Despite the demonstrated opportunities for revenue enhancement through digitalization, companies often experience a digitalization paradox. This paradox suggests that although companies may invest in ...digitalization, they often fail to achieve the expected revenue enhancement. In reporting research on 52 companies, we make the following four contributions: First, we focus on industrial companies in the business-to-business context, which largely have been neglected in previous research on digitalization. Second, we introduce the digitalization paradox as an important phenomenon in the discussion of revenue enhancement through digitalization. Third, we describe three growth paths: (1) commercializing digital solutions, (2) utilizing product connectivity, and (3) establishing an IoT-platform-based application business. For each growth path, the article takes a dynamic perspective on business models, highlighting triggers and modifications in business-model components (including value proposition, value-creation activities, and profit equation). Fourth, while the described modifications require initial investments to let these growth paths develop, we highlight how growth traps can prevent investments in business-model modifications from leading to revenue enhancement and how they can ultimately lead to the digitalization paradox.
Investigating ways to improve well-being in everyday situations as a means of fostering mental health has gained substantial interest in recent years. For many people, the daily commute by car is a ...particularly straining situation of the day, and thus researchers have already designed various in-vehicle well-being interventions for a better commuting experience. Current research has validated such interventions but is limited to isolating effects in controlled experiments that are generally not representative of real-world driving conditions. The aim of the study is to identify cause–effect relationships between driving behavior and well-being in a real-world setting. This knowledge should contribute to a better understanding of when to trigger interventions. We conducted a field study in which we provided a demographically diverse sample of 10 commuters with a car for daily driving over a period of 4 months. Before and after each trip, the drivers had to fill out a questionnaire about their state of well-being, which was operationalized as arousal and valence. We equipped the cars with sensors that recorded driving behavior, such as sudden braking. We also captured trip-dependent factors, such as the length of the drive, and predetermined factors, such as the weather. We conducted a causal analysis based on a causal directed acyclic graph (DAG) to examine cause–effect relationships from the observational data and to isolate the causal chains between the examined variables. We did so by applying the backdoor criterion to the data-based graphical model. The hereby compiled adjustment set was used in a multiple regression to estimate the causal effects between the variables. The causal analysis showed that a higher level of arousal before driving influences driving behavior. Higher arousal reduced the frequency of sudden events (P=.04) as well as the average speed (P=.001), while fostering active steering (P<.001). In turn, more frequent braking (P<.001) increased arousal after the drive, while a longer trip (P<.001) with a higher average speed (P<.001) reduced arousal. The prevalence of sunshine (P<.001) increased arousal and of occupants (P<.001) increased valence (P<.001) before and after driving. The examination of cause–effect relationships unveiled significant interactions between well-being and driving. A low level of predriving arousal impairs driving behavior, which manifests itself in more frequent sudden events and less anticipatory driving. Driving has a stronger effect on arousal than on valence. In particular, monotonous driving situations at high speeds with low cognitive demand increase the risk of the driver becoming tired (low arousal), thus impairing driving behavior. By combining the identified causal chains, states of vulnerability can be inferred that may form the basis for timely delivered interventions to improve well-being while driving.
The organisation of populist radical right parties significantly shapes their long-term electoral success. Within this party family, great organisational variation can be found, with the “Alternative ...for Germany” (AfD) representing a least-likely case: in terms of candidate selection (CS), it ranks much higher on democracy scales than the other Bundestag parties. This paper explores the reasons for this high level of intra-party democracy (IPD) by focusing on three explanatory dimensions: ideology, institutionalisation, and party unity. Methodologically, we apply multivariate analyses of representative quantitative data collected among AfD members at CS prior to the 2017 federal election. The results show that high political dissatisfaction and low levels of institutionalisation are important drivers of inclusive CS procedures. Overall, the article provides a deeper understanding of the underlying attitudes for the AfD’s inclusive IPD, and offers substantial theoretical and empirical implications for future research.
Due to environmental and resiliency benefits, distributed energy resources (DER) are a potential solution for meeting future electricity demand, but their integration into centralized power markets ...on the large scale is challenging. Many practitioners argue that blockchain technology can create new market structures for DER like local peer-to-peer energy markets which foster renewable generation. To get an understanding of the status quo of the research on blockchain-based energy exchange, we conducted a systematic literature review on the existing academic articles and industry projects. This article describes the design and technical specifications of the first real blockchain-based electricity market in Switzerland derived from this literature review and outlines the implementation of this market in the real world. The findings provide valuable guidelines for the integration of DER into future sustainable energy markets.
The growing adoption of photovoltaic panels on roof-tops increases the pressure on grid operators for offsetting surplus or deficiency in generation. A multi-carrier energy system allows energy to be ...converted and stored using different energy carriers, thus relieving the stress from grid operators. However, these systems require efficient operation to unfold their full potential.
This paper proposes a novel blockchain-enabled process to coordinate, allocate, and settle intra-day energy transactions in a district multi-carrier energy system with electricity and heating sub-networks. An incentive mechanism is designed for an optimal allocation of local green energy generation. The mechanism is implemented for the Ethereum blockchain and operates fully on-chain. The design leaves energy producers the freedom to choose their preferred pricing strategy for profit maximization while restricting them to behavior favoring the common good. We test three pricing strategies, with different levels of knowledge on users’ pricing behaviors, that energy producers may adopt. The price-availability-based allocation system guarantees consumers the lowest possible cost.
Internet of Things Wortmann, Felix; Flüchter, Kristina
Business & information systems engineering,
06/2015, Volume:
57, Issue:
3
Journal Article
Peer reviewed
Open access
It has been next to impossible in the past months not to come across the term Internet of Things (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the ...Internet of Things. Consortia have been formed to dene frameworks and standards for the IoT. Companies have started to introduce numerous IoT-based products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common denition or understanding today of what the IoT actually encompasses.
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.
This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception.
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.
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.
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.
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.
The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes.
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.
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 (F
=4.3; P<.05 and F
=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F
=3.9; P<.05), while blood glucose phase affected it in real-world driving (F
=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F
=2.46; P=.09, blood glucose phase: F
=0.3; P=.74), nor in the real-world driving study (modality: F
=0.8; P=.47, blood glucose phase: F
=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).
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.
ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
Background
To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for ...risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards.
Objective
In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches).
Methods
Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland.
Results
First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation.
Conclusions
Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system.
Trial Registration
Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834
An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may ...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 could 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. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply chains and energy microgrids. Our 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.
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