@phdthesis{SalinasSegura2016,
  author    = {Salinas Segura, Alexander},
  title     = {The Internet of Things: Business Applications, Technology Acceptance, and Future Prospects},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-131605},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {This dissertation explores the Internet of Things from three different perspectives for which three individual studies were conducted. The first study presents a business application within supply chain management. The second study addresses user acceptance of pervasive information systems, while the third study covers future prospects of the Internet of Things. The first study is about wireless sensor technologies and their possibilities for optimizing product quality in the cold chain. The processing of sensor data such as temperature information allows for the construction of novel issuing policies in distribution centers. The objective of the study was to investigate the possible economic potential of sensor-based issuing policies in a cold chain. By means of simulation, we analyzed a three-echelon supply chain model, including a manufacturer, a distribution center, and a retail store. Our analysis shows that sensor-based issuing policies bear the potential to become an effective complement to conventional issuing policies. However, the results also indicate that important trade-offs must be taken into account in the selection of a specific issuing policy. The second study deals with the increasing emergence of pervasive information systems and user acceptance. Based on the integration of the extended "Unified Theory of Acceptance and Use of Technology" (UTAUT2) and three pervasiveness constructs, we derived a comprehensive research model to account for pervasive information systems. Data collected from 346 participants in an online survey was analyzed to test the developed research model using structural equation modeling and taking into account multi-group and mediation analysis. The results confirm the applicability of the integrated UTAUT2 model to measure pervasiveness. The third study addresses future prospects of the Internet of Things within the retail industry. We employed a research framework to explore the macro- as well as microeconomic perspective. First, we developed future projections for the retail industry containing IoT aspects. Second, a two-round Delphi study with an expert panel of 15 participants was conducted to evaluate the projections. Third, we used scenario development to create scenarios of the most relevant projections evaluated by the participants.},
  subject      = {Internet der Dinge},
  language  = {en}
}
@phdthesis{Hauser2020,
  author    = {Hauser, Matthias},
  title     = {Smart Store Applications in Fashion Retail},
  doi       = {10.25972/OPUS-19301},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193017},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Traditional fashion retailers are increasingly hard-pressed to keep up with their digital competitors. In this context, the re-invention of brick-and-mortar stores as smart retail environments is being touted as a crucial step towards regaining a competitive edge. This thesis describes a design-oriented research project that deals with automated product tracking on the sales floor and presents three smart fashion store applications that are tied to such localization information: (i) an electronic article surveillance (EAS) system that distinguishes between theft and non-theft events, (ii) an automated checkout system that detects customers' purchases when they are leaving the store and associates them with individual shopping baskets to automatically initiate payment processes, and (iii) a smart fitting room that detects the items customers bring into individual cabins and identifies the items they are currently most interested in to offer additional customer services (e.g., product recommendations or omnichannel services). The implementation of such cyberphysical systems in established retail environments is challenging, as architectural constraints, well-established customer processes, and customer expectations regarding privacy and convenience pose challenges to system design. To overcome these challenges, this thesis leverages Radio Frequency Identification (RFID) technology and machine learning techniques to address the different detection tasks. To optimally configure the systems and draw robust conclusions regarding their economic value contribution, beyond technological performance criteria, this thesis furthermore introduces a service operations model that allows mapping the systems' technical detection characteristics to business relevant metrics such as service quality and profitability. This analytical model reveals that the same system component for the detection of object transitions is well suited for the EAS application but does not have the necessary high detection accuracy to be used as a component of an automated checkout system.},
  subject      = {Laden},
  language  = {en}
}