@inproceedings{5594, abstract = {{Design science is a fundamental research stream that contends its position in the information systems discipline. While ongoing debates address the relative importance of design science contributions in the information systems community, insights into the scientific impact of design science research (DSR) are missing and this lack of understanding arguably poses challenges to an informed discourse. To identify the most influential papers and those factors that explain their scientific impact, this paper presents an exploratory study of the scientific impact of DSR papers published in the AIS Senior Scholars' Basket of Journals. We uncover the current DSR landscape by taking stock of influential papers and theories and develop a model to explain the scientific impact of DSR papers. Our findings show that scientific impact is significantly explained by theorization and novelty. We discuss how the implications of our work can be projected on the overarching discourse on DSR.}}, author = {{Wagner, Gerit and Prester, Julian and Schryen, Guido}}, booktitle = {{38th International Conference on Information Systems}}, location = {{Seoul, South Korea}}, title = {{{Exploring the Scientific Impact of Information Systems Design Science Research: A Scientometric Study}}}, year = {{2017}}, } @article{5626, author = {{Schryen, Guido and Hristova, Diana}}, journal = {{Smart Data Radar (Deutsche Bank)}}, title = {{{High-Performance Business Computing - Effizienzsteigerung durch Parallelisierung}}}, year = {{2017}}, } @article{5633, abstract = {{Literature reviews (LRs) are recognized for their increasing impact in the information systems literature. Methodologists have drawn attention to the question of how we can leverage the value of LRs to preserve and generate knowledge. The panelists who participated in the discussion of ?Standalone Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?? at ICIS 2016 in Dublin acknowledged this significant issue and debated a) what the IS field can learn from other fields and where IS-specific challenges occur, b) how the IS field should move forward to foster the genre of LRs, and c) what best practices are to train doctoral IS students in publishing LRs. This article reports the key takeaways of this panel discussion. Guidance for IS scholars is provided on how to conduct LRs that contribute to the cumulative knowledge development within and across the IS field to best prepare the next generation of IS scholars.}}, author = {{Schryen, Guido and Benlian, Alexander and Rowe, Frantz and Shirley, Gregor and Larsen, Kai and Petter, Stacie and Par{\'e}, Guy and Wagner, Gerit and Haag, Steffi and Yasasin, Emrah}}, issn = {{1529-3181}}, journal = {{Communications of the AIS}}, keywords = {{Literature Review, Review Methodology, Research Methodology, Doctoral Training}}, pages = {{557 -- 569}}, publisher = {{Association for Information Systems (AIS)}}, title = {{{Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?}}}, volume = {{40}}, year = {{2017}}, } @article{5671, abstract = {{Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.}}, author = {{Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}}, journal = {{European Journal of Operational Research}}, keywords = {{E-Commerce, Recommender System, Attribute Weights, Configuration System, Decision Support}}, number = {{1}}, pages = {{205 -- 215}}, publisher = {{Elsevier}}, title = {{{A configuration-based recommender system for supporting e-commerce decisions}}}, volume = {{259}}, year = {{2017}}, } @inbook{14857, author = {{Beckschäfer, Michaela and Malberg, Simon and Tierney, Kevin and Weskamp, Christoph}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783319684956}}, issn = {{0302-9743}}, title = {{{Simulating Storage Policies for an Automated Grid-Based Warehouse System}}}, doi = {{10.1007/978-3-319-68496-3_31}}, year = {{2017}}, } @article{5676, author = {{Rauchecker, Gerhard and Schryen, Guido}}, journal = {{Im Einsatz}}, pages = {{44--46}}, publisher = {{Stumpf & Kossendey}}, title = {{{Projekt KUBAS: Koordination ungebundener Vor-Ort-Helfer}}}, volume = {{23}}, year = {{2016}}, } @inproceedings{5595, author = {{Wagner, Gerit and Prester, Julian and Roche, Maria and Benlian, Alexander and Schryen, Guido}}, booktitle = {{International Conference on Information Systems}}, title = {{{Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study}}}, year = {{2016}}, } @article{5617, abstract = {{CAPTCHAs are challenge-response tests that aim at preventing unwanted machines, including bots, from accessing web services while providing easy access for humans. Recent advances in artificial-intelligence based attacks show that the level of security provided by many state-of-the-art text-based CAPTCHAs is declining. At the same time, techniques for distorting and obscuring the text, which are used to maintain the level of security, make text-based CAPTCHAs diffcult to solve for humans, and thereby further degrade usability. The need for developing alternative types of CAPTCHAs which improve both, the current security and usability levels, has been emphasized by several researchers. With this study, we contribute to research through (1) the development of two new face recognition CAPTCHAs (Farett-Gender and Farett-Gender&Age), (2) the security analysis of both procedures, and (3) the provision of empirical evidence that one of the suggested CAPTCHAs (Farett-Gender) is similar to Google's reCAPTCHA and better than KCAPTCHA concerning effectiveness (error rates), superior to both regarding learnability and satisfaction but not effciency.}}, author = {{Schryen, Guido and Wagner, Gerit and Schlegel, Alexander}}, journal = {{Computers & Security}}, keywords = {{CAPTCHA, Usability, Facial features, Gender classiffcation, Age classification, Face recognition reverse Turing test}}, number = {{July}}, pages = {{95--116}}, publisher = {{Elsevier}}, title = {{{Development of two novel face-recognition CAPTCHAs: a security and usability study}}}, volume = {{60}}, year = {{2016}}, } @inproceedings{5678, abstract = {{Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Australasian Conference on Information Systems}}, keywords = {{scheduling, decision support, heuristic, high performance computing, parallel algorithms}}, pages = {{1--13}}, title = {{{High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic}}}, year = {{2015}}, } @article{5679, abstract = {{Cloud computing promises the flexible delivery of computing services in a pay-as-you-go manner. It allows customers to easily scale their infrastructure and save on the overall cost of operation. However Cloud service offerings can only thrive if customers are satisfied with service performance. Allow-ing instantaneous access and flexible scaling while maintaining the service levels and offering competitive prices poses a significant challenge to Cloud Computing providers. Furthermore services will remain available in the long run only if this business generates a stable revenue stream. To address these challenges we introduce novel policy-based service admission control mod-els that aim at maximizing the revenue of Cloud providers while taking in-formational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly out-perform first come first serve approaches, which are still state of the art. Furthermore the results give insights in how and to what extent uncertainty has a negative impact on revenue.}}, author = {{Püschel, Tim and Schryen, Guido and Hristova, Diana and Neumann, Dirk}}, journal = {{European Journal of Operational Research}}, keywords = {{admission control, informational uncertainty, revenue management, cloud computing}}, number = {{2}}, pages = {{637--647}}, publisher = {{Elsevier}}, title = {{{Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty}}}, volume = {{244}}, year = {{2015}}, }