Conventional exchanges and market makers facilitating initial public offerings and market making of tokens are prone to illicit practices. Automated Market Makers (AMMs) and, especially, Supply-sovereign AMMs (sAMMs) seem to tackle those challenges by employing persistent and transparent market making strategies. AMM designs typically serve two purposes: (1) facilitating the exchange of tokens not issued by the AMM and (2) enabling the issuance and exchange of tokens by the AMM itself, known as supply-sovereign AMM (sAMMs). In this work, we establish the foundations of bonding curve representations for these two AMM designs and explore their relationships. We introduce Undergirding Bonding Curve (UBC)-based sAMMs, a novel design of sAMMs well suited to represent continuous value growth and to support all project phases, from raising capital to mature and late phases. This is achieved by the novel type of shape-adapting bonding curve in our sAMM, enabling incentive-compatible value accumulation and distribution to shareholders. The provided formalized analysis shows that UBC-based sAMMs implement desirable characteristics of conventional share-based companies with accumulated future value derived from generated revenues.

Federated learning (FL) is a popular technique to train machine learning (ML) models for healthcare where institutions collaborate by sharing ML model gradients. An undesired phenomenon in ML models is bias, which may cause unfairness against specific subgroups. In FL research, it remains unclear to which extent bias occurs, and how to best incentivize institutions contributing toward trustworthy FL models. Besides bias, trustworthiness aspects include high predictive performance. Existing reward systems, incentivizing predictive performance only, can transfer model bias against patients to an institutional level. Therefore, we evaluate how to measure the contributions of institutions toward either predictive performance or bias in FL and design corresponding reward systems, before we propose a combined reward system that incentivizes both. We evaluate our work using multiple chest X-ray datasets focusing on sex- and age-related patient subgroups. Our results show that we can successfully measure contributions toward bias, and an integrated reward system successfully incentivizes contributions toward a well-performing model with low bias. Thereby, institutions with data predominantly from one subgroup introduce a favorable bias for this subgroup. In a label flip experiment, we furthermore show that reward systems can incentivize institutions to ensure a high dataset quality.

Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are known for their sample efficiency in identifying optimal configurations for machine learning (ML) models. However, practitioners often use less efficient methods, such as grid search, potentially resulting in under-optimized models. This discrepancy suggests that HPO method selection may be influenced by practitioner-specific motives, which remain insufficiently understood hindering user-centered advancement of HPO tools. To uncover these motives, we conducted 20 semi-structured interviews and an online survey with 49 ML practitioners. We revealed six primary goals (e.g., increasing ML model understanding) and 14 contextual factors (e.g., available computational resources) that influence practitioners’ choices of HPO methods. This study provides a conceptual foundation for understanding real-world HPO practices and informs the development of more user-centered and context-adaptive HPO tools in automated ML (AutoML).

Procrastination is the deliberate postponing of tasks knowing that it will have negative consequences in the future. Despite the potentially serious impact on mental and physical health, research has just started to explore the potential of information systems to help students combat procrastination. Specifically, while existing learning systems increasingly employ elements of game design to transform learning into an enjoyable and purposeful adventure, little is known about the effects of gameful approaches to overcome procrastination in academic settings. This study advances knowledge on gamification to counter procrastination by conducting a mixed-methods study among higher education students. Our results shed light on usage patterns and outcomes of gamification on self-efficacy, self-control, and procrastination behaviors. The findings contribute to theory by providing a better understanding of the potential of gamification to tackle procrastination. Practitioners are supported by implications on how to design gamified learning systems to support learners in self-organized work.

Large language models (LLMs) are prone to hallucinations, i.e., nonsensical, unfaithful, and undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which can lead to misinterpretations and errors. To tackle the problem of overreliance, we propose HILL, the "Hallucination Identifier for Large Language Models". First, we identified design features for HILL with a Wizard of Oz approach with nine participants. Subsequently, we implemented HILL based on the identified design features and evaluated HILL’s interface design by surveying 17 participants. Further, we investigated HILL’s functionality to identify hallucinations based on an existing question-answering dataset and five user interviews. We find that HILL can correctly identify and highlight hallucinations in LLM responses which enables users to handle LLM responses with more caution. With that, we propose an easy-to-implement adaptation to existing LLMs and demonstrate the relevance of user-centered designs of AI artifacts.

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.

Following the success of the Bitcoin blockchain, distributed ledger technology (DLT) has received extensive attention in health informatics research. Yet, the healthcare industry is highly complex with many different stakeholders, information systems, regulations, and challenges. Thus, DLT may be used in various settings and for different purposes. First surveys have started to synthesize our knowledge of the different use cases, in which healthcare may benefit from DLT implementations. However, an in-depth understanding of whether and how these use cases differ concerning their requirements of DLT characteristics (i.e., technical or administrative design features) is still lacking. In this work, we conducted a structured review of 185 studies on DLT-based applications in healthcare. The results reveal six pertinent use cases, each with its own combination of different purposes that DLT is used for. Furthermore, our study shows that each of these use cases has a unique set of requirements with regard to the most important DLT characteristics. In doing so, we seek to guide practitioners in the development of highly effective DLT-based applications in various healthcare settings and pave the way for future research to investigate the understudied areas of DLT-based applications in healthcare.

