College of Business, Iowa State University, 3337 Gerdin Business Building, Ames, IA, 50011-1350, USA

Alliance Manchester Business School, University of Manchester, Booth Street West, Manchester, M15 6PB, UK

Simos Chari

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Morgan, N.A., Whitler, K.A., Feng, H. et al. Research in marketing strategy. J. of the Acad. Mark. Sci. 47 , 4–29 (2019). https://doi.org/10.1007/s11747-018-0598-1

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Received : 14 January 2018

Accepted : 20 July 2018

Published : 18 August 2018

Issue Date : 15 January 2019

DOI : https://doi.org/10.1007/s11747-018-0598-1

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Should You Feed the Trolls? How Toxic Social Media Comments Can Increase Product Usage

This Journal of Interactive Marketing study explores the types of content that attract toxic comments, and it finds that toxic comments aren’t always a bad thing.

• Information Technologies and Consumers’ Well-Being

Check out the research from the latest Journal of Interactive Marketing special issue.

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Award-Winning Research

Montaguti, Valentini, and Vecchioni Win 2023 Journal of Interactive Marketing Best Paper Award

The winners of the 2023 Best Paper Award are Elisa Montaguti, Sara Valentini, and Federica Vecchioni. Click here to learn more about the winning article and view the finalists.

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Marketing →

• 20 Aug 2024
• Cold Call Podcast

Angel City Football Club: A New Business Model for Women’s Sports

Angel City Football Club (ACFC) was founded in 2020 by venture capitalist Kara Nortman, entrepreneur Julie Uhrman, and actor and activist Natalie Portman. As outsiders to professional sports, the all-female founding team had rewritten the playbook for how to build a sports franchise by applying lessons from the tech and entertainment industries. Unlike typical sports franchises that built their teams and track records over many years before extending their brand beyond a local base, ACFC had inverted the model, generating both global and local interest in the club during its first three years. The club’s early success was reflected in its market valuation of $250 million as of its sale in July 2024 — the highest in the National Women’s Soccer League. Equally important, ACFC had started to bend the curve toward greater pay equity in women’s sports — the club’s ultimate goal. But the founders knew there was much more to do to capitalize on the club’s momentum. As they developed ACFC’s first three-year strategic plan in 2024, they weighed the most effective ways to build value for the franchise. Was it better to allocate the incremental budget to investments in digital brand building or to investments in the on-field product? Senior Lecturer Jeffrey Rayport is joined by case co-author Nicole Keller and club co-founder Kara Nortman to discuss the case, “Angel City Football Club: Scoring a New Model.”

Why Competing With Tech Giants Requires Finding Your Own Edge

In the new book Smart Rivals, Feng Zhu and Bonnie Yining Cao show business leaders how to create competitive advantages by uncovering their hidden strengths and leveraging their individual capabilities.

• 06 Aug 2024

How EdTech Firm Coursera Is Incorporating GenAI into Its Products and Services

In early 2023, Jeff Maggioncalda, CEO of Coursera, started developing the EdTech firm’s strategy for incorporating GenAI into their offerings. By early 2024, the firm had made significant progress in bringing four key capabilities to market, but GenAI was evolving quickly and Coursera needed to continuously improve its offerings. While the firm had been an early mover, competitors were adapting fast. Was Coursera taking full advantage of the opportunities presented by the technology? What more could it do to remain competitive?

• 09 Jul 2024

Non-Fungible Tokens (NFTs) and Brand Building

Non-fungible tokens (NFTs), which allow individuals to own their digital assets and move them from place to place, are changing the interaction between consumers and digital goods, brands, and platforms. Professor Scott Duke Kominers and tech entrepreneur Steve Kaczynski discuss the case, “Bored Ape Yacht Club: Navigating the NFT World,” and the related book they co-authored, The Everything Token: How NFTs and Web3 Will Transform The Way We Buy, Sell, And Create. They focus on the rise and popularity of the Bored Ape Yacht Club NFTs and the new model of brand building created by owning those tokens.

