People in human-computer interaction have learned a great deal about how to persuade and influence users of computing technology. They have much less well-founded knowledge about how to help users choose for themselves. It’s time to correct this imbalance. A first step is to organize the vast amount of relevant knowledge that has been built up in psychology and related fields in terms of two comprehensive but easy-to-remember models: The ASPECT model answers the question “How do people make choices?” by describing six choice patterns that choosers apply alternately or in combination, based on Attributes, Social influence, Policies, Experience, Consequences, and Trial and error. The ARCADE model answers the question “How can we help people make better choices?” by describing six general high-level strategies for supporting choice: Access information and experience, Represent the choice situation, Combine and compute, Advise about processing, Design the domain, and Evaluate on behalf of the chooser. These strategies can be implemented with straightforward interaction design, but for each one there are also specifically relevant technologies. Combining these two models, we can understand virtually all existing and possible approaches to choice support as the application of one or more of the ARCADE strategies to one or more of the ASPECT choice patterns. After introducing the idea of choice architecture for humancomputer interaction and the key ideas of the ASPECT and ARCADE models, we discuss each of the ASPECT patterns in detail and show how the high-level ARCADE strategies can be applied to it to yield specific tactics. We then apply the two models in the domains of online communities and privacy. Most of our examples concern choices about the use of computing technology, but the models are equally applicable to everyday choices made with the help of computing technology.

In this survey paper, we focus on smart interactive technologies and providing a picture of the current state of the art, exploring the way new discoveries and recent technologies changed workers’ operations and activities on the factory floor. We focus in particular on the Industry 4.0 and 5.0 visions, wherein smart interactive technologies can bring benefits to the intelligent behavior machines can expose in a human-centric AI perspective. We consider smart technologies wherein the intelligence may be in and/or behind the user interfaces, and for both groups we try to highlight the importance of designing them with a human-centric approach, framed in the smart factory context. We review relevant work in the field with the aim of highlighting the pros and cons of each technology and its adoption in the industry. Furthermore, we try to collect guidelines for the human-centric integration of smart interactive technologies in the smart factory. In this wa y, we hope to provide the future designers and adopters of such technologies with concrete help in choosing among different options and implementing them in a user-centric manner. To this aim, surveyed works have been also classified based on the supported task(s) and production process phases/activities: access to knowledge, logistics, maintenance, planning, production, security, workers’ wellbeing, and warehousing.

Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing.

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.