IEEE Transactions on Big Data (T-Data)
Publication menu.
The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications. Research areas for big data include, but are not restricted to, big data analytics, big data visualization, big data curation and management, big data semantics, big data infrastructure, big data standards, big data performance analyses, intelligence from big data, scientific discovery from big data security, privacy, and legal issues specific to big data. Applications of big data in the fields of endeavor where massive data is generated are of particular interest.
No current call documents available.
![](http://ortec.site/777/templates/cheerup1/res/banner1.gif)
Past Call Documents:
No past call documents available.
- View on IEEE Xplore
IEEE BigData 2016
IEEE BigData 2021 Now Taking Place Virtually
IEEE Big Data 2021 Accepted Papers
Main conference, regular papers.
Paper ID | Regular Papers |
---|---|
BigD226 | Xiajiong Shen, Kunying Meng, Daojun Han, Kai Zhai, and Lei Zhang, |
BigD242 | KAI-YUAN HOU, Qiao Kang, Sunwoo Lee, Ankit Agrawal, Alok Choudhary, and Wei-keng Liao, Supporting Data Compression in |
BigD243 | Shen Wang, Xiaokai Wei, Cicero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, and Philip S. Yu, |
BigD264 | Da Yan, Shengbin Wu, Mirza Sami, Abdullateef Almudaifer, Zhe Jiang, Haiquan Chen, Rangaprakash Deshpande, Gopikrishna Deshpande, and Yueen Ma, |
BigD266 | Hongjie Chen, Ryan Rossi, Kanak Mahadik, and Hoda Eldardiry, Context Integrated Relational |
BigD267 | Huijuan Zhang, Lipeng Liang, and Dongqing Wang, |
BigD268 | Fuxin Ren, Zhongbao Zhang, Yang Yan, Zhi Wang, Sen Su, and Philip S. Yu, HAMLET: Hierarchical Attention-based Model with muLti-task |
BigD270 | Jonathan Bader, Lauritz Thamsen, Svetlana Kulagina, Jonathan Will, Henning Meyerhenke, and Odej Kao, |
BigD272 | Minh Vu, Truc Nguyen, NhatHai Phan, Ralucca Gera, and My Thai, |
BigD273 | Wei Rang, Donglin Yang, Zhimin Li, and Dazhao Cheng, |
BigD275 | Yuping Wang, Savvas Zannettou, Jeremy Blackburn, Barry Bradlyn, Emiliano De Cristofaro, and Gianluca Stringhini, |
BigD283 | Xianjun Yang, Xinlu Zhang, Julia Zuo, Stephen Wilson, and Linda Petzold, |
BigD296 | Sicong Liang, Chang Deng, and Yu Zhang, |
BigD298 | Huiyu Wu and Diego Klabjan, |
BigD303 | Kuan Feng, Yanmin Zhu, and Jiadi Yu, |
BigD323 | Panpan Zheng, Shuhan Yuan, Xintao Wu, and Yubao Wu, |
BigD325 | Shengzhong Liu, Franck Le, Supriyo Chakraborty, and Tarek Abdelzaher, |
BigD329 | Yangdi Lu and Wenbo He, MixNN: Combating Noisy Labels in Deep Learning by Mixing with Nearest |
BigD331 | Guobing Zou, Tengfei Li, Ming Jiang, Chenhong Cao, Bofeng Zhang, Yanglan Gan, and Yixin Chen, |
BigD337 | Kevin Kocon and Pascal Bormann, Point |
BigD346 | Cheng Hsu, Cheng-Te Li, Diego Saez-Trumper, and Yi-Zhan Hsu, |
BigD347 | Brandon Foggo and Nanpeng Yu, |
BigD361 | Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip Yu, and Kannan Achan, Pre-training Recommender Systems via Reinforced Attentive Multi-relational |
BigD369 | Xiao Han, He Cheng, Depeng Xu, and Shuhan Yuan, |
BigD370 | Ziqing Zhu, Jiuxin Cao, Tao Zhou, Huiyu Min, and Liu Bo, |
BigD371 | Qiao Kang, Scot Breitenfeld, Kaiyuan Hou, Wei-keng Liao, Robert Ross, and Suren Byna, |
BigD373 | Tiantian Zhang and Man Lan, |
BigD376 | Mingzhe Liu, Bowen Du, and Leilei Sun, Co-Prediction of Station-based Multimodal Transportation Demands With |
BigD383 | Dimitrios Karapiperis, Aris Gkoulalas-Divanis, and Vassilios Verykios, |
BigD389 | Daniel Zhang, Jonathan Hueser, Yao Li, and Sarah Campbell, |
BigD393 | Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, and Yuxiang Luo, GLOW : |
BigD394 | Raed Alharbi, Minh Vu, and My T. Thai, Learning Interpretation Representations withExplainable |
BigD397 | Abdullah-Al-Raihan Nayeem, Mohammed Elshambakey, Todd Dobbs, Huikyo Lee, Daniel Crichton, Yimin Zhu, Chanachok Chokwitthaya, William J. Tolone, and Isaac Cho, |
BigD404 | Wenlu Wang, Collectively Learned Multi-level |
BigD411 | Zhe Wang, Dylan Cashman, Mingwei Li, Jixian Li, Matthew Berger, Joshua Levine, Remco Chang, and Carlos Scheidegger, |
BigD414 | Yiming Xu and Diego Klabjan, |
BigD416 | Yiming Bian and Arun Somani, |
BigD417 | Yifan Guo, Qianlong Wang, Tianxi Ji, Xufei Wang, and Pan Li, |
BigD418 | Benedetta Iavarone and Anna Monreale, |
BigD421 | Xiaopeng Jiang, Shuai Zhao, Guy Jacobson, Rittwik Jana, Wen-Ling Hsu, Manoop Talasila, Syed Anwar Aftab, Yi Chen, and Cristian Borcea, |
BigD425 | Rahul Duggal, Scott Freitas, Sunny Dhamnani, Duen Chau, and Jimeng Sun, |
BigD426 | Yu Huang, Chao Zhang, Jaswanth Yella, Sergei Petrov, Xiaoye Qian, Yufei Tang, Xingquan Zhu, and Sthitie Bom, |
BigD430 | Sanjana Srinivas and Mahima Agumbe Suresh, |
BigD436 | Guanxiong Liu, Issa Khalil, Abdallah Khreishah, and NhatHai Phan, |
BigD439 | Daniel Tschernutter and Stefan Feuerriegel, |
BigD442 | Md Hasanuzzaman Noor and Leonidas Fegaras, |
BigD443 | Zihan Zhou and Mingxuan Sun, |
BigD444 | Wenqi Cao, Ling Liu, Gong Su, and Arun Iyengar, Efficient Huge Page Management with |
BigD453 | Guanyi Mou, Yichuan Li, and Kyumin Lee, |
BigD459 | Rameshwar Pratap, Hrushikesh Sudam Sarode, Suryakant Bhardwaj, and Raghav Kulkarni, |
