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Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share research.

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Zotero automatically senses research as you browse the web. Need an article from JSTOR or a preprint from arXiv.org? A news story from the New York Times or a book from a library? Zotero has you covered, everywhere.

Organize your way.

Zotero helps you organize your research any way you want. You can sort items into collections and tag them with keywords. Or create saved searches that automatically fill with relevant materials as you work.

Cite in style.

Zotero instantly creates references and bibliographies for any text editor, and directly inside Word, LibreOffice, and Google Docs. With support for over 9,000 citation styles, you can format your work to match any style guide or publication.

Stay in sync.

Zotero can optionally synchronize your data across devices, keeping your files, notes, and bibliographic records seamlessly up to date. If you decide to sync, you can also always access your research from any web browser.

Collaborate freely.

Zotero lets you co-write a paper with a colleague, distribute course materials to students, or build a collaborative bibliography. You can share a Zotero library with as many people you like, at no cost.

Zotero is open source and developed by an independent, nonprofit organization that has no financial interest in your private information. With Zotero, you always stay in control of your own data.

Still not sure which program to use for your research? See why we think you should choose Zotero .

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5 Mac Word Processors To Help You Write That College Paper

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This Is the One "Boring" Mac App You Won't Regret Installing

Here's the easy way to insert a table of contents in microsoft word, vpns don't do these 5 things: vpn myths busted.

Finding sources to cite is easy . Planning a paper is easy. Sitting down and writing the thing? Much harder, and though there's no shortage of word processors, not all are well-suited to academic writing.

As someone currently working on my dissertation, I know this problem all too well. So I found five popular Mac applications commonly used for academic writing and  reviewed each in order  to see which excelled the most when it comes to writing college papers and dissertations.

Here's what I found.

Ulysses  ($45)

At just short of $45, Ulysses is one of the more expensive applications in this rundown. I reviewed version 2.0, which runs exclusively on 64-bit Macs running Yosemite. There's also an iPad version  ($19.99), which Bakari reviewed recently .

Ulysses is, like Desk and iA Writer, a markdown-oriented text editor. Markdown allows you to format text using a special syntax, rather than pressing a button in an application. The advantage of this is that it doesn't break your workflow, and text written in MarkDown can be copied between applications without losing formatting.

Another advantage of Markdown is that it's incredibly easy to learn, not just because we published a guide to it last year. Ulysses is different from other markdown editors in a number of ways that distinguish it from the pack.

Firstly, it allows you to separate texts into individual sections, each within their own writing space. This is handy if your university project is effectively an anthology of texts, as most dissertations are.

Secondly, Ulysses allows you to change the theme from a bright one, to a more subdued night-mode version which looks great when working in the dark. It also comes with a command palette that feels oddly reminiscent of Sublime Text 2 , which allows you to navigate your document without endlessly scrolling,  just like Vim .

Ulysses also makes it easy to set goals, which is handy when you're unmotivated and trudging through the tedium of a literature review. Unfortunately it doesn't natively support any major reference managers, such as EndNote and Zotero , and it doesn't allow you to embed images or graphics.

Despite these limitations, it's a perfectly adequate markdown editor, and one that lends itself favorably to academic applications.

iA Writer Pro ($20)

I'm a fan of iA Writer. We  reviewed the non-pro version of it back in 2013 and it immediately became my writing application of choice. Why?

The app is markdown-based, so you can add formatting as you write without getting distracted or having your writing pane filled with superfluous toolbars and ribbons. It also allows you to focus on the writing, as it puts the text in the center of your screen and a simple, readable typeface contrasts with the austere, white background.

That's the cheaper, non-pro version. I've since moved on to the professional version, and I'm convinced it too is an excellent choice for markdown aficionados tasked with academic writing.

iA Writer Pro comes all the same features of the cheaper version that allow you to focus on the writing, but brings with it a 'night mode' theme, which is great for late night work.

It also allows you to drill-down on your text and identify parts of your writing you can remove and refactor, such as adverbs, verbs, and prepositions. Given academic writing strongly emphasizes conciseness and precision, this is really helpful.

But iA Writer Pro is lacking some features that are helpful when it comes to academic writing. It doesn't support third-party plugins, which makes it hard to import your citations in from Zotero, or any other reference manager. It also only lets you to work one document at a time, unlike Ulysses's multi-sheet approach to document editing.

Despite those drawbacks, it's only $20  and makes it easy to be focused and productive, and is therefore worth a consider.

Scrivener 2 ($45)

Scrivener is an inexpensive application with an excruciatingly steep learning curve. It's commonly used by people working in the creative industries, and has found a niche as a tool for writing screenplays and scripts. But despite this pedigree, it is also worth considering for your next academic paper.

Scrivener, like Ulysses, lets you break your document into manageable chunks, and tackle them one at a time. Editing is done through a graphical interface, with formatting added through the application, rather than using Markdown syntax.

But perhaps the killer feature of Scrivener is its 'cork board'. This allows you to manage, collect, and collate resources you might want to use in your paper, such as images, notes and references.

Scrivener supports a handful of popular third-party bibliography applications, which means you don't have to adjust your system of managing citations and references. It also allows you to create snapshots - or versions - of your text, and revert back to them when you want to return to an earlier form of your work. This is similar to how Git works , which is a version control system used by programmers.

However, Scrivener lacks the sleek, distraction-free aesthetics of iA Writer and Ulysses, which makes it less than ideal for long writing sprints where your focus might wander. It's also rather expensive, and takes a few hours (and a lot of reading) to fully get to grips with.

Microsoft Word 2016 Preview Edition (Free)

It's hard not to talk about word processors, and not mention Microsoft Word. It's the incumbent, and has been for a couple of decades now. Go to any university, and you'll find Microsoft Word is the de-facto word processor. This due to that fact that it's well understood, supported by Microsoft, and works well with other the packages in the Microsoft Office family.

Microsoft recently released the preview version of Word 2016 , and is currently available as a free download before being publicly released.

This latest version represents the biggest change to Microsoft Word on OS X for almost 5 years. It comes with a sleek new aesthetic that makes it feel like the modern, premium word processor it is. For once, you're going to want to write with Word.

But as a tool for writing Academic papers, how does it stand up? Well, it's not a distraction-free editor like iA Writer is, but that's fine. It makes up for that by being well-rounded and complete, boasting all the features any university student or academic could possibly need.

One of the most compelling features for any student is its built-in citation manager, which offers many of the features of Zotero, and can produce references in APA, MLM and Chicago style.

Unlike iA Writer Pro and Ulysses, Word allows you to insert and embed figures and graphics, and create charts that underscore the points you make.

This makes it one of the more compelling packages for academic writing. The only problem is that when it exits the beta phase, it will ultimately cost a good chunk of change. This free version will eventually cease to work, so you'll have to purchase Word as part of the Office 2016 release if you want to keep the functionality you've gotten used to. In the Apple Store, Office 2011 costs $139.95, so expect Office 2016 to cost something approaching that.

It's also worth noting that beta applications can ship with bugs that might end up destroying all your hard work. With that in mind, it's a good idea to make regular backups if you decide to use it.

Pages (Free/$19.99)

Pages is part of iWork , Apple's flagship productivity suite. Apple made it available free of charge to anyone who purchased Mac on or after October 1, 2013. Everyone else can purchase it for $19.99 on the Mac App Store, which is pretty good for a fully-fledged word processor.

As a tool for getting words on a page, it's solid. It comes with a number of templates for academic writing. However, these overwhelmingly are geared towards a style of academic writing that's more common in the American university system, than in the British and Antipodean ones. That said, it's easy enough to tweak a template, and formatting text in Pages is simple enough for this not to be too much of a barrier.

Pages also supports academic citations through EndNote , a perfectly competent though expensive reference manager, with a license costing around $250. The closest free alternative, Zotero, hasn't released a plugin for iWork and given the niche status of Apple's iWork when it comes to productivity software, I doubt they ever will.

Pages can also produce incredible graphics and charts with a button's press. This makes it ideal for those writing papers with a somewhat data-driven emphasis.

For those on a tight budget, it remains the best option, and poses a serious challenge to the likes of Scrivener and Ulysses.