Web seals have recently been subject to increasing skepticism from consumers. This study develops and tests a theoretical model of web seal skepticism by integrating multiple theoretical perspectives. We explore how web seal skepticism emerges and harms consumers’ perceptions. The results of an online experiment with 757 participants demonstrate that consumer skepticism increases consumers’ mistrust in information system providers and makes them feel less protected. Our findings also indicate that situational factors (web seal unreliability and seal authority incredibility) are significant antecedents of web seal skepticism and that consumers’ inferences of providers’ manipulative intent mediate the relationships.

Whereas the importance of information system (IS) certifications is increasing to prove compliance with regulatory and industry requirements, research reveals inconsistent findings concerning the effectiveness of IS certifications. Prior studies have concluded that such inconsistent findings stem partly from users’ limited understanding of the nature and role of certifications due to ineffective certification presentations. We follow a three-phase, theory-driven design science approach to examine how to design effective certification presentations. First, we identified sources of users’ limited understanding and formulated a revised certification presentation that facilitates users’ peripheral information processing. We tested our design proposal in an online experiment with 300 participants. Second, we derived meta-requirements and design guidelines by validating a theory-driven model of certification presentations through an online survey with 352 participants. Third, we implemented three certification presentations complying with example guidelines and ran an online experiment with 400 participants to test whether these presentations are effective. We contribute to research and practice by proposing a design theory for certification presentations composed of peripheral cues inducing authority, social proof and likeability to increase users’ trust perceptions.

In a thought-provoking paper published in EJIS more than a decade ago, Lyytinen and colleagues asked the question, why does the “old world” perform poorly in high academic impact publishing? This triggered a lively debate on the epistemological and methodological traditions, preferences, and research practices of Europeans versus North Americans that has lasted until today. Is it still true, we asked ourselves, that European scholars generate less academic impact than their North American colleagues? Is the European research context indeed disadvantageous for developing high academic impact research? In this paper, we set out to explore these questions by analysing the Google Scholar profiles of 1713 IS researchers from all over the world. Our findings show that Northern and Western European scholars no longer seem to differ significantly from their North American peers, even though the different research contexts still exercise a certain level of influence. However, is this a development the “old world” desires? We provide an assessment and some suggestions for the future of European IS research.

OBJECTIVE: Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. MATERIALS AND METHODS: We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. RESULTS: Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. DISCUSSION: Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people's attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. CONCLUSION: While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners.

Smart contracts are a promising means of formalizing and reliably enforcing agreements between entities using distributed ledger technology (DLT). Research has revealed that a significant number of smart contracts are subject to programming flaws, making them vulnerable to attacks and leading to detrimental effects, such as asset loss. Researchers and developers call for a thorough analysis of challenges to identify their causes and propose solutions. To respond to these calls, we conducted two literature reviews and diverse expert interviews and synthesized scattered knowledge on challenges and solutions. We identified 29 challenges (e.g., code visibility, code updateability, and encapsulation) and 60 solutions (e.g., gas limit specification, off-ledger computations, and shadowing). Moreover, we developed 20 software design patterns (SDPs) in collaboration with smart contract developers. The SDPs help developers adjust their programming habits and thus support them in their daily development practices. Our results provide actionable knowledge for smart contract developers to overcome the identified challenges and offer support for comparing smart contract integration concepts across three fundamentally different DLT protocols (i.e., Ethereum, EOSIO, and Hyperledger Fabric). Moreover, we support developers in becoming aware of peculiarities in smart contract development and the resulting benefits and drawbacks.

BACKGROUND: The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date. RESULTS: We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information. CONCLUSIONS: Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.

Cloud service certifications (CSC) attempt to assure a high level of security and compliance. However, considering that cloud services are part of an ever-changing environment, multi-year validity periods may put in doubt reliability of such certifications. We argue that continuous auditing (CA) of selected certification criteria is required to assure continuously reliable and secure cloud services, and thereby increase trustworthiness of certifications. CA of cloud services is still in its infancy, thus, we conducted a thorough literature review, interviews, and workshops with practitioners to conceptualize an architecture for continuous cloud service auditing. Our study shows that various criteria should be continuously audited. Yet, we reveal that most of existing methodologies are not applicable for third party auditing purposes. Therefore, we propose a conceptual CA architecture, and highlight important components and processes that have to be implemented. Finally, we discuss benefits and challenges that have to be tackled to diffuse the concept of continuous cloud service auditing. We contribute to knowledge and practice by providing applicable internal and third party auditing methodologies for auditors and providers, linked together in a conceptual architecture. Further on, we provide groundings for future research to implement CA in cloud service contexts.

Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35,000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy.

Cloud computing introduces several characteristics that challenge the effectiveness of current certification approaches. Particularly, the on-demand, automated, location-independent, elastic, and multi-tenant nature of cloud computing systems is in contradiction with the static, manual, and human process-oriented evaluation and certification process designed for traditional IT systems. Cloud-specific certification processes can improve trust in the cloud computing paradigm, and can lead to the wide adoption of cloud services in enterprises by mastery of uncertainty, lack of transparency, and trust. Through third party evaluation cloud customers could receive more unbiased information about cloud-based services and security measures implemented as well as they could compare different cloud service providers much easier. Common certificates are a backward look at the fulfillment of technical and organizational measures at the time of issue and therefore represent a snapshot. This creates a gap between the common certification of one to three years and the high dynamics of the market for cloud services and providers. The proposed dynamic certification approach adopts the common certification process to the increased flexibility and dynamics of cloud computing environments through using of automation potential of security controls and continuous proof of the certification status. Dynamic certification is based on a new semi-automated certification process and the continuous monitoring of critical parameters of cloud services.