BigD461 | Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zhenong Jin, and Vipin Kumar, Attention-augmented |
BigD467 | Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, and Michael W. Mahoney, |
BigD469 | Nikolaos Nikitas, Ioannis Konstantinou, Vana Kalogeraki, and Nectarios Koziris, |
BigD474 | Hamed Jalali, Martin Pawelczyk, and Gjergji Kasneci, |
BigD475 | James Hicks and Thilanka Munasinghe, |
BigD481 | Hiroto Akatsuka, Yasunori Kamata, Tomohiro Nagata, Naoko Komiya, Makoto Goto, and Masayuki Terada, |
BigD484 | Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P.Yurko, and Xiaowei Jia, |
BigD497 | Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, and Fenglong Ma, |
BigD512 | Aswin Kannan, Anamitra Roy Choudhury, Vaibhav Saxena, Saurabh Manish Raje, Parikshit Ram, Ashish Verma, and Yogish Sabharwal, |
BigD514 | Mohamed Ragab, Feras M. Awaysheh, and Riccardo Tommasini, Bench-Ranking: A First Step Towards Prescriptive Performance Analyses For |
BigD519 | Brian Wheatman and Randal Burns, |
BigD526 | Zichao Wang, Mengxue Zhang, Richard Baraniuk, and Andrew Lan, |
BigD537 | Juhyun Bae, Ling Liu, KaHo Chow, Yanzhao Wu, Gong Su, and Arun Iyengar, |
BigD540 | Maria Kalantzi and George Karypis, Position-based Hash Embeddings For |
BigD555 | Jian Wu, Shaurya Rohatgi, Sai Raghav Reddy Keesara, Jason Chhay, Kevin Kuo, Arjun Manoj Menon, Sean Parsons, Bhuvan Urgaonkar, and C. Lee Giles, |
BigD561 | Lan Wang, Yusan Lin, Yuhang Wu, Huiyuan Chen, Fei Wang, and Hao Yang, |
BigD568 | Edoardo Serra, Anna Squicciarini, Sujeet Ayyapureddi, and Qudrat Ratul, A Few Shot Transfer Learning Approach Identifying Private Images With |
BigD569 | Duy-Khoi Vo, Sergej Zerr, Xiaofei Zhu, and Wolfgang Nejdl, |
BigD571 | George Constantinou, Cyrus Shahabi, and Seon Ho Kim, |
BigD582 | Saurav Manchanda, Da Zheng, and George Karypis, Schema-Aware Deep Graph ConvolutionalNetworks |
BigD589 | Uday Kiran RAGE, Likitha P, Veena P., Yukata Watanobe, and Koji Zettsu, |
BigD593 | So Hirai and Kenji Yamanishi, |
BigD596 | Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo Jin Kim, and Min Chi, InferNet |
BigD601 | Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos, T. Noah Hutson, and Leon Iasemidis, |
BigD602 | Saed Rezayi, Nedim Lipka, Vishwa Vinay, Ryan A. Rossi, Franck Dernoncourt, Tracy H. King, and Sheng Li, |
BigD603 | Nushrat Humaira, Sadegh Sadeghi Tabas, Vidya Samadi, and Nina Hubig, |
BigD604 | Yun-Wei Chu, Elizabeth Tenorio, Laura Melissa Cruz Castro, Kerrie Douglas, Andrew Lan, and Christopher Brinton, |
BigD606 | Zishi Deng and Torsten Suel, Optimizing Iterative Algorithms for Social Network |
BigD607 | Ahmadreza Mosallanezhad, Kai Shu, and Huan Liu, |
BigD620 | Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, and Elke Rundensteiner, |
BigD623 | Prem Bhusal, AKM Mubashwir Alam, Keke Chen, Ning Jiang, and Jun Xiao, |
BigD625 | Guoyi Zhao, Tian Zhou, and Lixin Gao, |
BigD628 | Maminur Islam, Somdeb Sarkhel, and Deepak Venugopal, |
BigD631 | Hadi Mansourifar and Weidong Shi, |
BigD632 | Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang, A Multimodal Misinformation Detector for COVID-19 Short Videos on |
BigD650 | Weiwei Duan, Yao-Yi Chiang, Stefan Leyk, Johannes H. Uhl, and Craig A. Knoblock, |
BigD652 | Erick Skorupa Parolin, Yibo Hu, Latifur Khan, Javier Osorio, Patrick Brandt, and Vito D'Orazio, |
BigD653 | Sodiq Adewole, Philip Fernandes, James Jablonski, Andrew Copland, Michael Porter, Sana Syed, and Donald Brown, Graph Convolution Neural Network For |
BigD657 | Negin Entezari, Evangelos Papalexakis, Haixun Wang, Sharath Rao, and Shishir Kumar Prasad, |
BigD666 | Hangtao He, Kejiang Ye, and Cheng-Zhong Xu, |
BigD676 | Daixuan Cheng, Haifeng Sun, Qi Qi, Jingyu Wang, and Yan Qi, |
BigD680 | Luyi Ma, Jianpeng Xu, Jason H.D. Cho, Evren Korpeoglu, Sushant Kumar, and Kannan Achan, |
BigD685 | Colin Stephen, |
BigD687 | Yongshuai Liu, Jiaxin Ding, and Xin Liu, |
BigD690 | Shohaib Mahmud, Haiying Shen, Ying Natasha Zhang Foutz, and Joshua Anton, A Human Mobility Data Driven Hybrid GNN+RNN Based Model For |
BigD692 | Chunyi Liu, Hao Feng, Jiang Xu, Zhiwei Qin, and Hongtu Zhu, |
BigD701 | Uday Kiran Rage, Pradeep Pallikila, Luna J.M, Fournier-Viger Philippe, Toyoda Masashi, and Krishna Reddy P, Discovering Relative High Utility Itemsets |
Short Papers
Paper ID | Short Papers |
---|---|
BigD206 | Juan Nathaniel and Baihua Zheng, A Hybrid Graph Convolutional Network For |
BigD223 | John Martinsson, Edvin Listo Zec, Daniel Gillblad, and Olof Mogren, |
BigD238 | Xingyu Wang, Lida Zhang, and Diego Klabjan, |
BigD248 | Víctor Rampérez, Shima Zahmatkesh, and Emanuele Della Valle, |
BigD258 | Guanyi Mou and Kyumin Lee, |
BigD263 | Yichuan Li, Kyumin Lee, Nima Kordzadeh, Brenton Faber, Cameron Fiddes, Elaine Chen, and Kai Shu, |
BigD284 | Nikodimos Provatas, Ioannis Konstantinou, and Nectarios Koziris, Is Systematic Data Sharding |
BigD293 | Ting Guo, Xingquan Zhu, Yang Wang, and Fang Chen, |
BigD299 | Liang-Wei Tao, An-Fong Hwu, Yu-Jen Huang, Chi-Chung Chen, Chao-Yuan Yeh, and Shih-Hao Hung, |
BigD315 | Hamid Karimi, Jiliang Tang, Xochitl Weiss, and Jiangtao Huang, Automatic Identification of Teachers in Social Media |
BigD319 | Andreas Grafberger, Mohak Chadha, Anshul Jindal, Jianfeng Gu, and Michael Gerndt, |