No Surprises Here

It should come as absolutely no surprise that the two packages I'm ultimately going to recommend are ones made by Microsoft and Apple; both giants in what they do. Pages and Word are just too complete and functional to not recommend, and offer the most value for money (at least while Word is free).

As a close second, I'd also recommend iA Writer Pro, which despite lacking a number of killer features like EndNote integration and bibliography management, offers the best writing experience of any application listed in my opinion.

What do you use to write your academic papers? Leave me a comment below and we'll chat.

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r/MacApps is a one stop shop for all things related to macOS apps - featuring app showcases, news, updates, sales, discounts and even freebies.

Good PDF reader/annotation for research

Hello People!

I just got accepted for a PhD, and I want to start looking into improving my paper management and annotation. I was looking already into some options and found Paperpile however, it does not have a Mac desktop app. I will be working mostly with Latex, mac and iPad.

What I am looking for is an App that can:

Save and organize PDF's

Highlight and annotate PDFs

Generate a bibliography compatible with Latex

Browser extension to easily add papers

If it has an iPad version, it would be amazing

If you have tips for a workflow with different apps or anything, I would love to hear them as well!

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LITERATURE REVIEW SOFTWARE FOR BETTER RESEARCH

“Litmaps is a game changer for finding novel literature... it has been invaluable for my productivity.... I also got my PhD student to use it and they also found it invaluable, finding several gaps they missed”

Varun Venkatesh

Austin Health, Australia

As a full-time researcher, Litmaps has become an indispensable tool in my arsenal. The Seed Maps and Discover features of Litmaps have transformed my literature review process, streamlining the identification of key citations while revealing previously overlooked relevant literature, ensuring no crucial connection goes unnoticed. A true game-changer indeed!

Ritwik Pandey

Doctoral Research Scholar – Sri Sathya Sai Institute of Higher Learning

Using Litmaps for my research papers has significantly improved my workflow. Typically, I start with a single paper related to my topic. Whenever I find an interesting work, I add it to my search. From there, I can quickly cover my entire Related Work section.

David Fischer

Research Associate – University of Applied Sciences Kempten

“It's nice to get a quick overview of related literature. Really easy to use, and it helps getting on top of the often complicated structures of referencing”

Christoph Ludwig

Technische Universität Dresden, Germany

“This has helped me so much in researching the literature. Currently, I am beginning to investigate new fields and this has helped me hugely”

Aran Warren

Canterbury University, NZ

“I can’t live without you anymore! I also recommend you to my students.”

Professor at The Chinese University of Hong Kong

“Seeing my literature list as a network enhances my thinking process!”

Katholieke Universiteit Leuven, Belgium

“Incredibly useful tool to get to know more literature, and to gain insight in existing research”

KU Leuven, Belgium

“As a student just venturing into the world of lit reviews, this is a tool that is outstanding and helping me find deeper results for my work.”

Franklin Jeffers

South Oregon University, USA

“Any researcher could use it! The paper recommendations are great for anyone and everyone”

Swansea University, Wales

“This tool really helped me to create good bibtex references for my research papers”

Ali Mohammed-Djafari

Director of Research at LSS-CNRS, France

“Litmaps is extremely helpful with my research. It helps me organize each one of my projects and see how they relate to each other, as well as to keep up to date on publications done in my field”

Daniel Fuller

Clarkson University, USA

As a person who is an early researcher and identifies as dyslexic, I can say that having research articles laid out in the date vs cite graph format is much more approachable than looking at a standard database interface. I feel that the maps Litmaps offers lower the barrier of entry for researchers by giving them the connections between articles spaced out visually. This helps me orientate where a paper is in the history of a field. Thus, new researchers can look at one of Litmap's "seed maps" and have the same information as hours of digging through a database.

Baylor Fain

Postdoctoral Associate – University of Florida

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Top 11 Apps for Researchers in 2024

Published on 01 May 2022

The evolution of new technologies has caused a digital transformation in almost every industry and field of interest, including academia. Technology has changed the way that academics conduct research, document findings, and collaborate with peers. 

Academics can now rely on new avenues of collaboration that didn’t even exist when they launched their careers. Networks like SSRN and Mendeley provide opportunities for researchers to share their work for increased collaboration, and abstract management tools streamline the peer review process required by legitimate academic conferences and journals. 

As this digital transformation accelerates, researchers can now access a vast array of apps aimed at simplifying their workflows and facilitating information sharing. While these apps have the potential to improve the way scientists conduct and share their research, the selection can be overwhelming. 

Based on our experience and extensive research, here are the 11 best apps available for researchers in 2024.

1. Fourwaves 

Fourwaves is a conference management software for researchers. Their free web application allows you to create a complete event website, manage abstract submissions, peer reviews, host virtual poster sessions , manage registrations and more. 

It’s the easiest way to organize scientific events as the tool was crafted with researchers in mind every step of the way. 

Fourwaves can be used not only for in-person events but also for hybrid and virtual conferences . They offer a complete virtual venue to access live streams, chat or call other participants and attend virtual poster sessions.

You can go as far as mass email your attendees, automatically generate your event schedule or even print out your name tags; everything you need for your event is in one place.

Most interesting features:

• Ready-to-go event website ; all you have to do is enter your event’s content and you’re ready to publish. 
• Abstract management & Peer review tool ; you can easily collect submissions, review them according to your criterias, email authors and publish your material and the full conference schedule online.
• Registration and payment management ; attendees can easily register to your event and pay online on your Fourwaves event website. 

2. R Discovery: Academic Research

R Discovery is a free app that empowers researchers to save time wading through a sea of academic research papers by finding the articles that are most relevant to your work and delivering them to you each day. It curates over 96 million research articles which includes over 24 million open access articles. 

The app is mobile-only, available for download on the Google Play App Store and the Apple App store for mobile use on your Android device, iPhone or iPad. The app scans papers from all major disciplines in the arts and sciences. 

• As soon as you sign up and submit your areas of interest, R Discovery will serve you the top three related articles in a news feed each day.
• R Discovery uses AI to learn your reading interests over time and populate your news feed with content increasingly tailored to your specific interests.
• The app provides export functions for easy integration with reference managers to organize your citations.

3. LabArchives  

LabArchives is a web-based application that acts as a digital lab notebook, helping researchers keep their work and notes organized to improve productivity in their labs. Users can access LabArchives to make notes, store images and data, and use the search feature for simple access to all of their material. 

There are also Android and iOS versions of this app available in the Apple App Store and Google Play App Store that allow users to access their digital notebooks from their Android devices, iPhones and iPads and have instant access to all of their data, from anywhere. While there are Premium and Enterprise versions of the platform for more advanced use and collaboration, individuals and small teams can access a free version that still includes unlimited notebooks and 1GB of storage. 

Most interesting features: 

• Makes it easy to store and share data between your team members, with user-friendly search functions. You can even share DNA sequence files in over 30 formats! 
• Access information from your desktop or your phone, thanks to the free iOS app for your iPhone or iPad. There is also an Android app available in the Google Play store, but based on reviews it appears that functionality is limited. 
• Data security that lets you determine file access and sharing limitations, so you know exactly who is viewing your files and when.

Typeset is a web-based application that was created to help researchers write, collaborate, format and submit research papers for publication. Typeset allows you to upload your work to their platform, and use their AI to reformat your research and submissions to meet the publication requirements of various journal and conference organizers. 

Typeset works seamlessly with reference management software like Mendeley, Zotero, Paperpile and more. It allows users to choose from over 45,000 verified journal formats and export your work to Word, LaTex and PDF formats. 

Typeset does not offer mobile apps for Apple or Android devices. There are a variety of subscription levels available with pricing ranging from free to $20 per month. 

• Editing features that increase the chances of being published.
• Integrations that enable you to submit research for publication directly from the app.
• Plagiarism and grammar checker for increased quality and peace of mind.

5. BenchSci

The BenchSci platform was built to use advanced biomedical AI to help source the materials that scientific researchers need to move forward with their work. 

Once the app user enters their protein target into the BenchSci platform, the app will sift through thousands of reliable information sources like websites and scientific publications, delivering options that will help determine the antibody or reagent needed. BenchSci is a web-based application that is not available for Android or iOS. It is used by more than 48,000 individual scientists and over 4,000 institutions. BenchSci boasts that their tools can accelerate projects through their AI-powered reagent and antibody selection process, cutting the selection time from 12 weeks to 30 seconds. By empowering researchers to find the antibodies and reagents they need easier and faster, BenchSci reduces the number of materials they need to purchase and experiment with, therefore reducing costs. 

