How Proficiency and Feelings impact the Preference and Perception of Mobile Technology Support in Older Adults
Article No.: 116, Pages 1 - 5
Abstract
The kind of technology (tech) support that older adults prefer during continued mobile use varies widely. So does the perceived quality of that support. However, we know little about what influences these preferences and perceptions. We conducted an online survey with 138 U.S. older adults to understand how mobile device proficiency and feelings of anxiety and confidence during mobile use impact the preference for and perception of mobile tech support in older adults. Proficiency predicted a positive preference for self-reliant support but a negative preference for social support during continued mobile tech use. The effects of proficiency and confidence on the perceived quality of self-reliant mobile tech support in older adults were partially mediated by a preference for it.
References
[1]
Lan An, Diego Muñoz, Sonja Pedell, and Leon Sterling. 2022. Understanding confidence of older adults for embracing mobile technologies. In Proceedings of the 34th Australian Conference on Human-Computer Interaction. 38–50.
[2]
Yvonne Barnard, Mike D Bradley, Frances Hodgson, and Ashley D Lloyd. 2013. Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Computers in human behavior 29, 4 (2013), 1715–1724.
[3]
Priyankar Bhattacharjee, Steven Baker, and Jenny Waycott. 2021. Older adults and their acquisition of digital skills: A review of current research evidence. In Proceedings of the 32nd Australian Conference on Human-Computer Interaction (Sydney, NSW, Australia) (OzCHI ’20). Association for Computing Machinery, New York, NY, USA, 437–443. https://doi.org/10.1145/3441000.3441053
[4]
Robert J Campbell. 2004. Older women and the Internet. Journal of women & aging 16, 1-2 (2004), 161–174.
[5]
Avner Caspi, Merav Daniel, and Gitit Kavé. 2019. Technology makes older adults feel older. Aging & mental health 23, 8 (2019), 1025–1030.
[6]
Adeline Chu and Beth Mastel-Smith. 2010. The outcomes of anxiety, confidence, and self-efficacy with Internet health information retrieval in older adults: a pilot study. CIN: Computers, Informatics, Nursing 28, 4 (2010), 222–228.
[7]
Deborah R Compeau and Christopher A Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS quarterly (1995), 189–211.
[8]
Sho Conte and Cosmin Munteanu. 2019. Help! I’m Stuck, and there’s no F1 Key on My Tablet!. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services. 1–11.
[9]
Sara J Czaja, Neil Charness, Arthur D Fisk, Christopher Hertzog, Sankaran N Nair, Wendy A Rogers, and Joseph Sharit. 2006. Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE).Psychology and aging 21, 2 (2006), 333.
[10]
Dina Di Giacomo, Jessica Ranieri, Meny D’Amico, Federica Guerra, and Domenico Passafiume. 2019. Psychological barriers to digital living in older adults: computer anxiety as predictive mechanism for technophobia. Behavioral Sciences 9, 9 (2019), 96.
[11]
Xiaotong Du, Willow Yao, and Sunyoung Kim. 2021. Lessons Learned from Supporting Low-income Older Adults to Use a Tablet PC in the Age of Digital Divide. In Adjunct Publication of the 23rd International Conference on Mobile Human-Computer Interaction. 1–5.
[12]
Nelly Geerts, Werner Schirmer, Anina Vercruyssen, and Ignace Glorieux. 2023. Bridging the ‘instruction gap’: how ICT instructors help older adults with the acquisition of digital skills. International Journal of Lifelong Education 42, 2 (2023), 195–207.
[13]
Clarice P Gressard and Brenda H Loyd. 1986. Validation studies of a new computer attitude scale. AEDS Journal 19, 4 (1986), 295–301.
[14]
Spencer E Harpe. 2015. How to analyze Likert and other rating scale data. Currents in pharmacy teaching and learning 7, 6 (2015), 836–850.
[15]
Robert K Heinssen Jr, Carol R Glass, and Luanne A Knight. 1987. Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in human behavior 3, 1 (1987), 49–59.
[16]
Ioana Iancu and Bogdan Iancu. 2020. Designing mobile technology for elderly. A theoretical overview. Technological Forecasting and Social Change 155 (2020), 119977.
[17]
Xiaofu Jin, Xiaozhu Hu, Xiaoying Wei, and Mingming Fan. 2022. Synapse: Interactive Guidance by Demonstration with Trial-and-Error Support for Older Adults to Use Smartphone Apps. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3 (2022), 1–24.
[18]
Seongho Kim. 2015. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Communications for statistical applications and methods 22, 6 (2015), 665.
[19]
Sunyoung Kim, Abhishek Choudhury, 2020. Comparison of older and younger adults’ attitudes toward the adoption and use of activity trackers. JMIR mHealth and uHealth 8, 10 (2020), e18312.
[20]
Barry Kort, Rob Reilly, and Rosalind W Picard. 2001. An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. In Proceedings IEEE international conference on advanced learning technologies. IEEE, 43–46.
[21]
Qingchuan Li and Yan Luximon. 2016. Older adults and digital technology: A study of user perception and usage behavior. In Advances in physical ergonomics and human factors. Springer, 155–163.
[22]
Qingchuan Li, Yan Luximon, 2018. Understanding older adults’ post-adoption usage behavior and perceptions of mobile technology. (2018).
[23]
Shareen Mahmud, Jessalyn Alvina, Parmit K Chilana, Andrea Bunt, and Joanna McGrenere. 2020. Learning through exploration: how children, adults, and older adults interact with a new feature-rich application. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–14.