• 07 May 2024

Lessons in Business Innovation from Legendary Restaurant elBulli

Ferran Adrià, chef at legendary Barcelona-based restaurant elBulli, was facing two related decisions. First, he and his team must continue to develop new and different dishes for elBulli to guarantee a continuous stream of innovation, the cornerstone of the restaurant's success. But they also need to focus on growing the restaurant’s business. Can the team balance both objectives? Professor Michael I. Norton discusses the connections between creativity, emotions, rituals, and innovation – and how they can be applied to other domains – in the case, “elBulli: The Taste of Innovation,” and his new book, The Ritual Effect.

• 29 Feb 2024

Beyond Goals: David Beckham's Playbook for Mobilizing Star Talent

Reach soccer's pinnacle. Become a global brand. Buy a team. Sign Lionel Messi. David Beckham makes success look as easy as his epic free kicks. But leveraging world-class talent takes discipline and deft decision-making, as case studies by Anita Elberse reveal. What could other businesses learn from his ascent?

• 17 Jan 2024

Psychological Pricing Tactics to Fight the Inflation Blues

Inflation has slowed from the epic rates of 2021 and 2022, but many consumers still feel pinched. What will it take to encourage them to spend? Thoughtful pricing strategies that empower customers as they make purchasing decisions, says research by Elie Ofek.

• 05 Dec 2023

What Founders Get Wrong about Sales and Marketing

Which sales candidate is a startup’s ideal first hire? What marketing channels are best to invest in? How aggressively should an executive team align sales with customer success? Senior Lecturer Mark Roberge discusses how early-stage founders, sales leaders, and marketing executives can address these challenges as they grow their ventures in the case, “Entrepreneurial Sales and Marketing Vignettes.”

Tommy Hilfiger’s Adaptive Clothing Line: Making Fashion Inclusive

In 2017, Tommy Hilfiger launched its adaptive fashion line to provide fashion apparel that aims to make dressing easier. By 2020, it was still a relatively unknown line in the U.S. and the Tommy Hilfiger team was continuing to learn more about how to serve these new customers. Should the team make adaptive clothing available beyond the U.S., or is a global expansion premature? Assistant Professor Elizabeth Keenan discusses the opportunities and challenges that accompanied the introduction of a new product line that effectively serves an entirely new customer while simultaneously starting a movement to provide fashion for all in the case, “Tommy Hilfiger Adaptive: Fashion for All.”

• Research & Ideas

Are Virtual Tours Still Worth It in Real Estate? Evidence from 75,000 Home Sales

Many real estate listings still feature videos and interactive tools that simulate the experience of walking through properties. But do they help homes sell faster? Research by Isamar Troncoso probes the post-pandemic value of virtual home tours.

• 17 Oct 2023

With Subscription Fatigue Setting In, Companies Need to Think Hard About Fees

Subscriptions are available for everything from dental floss to dog toys, but are consumers tiring of monthly fees? Elie Ofek says that subscription revenue can provide stability, but companies need to tread carefully or risk alienating customers.

• 29 Aug 2023

As Social Networks Get More Competitive, Which Ones Will Survive?

In early 2023, TikTok reached close to 1 billion users globally, placing it fourth behind the leading social networks: Facebook, YouTube, and Instagram. Meanwhile, competition in the market for videos had intensified. Can all four networks continue to attract audiences and creators? Felix Oberholzer-Gee discusses competition and imitation among social networks in his case “Hey, Insta & YouTube, Are You Watching TikTok?”

• 26 Jun 2023

Want to Leave a Lasting Impression on Customers? Don't Forget the (Proverbial) Fireworks

Some of the most successful customer experiences end with a bang. Julian De Freitas provides three tips to help businesses invest in the kind of memorable moments that will keep customers coming back.

• 31 May 2023

With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines

Armed with more data than ever, many companies know what key customers need. But how many know exactly when they need it? An analysis of 2,000 ridesharing commuters by Eva Ascarza and colleagues shows what's possible for companies that can anticipate a customer's routine.

• 30 May 2023

Can AI Predict Whether Shoppers Would Pick Crest Over Colgate?

Is it the end of customer surveys? Definitely not, but research by Ayelet Israeli sheds light on the potential for generative AI to improve market research. But first, businesses will need to learn to harness the technology.