• AI-Assisted Reagent Selection, which uses AI and automation to reduce the errors and inefficiencies in the reagent and model system selection for scientists. 
• AI-Assisted Antibody Selection, which follows the same principle as the reagent selection but focuses on antibodies. This feature is free for you to use if you are a student or researcher at an academic, government, or nonprofit institution. 
• Things change quickly, so the platform is constantly updated to add new antibody and reagent products to ensure that users can access everything available.

6. eLabJournal

There are many Electronic Lab Notebooks (ELNs) available on the market, but the eLabJournal takes the concept of ELNs to the next step. eLabJournal was designed to increase productivity and efficiency in your research lab and simplify the process of organizing and locating data, collaborating with peers, and exporting files into a variety of formats. 

This is a web-based application with mobile versions available on the Google Play and Apple App Stores. Academics can purchase a subscription to the eLabJournal for $15.55 per month, while Industry users are charged $41.95 per month. 

• This ELN uses a simple, intuitive interface that was specifically designed to meet the needs of those in the life science research and development field. 
• Facilitates the ability to link data with functionality to upload images (via the Android and iPhone apps) and a wide range of file types. 
• Seamlessly integrates with eLab’s other products through their SDK and APIs, providing extensive customization opportunities to meet the specific needs of your lab.

7. Connected Papers

Connected Papers is a web-based application that provides a uniquely visual representation of the published research available in a certain field. This helps researchers and scientists browse the information available related to their field of study and ensure that nothing is being missed as they prepare their work for submission. 

The app works when a scientist enters their research topic into the search bar. Within seconds, Connected Papers reviews tens of thousands of papers related to that topic, and creates a visual map showcasing all of the work available for the scientist to review and consider in their research. Connected Papers is currently not available on the Apple App Store or Google Play App Store. It is completely free to use. 

• The visual maps create an easy-to-follow pathway that showcases how closely related particular sources are to the work you’re conducting.
• The app creates clusters that groups papers based on their level of similarities, and pushes less relevant papers away.
• Connected works scans the citations used by various sources and classified papers to be closely related based on how many citations overlap. 

8. Papership

The Papership app allows you to store, annotate, manage and share research papers from anywhere. Available on your Mac, iPhone, and iPad, Papership syncs with popular web-based platforms Zotero and Mendeley to allow app users to access their curated research libraries stored in their Zotero and Mendeley accounts conveniently and remotely. 

• You can choose a free version of the app which can integrate with annotation apps like Evernote, or purchase the annotation function of Papership for $9.99 per month.
• Documents annotated through Papership can be shared via email, SMS, iMessage, Facebook and Twitter. 
• Papership provides quantitative measurements of the significance of a publication to alert the reader as to the legitimacy of the research. It measures both peer-reviewed and non-peer reviewed sources. 

9. GanttPRO

Ganttpro is a web-based project management application that helps research teams plan and organize projects through the use of collaborative Gantt charts. By providing the ability to create interactive Gantt charts online, GanttPRO makes it possible to plan and control many projects at the same time. It empowers researchers to organize and schedule tasks, set deadlines, identify dependencies and manage resources, all while making this information readily available to all collaborators. GanttPRO is available in a mobile version that can be downloaded for your Android and Apple mobile devices. The company offers a free trial and once that is complete different app packages are available that range from $7.99 to $19.99 per month. 

• Drag and drop capabilities to make it simple to organize and reorganize as inputs, outputs and priorities change
• Allows for the creation of multiple workspaces to separate personal tasks from overall team projects
• Collaborative functions make it easy to track the progress of each team member and step in to help whenever needed. 

Trello is an app that can be used by academics, researchers, marketers, computer scientists and basically any other student, professor or business person interested in seamlessly collaborating and managing projects on-the-go. Trello is organized in boards, lists and cards that are customizable and expandable as the project and team grows. Trello easily integrates with other popular apps like Dropbox, Slack, Chrome, Teams and more. It is available for Android and Apple mobile devices on the App Store and Google Play App Store. 

• Timelines that allow all team members to stay on track and be held accountable to deadlines
• Table views that connect work across a variety of related Trello boards
• A handy Dashboard that highlights usage and engagement stats for all of your boards.

11. Researcher

The Researcher app was built to make it easier for researchers to find academic articles relevant to their work. By aggregating over 19,000 sources that include peer-reviewed academic journals, blogs, podcasts and recordings from live events, Researcher helps scientists stay up-to-date on emerging trends and information related to any given field of study or interest. The creators of Researcher claim that their app is “like social media, but better.” The Researcher app is free to use and is available for download on the Apple App Store, the Google Play App Store and the AppInChina App Store. 

• Filter options that allow you to sift through tens of thousands of sources in seconds
• Notification options to ensure that any time a new source is published that relates to your stated interests, you’ll find out about it right away.
• Bookmarks that make it easy for you to come back to an interesting piece when the time is right, without having to search.

Conclusion 

The apps listed above can help you be more efficient, collaborate better with your colleagues, and get more organized. We hope one or more of them considerably help you with your research. Let us know if we missed any! 

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Using This Guide

This guide will cover apps that I find useful for research, ebook reading,  and organizing references and notes.

iPhone/iPad Apps

• Evernote Create text, photo and audio notes ● Auto-synchronize your notes to your Mac, PC, and Web ● Magically makes text within snapshots searchable ● All notes include geo-location information for mapping and search
• Dropbox Dropbox is a free service that lets you bring your photos, docs, and videos anywhere and share them easily.

