Rising unemployment has led to many discouraged job seekers. While the impact of job seekers’ motivation on interview performance is acknowledged in previous research, little attention has been given to understanding the effect of training on interview motivation and performance. We present InterviewApp, an online interview training tool aiming to support interview motivation through autonomy, relatedness and competence needs derived from Self-Determination Theory and, in turn, performance. Through a four-month study (N=135), we assess its effectiveness in supporting job seekers’ interview motivation and performance. Our results demonstrate the role of autonomy in mediating the effect of training on performance. We found that the intervention significantly affected the job seekers’ perceived autonomy. Furthermore, engagement with the recording and feedback features of the tool positively impacted performance. Overall, job seekers found InterviewApp helpful for online interview training and valued the provided expert feedback. These findings have implications for the design of online interview training tools and for behaviour change interventions to support employment.

Individuals share increasing amounts of personal multimedia data, exposing themselves (uploaders) as well as others (data subjects). Non-consensual sharing of multimedia data that depicts others raises so-called multiparty privacy conflicts (MPCs), which can have severe consequences. To limit the incidence of MPCs, a family of Precautionary mechanisms have recently been developed that force uploaders to collaborate with the other data subjects to prevent MPCs. However, there is still very little work on understanding how users perceive the precautionary mechanisms together with which ones they prefer and why. In addition, precautionary mechanisms have some limitations, e.g., they require linking content to the co-owners' identity. Therefore, we also explore alternatives to precautionary mechanisms and propose a new class of solutions-Dissuasive mechanisms-that aim at deterring the uploaders from sharing without consent. We then present a user-centric comparison of precautionary and dissuasive mechanisms, through a large-scale survey (N = 1792; a representative sample of adult Internet users). Our results showed that respondents prefer precautionary to dissuasive mechanisms. These enforce collaboration, provide more control to the data subjects, but also they reduce uploaders' uncertainty around what is considered appropriate for sharing. We learned that threatening legal consequences is the most desirable dissuasive mechanism, and that respondents prefer the mechanisms that threaten users with immediate consequences (compared with delayed consequences). Dissuasive mechanisms are in fact well received by frequent sharers and older users, while precautionary mechanisms are preferred by women and younger users. We discuss the implications for design, including considerations about side leakages, consent collection, and censorship.

People do not exercise as much and as regularly as they should. To support users in adopting healthy exercise routines, app designers integrate persuasive techniques in their apps. In this study, we focus on two of these techniques, i.e., offering tangible rewards and sending motivational messages to users. Past research has demonstrated the effects of these techniques in nudging recipients to increase their physical activity levels. However, the effect of these interventions on the intrinsic motivation of the participants has not yet been studied. We conducted a 10-month study involving 208 participants; this research consisted of a 3-month baseline (pre-phase), a 4-month experiment and a 3-month follow-up (post-phase). The participants were randomly assigned to one of the following three interventions: either they receive money ((i.) through a fixed incentive or (ii.) a lottery), or (iii.) informative messages. Their daily goal was to walk 10K steps. Through their smart phones, we recorded how many steps they walked every day. These interventions had no effect on the main outcome variable (i.e., the number of steps). However, the manipulations produced a detrimental effect on the intrinsic motivation of the participants, measured through a standardized questionnaire. This negative effect extended into the follow-up period. Our study reveals that tangible rewards and motivational messages decrease the intrinsic motivation of the participants, hence their connected physical activity. In our findings, we highlight the importance of intrinsic motivation in setting up healthy exercise routines that will be carried on autonomously by the participants after the period of the intervention. Finally, we present implications for the design of persuasive apps.

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size.