• 24 Apr 2023

What Does It Take to Build as Much Buzz as Booze? Inside the Epic Challenge of Cannabis-Infused Drinks

The market for cannabis products has exploded as more states legalize marijuana. But the path to success is rife with complexity as a case study about the beverage company Cann by Ayelet Israeli illustrates.

• 07 Apr 2023

When Celebrity ‘Crypto-Influencers’ Rake in Cash, Investors Lose Big

Kim Kardashian, Lindsay Lohan, and other entertainers have been accused of promoting crypto products on social media without disclosing conflicts. Research by Joseph Pacelli shows what can happen to eager investors who follow them.

• 10 Feb 2023

COVID-19 Lessons: Social Media Can Nudge More People to Get Vaccinated

Social networks have been criticized for spreading COVID-19 misinformation, but the platforms have also helped public health agencies spread the word on vaccines, says research by Michael Luca and colleagues. What does this mean for the next pandemic?

• 02 Feb 2023

Why We Still Need Twitter: How Social Media Holds Companies Accountable

Remember the viral video of the United passenger being removed from a plane? An analysis of Twitter activity and corporate misconduct by Jonas Heese and Joseph Pacelli reveals the power of social media to uncover questionable situations at companies.

• 06 Dec 2022

Latest Isn’t Always Greatest: Why Product Updates Capture Consumers

Consumers can't pass up a product update—even if there's no improvement. Research by Leslie John, Michael Norton, and Ximena Garcia-Rada illustrates the powerful allure of change. Are we really that naïve?

Marketing Research Notes, PDF, Syllabus I MBA, BBA, BCOM 2024

• Post last modified: 5 April 2022
• Reading time: 9 mins read
• Post category: MBA Study Material / BBA Study Material / BCOM Study Material

Download Marketing Research Notes , PDF, Books, Syllabus for MBA, BBA, BCOM 2024. We provide a complete marketing research pdf. Marketing Research study material includes marketing research notes, book, courses, case study, syllabus, question paper, MCQ, questions and answers and available in marketing research pdf form.

Marketing Research subject is included in MBA so students are able to download marketing research notes for MBA, BBA, BCOM 2nd year and marketing research notes for MBA, BBA, BCOM 4th semester.

Table of Content

• 1 Marketing Research Syllabus
• 2 Marketing Research Notes PDF
• 3 Marketing Research Notes
• 4 Marketing Research Questions and Answers
• 5 Marketing Research Question Paper
• 6 Marketing Research Books

Marketing Research Notes can be downloaded in marketing research pdf from the below article.

Marketing Research Syllabus

A detailed marketing research syllabus as prescribed by various Universities and colleges in India are as under. You can download the syllabus in marketing research pdf form.

Introduction to marketing Research – marketing research as a tool of Management – relevance of marketing research in the Indian Context.

Basic concepts – Scientific method – Types of Research – basic method of collection data – Secondary Data – The Marketing research process – planning the research project.

The data collection forms – attitude measurement.

Introduction to sampling – applications of sampling methods of marketing problems.

UNIT V Data collection and the field force – tabulation of collected data – analysis techniques – research report presentations.

Marketing Research Notes PDF

Marketing Research Notes

Market research is defined as the process of evaluating the feasibility of a new product or service, through research conducted directly with potential consumers. This method allows organizations or businesses to discover their target market, collect and document opinions and make informed decisions.

Marketing Research Questions and Answers

If you have already studied the marketing research notes, then it’s time to move ahead and go through previous year marketing research question papers.

• What is primary data?
• What is secondary data?
• What are the classical methods of collecting primary data?
• Mention some important sources of economic data.
• Distinguish between primary and secondary data.
• Distinguish between cross section and time-series data.
• Distinguish between qualitative and quantitative data
• What is a sampling frame?
• What is a complete enumeration?
• Define sampling.
• Write a note on simple random sampling
• Write a note on stratified random sampling.
• Write a note on systematic sampling.
• Write a note on multi-stage sampling.
• Write a note on sequential sampling.

Marketing Research Question Paper

If you have already studied the marketing research notes, then it’s time to move ahead and go through previous year marketing research question paper.

It will help you to understand the question paper pattern and type of marketing research question and answer asked in MBA 2nd year marketing research exam. You can download the syllabus in marketing research pdf form.