BigD320 | Xiaofeng Zhu and Diego Klabjan, |
BigD321 | Wen Huang, Kevin Labille, Xintao Wu, Dongwon Lee, and Neil Heffernan, |
BigD324 | Zhaoheng Li, Xinyu Pi, Mingyuan Wu, and Hanghang Tong, REFORM: Fast and Adaptive Solution for Subteam |
BigD327 | AKM Shahariar Azad Rabby, Md. Majedul Islam, Nazmul Hasan, and Fuad Rahman, Towards |
BigD332 | Amel Hidouri, Said Jabbour, Imen Ouled Dlala, and Badran Raddaoui, On Minimal and Maximal High Utility Itemsets |
BigD343 | Bo Hui, Da Yan, and Wei-Shinn Ku, |
BigD353 | Arun Sathanur and Arif Khan, |
BigD354 | Saptashwa Mitra, Daniel Rammer, Shrideep Pallickara, and Sangmi Pallickara, |
BigD356 | Jasmine DeHart, Chenguang Xu, Lisa Egede, and Christan Grant, |
BigD358 | Radwa El Shawi, |
BigD366 | Wenting Qi and Charalampos Chelmis, |
BigD374 | Guanzhou Ke, Zhiyong Hong, Zhiqiang Zeng, Zeyi Liu, Yangjie Sun, and Yannan Xie, |
BigD380 | Md Adnan Arefeen, Sumaiya Tabassum Nimi, MD YUSUF SARWAR UDDIN, and Yugyung Lee, |
BigD382 | Dinusha Vatsalan, Raghav Bhaskar, Aris Gkoulalas-Divanis, and Dimitrios Karapiperis, |
BigD388 | Shaika Chowdhury, Halid Yerebakan, Yoshihisa Shinagawa, and Philip S. Yu, |
BigD390 | Vanja Ljevar, James Goulding, Gavin Smith, and Alexa Spence, |
BigD398 | Steven Carrell and Amir Atapour-Abarghouei, |
BigD401 | Guillaume Habault, Shinya Wada, and Chihiro Ono, |
BigD406 | Hiroshi Inoue, |
BigD407 | Rachel Zheng, Kunpeng Zhang, Harry Wang, Ling Fan, and Zhe Wang, |
BigD409 | Roxane Desrousseaux, Gilles Bernard, and Jean-Jacques Mariage, |
BigD410 | Gonzalo Munilla Garrido, Kaja Schmidt, Christopher Harth-Kitzerow, Andre Luckow, and Florian Matthes, |
BigD413 | Peter Xenopoulos and Claudio Silva, |
BigD415 | Alexander Lapanowski and Irina Gaynanova, |
BigD433 | Dong Li, Haomin Yu, Yangli-ao Geng, Xiaobao Li, and Qingyong Li, DDGNet: A Dual-Stage Dynamic |
BigD434 | Lei Cao, Peng Xu, and Wei* Shang*, |
BigD441 | Quanliang Jing, Yao Di, Chang Gong, Xinxin Fan, Baoli Wang, Haining Tan, and Jingping Bi, TRAJCROSS: Trajecotry Cross-Modal Retrieval with |
BigD446 | Andy Berres, Brett Bass, Mark Adams, Eric Garrison, and Joshua New, |
BigD452 | Andrew Kwok-fai Lui, Yin-hei Chan, and Man-fai Leung, |
BigD460 | Cristina Menghini, Aris Anagnostopoulos, and Eli Upfal, How Inclusive Are Wikipedia’s Hyperlinks inArticles |
BigD463 | Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Ankush Khandelwal, David Mulla, and Vipin Kumar, |
BigD464 | Yue Zhang, Wanying Ding, Ran Xu, and Xiaohua Hu, |
BigD466 | Khan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, and Deepak Venugopal, |
BigD478 | Wenke Ding, Congcong Zhang, Gaofei Xie, Xiaojie Hu, Xiajiong Shen, and Yatian Shen, |
BigD480 | Takako Hashimoto, Takeaki Uno, Yuka Takedomi, David Shepard, Toyoda Masashi, Naoki Yoshinaga, Masaru Kitsuregawa, and Ryota Kobayashi, |
BigD489 | Yikai Huang, Zhili Yao, and Jianlin Feng, |
BigD491 | Dimitrios Klagkos and Vana Kalogeraki, |
BigD498 | Zhiyuan Wu, Ning Liu, Guodong Li, Xinyu Liu, Yue Wang, and Lin Zhang, |
BigD500 | Erika Duriakova, Elias Tragos, Aonghus Lawlor, Barry Smyth, and Neil Hurley, |
BigD510 | Xiaolin Chen, Shuai Zhou, Kai Yang, Hao Fan, Yongji Wang, and Hu Wang, |
BigD516 | Ryo Kawahara and Mikio Takeuchi, |
BigD517 | Albert Asratyan, Sina Sheikholeslami, and Vladimir Vlassov, |
BigD518 | Toan V. Tran, Thanh-Nam Doan, and Mina Sartipi, |
BigD521 | Yeo Jin Kim, Markel Sanz Ausin, and Min Chi, |
BigD527 | Soumya Dutta, Humayra Tasnim, Terece Turton, and James Ahrens, In Situ Adaptive |
BigD529 | Kaiqun Fu, Taoran Ji, Nathan Self, Zhiqian Chen, and Chang-Tien Lu, |
BigD531 | Prabin Lamichhane and William Eberle, |
BigD532 | Ashutosh Kumar, Takehiro Kashiyama, Hiroya Maeda, and Yoshihide Sekimoto, |
BigD542 | Luke Buquicchio, Walter Gerych, Kavin Chandrasekaran, Abdulaziz Alajaji, Hamid Mansoor, Thomas Hartvigsen, Elke Rundensteiner, and Emmanuel Agu, |
BigD549 | Zhen Deng, Jiaoyang Huang, and Kenji Kawaguchi, |
BigD554 | Nguyen Ho, Van Long Ho, Torben Bach Pedersen, and Mai Vu, |
BigD557 | Petros Barmpas, Aristidis Vrahatis, and Sotiris Tasoulis, |
BigD562 | Peng Zhao and Guanyu Hu, |
BigD566 | Chiho Kim, Sang-Yoon Chang, Jonghyun Kim, Dongeun Lee, and Jinoh Kim, |
BigD580 | Yu Pan, Kwo-Sen Kuo, Michael Rilee, and Hongfeng Yu, |
BigD583 | Robert Fitzgerald and Farnoush Banaei-Kashani, |
BigD584 | Saurav Manchanda, Mohit Sharma, and George Karypis, |
BigD586 | Tianchuan Du, Keng-hao Chang, Paul Liu, and Ruofei Zhang, |
BigD588 | Zinat Ara and Mahdi Hashemi, |
BigD591 | Zhantong Liang and Abdou Youssef, |
BigD592 | Chetraj Pandey, Rafal Angryk, and Berkay Aydin, |
BigD597 | Ziyi Kou, Lanyu Shang, Huimin Zeng, Yang Zhang, and Dong Wang, ExgFair: A Crowdsourcing Data Exchange Approach To |
BigD598 | Song Ju, Yeo Jin Kim, Markel Sanz Ausin, Maria Mayorga, and Min Chi, |
BigD599 | Yuansheng Zhu, Weishi Shi, Deep Pandey, Yang Liu, Xiaofan Que, Daniel Krutz, and Qi Yu, |
BigD614 | Wesley Lin, |
BigD615 | Jack J Amend, Albatool Wazzan, and Richard Souvenir, |
BigD617 | Haiyang He, Xiaolin Li, Zhihong Wang, and Shiguo Huang, |
BigD630 | Alexander He and Thilanka Munasinghe, |
BigD634 | Xiaoyun Fu, Rishabh Rajendra Bhatt, Samik Basu, and A. Pavan, Multi-Objective Submodular Optimization with Approximate Oracles |
