University of Liechtenstein
  • Vaduz, Liechtenstein
Recent publications
In this paper, we provide unique experimental evidence of depositors’ behaviour in presence of a possibility to convert commercial bank deposits into central bank digital currency (CBDC). Theoretically and experimentally we analyse whether such an option incentivises bank runs. We find that the availability of the deposit conversion option does not lead to a significant outflow of deposits. However, when conversion is restricted, depositors are eager to actively use it as a coordination tool. These findings highlight the importance of considering coordination and decision time in determining the choice to convert deposits into CBDC. Our study evidences that policy-makers should balance accessibility and control measures to maintain financial stability, ensuring that CBDC implementation supports the resilience of the banking system.
Small devices, such as drills, are increasingly being equipped with iot functions that make it possible to collect usage data, adapt the way they work, and also gain insights into the further development of the devices; even in application areas with limited battery capacity and non-continuous Internet connection. Since the health of the battery is a crucial factor in the successful long-term deployment of these IoT devices, tracking their soh is important to avoid outages. To preserve energy, utilization data is aggregated and sent when an event occurs (e.g. charging). To avoid the need to introduce expensive intrinsic battery tracking sensors as done in large-scale iot devices, the paper uses the existing capacity tracking sensor of the Battery Management System (BMS) to track the SoH by applying the peak State of Charge (SoC) extraction technique. However, an SoH update can only be achieved and verified when the battery is peak cycled; which does not happen every charge-/discharge cycle and also depends on the charging behavior of the customer. As long as the battery is shallow cycled, existing approaches would not update the SoH. To ensure continuous SoH tracking, the novel solution, presented in this paper, called "Battery Health Index" (BHI) combines physical capacity-based measurements with data-driven machine learning predictions based on utilization data to provide an always up-to-date SoH. The proposed state-of-the-art method is evaluated on a hand-held battery platform with millions of batteries and it outperforms existing solutions. The presented model enables proactive battery exchange by predicting the Remaining Useful Lifetime (RuL) therefore increasing customer experience.
In today’s dynamic business environment, organizations constantly change their business models to respond to emerging digital technologies and shifting customer expectations. It is a fundamental challenge to translate these changes into the organization’s operating model. When organizations redesign their business models, significant adjustments to the operating model and its underlying business processes are necessary to ensure the effective delivery of the value proposition to customers. Existing research falls short in detailing how changes to the business model at the tactical level impact the operating model at the operational level. To address this gap, this paper introduces the Compass Method. This method provides guidance for decision-makers at the tactical and operational levels in identifying necessary changes to their operating model using a set of operating model design cards. The method has been developed following the design science research methodology and is grounded in extant knowledge from both business model research and process management research. Three rounds of design and evaluation of the method were completed in multiple settings. The study contributes to the understanding of the relationship between business models, operating models, and business processes, paving the way for the development of complementary methods and tools to further investigate this relationship.
Generative AI (GenAI) represents a shift from AI’s ability to “recognize” to its ability to “generate” solutions for a wide range of tasks. As generated solutions and applications grow more complex and multi-faceted, new needs, objectives, and possibilities for explainability (XAI) have emerged. This work elaborates on why XAI has gained importance with the rise of GenAI and the challenges it poses for explainability research. We also highlight new and emerging criteria that explanations should meet, such as verifiability, interactivity, security, and cost considerations. To achieve this, we focus on surveying existing literature. Additionally, we provide a taxonomy of relevant dimensions to better characterize existing XAI mechanisms and methods for GenAI. We explore various approaches to ensure XAI, ranging from training data to prompting. Our paper provides a concise technical background of GenAI for non-technical readers, focusing on text and images to help them understand new or adapted XAI techniques for GenAI. However, due to the extensive body of work on GenAI, we chose not to delve into detailed aspects of XAI related to the evaluation and usage of explanations. Consequently, the manuscript appeals to both technical experts and professionals from other fields, such as social scientists and information systems researchers. Our research roadmap outlines over ten directions for future investigation.
Process mining research has made tremendous progress in analyzing, visualizing, and predicting the perfor- mance of business processes through computational techniques. However, little attention has been brought to understanding why and how business processes behave as they do. Process mining results alone are not sufficient to arrive at meaningful interpretations about the dynamics and changes of a given business process. Rather, we need to account for contextual factors that underlie and explain the behavior of processes. In this paper, we make two central contributions. First, we develop a framework that depicts relevant factors to make sense of process mining results. The framework is intended to help researchers and practitioners explain why and how processes change across a variety of contexts. Second, we demonstrate the application of our framework within a real-world case: a customer onboarding process in a European financial institution.
