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BISE 58(2) - April 2016

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  • Zeitschriftenartikel
    Making Digital Freemium Business Models a Success: Predicting Customers’ Lifetime Value via Initial Purchase Information
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Voigt, Sebastian; Hinz, Oliver
    In digital freemium business models such as those of online games or social apps, a large share of overall revenue derives from a small portion of the user base. Companies operating in these and similar businesses are increasingly constructing forecasting models with which to identify potential heavy users as early as possible and create special retention measures to suit those users’ needs. In our study, we observe three digital freemium companies that sell virtual credits and investigate to what extent initial purchase information can be used to determine a given customer’s lifetime value. We find that customers represent higher future lifetime values if they (a) make a purchase early after registration, (b) spend a significant amount on their initial purchase, and (c) use credit cards to purchase credits. In addition, we see that users tend to spend increasing amounts on subsequent purchases.
  • Zeitschriftenartikel
    BISE and the Engineering Sciences
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Bichler, Martin; Heinzl, Armin; Aalst, Wil
  • Zeitschriftenartikel
    Design Principles for High-Performance Blended Learning Services Delivery
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Bitzer, Philipp; Söllner, Matthias; Leimeister, Jan Marco
    The “perfect” orchestration of training participants, IT and process design is one of the ongoing challenges within blended learning service research and practice. Blended learning services (BLS) offer a great variety of options to design learning processes, overcoming many shortcomings of pure e-learning services and providing better scalability and more advantages for learners than pure face-to-face class teaching. Nevertheless, due to inconclusive results of blended learning design research in the literature, BLS designers can hardly find support for the systematic design of efficient and successful blended learning processes, which would enable a high degree of learning success with a balanced degree of delivery effort. Based on major determinants of BLS processes’ quality, the authors identify, develop, and evaluate design principles for high performance BLS using an action design research approach. They first derive a set of initial design principles, based on insights from literature and own exploratory case studies as well as workshops with experts from the field. They then improve the design principles iteratively in expert workshops as well as apply the design principles in four software training sessions. Finally, they present seven evaluated design principles for BLS, which are the core of a nascent design theory and contribute to a time-efficient and successful BLS delivery. Furthermore, these principles enable practitioners to systematically apply the design knowledge formalized within the principles in order to improve BLS design and delivery.
  • Zeitschriftenartikel
    Call for Papers: Issue 5/2017
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Frank, Ulrich; Matthes, Florian
  • Zeitschriftenartikel
    Self-Service Business Intelligence
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Alpar, Paul; Schulz, Michael
  • Zeitschriftenartikel
    Data Analysis of Delays in Airline Networks
    (Business & Information Systems Engineering: Vol. 58, No. 2, 2016) Ionescu, Lucian; Gwiggner, Claus; Kliewer, Natalia
    Cost-optimized airline resource schedules often imply a lack of delay tolerance in case of unforeseen disruptions, e.g. late check-ins, technical defects or airport and airspace congestion. Therefore, the consideration of timeliness and robustness has become an important topic in robust resource scheduling and a wide range of sophisticated scheduling approaches has been developed in recent years. However, these approaches depend on assumptions made concerning delay occurrences. A better understanding of delay mechanisms may lead to a better trade-off between cost-efficiency and robustness and is therefore the purpose of this paper. We provide a data-driven detection of decision rules for daytime delay trends, depending on spatio-temporal attributes. The focus is on interpretable rules whose prediction accuracy is compared to random forests as a non-parametric, automated modeling approach. The obtained results give an insight into both the nature of primary delay occurrence and the methodical potential of delay prediction in the context of robust resource scheduling.