Informatica

Industries have increasingly adopted supply chain management practices to sustain competitive advantage, fostering collaboration among supply chain partners for effective coordination. While prior research has explored whether inter-partner relationships influence supply chain network performance, these studies have primarily focused on perceived effects rather than emprical observations. This study investigates the impact of trust on supply chain network performance through linguistic summarization. Its originality lies in integrating linguistic summarization with heterogeneous information network modelling, a novel method for evaluating trust-driven performance effects in supply chains. We modelled supply chain networks as heterogeneous information networks, representing companies and products as distinct node types, and their interactions as varied link types. A linguistic summarization framework was developed for these networks, and its application in the automotive industry enabled the validation of literature-derived hypotheses through the truth degree of linguistic summaries. The findings demonstrate that trust significantly enhances organizational performance, particularly in terms of profitability. Supply chain managers, analysts, and researchers especially gain from this study since it offers a data-driven, interpretable framework for assessing how trust affects network performance, which promotes cooperation, transparency, and decision-making.

Trust is an important factor for successful e-commerce and e-media applications. However, these media inherently disable many ordinary communication channels and means, and affect trust forming factors. Therefore cyber environment requires additional support when it comes to trust. This is also one key reason why computational trust management methods are being developed now for some fifteen years, while another key reason is to enable better decision making through mathematical modeling and simulations in other areas. These methods are grounded on certain premises, which are analyzed in this paper. On this basis, Qualitative assessment dynamics (QAD for short) is presented that complements the above methods. As opposed to other methods, it is aligned with certain principles of human reasoning. Therefore it further extends the scope of other computational trust management technologies that are typically concerned with artificial ways of reasoning, while QAD gives a basis also for applications in ordinary environments where humans are involved. By using this methodology, experimental work will be presented, applied to the area of organizations and human factor management.

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.