Android Apps

• Xodo PDF Reader & Editor Xodo is an all-in-one PDF reader and PDF editor. With Xodo, you can read, annotate, sign, and share PDFs and fill in PDF forms, open .docx/.pptx as PDFs, plus sync with Google Drive, Dropbox and OneDrive.
• Instapaper Save web pages for later offline reading, optimized for readability on your screen
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Positional description for numerical normalization, classifier-free guidance is a predictor-corrector, on the benefits of pixel-based hierarchical policies for task generalization, can you remove the downstream model for speaker recognition with self-supervised speech features, novel-view acoustic synthesis from 3d reconstructed rooms, realm: reference resolution as language modeling, repcnn: micro-sized, mighty models for wakeword detection, ape: active prompt engineering - identifying informative few-shot examples for llms, av-cpl: continuous pseudo-labeling for audio-visual speech recognition, toolsandbox: a stateful, conversational, interactive evaluation benchmark for llm tool use capabilities, generating gender alternatives in machine translation, kglens: towards efficient and effective knowledge probing of large language models with knowledge graphs, rephrasing the web: a recipe for compute and data-efficient language modeling, direct large language model alignment through self-rewarding contrastive prompt distillation, llm in a flash: efficient large language model inference with limited memory, biscuit: scaffolding llm-generated code with ephemeral uis in computational notebooks, convkgyarn: spinning configurable and scalable conversational knowledge graph qa datasets with large language models, model-driven heart rate estimation and heart murmur detection based on phonocardiogram, tuning llms with contrastive alignment instructions for machine translation in unseen, low-resource languages, apple intelligence foundation language models, datacomp-lm: in search of the next generation of training sets for language models, instruction-following speech recognition, lazyllm: dynamic token pruning for efficient long context llm inference, pre-trained foundation model representations to uncover breathing patterns in speech, federated learning with differential privacy for end-to-end speech recognition, instance optimal private density estimation in the wasserstein distance, samplable anonymous aggregation for private federated data analytics, improving gflownets for text-to-image diffusion alignment, pine: efficient norm-bound verification for secret-shared vectors, projected language models: a large model pre-segmented into smaller ones, towards automated accessibility report generation for mobile apps, ferretv2: an improved baseline for referring and grounding, on a neural implementation of brenier's polar factorization, a direct algorithm for multi-gyroscope infield calibration, codeact: your llm agent acts better when generating code, contrasting multiple representations with the multi-marginal matching gap, how smooth is attention, on the minimal degree bias in generalization on the unseen for non-boolean functions, revealing the utilized rank of subspaces of learning in neural networks, whispering experts: toxicity mitigation in pre-trained language models by dampening expert neurons, careful with that scalpel: improving gradient surgery with an ema, enhancing ctc-based speech recognition with diverse modeling units, omnipredictors for regression and the approximate rank of convex functions, on computationally efficient multi-class calibration, superposition prompting: improving and accelerating retrieval-augmented generation, accurate knowledge distillation via n-best reranking, toad: task-oriented automatic dialogs with diverse response styles, transfer learning for structured pruning under limited task data, bytes are all you need: transformers operating directly on file bytes, mia-bench: towards better instruction following evaluation of multimodal llms, private vector mean estimation in the shuffle model: optimal rates require many messages, applying rlaif for code generation with api-usage in lightweight llms, how far can transformers reason the locality barrier and inductive scratchpad, optimization without retraction on the random generalized stiefel manifold, revisiting non-separable binary classification and its applications in anomaly detection, towards robust evaluation: a comprehensive taxonomy of datasets and metrics for open domain question answering in the era of large language models, comparative analysis of personalized voice activity detection systems: assessing real-world effectiveness, conformer-based speech recognition on extreme edge-computing devices, multimodal large language models with fusion low rank adaptation for device directed speech detection, synthetic query generation using large language models for virtual assistants, espnet-spk: full pipeline speaker verification toolkit with multiple reproducible recipes, self-supervised front-ends, and off-the-shelf models, hypernetworks for personalizing asr to atypical speech, on-device query auto-completion for email search, server-side rescoring of spoken entity-centric knowledge queries for virtual assistants, time sensitive knowledge editing through efficient finetuning, transformer-based model for asr n-best rescoring and rewriting, evaluating the iwslt2023 speech translation tasks: human annotations, automatic metrics, and segmentation, improved modelling of federated datasets using mixtures-of-dirichlet-multinomials, introducing apple’s on-device and server foundation models, agrame: any granularity ranking with multi-vector embeddings, entity disambiguation via fusion entity decoding, embedding pose graph, enabling 3d foundation model capabilities with a compact representation, clip meets model zoo experts: pseudo-supervision for visual enhancement, affine-based deformable attention and selective fusion for semi-dense matching, efficient diffusion models without attention, kpconvx: modernizing kernel point convolution with kernel attention, odgen: domain-specific object detection data generation with diffusion models, probabilistic speech-driven 3d facial motion synthesis: new benchmarks, methods, and applications, swallowing the bitter pill: simplified scalable conformer generation, on efficient and statistical quality estimation for data annotation, automatic creative selection with cross-modal matching, contextq: generated questions to support meaningful parent-child dialogue while co-reading, kv-runahead: scalable causal llm inference by parallel key-value cache generation, pfl-research: simulation framework for accelerating research in private federated learning, generative modeling with phase stochastic bridges, knowledge transfer from vision foundation models for efficient training of small task-specific models, conformal prediction via regression-as-classification, only pay for what is uncertain: variance-adaptive thompson sampling, guiding instruction-based image editing via multimodal large language models, posterior uncertainty quantification in neural networks using data augmentation, pseudo-generalized dynamic view synthesis from a video, relu strikes back: exploiting activation sparsity in large language models, compressing llms: the truth is rarely pure and never simple, how far are we from intelligent visual deductive reasoning, large language models as generalizable policies for embodied tasks, manifold diffusion fields, mofi: learning image representation from noisy entity annotated images, poly-view contrastive learning, when can transformers reason with abstract symbols, direct2.5: diverse 3d content creation via multi-view 2.5d diffusion, jointnet: extending text-to-image diffusion for dense distribution modeling, label-efficient sleep staging using transformers pre-trained with position prediction, catlip: clip-level visual recognition accuracy with 2.7× faster pre-training on web-scale image-text data, hummuss: human motion understanding using state space models, matryoshka diffusion models, openelm: an efficient language model family with open training and inference framework, talaria: interactively optimizing machine learning models for efficient inference, think while you write hypothesis verification promotes faithful knowledge-to-text generation, the slingshot effect: a late-stage optimization anomaly in adam-family of optimization methods, hindsight priors for reward learning from human preferences, fedhyper: a universal and robust learning rate scheduler for federated learning with hypergradient descen, frequency-aware masked autoencoders for multimodal pretraining on biosignals, hierarchical and dynamic prompt compression for efficient zero-shot api usage, overcoming the pitfalls of vision-language model finetuning for ood generalization, vanishing gradients in reinforcement finetuning of language models, data filtering networks, model compression in practice: lessons learned from practitioners creating on-device machine learning experiences, mobileclip: fast image-text models through multi-modal reinforced training, streaming anchor loss: augmenting supervision with temporal significance, towards a world-english language model, efficient-3dim: learning a generalizable single image novel view synthesizer in one day, a multi-signal large language model for device-directed speech detection, tic-clip: continual training of clip models, mm1: methods, analysis & insights from multimodal llm pre-training, enhancing paragraph generation with a latent language diffusion model, construction of paired knowledge graph - text datasets informed by cyclic evaluation, personalizing health and fitness with hybrid modeling, cease: conversation embeddings for implicit summarisation in the continuous space, corpus synthesis for zero-shot asr domain adaptation using large language models, motionprint: ready-to-use, device-agnostic, and location-invariant motion activity models, randomized algorithms for precise measurement of differentially-private, personalized recommendations, veclip: improving clip training via visual-enriched captions, axnav: replaying accessibility tests from natural language, merge vision foundation models via multi-task distillation, moonwalk: advancing gait-based user recognition on wearable devices with metric learning, vision-based hand gesture customization from a single demonstration, humanizing word error rate for asr transcript readability and accessibility, human following in mobile platforms with person re-identification, privacy-preserving quantile treatment effect estimation for randomized controlled trials, synthdst: synthetic data is all you need for few-shot dialog state tracking, what can clip learn from task-specific experts, multichannel voice trigger detection based on transform-average-concatenate, keyframer: empowering animation design using large language models, resource-constrained stereo singing voice cancellation, efficient convbn blocks for transfer learning and beyond, the entity-deduction arena: a playground for probing the conversational reasoning and planning capabilities of llms, differentially private heavy hitter detection using federated analytics, scalable pre-training of large autoregressive image models, acoustic model fusion for end-to-end speech recognition, co-ml: collaborative machine learning model building for developing dataset design practices, flexible keyword spotting based on homogeneous audio-text embedding, investigating salient representations and label variance modeling in dimensional speech emotion analysis, large-scale training of foundation models for wearable biosignals, user-level differentially private stochastic convex optimization: efficient algorithms with optimal rates, bin prediction for better conformal prediction, fastsr-nerf: improving nerf efficiency on consumer devices with a simple super-resolution pipeline, hybrid model learning for cardiovascular biomarkers inference, improving vision-inspired keyword spotting using a streaming conformer encoder with input-dependent dynamic depth, simulation-based inference for cardiovascular models, unbalanced low-rank optimal transport solvers, personalization of ctc-based end-to-end speech recognition using pronunciation-driven subword tokenization, deploying attention-based vision transformers to apple neural engine, ferret: refer and ground anything anywhere at any granularity, private and personalized histogram estimation in a federated setting, protip: progressive tool retrieval improves planning, transformers learn through gradual rank increase, importance of smoothness induced by optimizers in fl4asr: towards understanding federated learning for end-to-end asr, advancing speech accessibility with personal voice, bootstrap your own variance, context tuning for retrieval augmented generation, datacomp: in search of the next generation of multimodal datasets, leveraging large language models for exploiting asr uncertainty, modality dropout for multimodal device directed speech detection using verbal and non-verbal features, training large-vocabulary neural language model by private federated learning for resource-constrained devices, lidar: sensing linear probing performance in joint embedding ssl architectures, deeppcr: parallelizing sequential operations in neural networks, hugs: human gaussian splats, multimodal data and resource efficient device-directed speech detection with large foundation models, controllable music production with diffusion models and guidance gradients, fast optimal locally private mean estimation via random projections, generating molecular conformers with manifold diffusion fields, how to scale your ema, pre-trained language models do not help auto-regressive text-to-image generation, 4m: massively multimodal masked modeling, adaptive weight decay, fleek: factual error detection and correction with evidence retrieved from external knowledge, one wide feedforward is all you need, agnostically learning single-index models using omnipredictors, characterizing omniprediction via multicalibration, federated learning for speech recognition: revisiting current trends towards large-scale asr, increasing coverage and precision of textual information in multilingual knowledge graphs, sam-clip: merging vision foundation models towards semantic and spatial understanding, what algorithms can transformers learn a study in length generalization, label shift estimators for non-ignorable missing data, marrs: multimodal reference resolution system, automating behavioral testing in machine translation, diffusion models as masked