Felix Wortmann

Title

Prof. Dr.

Last Name

Wortmann

First name

Felix

felix.wortmann@unisg.ch

ORCID

0000-0001-5034-2023

Phone

+41 71 224 7325

Web Site

http://www.item.unisg.ch

Now showing 1 - 10 of 168

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Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data
(Assoc. of Information Systems)
Chanson, Mathieu
;
Bogner, Andreas
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Bilgeri, Dominik
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Fleisch, Elgar
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Wortmann, Felix
A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.
Type:journal article
Journal:Journal of the Association for Information Systems
Volume:Vol. 20, Issue 9
Issue:Article 10
DOI:10.17705/1jais.00567
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/116717
Scopus© Citations 160
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How Managers Get B2B Marketplace Scaling Wrong
(2026)
Wlcek, Manuel
;
Gawer, Annabelle
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Gassmann, Oliver
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Wortmann, Felix
B2B marketplaces are experiencing a powerful resurgence, promising to simplify global sourcing amid increasing geopolitical tensions, trade tariffs and growing supply chain disruptions. However, managers strive to replicate B2C success stories but misjudge unique B2B scaling challenges. Drawing on two pioneering B2B marketplaces, we highlight how B2C platform thinking leads managers into latent scaling traps. Our cases underscore the importance of setting a viable focus, deliberate product building, as well as physical selling and right-touch service to establish and scale B2B marketplaces in traditional industries.
Type:journal article
Journal:MIS Quarterly Executive
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124348
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Unlocking the benefits of direct-to-consumer (DTC) strategies without alienating dealers
(Elsevier BV, 2025-04)
Johanna Knapp
;
Heiko Gebauer
;
Felix Wortmann
This study examines the evolving role of direct-to-consumer (DTC) strategies among product manufacturers, with a particular focus on their relationship with dealer-based customer engagement. While moving closer to customers through DTC models offers significant benefits—including enhanced brand identity, improved competitive positioning, valuable data insights, and increased customer retention—these strategies can also generate tension with traditional dealer networks. Such tensions give rise to the DTC paradox, where DTC initiatives fail to deliver anticipated returns and simultaneously strain dealer relationships. Drawing on four in-depth case studies from leading international manufacturers, this article outlines a framework for implementing DTC strategies across key phases of the customer journey: awareness, purchasing, usage, and retention. The findings demonstrate that rather than bypassing dealers, manufacturers can achieve better outcomes by integrating dealers into their DTC strategies. This hybrid approach reduces channel conflict, enhances digital engagement, and fosters stronger, long-term customer relationships. The study contributes to the literature on omnichannel marketing, hybrid sales models, and co-opetition, offering actionable insights for manufacturers navigating the challenges of digital transformation while maintaining productive dealer collaborations.
Type:journal article
Journal:Business Horizons
URL:https://www.sciencedirect.com/science/article/pii/S0007681325000849?via%3Dihub
DOI:10.1016/j.bushor.2025.04.011
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/125119
Scopus© Citations 5
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Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving
(2024)
Caterina Bérubé
;
Martin Maritsch
;
Vera Franziska Lehmann
;
Mathias Kraus
;
Stefan Feuerriegel
Background Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. Objective The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. Methods Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. Results The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84). Conclusions Despite the mixed results, this paper highlights the potential of implementing voice assistant–based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. Trial Registration ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095
Type:journal article
Journal:JMIR Human Factors
Volume:11
DOI:10.2196/46967
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/119992
Scopus© Citations 2
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Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial
(2024)
Caterina Bérubé
;
Vera Franziska Lehmann
;
Martin Maritsch
;
Mathias Kraus
;
Stefan Feuerriegel
Background Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology—a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants’ self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants’ verbal feedback. Results Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.
Type:journal article
Journal:JMIR Human Factors
Volume:11
DOI:10.2196/42823
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/119288
Scopus© Citations 6
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Data products, data mesh, and data fabric New paradigm(s) for data and analytics?
(2024-06-05)
Ivo Blohm
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Felix Wortmann
;
Christine Legner
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Köbler, Felix
Type:journal article
Journal:Business & Information Systems Engineering
Volume:66
Issue:5
DOI:10.1007/s12599-024-00876-5
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121001
Scopus© Citations 30
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Smartwatches for non‐invasive hypoglycaemia detection during cognitive and psychomotor stress
(2024)
Martin Maritsch
;
Simon Föll
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Vera Lehmann
;
Naïma Styger
;
Caterina Bérubé
Type:journal article
Journal:Diabetes, Obesity and Metabolism
Volume:26 (3)
DOI:10.1111/dom.15402
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118950
Scopus© Citations 6
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Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars
(2024)
Vera Lehmann
;
Thomas Zueger
;
Martin Maritsch
;
Michael Notter
;
Simon Schallmoser
BACKGROUND Hypoglycemia, one of the most dangerous acute complications of diabetes, poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of hypoglycemia while driving remain unmet needs, as current sensing approaches are restricted by diagnostic delay, invasiveness, low availability, and high costs. This research aimed to develop and evaluate a machine learning (ML) approach for the detection of hypoglycemia during driving through data collected on driving characteristics and gaze/head motion. METHODS We collected driving and gaze/head motion data (47,998 observations) during controlled euglycemia and hypoglycemia from 30 individuals with type 1 diabetes (24 male participants; mean ±SD age, 40.1±10.3 years; mean glycated hemoglobin value, 6.9±0.7% [51.9±8.0 mmol/mol]) while participants drove a real car. ML models were built and evaluated to detect hypoglycemia solely on the basis of data regarding driving characteristics and gaze/head motion. RESULTS The ML approach detected hypoglycemia with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.80±0.11). When restricted to either driving characteristics or gaze/head motion data only, the detection performance remained high (AUROC, 0.73±0.07 and 0.70±0.16, respectively). CONCLUSIONS Hypoglycemia could be detected noninvasively during real car driving with an ML approach that used only data on driving characteristics and gaze/head motion, thus improving driving safety and self-management for people with diabetes. Interpretable ML also provided novel insights into behavioral changes in people driving while hypoglycemic. (Funded by the Swiss National Science Foundation and others; ClinicalTrials.gov numbers, NCT04569630 and NCT05308095.)
Type:journal article
Journal:NEJM AI
URL:https://ai.nejm.org/doi/10.1056/AIoa2300013
DOI:10.1056/aioa2300013
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/119328
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Data-driven business and data privacy: Challenges and measures for product-based companies
(2023)
Fabian Alfred Schäfer
;
Heiko Gebauer
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Christoph Gröger
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Oliver Gassmann
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Felix Wortmann
Type:journal article
Journal:Business Horizons
Volume:66
DOI:10.1016/j.bushor.2022.10.002
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118227
Scopus© Citations 41
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Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes
(Wiley Online Library, 2023-02-15)
Lehmann, Vera
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Zueger, Thomas
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Maritsch, Martin
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Kraus, Mathias
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Albrecht, Caroline
Type:journal article
Journal:Diabetes, Obesity and Metabolism
Volume:26
DOI:10.1111/dom.15021
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/107754
Scopus© Citations 14

Felix Wortmann

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