BigD636 | Pei-Cheng Tu and Hsing-Kuo Pao, |
BigD642 | Parantapa Bhattacharya, Dustin Machi, Jiangzhuo Chen, Stefan Hoops, Bryan Lewis, Henning Mortveit, Srinivasan Venkatramanan, Mandy L. Wilson, Achla Marathe, Przemyslaw Porebski, Brian Klahn, Joseph Outten, Anil Vullikanti, Dawen Xie, Abhijin Adiga, Shawn Brown, Christopher Barrett, and Madhav Marathe, |
BigD644 | Dennis Huynh, Garrett Audet, Nikolay Alabi, and Yuan Tian, |
BigD645 | Sarwan Ali and Murray Patterson, |
BigD647 | Weiwei Duan, Yao-Yi Chiang, Stefan Leyk, Johannes H. Uhl, and Craig A. Knoblock, |
BigD651 | Henriette Röger, Sukanya Bhowmik, and Tobias Linn, |
BigD660 | Nassim Bouarour, Idir Benouaret, and Sihem Amer-Yahia, |
BigD662 | Huan Dai, Yue Yun, Yupei Zhang, and Xuequn Shang, VarSKD: A Varitional |
BigD664 | Hao Gao, Yongqing Wang, Jiangli Shao, Huawei Shen, and Xueqi Cheng, |
BigD665 | Nirupama Appikatala, SansWord Huang, Balachandar Sankar, Shweta Tripathi, and Eyan Goldman, |
BigD671 | Daniel Peralta, Lin Tang, Maxim Lippeveld, and Yvan Saeys, |
BigD677 | Hang Nguyen, MD YUSUF SARWAR UDDIN, and Nalini Venkatasubramanian, |
BigD682 | Hangyue Li, Xucheng Luo, Qinze Yu, and Haoran Wang, |
BigD689 | Taruna Seth and Vipin Chaudhary, |
BigD694 | Carlos Escobar, Debejyo Chakraborty, Jorge Arinez, and Ruben Morales-Menedez, |
BigD696 | Dai-Hai Ton That, Alexander Rasin, and Tanu Malik, |
BigD702 | Jinwei Liu, Long Cheng, Ankur Sarker, and Li Yan, |
Industrial Track Regular Papers
Paper ID | Regular Papers |
---|---|
N201 | Lohit VijayaRenu, Zhenzhao Wang, Praveen Killamsetti, Tisha Emmanuel, Abhishek Jagannath, Lakshman Ganesh Rajamani, and Joep Rottinghuis, |
N203 | Alex Shtoff and Yair Koren, |
N206 | Stefanos Antaris, Dimitrios Rafailidis, and Sarunas Girdzijauskas, |
N209 | Sean Rooney, Luis Garcés-Erice, Daniel Bauer, and Peter Urbanetz, |
N210 | Chao Zhang, Jaswanth Yelle, Yu Huang, Xiaoye Qian, Sergei Petrov, Andrey Rzhetsky, and Sthitie Bom, |
N211 | Xiaoye Qian, Chao Zhang, Jaswanth Yelle, Yu Huang, Ming-Chun Huang, and Sthitie Bom, |
N212 | Daisuke Moriwaki, Yuta Hayakawa, Akira Matsui, Yuta Saito, Isshu Munemasa, and Masashi Shibata, |
N213 | Jaswanth Yelle, Chao Zhang, Sergei Petrov, Yu Huang, Xiaoye Qian, Ali Minai, and Sthitie Bom, |
N216 | Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, and Stephan Priebe, |
N217 | Takashi Isobe and Yoshihiro Okada, |
N218 | Antoine Hébert, Ian Marineau, Gilles Gervais, Tristan Glatard, and Brigitte Jaumard, |
N219 | Amir Yaghoubi Shahir, Tilemachos Charalampous, Mohammad A. Tayebi, Uwe Glässer, and Hans Wehn, |
N222 | Vibhati Burman, Rajesh Kumar Vashishtha, Srividhya Sethuraman, Ganesh Radhakrishnan, Prashanth Ganesan, Suresh Kumar V, and Sharadha Ramanan, |
N225 | Oren Somekh, Rina Levi, Yohay Kaplan, and Yair Koren, |
N226 | Siavash Samiei, Nasrin Baratalipour, Pranjul Yadav, Amitabha Roy, and Dake He, |
N233 | Haytham Assem, Rajdeep Sarkar, and Sourav Dutta, |
N234 | Raj Nath, Edward Burgin, Haytham Assem, and Sourav Dutta, |
N236 | Eliot Kim, Peter Kruse, Skylar Lama, Jamal Bourne, Michael Hu, Sahara Ali, Yiyi Huang, and Jianwu Wang, |
N240 | Conrad Albrecht, Fernando Marianno, and Levente Klein, |
N242 | Wei Zhang and Chris Challis, |
N243 | Guixiang Ma, Yao Xiao, Mihai Capotă, Theodore Willke, Shahin Nazarian, Paul Bogdan, and Nesreen Ahmed, |
N246 | Anika Tabassum, Supriya Chinthavali, Sangkeun Lee, Nils Stenvig, Bill Kay, Teja Kuruganti, and B. Aditya Prakash, |
N250 | Max Wurfel, Qiwei Han, and Maximilian Kaiser, |
N251 | Mayank Mishra, Archisman Bhowmik, and Rekha Singhal, |
N252 | Bharath Sudharsan, |
Industrial Track Short Papers
Paper ID | Short Papers |
---|---|
N221 (Abs.) | Heesun Won, Minh Chau Nguyen, Myeong-Seon Gil, and Yang-Sae Moon, |
N223 (Abs.) | Marios Vodas, Konstantina Bereta, Dimitris Kladis, Dimitris Zissis, Elias Alevizos, Emmanouil Ntoulias, Alexander Artikis, Antonios Deligiannakis, Antonios Kontaxakis, Nikos Giatrakos, David Arnu, Edwin Yaqub, Fabian Temme, Mate Torok, and Ralf Klinkenberg, |
N227 | Koustava Goswami, Sourav Dutta, and Haytham Assem, |
N228 (Abs.) | Huijun Wu, Yao Li, and Chunxu Tang, |
N229 (Abs.) | Shulong Tan, Meifang Li, Weijie Zhao, Yandan Zheng, Xin Pei, and Ping Li, |
N230 | Ramya Bygari, Aayush Gupta, Shashwat Raghuvanshi, Aakanksha Bapna, and Birendra Sahu, |
N238 | Johannes Breitenbach, Tim Dauser, Hendrik Illenberger, Marius Traub, and Ricardo Buettner, |
N241 (Abs.) | Aming Wu, Jangsoo Lee, Irshad Khan, and Young-Woo Kwon, |
N247 | Miroslav Hodak and Ajay Dholakia, |
N248 | Eelaaf Zahid, Yuya Ong, Aly Megahed, and Taiga Nakamura, |
- IEEE Xplore Digital Library
- IEEE Standards
- IEEE Spectrum
![big data research papers ieee IEEE](https://bigdata.ieee.org/templates/ieeesitetemplate/images/ieee-mb-wh.png)
Test Tube Hard Drives Compute with Chemicals
"A group of scientists and engineers at Brown University is planning to use chemicals in a droplet of fluid to store huge amounts of data and, eventually, get them to do complex calculations instantly. They’ve just received US $4.1 million from the Defense Advanced Research Projects Agency to get started, and plan to borrow robots and automation from the pharmaceutical industry to speed their progress."