audio-video learners, improved ddim sampling with moment matching gaussian mixtures, planner: generating diversified paragraph via latent language diffusion model, eelbert: tiny models through dynamic embeddings, semand: self-supervised anomaly detection in multimodal geospatial datasets, steer: semantic turn extension-expansion recognition for voice assistants, identifying controversial pairs in item-to-item recommendations, delphi: data for evaluating llms' performance in handling controversial issues, towards real-world streaming speech translation for code-switched speech, livepose: online 3d reconstruction from monocular video with dynamic camera poses, never-ending learning of user interfaces, slower respiration rate is associated with higher self-reported well-being after wellness training, when does optimizing a proper loss yield calibration, single-stage diffusion nerf: a unified approach to 3d generation and reconstruction, fastvit: a fast hybrid vision transformer using structural reparameterization, hyperdiffusion: generating implicit neural fields with weight-space diffusion, neilf++: inter-reflectable light fields for geometry and material estimation, gender bias in llms, reinforce data, multiply impact: improved model accuracy and robustness with dataset reinforcement, self-supervised object goal navigation with in-situ finetuning, all about sample-size calculations for a/b testing: novel extensions and practical guide, intelligent assistant language understanding on-device, rapid and scalable bayesian ab testing, consistent collaborative filtering via tensor decomposition, dataset and network introspection toolkit (dnikit), finerecon: depth-aware feed-forward network for detailed 3d reconstruction, improving the quality of neural tts using long-form content and multi-speaker multi-style modeling, voice trigger system for siri, r^2: range regularization for model compression and quantization, conformalization of sparse generalized linear models, duet: 2d structured and equivariant representations, pdp: parameter-free differentiable pruning is all you need, population expansion for training language models with private federated learning, resolving the mixing time of the langevin algorithm to its stationary distribution for log-concave sampling, the role of entropy and reconstruction for multi-view self-supervised learning, upscale: unconstrained channel pruning, learning iconic scenes with differential privacy, nerfdiff: single-image view synthesis with nerf-guided distillation from 3d-aware diffusion, boot: data-free distillation of denoising diffusion models with bootstrapping, referring to screen texts with voice assistants, towards multimodal multitask scene understanding models for indoor mobile agents, 5ider: unified query rewriting for steering, intent carryover, disfluencies, entity carryover and repair, monge, bregman and occam: interpretable optimal transport in high-dimensions with feature-sparse maps, private online prediction from experts: separations and faster rates, spatial librispeech: an augmented dataset for spatial audio learning, stabilizing transformer training by preventing attention entropy collapse, the monge gap: a regularizer to learn all transport maps, cross-lingual knowledge transfer and iterative pseudo-labeling for low-resource speech recognition with transducers, symphony: composing interactive interfaces for machine learning, a unifying theory of distance from calibration, approximate nearest neighbour phrase mining for contextual speech recognition, latent phrase matching for dysarthric speech, matching latent encoding for audio-text based keyword spotting, near-optimal algorithms for private online optimization in the realizable regime, roomdreamer: text-driven 3d indoor scene synthesis with coherent geometry and texture, semi-supervised and long-tailed object detection with cascadematch, less is more: a unified architecture for device-directed speech detection with multiple invocation types, collaborative machine learning model building with families using co-ml, efficient multimodal neural networks for trigger-less voice assistants, fast class-agnostic salient object segmentation, application-agnostic language modeling for on-device asr, actionable data insights for machine learning, growing and serving large open-domain knowledge graphs, robustness in multimodal learning under train-test modality mismatch, learning language-specific layers for multilingual machine translation, modeling spoken information queries for virtual assistants: open problems, challenges and opportunities, learning to detect novel and fine-grained acoustic sequences using pretrained audio representations, pointconvformer: revenge of the point-based convolution, state spaces aren’t enough: machine translation needs attention, improved speech recognition for people who stutter, autofocusformer: image segmentation off the grid, joint speech transcription and translation: pseudo-labeling with out-of-distribution data, from robustness to privacy and back, generalization on the unseen, logic reasoning and degree curriculum, self-supervised temporal analysis of spatiotemporal data, considerations for distribution shift robustness in health, f-dm: a multi-stage diffusion model via progressive signal transformation, high-throughput vector similarity search in knowledge graphs, naturalistic head motion generation from speech, on the role of lip articulation in visual speech perception, facelit: neural 3d relightable faces, angler: helping machine translation practitioners prioritize model improvements, feedback effect in user interaction with intelligent assistants: delayed engagement, adaption and drop-out, text is all you need: personalizing asr models using controllable speech synthesis, pointersect: neural rendering with cloud-ray intersection, continuous pseudo-labeling from the start, mobileone: an improved one millisecond mobile backbone, neural transducer training: reduced memory consumption with sample-wise computation, tract: denoising diffusion models with transitive closure time-distillation, variable attention masking for configurable transformer transducer speech recognition, from user perceptions to technical improvement: enabling people who stutter to better use speech recognition, i see what you hear: a vision-inspired method to localize words, more speaking or more speakers, pre-trained model representations and their robustness against noise for speech emotion analysis, improvements to embedding-matching acoustic-to-word asr using multiple-hypothesis pronunciation-based embeddings, mast: masked augmentation subspace training for generalizable self-supervised priors, paedid: patch autoencoder-based deep image decomposition for unsupervised anomaly detection, rgi: robust gan-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection, robust hybrid learning with expert augmentation, self supervision does not help natural language supervision at scale, audio-to-intent using acoustic-textual subword representations from end-to-end asr, fastfill: efficient compatible model update, loss minimization through the lens of outcome indistinguishability, mobilebrick: building lego for 3d reconstruction on mobile devices, diffusion probabilistic fields, heimdal: highly efficient method for detection and localization of wake-words, designing data: proactive data collection and iteration for machine learning, improving human annotation effectiveness for fact collection by identifying the most relevant answers, rangeaugment: efficient online augmentation with range learning, languages you know influence those you learn: impact of language characteristics on multi-lingual text-to-text transfer, active learning with expected error reduction, stable diffusion with core ml on apple silicon, supervised training of conditional monge maps, modeling heart rate response to exercise with wearable data, shift-curvature, sgd, and generalization, destseg: segmentation guided denoising student-teacher for anomaly detection, symbol guided hindsight priors for reward learning from human preferences, beyond cage: investigating generalization of learned autonomous network defense policies, rewards encoding environment dynamics improves preference-based reinforcement learning, homomorphic self-supervised learning, continuous soft pseudo-labeling in asr, elastic weight consolidation improves the robustness of self-supervised learning methods under transfer, mean estimation with user-level privacy under data heterogeneity, learning to break the loop: analyzing and mitigating repetitions for neural text generation, learning to reason with neural networks: generalization, unseen data and boolean measures, subspace recovery from heterogeneous data with non-isotropic noise, a large-scale observational study of the causal effects of a behavioral health nudge, improving generalization with physical equations, maeeg: masked auto-encoder for eeg representation learning, mbw: multi-view bootstrapping in the wild, statistical deconvolution for inference of infection time series, ape: aligning pretrained encoders to quickly learn aligned multimodal representations, emphasis control for parallel neural tts, the slingshot mechanism: an empirical study of adaptive optimizers and the grokking phenomenon, a treatise on fst lattice based mmi training, non-autoregressive neural machine translation: a call for clarity, prompting for a conversation: how to control a dialog model, fusion-id: a photoplethysmography and motion sensor fusion biometric authenticator with few-shot on-boarding, latent temporal flows for multivariate analysis of wearables data, the calibration generalization gap, spin: an empirical evaluation on sharing parameters of isotropic networks, learning bias-reduced word embeddings using dictionary definitions, low-rank optimal transport: approximation, statistics and debiasing, safe real-world reinforcement learning for mobile agent obstacle avoidance, speech emotion: investigating model representations, multi-task learning and knowledge distillation, texturify: generating textures on 3d shape surfaces, 3d parametric room representation with roomplan, generative multiplane images: making a 2d gan 3d-aware, flair: federated learning annotated image repository, privacy of noisy stochastic gradient descent: more iterations without more privacy loss, two-layer bandit optimization for recommendations, gaudi: a neural architect for immersive 3d scene generation, physiomtl: personalizing physiological patterns using optimal transport multi-task regression, toward supporting quality alt text in computing publications, providing insights for open-response surveys via end-to-end context-aware clustering, layer-wise data-free cnn compression, rgb-x classification for electronics sorting, aspanformer: detector-free image matching with adaptive span transformer, mel spectrogram inversion with stable pitch, multi-objective hyper-parameter optimization of behavioral song embeddings, improving voice trigger detection with metric learning, neilf: neural incident light field for material and lighting estimation, combining compressions for multiplicative size scaling on natural language tasks, integrating categorical features in end-to-end asr, cvnets: high performance library for computer vision, space-efficient representation of entity-centric query language models, a dense material segmentation dataset for indoor and outdoor scene parsing, benign, tempered, or catastrophic: a taxonomy of overfitting, forml: learning to reweight data for fairness, regularized training of nearest neighbor language models, minimax demographic group fairness in federated learning, neuman: neural human radiance field from a single video, device-directed speech detection: regularization via distillation for weakly-supervised models, vocal effort modeling in neural tts for improving the intelligibility of synthetic speech in noise, reachability embeddings: self-supervised representation learning from spatiotemporal motion trajectories for multimodal geospatial computer vision, dynamic memory for interpretable sequential optimization, artonomous: introducing middle school students to reinforcement learning through virtual robotics, efficient representation learning via adaptive context pooling, leveraging entity representations, dense-sparse hybrids, and fusion-in-decoder for cross-lingual question answering, optimal algorithms for mean estimation under local differential privacy, position prediction as an effective pre-training strategy, private frequency estimation via projective geometry, self-conditioning pre-trained language models, style equalization: unsupervised learning of controllable generative sequence models, critical regularizations for neural surface reconstruction in the wild, a multi-task neural architecture for on-device scene analysis, deploying transformers on the apple neural engine, neural face video compression using multiple views, efficient multi-view stereo via attention-driven 2d convolutions, robust joint shape and pose optimization for few-view object reconstruction, forward compatible training for large-scale embedding retrieval systems, bilingual end-to-end asr with byte-level subwords, end-to-end speech translation for code switched speech, streaming on-device detection of device directed speech from voice and touch-based invocation, training a tokenizer for free with private federated learning, utilizing imperfect synthetic data to improve speech recognition, data incubation - synthesizing missing data for handwriting recognition, a platform for continuous construction and serving of knowledge at scale, neo: generalizing confusion matrix visualization to hierarchical and multi-output labels, low-resource adaptation of open-domain generative chatbots, differentiable k-means clustering layer for neural network compression, enabling hand gesture customization on wrist-worn devices, learning compressed embeddings for on-device inference, neural fisher kernel: low-rank approximation and knowledge distillation, towards complete icon labeling in mobile applications, understanding screen relationships from screenshots of smartphone applications, mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer, synthetic defect generation for display front-of-screen quality inspection: a survey, differential secrecy for distributed data and applications to robust differentially secure vector summation, information gain propagation: a new way to graph active learning with soft labels, can open domain question answering models answer visual knowledge questions, non-verbal sound detection