Smartphones are now the most widely used devices in the world, and their usage monitoring applications have become a general interest topic. However, few experimental studies investigate the reflexive effects of this monitoring on users. To address this point, this paper presents a longitudinal experiment on the effects of monitoring on various variables (e.g. screen time, types of uses). Objective and subjective data from 60 participants, divided into treatment and control groups, were collected over a 3 weeks period. Both groups had to estimate their daily usages, but the treatment group subsequently had access to their real data. Results have shown a normalizing influence of monitoring on smartphone usage, by improving estimation of screen time, reducing time spent on some underestimated applications and increasing use of others overestimated applications. This research paves the way for public policies promoting mastery of its own technological uses and responsible digital usage.

The Green IT approach questions the environmental, social and economic impact of digital technology. Design fiction is a discipline that can guide behaviors in favor of Green IT by building new imaginaries. However, to our knowledge, few studies assess the impact of narratives of possible future on behavioral intentions. To answer this question, we conducted a study with 388 participants, examining the impact of 14 different scenarios of possible digital futures on individual perceptions. The results showed that, for individuals with already a high level of Green IT practices, the fear dimension of scenarios has a preeminent impact on their intention to further increase these behaviors, whereas people with little practice of Green IT are more sensitive to confidence and the presence of solutions. These results pave the way for the integration of specific technological narratives within the construction of future public policies in favor of Green IT.

Scales are widely used to evaluate subjective dimensions in questionnaires. Two main formats are used: Likert scalesand Visual Analogue Scales (VAS). Previous studies have shown mixed results regarding which format to favor. The aimof the current study is to compare formats and presentation types for each type of scale. 658 participants participated inthe study and completed a trust scale. Several characteristics of scales (e.g., valence of anchors) were explored, and 11formats of scales were compared. The results show that participants’ responses were different according to the type ofscale (i.e., Likert or VAS), the initial cursor’s position in the VAS, and the anchors’ valence in the VAS. Differences interms of reliability were found between VAS formats and the number of categories in Likert scales. These findings suggestthat the scale format is crucial and may influence data collection as well as suggesting related conclusions.

Artificial Intelligence (AI) is an increasingly prevalent field that influences a number of other areas. It generates a multitudeof reactions among the general population, particularly anxiety, which impacts on the development, deployment, andregulation of AI. Nevertheless, experimental data on public perceptions toward AI are generally lacking. To fill thisgap, this paper presents a large-scale experiment conducted on the influence of the terms used to describe AI on peoplesperception. In a preliminary study (705 participants), words related to AI were extracted. In a second experiment (552participants), the impact of these terminologies on anxiety toward AI was explored. An unprecedented effect of wordingand a positive bias of perception toward computers was revealed by these experiments, compared to robots and newtechnologies. This research paves the way for future studies on the effect of words on perception in the field of AI andnew technologies.

Thanks to research breakthroughs, Artificial Intelligence (AI) has gained popularity in recent years. Nevertheless, itsacceptability by general public is a poorly researched subject. The unified theory of acceptance and use of technology(UTAUT), a popular model to evaluate acceptability, is generally used for technological products. The purpose of thisstudy is to ensure that UTAUT is an appropriated model to evaluate AI acceptability. In this paper, 705 participants wereinvited to evaluate the acceptability of tools that integrate AI at work in 2030. Relying on UTAUT, performance expectancy,effort expectancy, social influence, facilitating conditions, and intention to use were evaluated. Structural modeling sug-gests a significant influence of performance expectancy, social influence and facilitating conditions on intention to use AI(explained variance of 81%). This research paves the way for prospective research on the overall acceptability of AI.