Read more at IEEE Spectrum.
Feature Article
![big data research papers ieee Latest Trend in Big Data Analytics: Immediate Real-Time Streaming Insights](https://bigdata.ieee.org/images/files/data-streaming.jpg)
Latest Trend in Big Data Analytics: Immediate Real-Time Streaming Insights
"Data analytics takes enormous quantities of information about everything from traffic management and fraud detection to disease outbreak and natural disaster and tries to make sense of it. Cities, disaster relief agencies, doctors, and businesses rely heavily on it. Moreover, the more critical the situation, the faster the data analysis is needed, which is why edge devices are so crucial."
Read more at IEEE Cloud Computing Magazine.
Technology Spotlight
![big data research papers ieee Advanced Data Analytics](https://bigdata.ieee.org/images/files/advanced-data-analytic.jpg)
Advanced Data Analytics
"Organizations and companies have been using basic data analytics for years to uncover simple insights and trends. The appetite for more data and better analytics has grown over the years, and now most modern organizations track and record nearly all types of data: transactional, clickstream, social media, audio, video, sensor, text, image, and so on. This ever-increasing volume of data, along with the diversity of data sources, makes the process of extracting useful information and insights an ever more challenging and complex endeavor."
Read more at Computing Now.
Useful Links
- IEEE Communications Society's Technical Community on Big Data
- IEEE Transactions on Big Data
![Access the IEEE Big Data Community on IEEE Collabratec Access the IEEE Big Data Community on IEEE Collabratec](https://bigdata.ieee.org/images/files/ieee_collabratec.jpg)
![big data research papers ieee IEEE BigDataService 2024](https://ieeebigdataservice.com/wp-content/uploads/2022/10/bigDataLogo.png)
Accepted Papers
Full papers
- Vani Bhat, Sree Divya Cheerla, Jinu Rose Mathew, Nupur Pathak and Zeyu Gao, Retrieval Augmented Generation (RAG) based Restaurant Chatbot with AI Testability
- Neeraj Kulkarni, Katerina Potika and Petros Potikas, Learning to Play the Trading Game: Exploring Reinforcement Learning-Based Stock Trading Bots
- Christos Chronis, Iraklis Varlamis, Dimitrios Michail, Konstantinos Tserpes and Georgios Dimitrakopoulos, From Perception to Action: Leveraging LLMs and Scene Graphs for Intuitive Robotic Task Execution
- Navaneeth Sai Nidadavolu and William Andreopoulos, Resume Content Generation Using Llama 2
- Jerry Gao, Mahavir Chandaliya, Teja Sree Goli and Swapna Kotha, UAV-Based Powerline Problem Inspection and Classification using Machine Learning Approaches
- Pranav Chellagurki, Sai Prasanna Kumar Kumaru, Rahul Raghava Peela, Neeharika Yeluri, Carlos Rojas and Jorjeta Jetcheva, Biomedical Relation Extraction using LLMs and Knowledge Graphs
- Zhong Chen, Adaptive Sparse Online Learning through Asymmetric Truncated Gradient
- Emeka Ndupuechi and Christian Beecks, Fault Detection in Transmission Production Lines Based on Imbalanced Multivariate Time Series
- Andrew Selvia, Ankur Singh and Wencen Wu, Vehicular Traffic Flow Prediction via Decentralized Federated Meta-Learning
Short Papers
- Hirofumi Shimoe and Hiroyuki Fujioka, Selecting Attractive Images from 3D Captures of Buddhist Statues Using Grad-CAM++
- Mayank Choudhary and Naveen Chauhan, Deep Learning Based Skin Lesion Segmentation and Classification
- Amir Hamza, Yassine Himeur and Abbes Amira, Predicting Asthma Attacks Through AI-Powered Thermal Imaging Analysis of Breathing Patterns
- Edoardo Canti, Enrico Collini, Luciano Alessandro Ipsaro Palesi and Paolo Nesi, Comparing techniques for TEmporal eXplainable Artificial Intelligence
- Sana Bellili, Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur, Shadi Atalla, Wathiq Mansoor and Hussain Al Ahmad, Very Low-Resolution Face Recognition Based On Multilinear Side-Information based Discriminant Analysis
- Jikang Zhao and Yancong Deng, Stock Market Prediction Based on Time Series Data and Multimodal Sentiments
- Yourui Shao, An Accurate Classification Method of Competitive Math Problems
- Shaopeng Xie, Research on Named Entity Recognition Method Based on BERT Model
- Chengzu Dong, Zhiyu Xu, Qin Wang, Qi An, Shantanu Pal, Frank Jiang, Shiping Chen and Xiao Liu, Blockchain-Enabled NFTs as Certificates for Smart UAV Delivery Systems
- Sarvadnya Bhatlawande, Vikas Nandeshwar and Safalya Satpute, Comparative Analysis of Feature Descriptors and Classifiers for Real-Time Object Detection
- Lidan Liu, Florence Tydeman, Wangqing Xie and Yanzhong Wang, Multilingual Depression Detection Based on Speech Signals and Deep Learning
- Mohsen Azararjmand, Amir Masoud Eftekhari Moghadam and Mohammad Hossein Rezvani, multi-label data stream classification using Heterogeneous ensemble learning
- Pranav Yadav, Mudit Choubisa, Anuj Shukla and Dr. Radhika R Cardiac, Pulse Monitoring through Multi-Scale Spectrum Filtering
- Beilei Zhu Zhu and Chandrasekar Vuppalapati, Enhancing Supply Chain Efficiency through Retrieve-Augmented Generation Approach in Large Language Models
Subscribe to the PwC Newsletter
Join the community, search results, distributed and parallel time series feature extraction for industrial big data applications.