for disordered speech, hierarchical prosody modeling and control in non-autoregressive parallel neural tts, element level differential privacy: the right granularity of privacy, learning spatiotemporal occupancy grid maps for lifelong navigation in dynamic scenes, collaborative filtering via tensor decomposition, modeling the impact of user mobility on covid-19 infection rates over time, fast and explicit neural view synthesis, federated evaluation and tuning for on-device personalization: system design & applications, lyric document embeddings for music tagging, acoustic neighbor embeddings, model stability with continuous data updates, reconstructing training data from diverse ml models by ensemble inversion, fair sa: sensitivity analysis for fairness in face recognition, learning invariant representations with missing data, interpretable adaptive optimization, robust robotic control from pixels using contrastive recurrent state-space models, challenges of adversarial image augmentations, self-supervised semi-supervised learning for data labeling and quality evaluation, batchquant: quantized-for-all architecture search with robust quantizer, high fidelity 3d reconstructions with limited physical views, arkitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data, do self-supervised and supervised methods learn similar visual representations, enforcing fairness in private federated learning via the modified method of differential multipliers, individual privacy accounting via a renyi filter, probabilistic attention for interactive segmentation, rim: reliable influence-based active learning on graphs, stochastic contrastive learning, it’s complicated: characterizing the time-varying relationship between cell phone mobility and covid-19 spread in the us, plan-then-generate: controlled data-to-text, randomized controlled trials without data retention, interdependent variables: remotely designing tactile graphics for an accessible workflow, breiman's two cultures: you don't have to choose sides, cross-domain data integration for entity disambiguation in biomedical text, evaluating the fairness of fine-tuning strategies in self-supervised learning, learning compressible subspaces for adaptive network compression at inference time, mmiu: dataset for visual intent understanding in multimodal assistants, on-device neural speech synthesis, on-device panoptic segmentation for camera using transformers, entity-based knowledge conflicts in question answering, finding experts in transformer models, deeppro: deep partial point cloud registration of objects, using pause information for more accurate entity recognition, conditional generation of synthetic geospatial images from pixel-level and feature-level inputs, multi-task learning with cross attention for keyword spotting, screen parsing: towards reverse engineering of ui models from screenshots, self-supervised learning of lidar segmentation for autonomous indoor navigation, a survey on privacy from statistical, information and estimation-theoretic views, improving neural network subspaces, combining speakers of multiple languages to improve quality of neural voices, audiovisual speech synthesis using tacotron2, user-initiated repetition-based recovery in multi-utterance dialogue systems, dexter: deep encoding of external knowledge for named entity recognition in virtual assistants, managing ml pipelines: feature stores and the coming wave of embedding ecosystems, smooth sequential optimization with delayed feedback, learning to generate radiance fields of indoor scenes, estimating respiratory rate from breath audio obtained through wearable microphones, online automatic speech recognition with listen, attend and spell model, subject-aware contrastive learning for biosignals, hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding, model-based metrics: sample-efficient estimates of predictive model subpopulation performance, retrievalfuse: neural 3d scene reconstruction with a database, joint learning of portrait intrinsic decomposition and relighting, non-parametric differentially private confidence intervals for the median, recognizing people in photos through private on-device machine learning, unconstrained scene generation with locally conditioned radiance fields, trinity: a no-code ai platform for complex spatial datasets, a simple and nearly optimal analysis of privacy amplification by shuffling, when is memorization of irrelevant training data necessary for high-accuracy learning, implicit acceleration and feature learning in infinitely wide neural networks with bottlenecks, a discriminative entity aware language model for virtual assistants, analysis and tuning of a voice assistant system for dysfluent speech, implicit greedy rank learning in autoencoders via overparameterized linear networks, learning neural network subspaces, lossless compression of efficient private local randomizers, private adaptive gradient methods for convex optimization, private stochastic convex optimization: optimal rates in ℓ1 geometry, streaming transformer for hardware efficient voice trigger detection and false trigger mitigation, tensor programs iib: architectural universality of neural tangent kernel training dynamics, uncertainty weighted actor-critic for offline reinforcement learning, spatio-temporal context for action detection, bootleg: self-supervision for named entity disambiguation, instance-level task parameters: a robust multi-task weighting framework, morphgan: controllable one-shot face synthesis, evaluating entity disambiguation and the role of popularity in retrieval-based nlp, hdr environment map estimation for real-time augmented reality, extracurricular learning: knowledge transfer beyond empirical distribution, learning to optimize black-box evaluation metrics, on the role of visual cues in audiovisual speech enhancement, leveraging ml compute for accelerated training on mac, an attention free transformer, cread: combined resolution of ellipses and anaphora in dialogues, dynamic curriculum learning via data parameters for noise robust keyword spotting, error-driven pruning of language models for virtual assistants, knowledge transfer for efficient on-device false trigger mitigation, multimodal punctuation prediction with contextual dropout, noise-robust named entity understanding for virtual assistants, on the transferability of minimal prediction preserving inputs in question answering, open-domain question answering goes conversational via question rewriting, optimize what matters: training dnn-hmm keyword spotting model using end metric, progressive voice trigger detection: accuracy vs latency, sapaugment: learning a sample adaptive policy for data augmentation, making mobile applications accessible with machine learning, video frame interpolation via structure-motion based iterative feature fusion, when can accessibility help an exploration of accessibility feature recommendation on mobile devices, streaming models for joint speech recognition and translation, neural feature selection for learning to rank, generating natural questions from images for multimodal assistants, a comparison of question rewriting methods for conversational passage retrieval, screen recognition: creating accessibility metadata for mobile applications from pixels, set distribution networks: a generative model for sets of images, sep-28k: a dataset for stuttering event detection from podcasts with people who stutter, question rewriting for end to end conversational question answering, leveraging query resolution and reading comprehension for conversational passage retrieval, learning soft labels via meta learning, whispered and lombard neural speech synthesis, frame-level specaugment for deep convolutional neural networks in hybrid asr systems, cinematic l1 video stabilization with a log-homography model, on the generalization of learning-based 3d reconstruction, improving human-labeled data through dynamic automatic conflict resolution, what neural networks memorize and why: discovering the long tail via influence estimation, collegial ensembles, faster differentially private samplers via rényi divergence analysis of discretized langevin mcmc, on the error resistance of hinge-loss minimization, representing and denoising wearable ecg recordings, stability of stochastic gradient descent on nonsmooth convex losses, stochastic optimization with laggard data pipelines, a wrong answer or a wrong question an intricate relationship between question reformulation and answer selection in conversational question answering, conversational semantic parsing for dialog state tracking, efficient inference for neural machine translation, how effective is task-agnostic data augmentation for pre-trained transformers, generating synthetic images by combining pixel-level and feature-level geospatial conditional inputs, making smartphone augmented reality apps accessible, mage: fluid moves between code and graphical work in computational notebooks, rescribe: authoring and automatically editing audio descriptions, class lm and word mapping for contextual biasing in end-to-end asr, complementary language model and parallel bi-lrnn for false trigger mitigation, controllable neural text-to-speech synthesis using intuitive prosodic features, hybrid transformer and ctc networks for hardware efficient voice triggering, improving on-device speaker verification using federated learning with privacy, stacked 1d convolutional networks for end-to-end small footprint voice trigger detection, downbeat tracking with tempo-invariant convolutional neural networks, modality dropout for improved performance-driven talking faces, enhanced direct delta mush, learning insulin-glucose dynamics in the wild, double-talk robust multichannel acoustic echo cancellation using least squares mimo adaptive filtering: transversal, array, and lattice forms, mkqa: a linguistically diverse benchmark for multilingual open domain question answering, improving discrete latent representations with differentiable approximation bridges, adascale sgd: a user-friendly algorithm for distributed training, a generative model for joint natural language understanding and generation, equivariant neural rendering, learning to branch for multi-task learning, variational neural machine translation with normalizing flows, predicting entity popularity to improve spoken entity recognition by virtual assistants, robust multichannel linear prediction for online speech dereverberation using weighted householder least squares lattice adaptive filter, scalable multilingual frontend for tts, generalized reinforcement meta learning for few-shot optimization, learning to rank intents in voice assistants, detecting emotion primitives from speech and their use in discerning categorical emotions, lattice-based improvements for voice triggering using graph neural networks, automatic class discovery and one-shot interactions for acoustic activity recognition, tempura: query analysis with structural templates, understanding and visualizing data iteration in machine learning, multi-task learning for voice trigger detection, speech translation and the end-to-end promise: taking stock of where we are, embedded large-scale handwritten chinese character recognition, generating multilingual voices using speaker space translation based on bilingual speaker data, leveraging gans to improve continuous path keyboard input models, least squares binary quantization of neural networks, unsupervised style and content separation by minimizing mutual information for speech synthesis, sndcnn: self-normalizing deep cnns with scaled exponential linear units for speech recognition, on modeling asr word confidence, capsules with inverted dot-product attention routing, improving language identification for multilingual speakers, multi-task learning for speaker verification and voice trigger detection, stochastic weight averaging in parallel: large-batch training that generalizes well, adversarial fisher vectors for unsupervised representation learning, app usage predicts cognitive ability in older adults, filter distillation for network compression, multiple futures prediction, an exploration of data augmentation and sampling techniques for domain-agnostic question answering, data parameters: a new family of parameters for learning a differentiable curriculum, nonlinear conjugate gradients for scaling synchronous distributed dnn training, modeling patterns of smartphone usage and their relationship to cognitive health, worst cases policy gradients, empirical evaluation of active learning techniques for neural mt, skip-clip: self-supervised spatiotemporal representation learning by future clip order ranking, single training dimension selection for word embedding with pca, overton: a data system for monitoring and improving machine-learned products, leveraging user engagement signals for entity labeling in a virtual assistant, reverse transfer learning: can word embeddings trained for different nlp tasks improve neural language models, variational saccading: efficient inference for large resolution images, jointly learning to align and translate with transformer models, connecting and comparing language model interpolation techniques, active learning for domain classification in a commercial spoken personal assistant, coarse-to-fine optimization for speech enhancement, developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams, raise to speak: an accurate, low-power detector for activating voice assistants on smartwatches, language identification from very short strings, learning conditional error model for simulated time-series data, bridging the domain gap for neural models, improving knowledge base construction from robust infobox extraction, protection against reconstruction and its applications in private federated learning, data platform for machine learning, speaker-independent speech-driven visual speech synthesis using domain-adapted acoustic models, addressing the loss-metric mismatch with adaptive loss alignment, lower bounds for locally private estimation via communication complexity, exploring retraining-free speech recognition for intra-sentential code-switching, parametric cepstral mean normalization for robust speech recognition, voice trigger detection from lvcsr hypothesis lattices using bidirectional lattice recurrent neural networks, neural network-based modeling of phonetic durations, mirroring to build trust in digital assistants, foundationdb record layer: a multi-tenant structured 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I review Macs for a living, and I install these 5 apps first on every MacBook

