Comparing the effect of cross-group friendship on generalized trust to its effect on prejudice: The mediating role of threat perceptions and negative affect

Intergroup relations theory posits that cross-group friendship reduces threat perceptions and negative emotions about outgroups. This has been argued to mitigate the negative effects of ethnic diversity on generalized trust. Yet, direct tests of this friendship-trust relation, especially including perceptions of threat and negative affect as mediators, have remained rare at the individual level. In this article, we bridge this research gap using representative data from eight European countries (Group-Focused Enmity). We employ structural equation modelling (SEM) to model mediated paths of cross-group friendship on generalized trust via perceptions of threat and negative affect. We find that both the total effect as well as the (mediated) total indirect effect of cross-group friendship on generalized trust are weak when compared with similar paths estimated for prejudice.

Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

Voluntary Tax-compliance Behavior in SMEs; Evidence from Pakistan and Turkey

This research article aims at exploring the Voluntary Tax-compliance behaviour of the small and medium-size business enterprises for two countries, Pakistan and Turkey. Voluntary tax-compliance from SME’s is considered as major risk area by the tax-collection authorities of both developing and developed countries, authorities from both desire to bring individuals belonging to SME’s to pay taxes on a voluntary basis, in order to generate higher revenues for their governments. Thus, data was collected from the owners of SME’s of both countries through a questionnaire, data thus collected was analysed using SPSS and PLS-Smart. The results suggested that voluntary tax-compliance can be achieved by building a mutual trust relation and by exercising legitimate use of power by the tax authorities, results also signify that subjective norms, perceived behavioural control, attitude towards taxes and moral obligation are also key factors in determining the voluntary tax-compliance behaviour as suggested by Kirchler, Hoelzl and Wahl, (2008). Also, this article tests the mediating role of the intention of building positive voluntary tax-compliance behaviour as suggested by Ajzen (1991). This study is an important contribution in literature as it incorporates data from two countries, Pakistan (a developing nation) and Turkey (that is making its mark in the list of developed countries). This research work can be further extended by incorporating comparative analysis among the business owners belonging to developed, semi-developed and developing countries.

Effects of misinformation diffusion during a pandemic

The role of misinformation diffusion during a pandemic is crucial. An aspect that requires particular attention in the analysis of misinfodemics is the rationale of the source of false information, in particular how the behavior of agents spreading misinformation through traditional communication outlets and social networks can influence the diffusion of the disease. We studied the process of false information transmission by malicious agents, in the context of a disease pandemic based on data for the COVID-19 emergency in Italy. We model communication of misinformation based on a negative trust relation, supported by findings in the literature that relate the endorsement of conspiracy theories with low trust level towards institutions. We provide an agent-based simulation and consider the effects of a misinfodemic on policies related to lockdown strategies, isolation, protection and distancing measures, and overall negative impact on society during a pandemic. Our analysis shows that there is a clear impact by misinfodemics in aggravating the results of a current pandemic.

TRUST EVALUATION & ENHANCEMENT: AN APPARATUS TO GET COMPETITIVE ADVANTAGE

The reparations offered by cloud computing, make it admired & accepted among enterprises and individuals. This IT paradigm has seen a sharp rise in the last one and half decade. At its infancy stage, this technology has changed every body's perception of storage, infrastructure, software installation, software support, and development. The credit goes to the architecture of the technology which makes it possible to offer premium resources at economical prices, scalability of resources depending upon requirements and the most important financials are based on usage. In this success & acceptance ride, technology also had its share of breakers Like security, Trust, Privacy. These hurdles are making current and future consumers hesitant about adopting the technology. Along with technology growth, last few years have witnessed a sharp rise in security lapse cases. These incidences are raising questions about security & privacy of data and information and finally leaving marks on the "Trust" relation between service consumer and service provider. In order to strengthen the relationship with service consumer, it is the responsibility of the provider to endow consumers with secure enclosures to keep their data and information protected. Standards and certifications are considered as best practices for providing security assurances in Information Technology. The objective of the paper is to design a framework that can help the provider to overcome this un-trusty situation, by converting one of the hesitant factors "Trust" into competitive advantage component by attaining suggested certifications or adopting recommended standards.

Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

Privacy-Protecting Algorithms for Digraph Shortest Path Queries

Trust relation in this work refers to permission that is given to a user at a source-host to access another user at a target-host through an authentication key with a unique fingerprint. The database owner can form a directed graph out of these trust relations, such that user-host pairs are considered nodes and fingerprints as arrows. The authors of this article present a novel protocol to query the shortest path from node A to node B, in a privacy preserving manner. The authors would like to use a cloud to perform such queries, but they do not allow the cloud to learn any information about the graph, nor the query. Also, the database owner is prevented from learning any information about the query, except that it happened.