Marketing Research Books

Below is the list of marketing research books recommended by the top university in India.

• Boyd, Harper W. Jr., Westfall, Ralph and Stasch, Stanley, Marketing Research: Text and Cases, Richard D. Irwin Inc., Homewood, Illinois.
• Green, P. E. and Tull, D. S., Research for Marketing Decisions, 5th edition, Prentice-Hall of India, New Delhi.
• Luck D. J., Wales, H.G., Taylor, D. A. and Rubin R. S., Marketing Research, 7th Edition, Prentice-Hall of India, New Delhi.
• Tull, D. S. and Hawkins D. I., Marketing Research : Measurement and Method, 6th Edition, Prentice-Hall of India, New Delhi.

In the above article, a student can download marketing research notes for MBA, BBA, BCOM 2nd year and marketing research notes for MBA, BBA, BCOM 4th semester. Marketing Research study material includes marketing research notes, marketing research books, marketing research syllabus, marketing research question paper, marketing research case study, marketing research questions and answers, marketing research courses in marketing research pdf form.

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Market research templates: what they are and how to use them.

18 min read Interested in market research but need some templates to start with? In this guide, we unpack market research, survey planning best practice and share some of our best templates for brand, customer, product and employee research.

What is a market research template?

While you’re no doubt familiar with the concept of market research and how it can help you to reach your target audiences and improve your product or service , the real challenge is designing a market research plan that is conducive to excellent results.

All of this starts with the right market research template(s) to help you analyze specific target audiences, collect the right data and uncover insights that can drive actionable change.

In this article, we’re going to:

• talk about market research and its use cases,
• provide you with a standard template that allows you to plan your research,
• and share several other templates to help you with specific types of market research

You can also check out our free template library.

But first, let’s revisit market research.

What is market research?

Market research is the process of determining the viability of a new service or product through surveys and questionnaires with prospects and/or customers. It involves gathering information about market needs and prospect/customer preferences .

Through market research, you can discover and/or refine your target market, get opinions and feedback on what you provide to them and uncover further prospect/customer pain points and expectations of your service or product .

Market research can be conducted in-house, either by you and your research team, or through a third-party company that specializes in it (they will typically have their own research panels or be capable of creating a research panel to suit your requirements).

The four common types of market research

There are lots of different ways to conduct market research to collect customer data and feedback , test product concepts , and do brand research, but the four most common are:

The most commonly used form of market research, surveys are a form of qualitative research that asks respondents a series of open or closed-ended questions , delivered either as an on-screen questionnaire or email.

Surveys are incredibly popular because they’re cheap, easy to produce, and can capture data very quickly, leading to faster insights.

2) Focus groups

Why not bring together a carefully selected group of people in your target market using focus groups? Though more expensive and complex than surveys and interviews, focus groups can offer deeper insight into prospect and customer behavior – from how users experience your products and services to what marketing messages really resonate with them.

Of course, as a market research method that’s reliant on a moderator to steer conversation, it can be subject to bias (as different moderators might have preferred questions or be more forceful) and if you cut corners (not asking all the necessary questions or making assumptions based on responses), the data could get skewed.

3) Observation

As if you were a fly-on-the-wall, the observation market research method can be incredibly powerful. Rather than interviewing or surveying users, you simply take notes while someone from your target market/target audience engages with your product . How are they using it? What are they struggling with? Do they look as though they have concerns?

Observing your target audience/target market in this fashion is a great alternative to the other more traditional methods on this list. It’s less expensive and far more natural as it isn’t guided by a moderator or a predefined set of questions. The only issue is that you can’t get feedback directly from the mouth of the user, so it’s worth combining this type of research with interviews, surveys, and/or focus groups.

4) Interviews

Interviews allow for face-to-face discussions (both in-person and virtually), allowing for more natural conversations with participants.

For gleaning deeper insights (especially with non-verbal cues giving greater weight to opinions), there’s nothing better than face-to-face interviews. Any kind of interview will provide excellent information, helping you to better understand your prospects and target audience/target market.

Use cases for market research

When you want to understand your prospects and/or customers, but have no existing data to set a benchmark – or want to improve your products and services quickly – market research is often the go-to.

