Contributions in Merlin

This paper examines how workers’ earnings change after involuntary job separations depending on the workers’ acquired IT skills and the specificity of their occupational training. We categorize workers’ occupational skill bundles along two independent dimensions. First, we distinguish between skill bundles that are more specific or less specific compared to the skill bundles needed in the overall labor market. Second, as digitalization becomes ever more important, we distinguish between skill bundles that contain two different types of IT skills, generic or expert IT skills. We expect that after involuntary separations, these different types of IT skills can have opposing effects, either reducing or amplifying earnings losses of workers with specific skill bundles. We find clearly opposing results for workers in specific occupations—but not in general occupations: Having more generic IT skills is positively correlated with earnings after involuntary separations, whereas more expert IT skills is negatively correlated.

To address global problems, intergovernmental collaboration is needed. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. However, data sharing between governments is limited. A possible solution is federated learning (FL), a decentralised AI method created to utilise personal information on edge devices. Instead of sharing data, governments can build their own models and just share the model parameters with a centralised server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data sharing challenges like disincentives, legal and ethical issues as well as technical constraints can be solved through FL. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, which will positively impact governments and citizens.

Fintech and big tech companies are making rapid inroads into credit markets. We hand construct a global database of fintech and big tech lending volumes for 79 countries over 2013-2018. Using a panel regression analysis, we find these new forms of digital lending are larger in countries with higher GDP per capita (albeit at a declining rate), where banking sector mark-ups are higher, and where banking regulation is less stringent. We also find that these alternative forms of credit are more developed where the ease of doing business is greater, investor protection disclosure and the efficiency of the judicial system are more advanced, and where bond and equity markets are more developed. Overall, fintech and big tech credit seem to complement other forms of credit, rather than substitute for them.

The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge; Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of Embeddings, we must thus integrate available embedding spaces into a uniform space. Current integration approaches are limited to two spaces and presume that both of them were embedded with the same method — both assumptions are unlikely to hold in the context of a Web of Embeddings. In this paper, we present FedCoder— an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked entities. Our experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of Embeddings.

Corporate–startup collaboration (CSC) allows the co-development of innovations for pressing societal needs. Paradoxically, CSC is both fueled and challenged by diverging interests and approaches of the unequal actors. We apply a paradox lens to better understand the complex collaborative demands of CSC from the perspective of the corporate actors involved. Over the course of three years, we conducted 52 contextualized semi-structured interviews in a corporate-sponsored accelerator pursuing sustainability improvements. We identify five CSC paradoxes, which we translate into guiding principles for managing such paradoxes with a both/and mindset. Further, we show how these guiding principles help to address interdependencies between the CSC paradoxes. By disentangling the inherently paradoxical nature of the collaborative demands, we contribute to a fuller theoretical understanding of how organizational actors can manage these demands. We encourage companies engaging in CSC to use the guiding principles for empowering organizational actors’ understanding and approaches to CSC paradoxes.

Research on key determinants of negative attitudes toward immigration has often suggested that values held by individuals systematically explain such sentiments. Universalists appear to have more positive and conservatives more negative attitudes. So far, however, these insights are based on studies using adult samples. In our study, we analyze these relations among children and adolescents. For the analysis, we utilized a Swiss-Polish panel dataset (2015–2017, N = 5,332) with three time points collected among school children aged 8–19 years. We employed autoregressive cross-lagged models. The results indicated that while universalism decreased negative attitudes toward immigrants, the expected effect for conformity-tradition was not found.

We investigate the impact of the Bank of England's asset purchase program (APP) on the composition of assets of UK banks with unique data on the received reserves injections. The Monetary Policy Committee (MPC) didn’t expect there to be strong transmission of the APP’s impact through the bank lending channel. We find that compared to the control group, treated banks reallocated their assets towards lower risk-weighted investments, such as government securities, but did not provide more credit to the real economy. Overall, our findings suggest that when banks are not adequately capitalised, risk-based capital constraints can limit the effectiveness of expansionary unconventional monetary policies and provide incentives for carry trade activities.

Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.

Over the last 30 years, mounting insights into paradox have enabled a paradigm shift in organizational theory from linear, static, and rational toward more holistic, dynamic, and dualistic thinking. To gain insight into the nature and development of this scholarship, we curated articles from Academy of Management journals. We identified four approaches to paradox—as a label, a lens, a theory, and a metatheory. Pioneering and prototypical articles have illustrated how each approach expands our understanding of paradox, elucidating unresolved issues in and between established literatures. The collection displays both the progression of abstraction and complexity in paradox scholarship over time, and the recursive process accentuating the value of each approach and their interplay, thus offering three contributions. First, our delineation of these approaches demonstrates the development of paradox scholarship, helping scholars situate their own work in this expanding canon, while inviting new scholars to find their entry point to engage with paradox. Second, by tracking the journey from label to metatheory, we offer a model that may inform similar paths for other literatures. Third, the collection suggests that insights into paradox are fostering a paradigm shift from linear and binary toward dynamic and holistic ontologies in the organizational sciences.

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