BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220608T221758Z LOCATION: DTSTART;TZID=America/New_York:20220609T120000 DTEND;TZID=America/New_York:20220609T123000 UID:HFESHCS_2022 International Symposium on Human Factors and Ergonomics i n Health Care_sess175_INDLEC288@linklings.com SUMMARY:The Role UX Design Attributes Play in the Perceived Persuasiveness of COVID-19 Contact Tracing Apps DESCRIPTION:Oral Presentations\n\nThe Role UX Design Attributes Play in th e Perceived Persuasiveness of COVID-19 Contact Tracing Apps\n\nOyibo, Mori ta\n\nINTRODUCTION\n\nContact tracing apps (CTAs) were deployed worldwide in 2020 to combat COVID-19. Due to their low uptake, a growing amount of e mpirical research is being conducted to understand the factors that drive their adoption. We argue that for them to be adopted, users, first and for emost, must find them persuasive. However, there is little research to und erstand the role user experience (UX) plays in their perceived persuasiven ess. Consequently, we conducted an online study on Amazon Mechanical Turk among Canadian and American residents (n = 446) to investigate the most im portant UX design attributes associated with the perceived persuasiveness of CTAs. The study was based on two app designs (control and persuasive), each of which comprises three use cases: no-exposure, exposure, and diagno sis report interfaces. One interface (screenshot) was randomly presented t o a participant to view and provide their responses on the perceived UX de sign attributes and perceived persuasiveness of the interface.\n\nIn the o verall model, we found that perceived usefulness is the most important and consistent UX design attribute that influences perceived persuasiveness ( β = 0.29, p < 0.001), followed by perceived trustworthiness (β = 0.24, p < 0.001), and perceived privacy protection (β = 0.16, p < 0. 05). Respectively, the three predictors were consistently significant in t wo-third, half, and one-third of the 12 submodels based on app design, use case, adoption status, and country of residence. Overall, the relationshi ps regarding the persuasive designs are more likely to be significant, wit h the variance of the target construct explained by the predictors ranging from 71% to 89% compared with 54% to 69% for the control designs. \n\nThe three significant attributes will help designers know which UX design att ributes to focus on when designing CTAs for future pandemics/epidemics. Mo re importantly, in predictive modeling, if their ratings are known, they h old potential in predicting new users’ responsiveness to a number of persuasive strategies/messages featured in a behavior change support syst em.\n\n\nOVERVIEW OF COVID ALERT\n\nThe study was based on COVID Alert, Ca nada’s official CTA deployed in July 2020. In this study, we impleme nted two app designs (persuasive and control), with each design comprising the three key interfaces. \n\nNo-Exposure Status Interface. The control v ersion (C1) lets the user know that they have not been exposed by being in close contact with someone infected by COVID-19. In addition to this func tionality, the persuasive version (P1) supports a self-monitoring feature that enables the user to track their daily exposure level: number and dura tion of contacts.\n\nExposure Status Interface. The control version (C2) n otifies the user that they may have been exposed to COVID-19 and provide i nformation on what to do next. In addition to this functionality, the pers uasive version (P2) supports a self-monitoring feature that enables the us er to track their exposure level: the total number and duration of contac ts within the last 14 days.\n\nDiagnosis Report Interface. This control ve rsion (C3) allows the user to report their one-time key given to them by t he public health authority after testing positive. In addition to this fun ctionality, the persuasive version (P3) supports a social-learning feature that enables the user to see the number of other users who have reported their COVID-19 diagnosis using the app.