Market research (as mentioned above), helps you to discover how prospects and customers feel about your products and services, as well as what they would like to see .

But there are more use cases and benefits to market research than the above.

Reduce risk of product and business failure

With any new venture, there’s no guarantee that the new idea will be successful. As such, it’s up to you to establish the market’s appetite for your product or service. The easiest way to do this is through market research – you can understand the challenges prospects face and quickly identify where you can help. With the data from your market survey, you can then create a solution that addresses the needs and expectations of would-be customers.

Forecast future trends

Market research doesn’t just help you to understand the current market – it also helps you to forecast future needs. As you conduct your research and analyze the findings, you can identify trends – for example, how brands and businesses are adopting new technology to improve customer experiences or how sustainability is becoming a core focus for packaging. Whatever it is you’re looking to understand about the future of business in your market, comprehensive market research can help you to identify it.

Stay ahead of the competition

Understanding your market and what prospects and customers want from you will help to keep you ahead of the competition . The fact is that the top businesses frequently invest in market research to get an edge, and those that don’t tap into the insights of their audience are missing low-hanging fruit.

As well as helping you to stay in front, you can also use market research to identify gaps in the market, e.g. your competitors’ strengths and weaknesses . Just have participants answer questions about competitor products/services – or even use the products/services – and work out how you can refine your offerings to address these issues.

Plan more strategically

What’s the foundation of your business strategy? If it’s based on evidence, e.g. what people expect of your products and services, it’ll be much easier to deliver something that works. Rather than making assumptions about what you should do, market research gives you a clear, concrete understanding of what people want to see.

Check out our guide to market research for a more comprehensive breakdown.

How do you write a market research plan/template?

A market research plan is very similar to a brief in that it documents the most vital information and steps about your project. Consider it a blueprint that outlines your main objective (summary), key questions and outcomes, target audience and size, your timeline, budget, and other key variables.

Let’s talk about them in more detail.

Elements of a great market research plan

1) overview or summary.

Use the first section of your market research plan to outline the background to the problem that you are attempting to solve (this is usually your problem statement or problem question). Include background information on the study’s purpose and the business to provide context to those who would read the report, as well as the need for the research. Keep the overview simple and concise; focus on the most salient elements.

2) Objectives

What is it that you hope to achieve with this survey? Your objectives are the most important part of the survey. Make sure to list 3-5 of the decisions or initiatives that the research will influence.

For example:

Understand the most-used channels for customer engagement and purchasing to decide where to prioritize marketing and sales budget in Q1 2022. Determine what’s causing customer churn at the later stages of the buyer journey and implement a new retention and sales strategy to address it.

Your objectives should be smart, that is: Specific, Measurable, Attainable, Relevant, and Timely.

3) Deliverables (or outcomes)

This section should focus on what you expect to have at the end of the project. How many responses are you looking for? How will the data be presented? Who will the data be shared with? (Stakeholders, executives) What are your next steps? Make sure you state how you will collect and analyze the data once it’s available.

Products such as Qualtrics CoreXM make this process fast and incredibly easy to do, drastically reducing the time to insights so you can make more meaningful changes, faster.

4) Target audience

Not to be confused with your market research sample, your target audience represents who you want to research. Of course, your sample may include ideal buyers from your target audience. Here you want to define the main variables or factors of your audience: demographic , age, location , product interaction, experience, and so on. It’s worth building out your buyer personas (if you haven’t already) and including a quick breakdown of them here.

5) Sample plan

How many participants do you want to research and what kind of groups do you want to reach? Depending on these two variables, you may have to use qualitative, quantitative , or multi-method approaches.

6) Research methods

What methods will you use in your market research project? The insights (and the granularity of those insights) will depend on the methods and tools you choose. For example, and as mentioned earlier, surveys are often the go-to for many organizations as they’re affordable and straightforward, but if you want to get more personal views from your respondents, one-to-one interviews might be more applicable. You might even want to take a hands-off approach and simply observe participants as they use your products, or try a combination of research methods. Make sure to outline what methods you will use as part of your research plan.

