Contributions in Merlin

User-centricity in e-government is a double-edged sword. While it helps governments design digital services tailored to the needs of citizens, it may also increase the burden on users and deepen the digital divide. From an institutional perspective, these fundamental conflicts are inevitable. To better understand the role and effect of user-centricity in e-government, this paper analyses academic literature on user-centricity and public values. The analysis leads to three main insights: First, there is a conflict in citizen representation that may result from the normative dominance of decision-makers. Second, we identify an accountability conflict that can prevent user-centric innovation from thriving in a highly institutionalized environment. Third, we identify a pluralism conflict that emerges from a clash between the reality of a diverse society and the assumed homogeneity of actors. The need to address these conflicts increases with rapid technological innovation, such as distributed ledger technologies, artificial intelligence, and trust infrastructures. These technologies put the user at the center stage and permeate aspects of social life beyond government. In response to these insights, we outline suggestions for further research and practice.

The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades old and neglect the human aspect. Research has shown that considering how a developer reads and experiences the code can be an indicator of its quality. In our research, we built on these insights and designed, trained, and evaluated the first deep neural network that aligns a developer’s eye gaze with the code tokens the developer looks at to predict code comprehension and perceived difficulty. To train and analyze our approach, we performed an experiment in which 27 participants worked on a range of 16 short code comprehension tasks while we collected fine-grained gaze data using an eye tracker. The results of our evaluation show that our deep neural sequence model that integrates both the human gaze and the stimulus code, can predict (a) code comprehension and (b) the perceived code difficulty significantly better than current state-of-the-art reference methods. We also show that aligning human gaze with code leads to better performance than models that rely solely on either code or human gaze. We discuss potential applications and propose future work to build better human-inclusive code evaluation systems.

Digitalization and sustainability have been the core drivers of transformation of the financial industry in recent years. In this context, green fintech plays a major role, which, however, is still an unexplored field in business, information systems and finance research. This paper conducts a systematic literature analysis and develops a research agenda based on a framework, which is derived from clustering 74 academic research papers. The framework consists of the four clusters strategy, organization, technology, and potentials along nine dimensions. The research agenda reveals that green fintech is still a very premature field of research. The analysis shows that areas like customer- and government-related services, insurance-oriented approaches and SDGs which focus on life on land and life below water are still rare and that most of the approaches focus on blockchain technology, while other financial technologies like artificial intelligence are still underrepresented.

This article introduces a new measure of a firm’s negative impact on biodiversity, the corporate biodiversity footprint (CBF), and studies whether it is priced in an international sample of stocks. On average, the CBF does not explain the cross-section of returns between 2019 and 2022. However, a biodiversity footprint premium (higher returns for firms with larger footprints) began emerging in October 2021 after the Kunming Declaration, which capped the first part of the UN Biodiversity Conference (COP15). Consistent with this finding, stocks with large footprints lost value in the days after the Kunming Declaration. The launch of the Taskforce on Nature-related Financial Disclosures (TNFD) in June 2021 had a similar effect. These results indicate that investors have started to require a risk premium upon the prospect of, and uncertainty about, future regulation or litigation to preserve biodiversity.

The so‐called BigTech companies Amazon, Alphabet, Apple and Meta and are constantly attempting to grow into new business areas. Due to their expertise in data analysis, they have managed to quickly establish themselves in many industries as digital ecosystems and, as part of this strategy, are also expanding their footprint in financial services. However, the biggest challenge that BigTech companies are facing in financial services is customer acceptance. This paper contributes to the emerging field of digital ecosystems and the acceptance of sensitive services by users. Although the BigTech firms have gained much attention, only little empirical analysis is available. This paper aims to shed light on the determinants of customer acceptance of BigTech banking services. Based on a survey in Switzerland this research develops an analytic model to identify and test the relevant determinants. The results indicate that the strongest significant influences were found in subjective risk and trust which clearly demonstrates the still‐existing incumbents' advantage over the BigTech companies.

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly-connected “hub” individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals’ influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals’ estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights on the network position of the superspreaders.

Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.

Return event studies generally involve several firms but there are also cases when only one firm is involved. This makes the relevant testing problems, abnormal return and cumulative abnormal return, more difficult since one cannot exploit the multitude of firms (by using a relevant central limit theorem, say) to design hypothesis tests. We propose a permutation test which is of nonparametric nature and more generally valid than the tests that have previously been proposed in the literature in this context. We address the question of the power of the test via a brief simulation study and also illustrate the method with two applications to real data.

This paper provides a novel theory of research joint ventures for financially constrained firms. When firms choose R&D portfolios, an RJV can help to coordinate research efforts, reducing investments in duplicate projects. This can free up resources, increase the variety of pursued projects and thereby increase the probability of discovering the innovation. RJVs improve innovation outcomes when market competition is weak or external financing conditions are bad. An RJV may increase the innovation probability and nevertheless lower total R&D costs. RJVs that increase innovation also increase consumer surplus and tend to be profitable, but innovation-reducing RJVs also exist. Finally, we compare RJVs to innovation-enhancing mergers.

The advent of increasingly powerful language models has raised expectations for conversational interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE (Available at: https://github.com/zhaw-iwi/promise), a framework that facilitates the development of complex conversational interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of language models’ behavior and thus enables their effective and efficient use. We show the applications of PROMISE in health information systems and demonstrate its ability to handle complex interactions.