Agility & Loose Coupling of Enterprise Systems

Enterprise Systems (ES) are large, integrated information systems that combine various ICT functions of organizations. Our interest lies in the question to what extent these recent ES developments contribute to this ‘loose coupling’ and consequently, how this influences business agility.

Automated testing of data integrity in the social sciences

Social science research communities worldwide are working hard to improve their research, analysis, reporting, and publication practices. The aim of the current project is to build a platform that (1) facilitates the use of, and (2) extends, Simonsohn’s detection method.

Conversational Success in Twitter Webcare Dialogues

Social media have radically changed the communication between organizations and their customers. Organizations are faced with the questions whether they should be active in social media, and if so, how they should respond in a simultaneously efficient and effective manner. This project addresses these questions by investigating the relationship between the course, outcome, and linguistic characteristics of social media conversations.

Helping Humanities Scholars to Search the Rijksmuseum Prints Online Collection

Humanities researchers depend in their research on the efficiency and effectiveness of the search functionality provided in various cultural heritage collections online. The main goal of this project is to study the user needs of humanities scholars and implement them in a demonstrator, which will serve as a basis for an evaluation pilot to collect user feedback on a novel semantic search approach.

Leveraging Expertise Diversity

Cross-domain collaboration is considered an important source of creativity and innovation, particularly important to deal with complex tasks. Tackling such complex tasks requires insights from different disciplines. This project proposes to assess intercoder reliability and design an approach through which experts from the relevant fields can rate the expertise areas to further advance disambiguation.

New Independance Indicators for Science

Selecting scientific talent for positions and grants is a complex phenomenon. Where peer review traditionally played a dominant role, increasingly, this is replaced by expert (but nonpeer) committees who decide partly based on bibliometrics. From an evaluation and analytical perspective, this is not very satisfying. In talent selection, many criteria are deployed (implicitly or explicitly) that ultimately determine who gets a job or a grant and who doesn’t. As current research mainly focuses on traditional bibliometric performance indicators, we (i) do not understand the process of talent identification very well and as far as the indicators used are (normatively) used, we (ii) misinform decision makers by providing them with wrong (or one sided) indicators.
For selecting scholars for jobs, we showed elsewhere (e.g Van den Besselaar et al 2009) that bibliometric indicators actually do not work very well; interviewing a series of panel members suggests that scholarly independence is seen as a major criteria (Van Arensbergen et al 2013). Therefore we started to develop indicators that show candidates’ independence, but mainly focusing on coauthor networks and topical orientation of the candidates (van den Besselaar et al 2012). Other dimensions should be included, such as the independence of the scientist’s academic network. This project will develop a new more-dimensional concept of independence that combines bibliometrics withweb-based data (e.g. social media, digital CFPS).

Shifts and context in political news 1945 – now

This project focuses on determining the way in which the topic, political context, and involved politicians determine the relation between political discourse and preceding and following news coverage, where we are especially interested in long term shifts in the relation between press and politics.

SIRUP: Enhancing Serendipity In Recommendations via User Perceptions

Creating serendipity (i.e. “pleasant surprises for users”) is a primary goal of intelligent recommender systems. This project proposes an interdisciplinary approach to enhance the serendipity of TV recommendations that combines complementary knowledge from three disciplines – Computer Science, Language & Cognition and Communication Science.