7) Timeline

How long will your research project run? It’s worth putting together a Gantt chart to highlight key milestones in the project, along with dependencies, and to break down tasks as much as possible. Schedule in contingency time in case some tasks or research runs over – or you need more responses.

Set a budget for the overall program and list it in your plan. Though this might be the most difficult aspect of any research plan, it helps you to be more strategic about tasks and hold people accountable at each stage of the process. If costs go over, that’s good to know for future market research. If costs are lower than anticipated, you then have the opportunity to do further research or prop up other areas of the study.

9) Ethical concerns or conflicts of interest

One of the most important parts of your market research plan, you should highlight any ethical concerns. To begin with, it’s your duty to state whether or not responses will be kept confidential and anonymous as part of the study. It’s also important to allow participants to remain anonymous and ensure you protect their privacy at all times.

Another issue to consider is stereotyping. Any analysis of real populations needs to make approximations and place individuals into groups, but if conducted irresponsibly, stereotyping can lead to undesirable results.

Lastly, conflicts of interest – it may be that researchers have interests in the outcome of the project that lead to a personal advantage that might compromise the integrity of your market research project. You should clearly state in your market research report that any potential conflicts of interest are highlighted and addressed before continuing.

But I want a faster solution!

Well, there’s a quicker and far easier way to do all of the above and get the data you need – just use a market research survey template. In our next section, we’re going to share a whole list of templates that you can use.

Free market research survey templates

No matter what kind of research you want to conduct, we have templates that will remove the complexity of the task and empower you to get more from your data. Below we’ve compiled a list of templates for four key experience areas: Brand , Customer , Employee , and Product .

All of our research templates are free. All you need to do is sign up for a free Qualtrics account to access them.

Brand experience market research templates:

• Logo testing : Collect feedback to help you evaluate and iterate on your logo designs and concepts
• Brand awareness : Track the level of brand awareness in your target market, including current and potential future customers
• Ad testing : Evaluate your consumers’ reaction to an advertisement so you know which campaigns to deploy before you invest
• A/B testing : Quickly and easily compare to versions or options in a study, whether it’s a design, headline, color palette or a mock-up of your latest ad campaign

Customer experience market research templates

• Student satisfaction : Gather feedback on how your institution is delivering on the student experience
• Net promoter score (NPS) : Measure customer loyalty and understand how they feel about your product or service using one of the world’s best-recognized metrics
• Customer satisfaction : Evaluate how satisfied your customers are with your company, including the products and services you provide, and how they are treated when they buy from you
• Customer service : Gain insights into the contact center experience, so you can achieve and maintain optimum levels of customer experience (CX) performance
• Event feedback : Measure the effectiveness of your events and how well they meet attendee expectations so that you can continuously improve your offering
• IT help desk : Understand how satisfied your employees and customers are with your IT help desk experience
• Website suggestion box : Collect visitor feedback on how your website can be improved
• Website satisfaction : Find out how satisfied visitors are with your website’s design, usability, and performance
• Store purchase feedback : Capture customer experience data at the point of purchase to help you improve the in-store experience
• Online purchase feedback : Find out how well your online shopping experience performs against customer needs and expectations

Employee experience market research templates

• Employee satisfaction : Get an overview of your current employee experience
• Manager feedback : Improve your skills as a leader with valuable feedback from your team
• Employee engagement : Find out how employees find the current experience at your workplace with this entry-level engagement survey
• Employee exit interview : Understand why your employees are leaving and how they’ll speak about your company once they’re gone with this survey template
• Employee onboarding : Improve your onboarding program by understanding what’s working and what’s not
• Team event planning : Collect inputs from employees to plan a team event that works for everyone
• Meeting feedback : Check-in with team members after a meeting to see how well your company is running and what improvements can be made
• Interview feedback : Improve your candidate experience by gathering actionable insights about the interview process
• Employee suggestion box : Gather anonymous data to help address concerns and improve the employee experience in your organization
• Candidate experience : Improve your candidate experience to increase brand perception, offer acceptance rates, and hiring process efficiency with this single-touchpoint survey template
• Employee suggestion action : Take employee feedback a step further by working with your staff to quantify solutions based on their experience data

Product experience market research templates

• Product research : Evaluate your consumers’ reaction to a new product or product feature across every stage of the product development journey
• Pricing : Understand how to set the exact price point for your product or service, according to your target consumers
• Feature prioritization : Compare and contrast product features using conjoint analysis to find the optimal mix for your customers
• Product package testing : Collect feedback on your product packaging to see how well it meets the needs and expectations of your customers

Armed with the right market research templates, getting the information you need across brand, product, customer and employee disciplines — as well as beyond — is significantly easier.