[24]
João Mariano, Sibila Marques, Miguel R Ramos, Filomena Gerardo, Cátia Lage da Cunha, Andrey Girenko, Jan Alexandersson, Bernard Stree, Michele Lamanna, Maurizio Lorenzatto, 2022. Too old for technology? Stereotype threat and technology use by older adults. Behaviour & Information Technology 41, 7 (2022), 1503–1514.
[25]
Tamir Mendel, Debin Gao, David Lo, and Eran Toch. 2021. An Exploratory Study of Social Support Systems to Help Older Adults in Managing Mobile Safety. In Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction. 1–13.
[26]
Dr Antonette Mendoza, Dr Tim Miller, Dr Sonja Pedell, Prof Sterling, 2013. The role of users’ emotions and associated quality goals on appropriation of systems: two case studies. (2013).
[27]
John B Nash and Pauline A Moroz. 1997. An examination of the factor structures of the computer attitude scale. Journal of educational computing research 17, 4 (1997), 341–356.
[28]
Tobias Olsson and Dino Viscovi. 2018. Warm experts for elderly users: Who are they and what do they do?Human Technology 14, 3 (2018).
[29]
Carolyn Pang, Zhiqin Collin Wang, Joanna McGrenere, Rock Leung, Jiamin Dai, and Karyn Moffatt. 2021. Technology Adoption and learning preferences for older adults: Evolving perceptions, ongoing challenges, and emerging design opportunities. In Proceedings of the 2021 CHI conference on human factors in computing systems. 1–13.
[30]
Andraž Petrovčič, Walter R Boot, Tomaž Burnik, and Vesna Vesna Dolničar. 2019. Improving the Measurement of Older Adults’ Mobile Device Proficiency: Results and Implications from a Study of Older Adult Smartphone Users. IEEE Access 7 (2019), 150412–150422.
[31]
Andraž Petrovčič, Sakari Taipale, Ajda Rogelj, and Vesna Dolničar. 2018. Design of mobile phones for older adults: An empirical analysis of design guidelines and checklists for feature phones and smartphones. International Journal of Human–Computer Interaction 34, 3 (2018), 251–264.
[32]
AARP Research. 2024. 2024 Tech Trends and Adults 50+. Retrieved January, 2024 from https://www.aarp.org/content/dam/aarp/research/topics/technology/internet-media-devices/2024-tech-trends-adults-50-plus.doi.10.26419-2Fres.00772.001.pdf.
[33]
Hasti Sharifi and Debaleena Chattopadhyay. 2023. A Cross-Cultural Study of Relational Maintenance in Tech Caregiving. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. 1–10.
[34]
Hasti Sharifi and Debaleena Chattopadhyay. 2023. Senior Technology Learning Preferences Model for Mobile Technology. Proceedings of the 25th International Conference on Mobile Human-Computer Interaction 7, Article 222 (2023), 39 pages. https://doi.org/10.1145/3447526.3472040
[35]
Hasti Sharifi, Joseph Michaelis, and Debaleena Chattopadhyay. 2024. Development and Evaluation of the Mobile Tech Support Questionnaire for Older Adults. In Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility. in press.
[36]
Gail M Sullivan and Anthony R Artino Jr. 2013. Analyzing and interpreting data from Likert-type scales. Journal of graduate medical education 5, 4 (2013), 541–542.
[37]
Xinru Tang, Yuling Sun, Bowen Zhang, Zimi Liu, RAY LC, Zhicong Lu, and Xin Tong. 2022. " I Never Imagined Grandma Could Do So Well with Technology" Evolving Roles of Younger Family Members in Older Adults’ Technology Learning and Use. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–29.
[38]
Teerapaun Tanprasert, Jiamin Dai, and Joanna McGrenere. 2024. HelpCall: Designing Informal Technology Assistance for Older Adults via Videoconferencing. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–23.
[39]
Dustin Tingley, Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. 2014. Mediation: R package for causal mediation analysis. (2014).
[40]
Hsin-Yi Sandy Tsai, RV Rikard, Shelia R Cotten, and Ruth Shillair. 2019. Senior technology exploration, learning, and acceptance (STELA) model: from exploration to use–a longitudinal randomized controlled trial. Educational Gerontology 45, 12 (2019), 728–743.
[41]
Hsin-yi Sandy Tsai, Ruth Shillair, and Shelia R Cotten. 2017. Social support and “playing around” an examination of how older adults acquire digital literacy with tablet computers. Journal of Applied Gerontology 36, 1 (2017), 29–55.
[42]
Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly (2003), 425–478.
[43]
John Vines, Gary Pritchard, Peter Wright, Patrick Olivier, and Katie Brittain. 2015. An age-old problem: Examining the discourses of ageing in HCI and strategies for future research. ACM Transactions on Computer-Human Interaction (TOCHI) 22, 1 (2015), 1–27.
[44]
Ja Eun Yu and Debaleena Chattopadhyay. 2020. Supporting Older Adults in Locating Mobile Interface Features with Voice Input. In Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility. 1–4.
[45]
Ja Eun Yu and Debaleena Chattopadhyay. 2024. Reducing the Search Space on demand helps Older Adults find Mobile UI Features quickly, on par with Younger Adults. In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, NY, USA, Article 23, 22 pages. https://doi.org/10.1145/3613904.3642796
[46]
Ja Eun Yu, Natalie Parde, and Debaleena Chattopadhyay. 2023. “Where is history”: Toward Designing a Voice Assistant to help Older Adults locate Interface Features quickly. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19.
Index Terms
- How Proficiency and Feelings impact the Preference and Perception of Mobile Technology Support in Older Adults
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October 2024
1475 pages
ISBN:9798400706776
DOI:10.1145/3663548
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Published: 27 October 2024
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