Process science is the interdisciplinary study of socio-technical processes. Socio-technical processes involve coherent series of changes over time, entailing actions and events that include humans and digital technologies. The ubiquitous availability of digital trace data, combined with advanced data analytics capabilities, offer new and unprecedented opportunities to study such processes through multiple data sources. Process science is concerned with describing, explaining, and intervening in socio-technical change. It is based on four key principles; it (1) puts socio-technical processes at the center of attention, (2) investigates socio-technical processes scientifically, (3) embraces perspectives of multiple disciplines, and (4) aims to create impact by actively shaping the unfolding of socio-technical processes.
Information technologies are expected to improve organizational routines in a number of ways, yet their implementations often lead to unexpected, unintended, and even undesired effects. In this research, we investigate how IT-based changes affect the complexity of an organizational routine over time, that is, the number of ways through which the routine can be performed. We present the findings of a computationally-intensive research study of a customer onboarding routine at a financial institution in Central Europe. To this end, we investigate how IT-based changes in the associated low-code platform affect the dynamics of how the routine is performed over the course of 2 years. We explain the effects of IT-based change on the routine’s complexity along four core dimensions—the type of change, the strength of the effect, the temporal unfolding of the effect, and the permanence of the effect—, where each dimension is characterized by different change patterns. We further distinguish between two types of IT-based effects: intended and unintended effects.
Background The Lewis (Le) blood group system, unlike most other blood groups, is not defined by antigens produced internally to the erythrocytes and their precursors but rather by glycan antigens adsorbed on to the erythrocyte membrane from the plasma. These oligosaccharides are synthesized by the two fucosyltransferases FUT2 and FUT3 mainly in epithelial cells of the digestive tract and transferred to the plasma. At their place of synthesis, some Lewis blood group carbohydrate antigen variants also seem to be involved in various gastrointestinal malignancies. However, relatively little is known about the transcriptional regulation of FUT2 and FUT3. Summary To address this question, we screened existing literature and additionally used in silico prediction tools to identify novel candidate regulators for FUT2 and FUT3 and combine these findings with already known data on their regulation. With this approach, we were able to describe a variety of transcription factors, RNA binding proteins and microRNAs, which increase FUT2 and FUT3 transcription and translation upon interaction. Key Messages Understanding the regulation of FUT2 and FUT3 is crucial to fully understand the blood group system Lewis (ISBT 007 LE) phenotypes, to shed light on the role of the different Lewis antigens in various pathologies, and to identify potential new diagnostic targets for these diseases.
Organizations have to adjust to changes in the ecosystem, and customer feedback systems (CFS) provide important information to adapt products and services to changing customer preferences. However, current systems are limited to single-dimensional rating scales and are subject to self-selection biases. The work contributes design principles for CFS and implements a CFS that advances current systems by means of contextualized feedback according to specific organizational objectives. The authors apply Design Science Research (DSR) methodology and report on a longitudinal DSR journey considering multiple stakeholder values by utilizing value-sensitive design methods. They conducted expert interviews, design workshops, demonstrations, and a four-day experiment in an organizational setup, involving 132 customers of a major Swiss library. In the process, the identified design principles and the implemented software artifact were validated qualitatively and quantitatively, leading to conclusions for their efficient instantiation. The authors found that i) blockchain technology can afford four design principles of effective CFS. Also, ii) combining DSR with value-sensitive design methods explicitly provides rationale for design principles in the form of identified important values. Moreover, iii) combining DSR with value-sensitive design methods makes the construction of software artifacts more efficient it terms of design time by restricting the design space of a software artifact to those options that align with stakeholder values. The findings of this work thus extend the knowledge about the design of CFS and offer both researchers a theoretical contribution to reasoning about design principles and managers and decision makers a guide for the efficient design of software artifacts.
Investment in leadership development programs (LDPs) does not reliably increase leaders’ competence in core socioemotional skills related to self-management, self-awareness, and relationship-building with employees. Training programs focused on self-leadership, in combination with mindfulness practices, have the potential to address this gap. However, robust research that assesses the suitability and efficacy of such programs is lacking. In this article, the results of a systematic review of the literature on self-leadership and mindfulness in the context of LDPs are reported. A total of 52 articles were selected from an initial pool of 284 articles, subjected to textual analysis, and coded in terms of the reported impact levels for all of the examined training programs. This study revealed that training in self-leadership competencies and skills improved stress resilience, job performance and satisfaction, and positive attitudes and increased leaders’ abilities to organize and motivate their teams. Mindfulness training was strongly linked to stress reduction and self-regulation as well as to enhanced sleep and reduced burnout. Mindfulness also appeared to improve job performance and emotional regulation and to increase the ability to establish positive relationships with employees.
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763 members
Janine Viol Hacker
  • Institute of Information Systems
Christian Sonnenberg
  • Information Systems
Giovanni Apruzzese
  • Institute of Information Systems
Marco Furtner
  • Institute of Entrepreneurship
Urs Baldegger
  • Entrepreneurship
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