3 code implementations • 25 Oct 2016
This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.
![big data research papers ieee big data research papers ieee](https://production-media.paperswithcode.com/thumbnails/task/d0eafcb3-1a12-430b-8bb5-6f6bbff1a4b3.jpg)
Gaussian Processes for Big Data
8 code implementations • 26 Sep 2013
We introduce stochastic variational inference for Gaussian process models.
Escaping the Big Data Paradigm with Compact Transformers
8 code implementations • 12 Apr 2021
Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.
![big data research papers ieee big data research papers ieee](https://production-media.paperswithcode.com/sota-thumbs/image-classification-on-flowers-102-small_46a0f46c.png)
Satellite Image Time Series Analysis for Big Earth Observation Data
2 code implementations • 24 Apr 2022
Solutions that are efficient for specific hardware architectures can not be used in other environments.
Deep learning in bioinformatics: introduction, application, and perspective in big data era
1 code implementation • 28 Feb 2019
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics.
What's In My Big Data?
1 code implementation • 31 Oct 2023
We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them.
Hillview: A trillion-cell spreadsheet for big data
1 code implementation • 10 Jul 2019
As a spreadsheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim.
Distributed, Parallel, and Cluster Computing
Towards Interactive, Adaptive and Result-aware Big Data Analytics
1 code implementation • 14 Dec 2022
The importance of initial results in the iterative process of data wrangling has motivated a result-aware approach to big data analytics.
An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark
1 code implementation • IEEE 2017 2017
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.
![big data research papers ieee big data research papers ieee](https://production-media.paperswithcode.com/thumbnails/task/task-0000000831-cdc3967d.jpg)
VDMS: Efficient Big-Visual-Data Access for Machine Learning Workloads
1 code implementation • 28 Oct 2018
We introduce the Visual Data Management System (VDMS), which enables faster access to big-visual-data and adds support to visual analytics.
Journal of Big Data
![big data research papers ieee Journal of Big Data Cover Image](https://media.springernature.com/w110/springer-static/cover/journal/40537.jpg)
Featured Collections on Computationally Intensive Problems in General Math and Engineering
This two-part special issue covers computationally intensive problems in engineering and focuses on mathematical mechanisms of interest for emerging problems such as Partial Difference Equations, Tensor Calculus, Mathematical Logic, and Algorithmic Enhancements based on Artificial Intelligence. Applications of the research highlighted in the collection include, but are not limited to: Earthquake Engineering, Spatial Data Analysis, Geo Computation, Geophysics, Genomics and Simulations for Nature Based Construction, and Aerospace Engineering. Featured lead articles are co-authored by three esteemed Nobel laureates: Jean-Marie Lehn, Konstantin Novoselov, and Dan Shechtman.
Open Special Issues
Customization and fine-tuning of machine learning models Submission Deadline: 15 December 2024
Advancements on Automated Data Platform Management, Orchestration, and Optimization Submission Deadline: 30 September 2024
Emergent architectures and technologies for big data management and analysis Submission Deadline: 1 October 2024
View our collection of open and closed special issues
Read our guidelines for special issue proposals here .
- Most accessed
Predicting startup success using two bias-free machine learning: resolving data imbalance using generative adversarial networks
Authors: Jungryeol Park, Saesol Choi and Yituo Feng
CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction
Authors: I Nyoman Mahayasa Adiputra and Paweena Wanchai
Cartographies of warfare in the Indian subcontinent: Contextualizing archaeological and historical analysis through big data approaches
Authors: Monica L. Smith and Connor Newton
Automated subway touch button detection using image process
Authors: Junfeng An, Mengmeng Lu, Gang Li, Jiqiang Liu and Chongqing Wang
Cybersecurity vulnerabilities and solutions in Ethiopian university websites
Authors: Ali Yimam Eshetu, Endris Abdu Mohammed and Ayodeji Olalekan Salau
Most recent articles RSS
View all articles
A survey on Image Data Augmentation for Deep Learning
Authors: Connor Shorten and Taghi M. Khoshgoftaar
Big data in healthcare: management, analysis and future prospects
Authors: Sabyasachi Dash, Sushil Kumar Shakyawar, Mohit Sharma and Sandeep Kaushik
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Authors: Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie and Laith Farhan
Deep learning applications and challenges in big data analytics
Authors: Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald and Edin Muharemagic
Short-term stock market price trend prediction using a comprehensive deep learning system
Authors: Jingyi Shen and M. Omair Shafiq
Most accessed articles RSS
Aims and scope
Top 10 most cited articles 2023.