Install these things first to make your Mac your own

Google Chrome

Setting up a new Mac? Whether it's a MacBook or a Mac mini , I have some suggestions for the apps you should install right away.

Sure, Safari and the rest of Apple's default apps are good enough to get by with in a pinch. But I find nothing makes a Mac feel more comfortable to use right out of the box than kitting it out with all the apps and tools I use on a daily basis.

I've had a lot of time to refine my list, too, because I regularly review Macs and MacBooks as part of the Computing team here at Tom's Guide. And every time I unbox a new Apple computer, one of the first things I do is install all the software on this list.

(The very first thing I do is turn on dark mode , of course!)

I have a similar list of 7 apps I install on every Windows 11 PC I get in for review, and I like to use the service Ninite to create a custom installer package with all those apps in one handy file. Unfortunately, Ninite isn't supported on Mac, so you may have to just head to each app's website and download it the old-fashioned way.

You could also try using macapps.link , which offers a similar one-stop-shop for Mac apps: you just click the ones you want from the big list, and macapps packages them all up for you in one easy-to-download installer. It has a pretty good selection that includes some of the best Mac apps we recommend, including everything on this list.

The site can look a little dodgy if you don't use an ad blocker since it displays a significant number of ads, but I've used it many times myself and never had an issue.

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Whether you choose to try macapps for yourself or want to click through to every app before downloading, these are the 5 apps I recommend you install first on your new Mac!

I didn't think much of Discord back when it launched in 2015, but nowadays it's become one of my most-used tools for staying in touch with old friends and colleagues around the world. For that reason, it's one of the first things I install on any new Mac.

Once upon a time it felt like Twitter was the best place to stay in touch with people, but these days I prefer the more comfortable intimacy of a Discord server with pals. The service has evolved significantly over the years, too, and now you can use it for cross-platform voice chat, so friends on Macs, PCs, and even a PS5 can all hop into the same voice channel to catch up after work.