\n\n\nMETHOD\n\nThe six interfaces were randomly administered to the participants on Amazon Mechanical Turk, with each participant seeing one interface only. A total of 446 participan ts took part in the study: 204 Canadian residents and 242 American residen ts.The distribution of the interfaces among the participants is as follows : C1 (n = 74), P1 (n = 75), C2 (n = 77), P2 (n = 80), C3 (n = 72), and P3 (n = 68). The constructs measured include perceived usefulness, perceived trust, perceived security, perceived ease of use, perceived privacy prote ction, perceived compatibility, perceived enjoyment, and perceived persuas iveness. The first seven are predictors and the last one is the target con struct. Each construct was measured using three items ranging from “ strongly disagree – 1” to “strongly agree – 7.R 21;\n\n\nRESULTS\n\nWe used Partial Least Square Path Modeling to explore the relationships between each of the seven predictors and perceived persu asiveness. The overall and subgroup models are as follows:\n\n1. Overall M odel: usefulness (β = 0.29, p < 0.001), trustworthiness (β = 0 .24, p < 0.001), privacy protection (β = 0.16, p < 0.05), ease of u se (β = 0.12, p < 0.01).\n\n2. Submodels based on Country of Residenc e: \na) Canada: usefulness (β = 0.19, p < 0.01), trustworthiness (& #946; = 0.24, p < 0.01), privacy protection (β = 0.25, p < 0.01).\nb ) United States: usefulness (β = 0.43, p < 0.001), trustworthiness (β = 0.28, p < 0.05).\n\n3. Submodels based on Adoption Status: \na) Adopters: privacy protection (β = 0.42, p < 0.01), ease of use ( 46; = 0.10, p < 0.05).\nb) Non-Adopters: usefulness (β = 0.43, p < 0 .01), trustworthiness (β = 0.22, p < 0.01), ease of use (β = 0 .13, p < 0.05).\n\n4. Submodels based on App Design: \na) Control: useful ness (β = 0.30, p < 0.01), trustworthiness (β = 0.20, p < 0.01) .\nb) Persuasive: usefulness (β = 0.32, p < 0.01), trustworthiness (β = 0.23, p < 0.05), privacy protection (β = 0.12, p < 0.05).\ n\n5. No-Exposure Submodels based on App Design: \na) C1: No attribute is significant.\nb) P1: trustworthiness (β = 0.79, p = 0.055).\n\n6. Ex posure Submodels based on App Design: \na) C2: usefulness (β = 0.41, p < 0.05).\nb) P2: usefulness (β = 0.29, p < 0.05), trustworthines s (β = 0.22, p < 0.01), enjoyment (β = 0.27, p < 0.05).\n\n7. D esign Report Submodels based on App Design: \na) C3: usefulness (β = 0.31, p < 0.01).\nb) P3: privacy protection (β = 0.75, p < 0.001).\ n\nDISCUSSION\n\nPerceived usefulness and perceived trustworthiness are si gnificantly and strongly associated with perceived persuasiveness. These a ssociations are (near) strong, despite controlling for country of residenc e and app design. However, when app design and use case are controlled for , some of the significant associations become non-significant, especially regarding C1 and P1. Nevertheless, regarding C2 and P2 the association be tween perceived usefulness and perceived persuasiveness remains significan t. Moreover, the association between perceived trust/perceived enjoyment a nd perceived persuasiveness remains strong regarding P2 and non-significan t regarding C2. Secondly, regarding the diagnosis report interface, the as sociation between perceived usefulness and perceived persuasiveness remain significantly strong for C3, while that between perceived privacy and per ceived persuasiveness becomes strong for P3. Finally, the control for adop tion status shows that perceived usefulness and perceived trustworthiness remain strong in the non-adopter models, while perceived privacy protectio n becomes significantly strong in the adopter model.\n\nOverall, perceived usefulness has a more consistent strong relationship with perceived persu asiveness, followed by perceived trust, perceived privacy protection, and perceived enjoyment. The other constructs (perceived ease of use, perceive d compatibility, and perceived security) have no strong or significant rel ationship with perceived persuasiveness.\n\nThe significantly strong relat ionships hold potential for building machine-learning predictive models by using the significant UX design attributes to predict first-time users 217; responsiveness to persuasive strategies implemented in CTAs and other persuasive apps aimed at motivating behavior change. In other words, the significant relationships have the potential to address the cold start pro blem experienced by persuasive health technologies. The cold start problem is the inability for a persuasive app to know what persuasive strategies or messages that will be effective in changing a first user’s behavi or. This means by asking first-time users few UX-related questions prior t o using the app, their responsiveness to certain persuasive strategies and messages may be predicted. END:VEVENT END:VCALENDAR