https://doi.org/10.1177/0733464819895208 Journal of Applied Gerontology 1–10 © The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0733464819895208 journals.sagepub.com/home/jag Original Manuscript Given its ubiquity in the modern world, technology adoption may benefit older adults by supporting independent living (Czaja et al., 2006). However, in general, older adults reported less comfort in using new technology and were less likely to accept it than younger adults (Czaja et al., 2006; Porter & Donthu, 2006). For instance, in 2019, only 53% of US adults age 65 and above own smartphones compared to 96% of those aged 18 to 29 (https://www.pewinternet.org/ fact-sheet/mobile/). Previous studies have demonstrated that adoption of technology in older adults is not a purely techni- cal issue but rather a complex issue that requires consider- ations of multiple aspects, such as demographic factors, cognitive abilities, and relevant psychological factors (Chung et al., 2010; Czaja et al., 2006; see C. Lee & Coughlin, 2015; Schulz et al., 2014, for reviews). Particularly, the adoption of new technology can signifi- cantly be explained by attitudes toward or perception of technology. The technology acceptance model (TAM; Davis, 1989) is one of the early frameworks that has been effectively used and extended to explain the adoption pat- terns of different types of new technology. TAM suggested that a user’s intentions to adopt a technology are the single best predictor of use behavior (Davis & Venkatesh, 2004). The Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al., 2012) is another theoreti- cal framework suggesting that people’s intentions to use a new technology and actual technology acceptance are influ- enced by four key constructs: performance expectancy (use- fulness), effort expectancy (ease of use), social influence, and facilitating conditions (Venkatesh et al., 2012). Based on the models, researchers have also attempted to find how social context (Kulviwat et al., 2009; Venkatesh et al., 2003) and individual differences (Chen & Chan, 2014; Porter & Donthu, 2006; Sarker & Wells, 2003), such as age, income, cultural background, technology, and self-efficacy, affect the behavioral intentions and actual acceptance of new tech- nology. The models were recently extended to account for technology acceptance in older adults by adding age-related 895208JAG XX X 10.1177/0733464819895208Journal of Applied GerontologyYoon et al. research-article 2019 Manuscript received: April 2, 2019; final revision received: November 18, 2019; accepted: November 20, 2019. 1 University of South Dakota, Vermillion, USA 2 Florida State University, Tallahassee, USA 3 The Economist Corporate Network, Tokyo, Japan Corresponding Author: Jong-Sung Yoon, Department of Psychology, University of South Dakota, 414 E. Clark St, Vermillion, SD 57069, USA. Email: jongsung.yoon@usd.edu Shaking Confidence in Technology: Effects of an Earthquake-Induced Nuclear Disaster on Technology Adoption in Middle-Aged and Older Adults Jong-Sung Yoon 1 , Neil Charness 2 , and Florian Kohlbacher 3 Abstract Using the coincidental timing of a national survey conducted in Japan before and after the Fukushima Daiichi nuclear disaster in 2011, this study reports a rare natural experiment that explored how the experience of a nuclear disaster influenced technology adoption in middle-aged and older adults. We conducted path analyses assessing how technology or nontechnology adoption intention and behavior changed before and after the nuclear disaster and whether age could moderate the potential change over and above other relevant factors. Our models supported that Japanese middle-aged to older adults reported fewer technology adoption behaviors after experiencing of the earthquake. However, the negative impact of the earthquake was not more pronounced in older adults. Our results suggest that researchers need to pay more attention to the issue of how loss of trust and/or perceived risk affect technology adoption interacting with other relevant factors, particularly, age- related factors and abilities. Keywords technology adoption, disaster, path analysis