Welcome to Merlin

Many blockchain consortia have been established to build blockchain information systems. While the developed blockchain information systems were promising, few have reached market entry. Indeed, blockchain consortia often lost development focus due to high system complexity and a lack of understanding of how to create a system that will serve the needs and bring value to all stakeholders. Thus, stakeholders struggled to leverage blockchain information systems' full value. Prior studies demonstrated that blockchain systems pose not only technical but also organizational challenges. Analysing six blockchain consortia, we identify their value mechanisms, organizational problems, and organizational solutions that successful blockchain consortia experience while organizing themselves for value. As a result, we propose a new organizational form, i.e., a layered organization, for blockchain consortia to achieve better value creation.

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.

Climate physical risk assessment is crucial to inform adaptation policies and finance. However, science-based and transparent solutions to assess climate physical risks are limited, compounding the adaptation gap. This is a main limitation to fill the adaptation gap. We provide a methodology that quantifies physical risks on geolocalized productive assets, considering their exposure to chronic and acute impacts (hurricanes) across the scenarios of the Intergovernmental Panel on Climate Change. Then, we translate asset-level shocks into economic and financial losses. We apply the methodology to Mexico, a country highly exposed to physical risks, recipient of adaptation finance and foreign investments. We show that investor losses are underestimated up to 70% when neglecting asset-level information, and up to 82% when neglecting tail acute risks. Therefore, neglecting the asset-level and acute dimensions of physical risks leads to large errors in the identification of adaptation policy responses, investments and finance tools aimed to build resilience to climate change.

The n-player Tullock contest with complete information is known to admit explicit solutions in special cases, such as (i) homogeneous valuations, (ii) constant returns, and (iii) two contestants. But can that model be solved more generally? In this paper, we show that key characteristics of the equilibrium, such as individual efforts, winning probabilities, and payoffs cannot, in general, be expressed in terms of the primitives of the model using basic arithmetic operations plus the extraction of roots alone. In this sense, the Tullock contest is intractable. We argue that our formal concept of tractability captures the intuitive understanding of the notion.

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.

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.

How do banks offer and price mortgages when an online platform enables them to reach regions where they have no branches? With unique data on responses from differently located banks to each applying household and a shift-share instrument for market concentration, we find banks to make more and cheaper offers to more concentrated local markets. We rationalize this as investments in lucrative market shares given customer switching costs. Banks also improve their inter-regional portfolio diversification with more attractive offers to regions more complementary to their home locales. Finally, banks` choices become increasingly automated, reducing their operating costs.

The spread of propaganda, misinformation, and biased narratives from autocratic regimes, especially on social media, is a growing concern in many democracies. Can censorship be an effective tool to curb the spread of such slanted narratives? In this paper, we study the European Union’s ban on Russian state-led news outlets after the 2022 Russian invasion of Ukraine. We analyze 775,616 tweets from 133,276 users on Twitter/X, employing a difference-in-differences strategy. We show that the ban reduced pro-Russian slant among users who had previously directly interacted with banned outlets. The impact is most pronounced among users with the highest pre-ban slant levels. However, this effect was short-lived, with slant returning to its pre-ban levels within two weeks post-enforcement. Additionally, we find a detectable albeit less pronounced indirect effect on users who had not directly interacted with the outlets before the ban. We provide evidence that other suppliers of propaganda may have actively sought to mitigate the ban’s influence by intensifying their activity, effectively counteracting the persistence and reach of the ban.

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.

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.