But if you want help putting together complex market research and scaling your in-house research team to get agile insights, check out our guide to building an agile research function.

Insights are more important than ever, especially during times of change, but building a great team takes a lot of time and money.

In our eBook, we’ll explain how you can:

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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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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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Beyond Peers: Cross-Industry Competition  and Strategic Financing

Swiss Finance Institute Research Paper No. 24-45

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Boris Nikolov

University of Lausanne; Swiss Finance Institute; European Corporate Governance Institute (ECGI)

Norman Schuerhoff

Swiss Finance Institute - HEC Lausanne

Zepeng Wang

University of Lausanne

Date Written: September 01, 2024

Corporate financial leverage within competition networks is determined by both direct and  indirect competitors. Using data on firms’ self reported competitors, we identify eleven  stable competition communities within the U.S. economy, where firms are grouped into  communities based on competitive interactions both within and across industries. We find  a strong complementarity between a firm’s leverage and that of its community members,  consistent with strategic interactions with both immediate peers and chain effects from  the propagation of shocks affecting indirect peers. To achieve identification, we employ a  granular instrumental variable approach. Our results highlight that firms’ financial strategies  are shaped not only by direct competition but also by the broader competitive environment.

Keywords: capital structure, strategic competition, financial complementarity, competitor networks

JEL Classification: G31, G32, L13

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Boris Nikolov (Contact Author)

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Swiss Finance Institute ( email )

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Quinoa Market Research Report - Global Forecast to 2024 - Cumulative Impact for COVID-19 Recovery | SpendEdge

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Spend Growth and Demand Segmentation

• The Quinoa market will register an incremental spend of USD 357.31 million, growing at a CAGR of 7.03% from 2020-2024
• On the supply side, North America, South America, Europe, Middle East and Africa, and APAC will have the maximum influence owing to the supplier base.

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Some of the top Quinoa suppliers enlisted in this report

This Quinoa procurement intelligence report has enlisted the top suppliers and their cost structures, SLA terms, best selection criteria, and negotiation strategies.

• Cargill Inc.
• Archer-Daniels-Midland Co.
• Olam International Ltd.
• Quinoa Foods Co.
• Northern Quinoa Production Corp.
• Irupana Andean Organic Food SA
• The British Quinoa Co.
• Andean Valley Corp.
• Quinoa Corp.

This procurement report answers help buyers identify and shortlist the most suitable suppliers for their Quinoa requirements following questions:

• Am I engaging with the right suppliers?
• Which KPIs should I use to evaluate my incumbent suppliers?
• Which supplier selection criteria are relevant for?
• What are the workplace computing devices category essentials in terms of SLAs and RFx?

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Table of Content

• Executive Summary
• Market Insights
• Category Pricing Insights
• Cost-saving Opportunities
• Best Practices
• Category Ecosystem
• Category Management Strategy
• Category Management Enablers
• Suppliers Selection
• Suppliers under Coverage
• US Market Insights
• Category scope

About SpendEdge:

SpendEdge shares your passion for driving sourcing and procurement excellence. We are the preferred procurement market intelligence partner for 120+ Fortune 500 firms and other leading companies across numerous industries. Our strength lies in delivering robust, real-time procurement market intelligence reports and solutions.

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View source version on businesswire.com: https://www.businesswire.com/news/home/20210120005207/en/

CONTACT: SpendEdge

Anirban Choudhury

Marketing Manager

Ph No: +1 (872) 206-9340

INDUSTRY KEYWORD: FOOD/BEVERAGE RETAIL

SOURCE: SpendEdge

Copyright Business Wire 2021.

PUB: 01/20/2021 10:00 AM/DISC: 01/20/2021 10:00 AM

http://www.businesswire.com/news/home/20210120005207/en

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