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications Alzubaidi L., Bai J., Al-Sabaawi A. et al., (2023)
IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset Yin Y., Jang-Jaccard J., Xu W. et al., (2023)
A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis Gagandeep Kaur, Amit Sharma (2023)
Skin-Net: a novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier Yousef S. Alsahafi, Mohamed A. Kassem, Khalid M. Hosny
Read the rest of the list here .
Latest Tweets
Your browser needs to have JavaScript enabled to view this timeline
- Editorial Board
- Sign up for article alerts and news from this journal
- Follow us on Twitter
Annual Journal Metrics
Citation Impact 2023 Journal Impact Factor: 8.6 5-year Journal Impact Factor: 12.4 Source Normalized Impact per Paper (SNIP): 3.853 SCImago Journal Rank (SJR): 2.068
Speed 2023 Submission to first editorial decision (median days): 56 Submission to acceptance (median days): 205
Usage 2023 Downloads: 2,559,548 Altmetric mentions: 280
- More about our metrics
- ISSN: 2196-1115 (electronic)
IEEE BigData 2023
IEEE BigData 2024 Washington DC, USA
![big data research papers ieee big data research papers ieee](https://www3.cs.stonybrook.edu/images/lefttop_logo.png)
Welcome! 2024 IEEE International Conference on Big Data (IEEE BigData 2024) Dec 15-18, 2024 @ Washington DC, USA
![big data research papers ieee big data research papers ieee](https://www3.cs.stonybrook.edu/./images/IEEEBigData-logo-color.png)
IEEE Big Data 2020 Promotion video
- © IEEE BigData 2024. All rights reserved.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
- View all journals
- Explore content
- About the journal
- Publish with us
- Sign up for alerts
- Open access
- 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.
Similar content being viewed by others
![big data research papers ieee big data research papers ieee](https://media.springernature.com/w215h120/springer-static/image/art%3A10.1057%2Fs41599-024-03552-x/MediaObjects/41599_2024_3552_Fig1_HTML.png)
![](http://ortec.site/777/templates/cheerup1/res/banner1.gif)
Research progress and intellectual structure of design for digital equity (DDE): A bibliometric analysis based on citespace
![big data research papers ieee big data research papers ieee](https://media.springernature.com/w215h120/springer-static/image/art%3A10.1057%2Fs41599-024-03380-z/MediaObjects/41599_2024_3380_Fig1_HTML.png)
Exploring the role of interaction in older-adult service innovation: insights from the testing stage
![big data research papers ieee big data research papers ieee](https://media.springernature.com/w215h120/springer-static/image/art%3Aplaceholder%2Fimages/placeholder-figure-nature.png)
Smart device interest, perceived usefulness, and preferences in rural Alabama seniors
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 .
![big data research papers ieee figure 1](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig1_HTML.png)
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.
![big data research papers ieee figure 2](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig2_HTML.png)
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 .
![big data research papers ieee figure 3](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig3_HTML.png)
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.
![big data research papers ieee figure 4](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig4_HTML.png)
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 .
![big data research papers ieee figure 5](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig5_HTML.png)
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.
![big data research papers ieee figure 6](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig6_HTML.png)
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.
![big data research papers ieee figure 7](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig7_HTML.png)
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 ).
![big data research papers ieee figure 8](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig8_HTML.png)
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.
![big data research papers ieee figure 9](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03658-2/MediaObjects/41599_2024_3658_Fig9_HTML.png)
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 .
Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286
Article PubMed PubMed Central Google Scholar
Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566
Article Google Scholar
Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014
Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120
Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608
Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879
Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006
Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002
Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004
Article MathSciNet Google Scholar
Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052
Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317
Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813
Article PubMed Google Scholar
Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968
Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150
Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955
Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00
Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855
Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005
Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7
Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009
Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83
Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008
Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157
Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043
Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001
Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681
Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380
Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197
Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575
González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314
Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4
Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566
He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732
Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5
Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9
Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4
Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024
Article ADS Google Scholar
Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6
Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934
Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w
Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1
Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013
Article MathSciNet PubMed Google Scholar
Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061
Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359
Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1
Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254
Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176
Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874
Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003
Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006
Article ADS PubMed Google Scholar
Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871
Google Scholar
Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458
Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165
Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0
Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5
Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999
Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317
López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13
Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015
Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005
Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013
Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944
Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496
Article ADS PubMed PubMed Central Google Scholar
Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428
Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020
Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113
Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5
Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049
Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297
Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536
Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569
Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073
Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015
Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005
Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063
Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949
Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5
Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004
Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9
Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653
Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785
Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500
Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012
Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298
Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359
Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3
Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463
Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564
Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522
Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19
Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774
Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681
Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1
Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695
Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793
Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217
Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1
United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220
Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018
Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3
Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540
Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029
Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927
Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y
Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609
Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033
Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915
Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369
Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778
Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060
Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557
Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642
Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w
Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x
Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210
Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896
Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765
Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7
Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004
Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826
Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w
Article ADS MathSciNet PubMed PubMed Central Google Scholar
Download references
Acknowledgements
This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).
Author information
Authors and affiliations.
School of Art and Design, Shaanxi University of Science and Technology, Xi’an, China
Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu
Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China
Chengliang Wang
You can also search for this author in PubMed Google Scholar
Contributions
Conceptualization, XS, YW, CW; methodology, XS, ZL, CG, CW; software, XS, CG, YW; writing-original draft preparation, XS, CW; writing-review and editing, XS, CG, ZH, CW; supervision, ZL, ZH, CW; project administration, ZL, ZH, CW; funding acquisition, XS, CG. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.
Corresponding author
Correspondence to Chengliang Wang .
Ethics declarations
Competing interests.
The authors declare no competing interests.
Ethical approval
Ethical approval was not required as the study did not involve human participants.
Informed consent
Informed consent was not required as the study did not involve human participants.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .
Reprints and permissions
About this article
Cite this article.
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
Download citation
Received : 20 June 2024
Accepted : 21 August 2024
Published : 31 August 2024
DOI : https://doi.org/10.1057/s41599-024-03658-2
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Quick links
- Explore articles by subject
- Guide to authors
- Editorial policies
![big data research papers ieee big data research papers ieee](https://verify.nature.com/verify/nature.png)
IEEE Account
- Change Username/Password
- Update Address
Purchase Details
- Payment Options
- Order History
- View Purchased Documents
Profile Information
- Communications Preferences
- Profession and Education
- Technical Interests
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
![](http://ortec.site/777/templates/cheerup1/res/banner1.gif)
IMAGES
VIDEO
COMMENTS
Publications. IEEE Talks Big Data - Check out our new Q&A article series with big Data experts!. Call for Papers - Check out the many opportunities to submit your own paper. This is a great way to get published, and to share your research in a leading IEEE magazine! Publications - See the list of various IEEE publications related to big data and analytics here.
