@article{AlKassabThiesseBuckel2013,
  author    = {Al-Kassab, Jasser and Thiesse, Frederic and Buckel, Thomas},
  title     = {RFID Data Analytics in Retail Logistics: A Case Example},
  series = {Journal of Theoretical and Applied E-Commerce Research},
  volume    = {8},
  journal   = {Journal of Theoretical and Applied E-Commerce Research},
  number    = {2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-129500},
  pages     = {112-137},
  year      = {2013},
  abstract  = {The growing interest in Radio Frequency Identification (RFID) technology in recent years has sparked an intensive debate on the benefits to be expected. With the growth of RFID implementations in size and scope comes a shift away from infrastructural aspects to the question of how to draw value from the large amounts of collected data. However, the necessary procedures for the handling of massive RFID data sets are still an under-researched issue. Against this background, the study presents results from a real-world trial conducted by a large apparel retailer. The objective of the trial was to explore the opportunities for generating novel performance indicators and reports on the reality of store processes and customer behavior on the sales floor. We give an overview of the algorithms used for RFID data processing and the interpretation of the resulting insights from a practitioner's point of view. The case example thus provides an overview of the potential of RFID as a powerful tool for assortment optimization, customer research, store layout design, and other management tasks in retail.},
  language  = {en}
}
@article{FoellThiesse2021,
  author    = {F{\"o}ll, Patrick and Thiesse, Fr{\´e}d{\´e}ric},
  title     = {Exploring Information Systems Curricula},
  series = {Business \& Information Systems Engineering},
  volume    = {63},
  journal   = {Business \& Information Systems Engineering},
  number    = {6},
  issn      = {1867-0202},
  doi       = {10.1007/s12599-021-00702-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270178},
  pages     = {711-732},
  year      = {2021},
  abstract  = {The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.},
  language  = {en}
}