Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.@en

Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations. Here, explanations convey reasons why a recommendation is given or how the system forms its recommendations. This paper focuses on the question how such claims about effectiveness of explanations can be evaluated. Accordingly, we investigate various models that are used to assess the effects of explanations and recommendations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.@en

The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.@en

Nowadays, as international trade with cross-border logistics increases, the administrative burden of regulatory authorities has been dramatically raised. In order to reduce repetitive and redundant supervisory controls and promote automatic administration procedures, electronic data interchange (EDI) 1 and other forms of information sharing are introduced and implemented. Compliance monitoring ensures data quality for information exchange and audit purpose. However, failure to be compliant with various regulations is still a general phenomenon globally among stakeholders in supply chains, leading to more problems such as delay of goods delivery, missing inventory, and security issues. To address these problems, traditional physical auditing methods are widely used but turned out to be timeconsuming and costly, especially when multiple stakeholders are involved. Since there is limited empirical research on compliance monitoring for regulatory supervision in international supply chains, we propose a compliance monitoring framework that can be applied with data sharing and analytics. "e framework implementation is validated by an extensive case study on customs supervision in the Netherlands using process mining techniques. Practically, both public and private sectors will benefit from our descriptive and prescriptive analytics for audit purposes. "eoretically, our control strategies developed at the operational level facilitates mitigation of risks at root causes.@en

There has been a lot of research on the use of open data in the fight against corruption. Although there are some promising examples, it appears that a systematic approach is lacking. What are the design principles for an architecture to open up data and thereby reduce corruption? In this paper we use theory about fraud, and about public accountability to derive design principles for an open data architecture. Crucial is the sustained presence of a specific forum: A group of people who are critical, have expertise, are free to challenge the authorities. Unlike the general public, a specific forum has an interest in reviewing the data. The architecture is motivated and illustrated by an extensive example of an E-procurement system in the context of an anticorruption program in Palembang, Indonesia.@en