BPMN has become the de facto standard notation for process modelling. Past research has demonstrated the need for modelling guidelines to improve the quality of process models. In previous research we collected a set of practical guidelines through a systematic literature survey and classified those in different categories. In this paper we test a selection of BPMN tools for their support for these guidelines, and report on existing support per category of guideline and the kinds of support used by the tool to support the different guidelines. The results give insight into which domains of guidelines are well supported and which lack support from BPMN tools. Further, different preferences of the vendors are observed regarding the methods of support they implement in their tools.
We present a framework for organisations to prevent errors in data entry. It states that data entry errors can be prevented by a strong intention of data producers to enter data correctly and by a high task-technology fit. Two empirical studies support the framework and demonstrate that a high task-technology fit is relatively more important than the data producers' intention. The framework refines the theory of planned behaviour, and extends the explanatory domain of the task-technology fit construct. The empirical evidence underlines the importance of the task-technology fit construct, an often-neglected construct in information systems research.
Current methods of evaluating the quality of recommender systems are based on averages of metrics such as the average normalized discounted cumulative gain, average diversity and average reciprocity. Averages of metrics give a good sense of the overall quality of the recommendations, but not of how their quality is distributed with respect to the recommendation system's users or items. This paper presents a visual method, based on embedding a high dimensional content feature-space into a 2D image, that is capable of providing insights in which users are receiving high quality recommendations and how biased recommendation quality is with respect to different types of users. Through a proof of concept in the domain of job recommendation we show that our method allows business people to come to relevant answers to the question "For which of my users does my recommender system work well/poorly?".
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