The Performance and Reliability of a RFID Cycle-Count – A Quantitative Approach from Fashion Retail

In recent years, several retailers gained experience with the implementation and use of radio-frequency identification (RFID). Although extensive benefits through the use of RFID have been discussed in academia and practice for many years, strategic approaches to reach the promised targets still are largely unexplored. To support practitioners with using RFID in retailing, the reliability of data generated by RFID in-store processes has to be investigated. As a starting point, we compare in the present study the quality of a RFID cycle-count to a physically conducted count. We collected data from a real-world implementation and completed parallel counts in 9 RFID pilot stores at a global fashion retailer. Our results based on an item-level error investigation show, that the error rate of a RFID cycle-count nearly is as low as the error rate of a physical conducted count. As a consequence for further research, more reliable data collected in a cycle-count lead to a better stock accuracy, more effective in-store replenishment processes and less stock-out situations

Predicting The Disclosure of Personal Information on Social Networks: An Empirical Investigation

The present study considers factors that motivate users of social networks to publish different types of privacy-related information to friends or even the public. In contrast to prior research, we do not limit our research scope to an individual\u27s decision-making process (i.e., the formation of behavioral intentions) but also include actual behavior as observed among a group of real Facebook users. Our objective is to test to what extent existing theory is not only capable of explaining self-disclosure decisions but also to predict subsequent behavior. We test our model using a combination of structural equation modeling and logistic regression with questionnaire data and data collected from the Facebook platform. Our results indicate that the way self-disclosure was operationalized in prior research shows low predictive power, especially when compared to predictions based on simple questions regarding an individual\u27s sensitivity to the disclosure of personal information

LEVERAGING TEXT MINING FOR THE DESIGN OF A LEGAL KNOWLEDGE MANAGEMENT SYSTEM

In today’s globalized world, companies are faced with numerous and continuously changing legal requirements. To ensure that these companies are compliant with legal regulations, law and consulting firms use open legal data published by governments worldwide. With this data pool growing rapidly, the complexity of legal research is strongly increasing. Despite this fact, only few research papers consider the application of information systems in the legal domain. Against this backdrop, we pro-pose a knowledge management (KM) system that aims at supporting legal research processes. To this end, we leverage the potentials of text mining techniques to extract valuable information from legal documents. This information is stored in a graph database, which enables us to capture the relation-ships between these documents and users of the system. These relationships and the information from the documents are then fed into a recommendation system which aims at facilitating knowledge transfer within companies. The prototypical implementation of the proposed KM system is based on 20,000 legal documents and is currently evaluated in cooperation with a Big 4 accounting company

ALIGNING IS CURRICULUM WITH INDUSTRY SKILL EXPECTATIONS: A TEXT MINING APPROACH

Digitalization offers both great opportunities as well as new challenges and uncertainties. In particular, students in their role as future employees will have to cope with the new digital environments, which makes lifelong learning and up-to-date skills even more important than they already are. Key players in this long-term development are the universities as providers of the necessary skills and knowledge. By now, it is clear that digitalization will have a broad impact on the future conditions of universities. But are they already prepared for it? Against this backdrop, we present an approach to combine universities’ offerings with the required industry job skills to identify potential curricular gaps at course level that arise through ongoing digitalization and, as a consequence, changing skill requests for employees. We identify an appropriate set of methods for our project including text min-ing methods, an expert survey and an interview phase for evaluation. We illustrate our approach using a large data set of German IS curricular module descriptions and offers for IS job starters

The Impact of Goal-Congruent Feature Additions on Core IS Feature Use: When More Is Less and Less Is More

This research investigates the impact of feature additions on the use of an information system’s (IS) existing core features. Based on prior work in marketing and IS, we hypothesize conflicting effects on the usage of the system as a whole and the IS core due to the goal congruence of the two feature sets. In three consecutive empirical studies, we consider the example of a utilitarian consumer IS in the form of a mobile insurance app with additional weather-related functionality. The statistical results indicate that the goal-congruent feature addition exerts a positive influence on system use, whereas the impact on core IS use is negative. More specifically, we show that the latter effect can be explained by changes in the users’ perceptions of the usefulness and ease of use of the core features. From a theoretical perspective, our work goes beyond the predominant system view of technology acceptance and use by employing a more fine-grained, feature-oriented level of investigation, which opens several avenues for further research regarding the relationships between information systems and the features they comprise. From a managerial perspective, the results help to characterize the detrimental effects that feature additions may have on IS usage. These consequences become particularly relevant when revenue, cost savings, or other benefits on the part of IS operators are linked only to a subset of the entire IS functionality, as in the case of certain web portals or mobile apps

Explaining Adoption of Pervasive Retail Systems with a Model based on UTAUT2 and the Extended Privacy Calculus

The advent of e-commerce puts traditional retail companies under a lot of pressure. A way retailers try to attract more customers to their physical stores is by offering online services on the retail sales floor. Such services are enabled through pervasive retail systems. These systems, however, do not only offer new opportunities but also bear risks for retailers because they heavily depend on privacy-related data, which customers could perceive as a potential privacy threat. In the present paper, we thus investigate the antecedents of customers’ usage intention towards such systems and the trade-off between the perceived benefits and the perceived privacy costs that are associated with their use. To this end, we propose a model based on the most recent version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Extended Privacy Calculus Theory. We validate our model considering a smart fitting room application and show that the model is able to explain 67.1% of the variance in the behavioral intention to use the system and 43.1% of the variance in a person’s willingness to disclose private information. Our results can be leveraged to design pervasive systems that are perceived as valuable instead of privacy threatening

TRACING DOWN THE VALUE OF CO-CREATION IN FEDERATED AI ECOSYSTEMS

Methods and software components for developing novel IT solutions based on artificial intelligence (AI) technology are broadly available to organizations of any size. However, as AI typically requires large amounts of data, smaller organizations are at a disadvantage compared to large competitors as the latter often have more training and test data at their disposal. Collaboration and data sharing between multiple smaller actors might offer a solution to this issue, but also poses a potential risk to privacy and confidentiality. Our research considers the concept of federated learning, which enables collaborative training without exchanging the actual data. Still, the benefits of value co-creation within federated AI ecosystems are unclear. To shed light on this topic, we present a data-driven analysis using the example of sales forecasting in retail. We show that three types of benefits can be expected in federated AI ecosystems, namely collaboration, privacy preservation, and generalizability

Personalization & Trust-Enhancing Signals in E-Commerce

Despite worldwide growing revenue rates in e-Commerce, a lot of economic potential remains unused, which is manifesting in low conversion rates. Only a fraction of website visitors can be transformed to website buyers, which may be explained by a lack of trust in the retailer. In e-Commerce, trustworthiness can be signaled through special stimuli presented on the website as interaction platform between customer and retailer. By personalization of these signals, consumers can conveniently collect information needed to reduce their individual risk concerns. The objective of this study is to understand whether and how the personalization of trust-enhancing signals has an effect on trusting attitudes, buying intentions and buying behaviors. First promising preliminary results refer to the central importance of trust-enhancing signals for both a trustworthy impression and trust-related buying behavior. These insights will hold practical and managerial implications for web designers, online retailers and the integration of personalization into the business model