While you can get by with the browser version of Discord, I like to download the app because I feel like the quality of my audio sounds better when I'm broadcasting using the app vs. the browser version—but that's entirely subjective, and your mileage may vary. 

As a working journalist I need to edit images on a regular basis, and the Photos app just doesn't cut it. I like to download GIMP (GNU Image Manipulation Program) on every new MacBook I get my hands on because it's capable, quick to download and most importantly, free.

I've also spent years using it, so I know my way around this image editor in a way I'll never understand Photoshop. And while there are lots of great free image editors available out there, including lots you can access right in your browser, I prefer to have an app downloaded to my Mac hard drive so I can work with images even when I'm on the go and don't have a reliable Internet connection. 

Sure, the name is a little silly and the interface could be better, but for the asking price you can't beat GIMP.

Like I said up top, Safari is fine—in fact, it's better than ever thanks to the upgrades in macOS Sequoia —but I still prefer to use Chrome on my Macs.

Admittedly, partly that's just laziness and habit: I have Google accounts for both work and personal business, so it's convenient to have Chrome because I can stay on top of both with a browser logged into each account. 

But I also love Chrome because while it's demanding, it feels faster and more responsive than Safari. I also like how it supports every website and web service I need to use. Plus, it has robust cross-platform support so I can open a link on my phone using Chrome for iOS, then hit a button to send it to my desktop Chrome instance running on my MacBook for easier reading when I'm back at my desk.

Macs are better gaming platforms than ever thanks to the work Apple's been doing to help game developers get their games running well on the company's M-series chips, so Steam is a must-have.

You can get games off the Mac App Store, the Epic Games Store or a few other places, but Steam is the first (and often only) game storefront I download because it simply has more games than anywhere else. And with the power of Apple's top-tier chips in premium Macs like the MacBook Pro 16-inch M3 Max you can enjoy the best Mac games  (including my fav, Baldur's Gate 3 ) at killer framerates.

It's not just about the games either—I also keep in touch with a few friends on my Steam friends list, so it's nice to have it installed and running on my PC to keep abreast of who's playing what.

Sometimes you need to watch a .wmv (Windows Media Video) on a Mac, and QuickTime Player isn't up to the task. For those moments, you want a capable alternative like VLC media player installed.

I put this media player on basically every laptop I review because I watch a lot of videos while testing display quality and conducting research, and I can't afford to be constrained by the limits of Apple's default software. I recommend VLC because its lightweight and easy to download, yet supports a broad variety of file formats and codecs. 

Plus, it's free!

Bottom line

Every Mac I get my hands on gets these apps installed as soon as possible, because navigating the web in Safari or editing images in Photos feels terrible. And when work is done, I'm ready to fire up Discord and Steam and have some fun with my friends.

I wish Ninite supported Mac because I already regularly use it all the time for Windows 11 PC software, but at least there's macapps.link. I've been using it with my new MacBooks for a while now, and as long as it stays as useful as it is, I think it's a great way to grab all of these apps in one fell swoop. 

And of course, you don't have to stick to my suggestions--macapps offers a wide variety of free software, so have a look around and try out some new finds!

Alex Wawro is a lifelong tech and games enthusiast with more than a decade of experience covering both for outlets like Game Developer, Black Hat, and PC World magazine. A lifelong PC builder, he currently serves as a senior editor at Tom's Guide covering all things computing, from laptops and desktops to keyboards and mice. 

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• lukasmull While setting up your new Mac, remember to install TextSniper! It's a game-changer for quickly extracting text from images or screenshots. It's perfect for reviewing apps or grabbing info from non-editable sources. It'll save you tons of time and hassle. Reply
• tmmr Wow, no security or antivirus apps? My very first two installs are always Little Snitch and Malware Bytes. After that Firefox, and then in no particular order, come crucial MacOS improvements such as Jumpcut, Bartender, Moom, and Tuxera NTFS. Then iTerm, Mimestream, BBEdit, Syncthing, openVPN Connect and Google Drive. Gimp is the only one on your list that might crack my first twenty. Reply
• dfcloseupman "you can't beat GIMP." Yeah you shouldn't be beating a GIMP! Reply

tmmr said: Wow, no security or antivirus apps? My very first two installs are always Little Snitch and Malware Bytes. After that Firefox, and then in no particular order, come crucial MacOS improvements such as Jumpcut, Bartender, Moom, and Tuxera NTFS. Then iTerm, Mimestream, BBEdit, Syncthing, openVPN Connect and Google Drive. Gimp is the only one on your list that might crack my first twenty.

• bkkcanuck8 Have to say -- that was one of the most boring and disappointing - must install I have ever read. Some of the apps I install early on is: Keyboard Maestro, TextSniper, Alfred, DevonThink Pro (use it for organizing all my documents and notes etc.), iTerm, Sublime Text, and MacUpdater.... Reply
• djggettys must install apps? This is a really disappointing Article from a site that I still went to more than others for actual technical information. The senior writer who looks about 20 obviously being paid for the sponsoring of these apps? And setting up a new Mac doesn’t always mean it’s a brand new Mac -it might be new to me, but actually 5 to 10 years old. I am not installing any of these apps on said Mac Until I have fully upgraded to whatever current OS system that mac can handle- Especially for the older ones, a significant amount of space needs to be open, and boot installers might have to be Put into action. This is really a terrible article that turned me and my fairly decent opinion of this site in the other direction. Reply

bkkcanuck8 said: When I first came over to the Mac I installed an antivirus program thinking it would do something.. (intego -- before all the others arrived)... all it did was cause random kernel panics. I then read what it protected against at the time, and the list was basically a list of things that a person with physical access would have to install on the computer. macOS already has built in anti-virus software for known threats (XProtect). Little Snitch as a monitor/firewall against unwanted information gathering for advertising - is nice though. I also install pretty much only user space software and software from known publishers... though I do have a few terminal apps through homebrew (though they are known sources - not randomly selected ones).

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• Published: 31 August 2024

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

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

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

Metrics details

• Science, technology and society

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

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Introduction.

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

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

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

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

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

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

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

Methodology and materials

Research method.

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

Data source

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

Presentation of the data culling process in detail.

Data standardization

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

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

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

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

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

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

Trends in publications and disciplinary distribution

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

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

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

Knowledge flow analysis

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

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

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

Main research journals analysis

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

Research power (RQ2)

Countries and collaborations analysis.

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

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

Institutions and authors analysis

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

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

Knowledge base and theme progress (RQ3)

Research knowledge base.

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

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

Seminal literature analysis

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

Research thematic progress

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

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

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

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

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

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

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

Core keywords analysis.

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

Research hotspots analysis

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

A Co-occurrence clustering network. B Keyword density.

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

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

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

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

Evolutionary trends analysis

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

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

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

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

Research quality distribution

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

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

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

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

Discussion on distribution power (RQ1)

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

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

Discussion on research power (RQ2)

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

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

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

Discussion on knowledge base and thematic progress (RQ3)

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

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

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

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

Discussion on research hotspots and evolutionary trends (RQ4)

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

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

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

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

Research agenda

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

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

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

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

Conclusions

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

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

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

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

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

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

Limitations

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

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

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

Data availability

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

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

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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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Research status and development trend of wire arc additive manufacturing technology for aluminum alloys.

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Dai, P.; Li, A.; Zhang, J.; Chen, R.; Luo, X.; Wen, L.; Wang, C.; Lv, X. Research Status and Development Trend of Wire Arc Additive Manufacturing Technology for Aluminum Alloys. Coatings 2024 , 14 , 1094. https://doi.org/10.3390/coatings14091094

Dai P, Li A, Zhang J, Chen R, Luo X, Wen L, Wang C, Lv X. Research Status and Development Trend of Wire Arc Additive Manufacturing Technology for Aluminum Alloys. Coatings . 2024; 14(9):1094. https://doi.org/10.3390/coatings14091094

Dai, Pan, Ao Li, Jianxun Zhang, Runjie Chen, Xian Luo, Lei Wen, Chen Wang, and Xianghong Lv. 2024. "Research Status and Development Trend of Wire Arc Additive Manufacturing Technology for Aluminum Alloys" Coatings 14, no. 9: 1094. https://doi.org/10.3390/coatings14091094

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