Institut für Informatik und Computational Science
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"Deal of the Day" (DoD) platforms have quickly become popular by offering savings on local services, products and vacations. For merchants, these platforms represent a new marketing channel to advertise their products and services and attract new customers. DoD platform providers, however, struggle to maintaining a stable market share and profitability, because entry and switching costs are low. To sustain a competitive market position, DoD providers are looking for ways to build a loyal customer base. However, research examining the determinants of user loyalty in this novel context is scarce. To fill this gap, this study employs Grounded Theory methodology to develop a conceptual model of customer loyalty to a DoD provider. In the next step, qualitative insights are enriched and validated using quantitative data from a survey of 202 DoD users. The authors find that customer loyalty is in large part driven by monetary incentives, but can be eroded if impressions from merchant encounters are below expectations. In addition, enhancing the share of deals relevant for consumers, i.e. signal-to-noise ratio, and mitigating perceived risks of a transaction emerge as challenges. Beyond theoretical value, the results offer practical insights into how customer loyalty to a DoD provider can be promoted.
(Near-)inverses of sequences
(2006)
We introduce the notion of a near-inverse of a non-decreasing sequence of positive integers; near-inverses are intended to assume the role of inverses in cases when the latter cannot exist. We prove that the near-inverse of such a sequence is unique; moreover, the relation of being near-inverses of each other is symmetric, i.e. if sequence g is the near-inverse of sequence f, then f is the near-inverse of g. There is a connection, by approximations, between near- inverses of sequences and inverses of continuous strictly increasing real-valued functions which can be exploited to derive simple expressions for near-inverses
In recent years, there has been a large amount of disparate work concerning the representation and reasoning with qualitative preferential information by means of approaches to nonmonotonic reasoning. Given the variety of underlying systems, assumptions, motivations, and intuitions, it is difficult to compare or relate one approach with another. Here, we present an overview and classification for approaches to dealing with preference. A set of criteria for classifying approaches is given, followed by a set of desiderata that an approach might be expected to satisfy. A comprehensive set of approaches is subsequently given and classified with respect to these sets of underlying principles