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Although trust is a lively area of research, it is rarely investigated in countries outside of commonly available cross-national public-opinion datasets. In an effort to fill this empirical void and to draw conclusions concerning the general determinants of trust, the current article employs detailed survey data from a frequently overlooked Central Asian country, Uzbekistan, to test the relationship between particularized trust and demographic traits previously identified as influential. While a number of Uzbek demographic characteristics coincide with previously identified determinants of trust, age and education yield negative effects not previously found. Interestingly, individual-level demographic variables become insignificant when controlling for regional, religious, and linguistic variation. We conclude with a discussion of the theoretical implications. [Reprinted by permission of Sage Publications Ltd., copyright, the International Political Science Association.]

Trust in the news media received wide scholarly attention for almost a century, which was further boosted as a result of recent developments in the media landscape and changes in how news is made and consumed. Despite that, the conceptualization of trust in the news media is still debated, and its measurement comparability has not yet been established. In this paper, I build up on earlier conceptualizations of trust in the news media, and test three theoretically derived measurement models to determine their cross-cultural equivalence in 28 EU countries. Using Eurobarometer data, I test the validity and comparability of these measurements employing multi-group confirmatory factor analysis. The findings indicate that trust is a unidimensional latent construct, equally interpreted across contexts. People's level of trust in the news media reflects their general attitude to the news stories and reporters in all sources of media they are exposed to. While bifactorial measurements of news media trust, differentiating between legacy and online sources, have some merit in single case-studies, they are non-invariant and therefore non comparable. This means that any cross-population differences found employing them are likely a function of measurement idiosyncrasies or other unknown factors.

Although many consider trust as a desirable value in administration, they also find it elusive. The meaning of trust gets more elusive when one seeks to optimize it in administration. The article takes on this quest first by addressing trust as a relational fact, and then, identifying the dynamic conditions of its optimization. Organizational control and organizational learning are discussed as two such conditions—in their embedded as well as enacted forms. Using the contingencies of control and learning, the article delineates a model of optimal trust, where optimality is located in the dynamic interfaces of control and learning that simultaneously maximize the enabling conditions of trust and minimize its disabling conditions.

Part 7: IFIPTM 2017 Graduate Symposium ; International audience ; Information has been an essential element in the development of collaborative and cooperative models, from decision making to the attainmentof varying goals, people are adept at making judgment on the trustworthiness of information, based on knowledge and understanding of a normative model of information. Contemporary narrative especially in high-impact contexts like politics, health, business, government and technology, is eroding trust in information, its source, its value and the ability to objectively determine the trustworthiness of a piece of information, a situation made more complex by social networks, social media have made the spread of information easier and faster irrespective of their trustworthiness, hence the need for judgment on the trustworthiness of a piece of information based on social cognitive construct, a trust model for information.

Social media greatly enables people to participate in online activities and shatters the barrier for online users to create and share information at any place at any time. However, the explosion of user-generated content poses novel challenges for online users to find relevant information, or, in other words, exacerbates the information overload problem. On the other hand, the quality of user-generated content can vary dramatically from excellence to abuse or spam, resulting in a problem of information credibility. The study and understanding of trust can lead to an effective approach to addressing both information overload and credibility problems. Trust refers to a relationship between a trustor (the subject that trusts a target entity) and a trustee (the entity that is trusted). In the context of social media, trust provides evidence about with whom we can trust to share information and from whom we can accept information without additional verification. With trust, we make the mental shortcut by directly seeking information from trustees or trusted entities, which serves a two-fold purpose: without being overwhelmed by excessive information (i.e., mitigated information overload) and with credible information due to the trust placed on the information provider (i.e., increased information credibility). Therefore, trust is crucial in helping social media users collect relevant and reliable information, and trust in social media is a research topic of increasing importance and of practical significance. This book takes a computational perspective to offer an overview of characteristics and elements of trust and illuminate a wide range of computational tasks of trust. It introduces basic concepts, deliberates challenges and opportunities, reviews state-of-the-art algorithms, and elaborates effective evaluation methods in the trust study. In particular, we illustrate properties and representation models of trust, elucidate trust prediction with representative algorithms, and demonstrate real-world applications where trust is explicitly used. As a new dimension of the trust study, we discuss the concept of distrust and its roles in trust computing