The objective of this research was to develop a short, reliable, and valid perceived website usability measurement scale. A sample of 350 participants was used to collect the necessary data. Exploratory and confirmatory factor analyses were performed to purify the proposed scale. Analysis indicated that the proposed multi-dimensional usability scale is reliable and shows evidence of construct and predictive validity. Academic and managerial implications were discussed.

Despite rapid growth of the sharing economy, little is known about consumers’ reactions when sharing services fail. Drawing on attribution theory, in three studies we show that consumers forgive such service failures varyingly, depending on the controllability and the locus of attribution of the failures. Specifically, when a failure has low controllability, consumers are more forgiving when it is attributed to an individual service provider than when it is attributed to a service enabling organization. Empathy toward the service provider explains the increased forgiveness. However, no difference in forgiveness is observed in the case of highly controllable failures, irrespective of the source of attribution. Furthermore, the effect of two recovery strategies – compensation and apology – also varies depending on these conditions. Theoretical and managerial implications are discussed.

Although several meta-analyses have been conducted on the effectiveness of warning labels, many questions regarding their effectiveness remain unanswered. The authors identify 243 effect sizes from 66 primary articles, more than three times the number of effect sizes included in the most comprehensive meta-analysis to date. This updated and substantially larger data set shows that label effectiveness is contingent on the type of expected behavioral outcome. Labels aimed at moderation/cessation display a generally diminishing cascade of effects from attention (r = .32), comprehension (r = .37), recall (r = .31), judgment (r = .22), and behavior (r = .18). Labels targeting safe use show stronger effect sizes for behavior (r = .39) despite displaying a downward trend for attention (r = .35), comprehension (r = .29), recall (r = .32), and judgment (r = .21). The authors also find evidence of increased effectiveness when preactivating the label by means of an integrated communication strategy (r = .49). In addition, the results show the impact of several contextual factors (e.g., social influence [r = .33] and exposure frequency [r = .12]).

As the world adapts to COVID-19, the transport behaviour of commuters has been greatly modified. Governments and transit authorities will need strong, well-received mitigation measures and education campaigns to maintain the historically upward trend of sustainable mass transit usage following this pandemic. This study, from a survey of 1968 Canadians in early May 2020, reveals that, following the end of stay-at-home orders, commuters intend to use their cars more and mass transit less. Driving these behavioural changes are commuters’ perceptions that mass transit use will negatively impact their health safety, peace of mind, and travel experience. The results also show that certain mitigation measures, such as more frequent cleaning and mandatory hand washing, are likely to reduce this decline, whereas e-monitoring and the use of health certificates will be detrimental to mass transit ridership through user perception. These results can help lessen the environmental impact of the public returning to work by encouraging their continued use of more environmentally friendly modes of transportation.

Background: Psychological distress increases across adolescence and has been associated with several important health outcomes with consequences that can extend into adulthood. One type of technological innovation that may serve as a unique intervention for youth experiencing psychological distress is the conversational agent, otherwise known as a chatbot. Further research is needed on the factors that may make mental health chatbots destined for adolescents more appealing and increase the likelihood that adolescents will use them. Objective: The aim of this study was to assess adolescents’ emotional reactions and likelihood of responding to questions that could be posed by a mental health chatbot. Understanding adolescent preferences and factors that could increase adolescents’ likelihood of responding to chatbot questions could assist in future mental health chatbot design destined for youth. Methods: We recruited 19 adolescents aged 14 to 17 years to participate in a study with a 2×2×3 within-subjects factorial design. Each participant was sequentially presented with 96 chatbot questions for a duration of 8 seconds per question. Following each presentation, participants were asked to indicate how likely they were to respond to the question, as well as their perceived affective reaction to the question. Demographic data were collected, and an informal debriefing was conducted with each participant. Results: Participants were an average of 15.3 years old (SD 1.00) and mostly female (11/19, 58%). Logistic regressions showed that the presence of GIFs predicted perceived emotional valence (β=–.40, P<.001), such that questions without GIFs were associated with a negative perceived emotional valence. Question type predicted emotional valence, such that yes/no questions (β=–.23, P=.03) and open-ended questions (β=–.26, P=.01) were associated with a negative perceived emotional valence compared to multiple response choice questions. Question type also predicted the likelihood of response, such that yes/no questions were associated with a lower likelihood of response compared to multiple response choice questions (β=–.24, P=.03) and a higher likelihood of response compared to open-ended questions (β=.54, P<.001). Conclusions: The findings of this study add to the rapidly growing field of teen-computer interaction and contribute to our understanding of adolescent user experience in their interactions with a mental health chatbot. The insights gained from this study may be of assistance to developers and designers of mental health chatbots.