Big data is a popular phenomenon among practitioners as well as scholars. Due to its multidisciplinary background, big data research literature includes a wide spectrum of scientific publications in various research areas. With the aim of identification of research trends in big data literature, an empirical analysis based on probabilistic topic models was performed on peer reviewed articles ...
In this paper, we explain the concept, characteristics & need of Big Data & different offerings available in the market to explore unstructured large data. This paper covers Big Data adoption trends, entry & exit criteria for the vendor and product selection, best practices, customer success story, benefits of Big Data analytics, summary and conclusion. Our analysis illustrates that the Big ...
Big data is concerned with voluminous, complex, highly unstructured data produced from numerous sources. It is expanding at immense rate these days and is a crucial issue to handle and manage the data for the analysis of required information to save both time and cost. The data extracted can be useful for the organization in various aspects. A lot of decisions have to be taken by business ...
The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications. Research areas for big data include, but are not restricted to, big data analytics ...
The 2022 IEEE International Conference on Big Data (IEEE BigData 2022) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress ...
Regular Papers. BigD226. Xiajiong Shen, Kunying Meng, Daojun Han, Kai Zhai, and Lei Zhang, Weather radar echo prediction method based on recurrent convolutional neural network. BigD242. KAI-YUAN HOU, Qiao Kang, Sunwoo Lee, Ankit Agrawal, Alok Choudhary, and Wei-keng Liao, Supporting Data Compression in PnetCDF. BigD243.
IEEE Transactions on Big Data. IEEE Big Data Initiative is a new IEEE Future Directions initiative. Big data is much more than just data bits and bytes on one side and processing on the other. IEEE, through its Cloud Computing Initiative and multiple societies, has already been taking the lead on the technical aspects of big data.
IEEE Big Data 2021 Accepted Papers. 1. Big Data Science and Foundations. Paper ID. Regular Papers. BigD310. "Trie-based Output Space Itemset Sampling". Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, and Arnaud Soulet. BigD390.
The 2024 IEEE International Conference on Big Data (IEEE BigData 2024) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress ...
The Big Data Access (BDA) working group was established in 2017 as part of the IEEE PES Subcommittee on Big Data and Analytics for Power Systems. The objective of the BDA working group is to facilitate public access to power systems data to promote big data research and development. The electric power
Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT, meteorology, fraud detection. It aimed to uncover hidden trends and has prompted a development from a model-driven perspective to a data-driven approach. Among ...
With this paper, readers can have a more thorough understanding of cybersecurity in the big data era, as well as research trends and open challenges in this active research area. Published in: IEEE Transactions on Services Computing ( Volume: 14 , Issue: 6 , 01 Nov.-Dec. 2021 )
7 6 to, best practices in and standards for BDA/ML/AI in the smart grid. 9 8 The IEEE Smart Grid BDA/ML/AI White Paper Series will comprise the following white papers: 10 1. Introduction to BDA/ML/AI, Benefits, Challenges and Issues. 11 2. Best Practices in Big Data Analytics for the Smart Grid.
Over the past 15 years, Big Data has emerged as a foundational pillar providing support to an extensive range of different scientific fields, from medicine and healthcare [] to engineering [], finance and marketing [3,4,5], politics [], social networks analysis [7, 8], and telecommunications [], to cite only a few examples.This 15-year period has witnessed a significant increase in research ...
Accepted Papers. Full papers. Vani Bhat, Sree Divya Cheerla, Jinu Rose Mathew, Nupur Pathak and Zeyu Gao, Retrieval Augmented Generation (RAG) based Restaurant Chatbot with AI Testability. Neeraj Kulkarni, Katerina Potika and Petros Potikas, Learning to Play the Trading Game: Exploring Reinforcement Learning-Based Stock Trading Bots.
IEEE Big Data 2020 Accepted Papers. 1. Big Data Science and Foundations. Paper ID. Regular Papers. BigD273. "Connecting MapReduce Computations to Realistic Machine Models" Peter Sanders. BigD274. ""To Tell You the Truth" by Interval-Private Data" Jie Ding and Bangjun Ding.
Distributed and parallel time series feature extraction for industrial big data applications. 3 code implementations • 25 Oct 2016. This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and ...
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.. The journal will accept papers on foundational aspects in … View full aims & scope $2760
Aims and scope. The Journal of Big Data publishes open-access original research on data science and data analytics. Deep learning algorithms and all applications of big data are welcomed. Survey papers and case studies are also considered. The journal examines the challenges facing big data today and going forward including, but not limited to ...
Keynote Speeches. Phd Forum. Conference Venue & Hotels. Registration. Welcome!2024 IEEE International Conference on Big Data (IEEE BigData2024) Dec 15-18, 2024 @ Washington DC, USA. Welcome!2024 IEEE International Conference on Big Data (IEEE BigData2024) Dec 15-18, 2024 @ Washington DC, USA.
Big Data refers to data that can't be processed with traditional applications due the challenge of capturing, storing, transferring, querying, fast processing and updating data in such large amounts. The Big Data concept often uses analytics involving Artificial Intelligence (AI), Machine Learning and Deep Learning. The paper investigates the impact of Big Data in the use of AI methods and ...
Industrial intelligent systems (IIS) play a huge role in modern industry, and their intelligent models of IIS enable diagnosis of faults, key performance indicator (KPI) prediction, and other important industrial process analysis in a data-driven way. However, the performance of intelligent models is limited by the quantity and quality of local data in specific factories. At the same time, the ...
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 ...
Nowadays, big data has become a constantly extended and widely mentioned term. It can excavate, describe and utilize a large amount of structured, unstructured and semi-structured data to obtain more information. With the rapid increase of data, big data has become more and more diverse, and the big data technology has emerged consequently. This paper reviews the literature of big data and the ...
First, data coding was related to research questions. Next, the coding used an inductive approach to identify the interacting variables in this process. ... Big data will play one of the prominent roles in full automation (Kosolapov and Krysin, ... IEEE Industrial Electronics Magazine 15(3). IEEE: 6-12. Crossref. Web of Science. Google ...
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining ...
Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typical workflows in visualization and visual analytics (VIS), unveiling the characteristics shared by these workflows. We explore the research landscape of VIS to study where GT is applied to generate VIS theories ...