Contributions in Merlin

This chapter introduces the practice theory developed by Theodore Schatzki and highlights its implications for organization studies. In particular, it shows how his theory contributes to our understanding of the micro-foundations of organizations, the embeddedness of organizations in their wider social context, and the temporal and spatial dimensions of organizations. It also discusses how Schatzki’s theory can be applied in empirical research on organizations.

We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.

The updates on knowledge graphs (KGs) affect the services built on top of them. However, changes are not all the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation. Estimating the impact of a change ex-ante is highly important, as it might make KG engineers aware of the consequences of their action during KG editing or may be used to highlight the importance of a new fragment of knowledge to be added to the KG for some application. The main goal of this contribution is to offer a formalization of the problem. Additionally, it presents some preliminary experiments on three different datasets considering embeddings as operation.Results show that the estimation can reach AUCs of 0.85, suggesting the feasibility of this research.

Understanding the evolution of a project is crucial in reverse-engineering, auditing and otherwise understanding existing software. Visualizing how software evolves can be challenging, as it typically abstracts a multi-dimensional graph structure where individual components undergo frequent but localized changes. Existing approaches typically consider either only a small number of revisions or they focus on one particular aspect, such as the evolution of code metrics or architecture. Approaches using a static view with a time axis (such as line charts) are limited in their expressiveness regarding structure, and approaches visualizing structure quickly become cluttered with an increasing number of revisions and components. We propose a novel trade-off between displaying global structure over a large time period with reduced accuracy and visualizing fine-grained changes of individual components with absolute accuracy. We demonstrate how our approach displays changes by blending redundant visual features (such as scales or repeating data points) where they are not expressive. We show how using this approach to explore software evolution can reveal ephemeral information when familiarizing oneself with a new project. We provide a working implementation as an extension to our open-source library for fine-grained evolution analysis, LISA.

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The recent advances in 3D laser range scanning have led to significant improvements in capturing and modeling 3D envi- ronments, allowing the creation of highly expressive and semantically rich 3D models from indoor environments, generally known as building information models. Despite the capabilities of state-of-the-art methods to generate faithful architectural 3D building models, the majority of them rely explicitly on the prior knowledge of scanner positions in order to reconstruct them successfully. However, in real-world applications, this metadata information gets typically lost after the point cloud registration, which means that none of these methods could work in practice and the creation of their building models would be impossible. Therefore, we present a novel pipeline that allows to automatically and accurately reconstruct the original scanner positions under very challenging conditions, without requiring any prior knowledge about the environment or the dataset. Being independent from laser range scanner manufacturers, it can be applied to almost every real-world LiDAR appli- cation. Our method exploits only information derived from the raw point data and is applicable to all scientific and industrial applications, where the original scan positions typically get lost after registration by the proprietary software provided by the scanner manufacturers. We demonstrate the validity of our approach by evaluating it on several real-world and synthetic indoor environments.

One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926–2010) to test our ability to early identify a list of expert-selected historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers. In the context of technology management, our rescaled metrics can be useful to early detect recent trends in technical improvement, which is of fundamental interest for companies and investors.

In this paper, Gloor, Okimoto, and Li examine how bystanders react to incivility toward women at work in two studies. They first surveyed 1,896 scholars to examine the cross-lagged relationship between identification (gender and organizational identification) and their subsequent perceptions that incivility toward women is attributable to gender discrimination. While they observed no effect of gender identification, they found a negative effect of organizational identification, such that those who identify with their employer were less likely to attribute the incivility to gender discrimination. Then, they ran an experiment to better assess the possibility that gender discrimination may be “missed”. From this experiment they found that, after observing incivility toward a woman, participants with higher organizational identification were less likely to perceive the incivility as discrimination and less likely to act in response to such incivility. Their research adds to the selective incivility and the broader workplace mistreatment literatures by focusing on a bystander perspective and has implications for how coworkers (especially bystanders of incivility) can affect inclusive workplaces.

Humor, a social communication intended to be amusing (Cooper, 2005), is ubiquitous at work. Although research on humor in organizations has increased in recent years, existing work heavily focuses on leader humor. As leaders are by definition higher in the hierarchy and formal status than employees relatively lower or within the same hierarchy, leaders enjoy a wider range of acceptable behaviors, including humor. Thus, the research within this symposium extends our knowledge of humor, its motives and effects by examining a broader range of humor users and humor use within and across hierarchies (e.g., among team members and bottom-up or top-down, respectively). Within their respective hierarchical niche, each of the four papers also focuses on a specific context that shapes humor use (i.e., front-line workers, workplace incivility, interpersonal tension, and followership), while also exploring the theoretical mechanisms through which humor exerts its effects (i.e., social or relational capital accumulation, power approach theory, and territoriality theory). In this way, this symposium also builds on existing humor research that has examined general humor use collapsed across all types of situations, often overlooking potentially meaningful differences in context and the theoretical mechanisms by which humor influences outcomes. In summary, this symposium contributes mixed methods research to paint a more comprehensive picture of humor use within and across hierarchies and in specific contexts.

Der Finanzwissenschafter und Experte für Behavioral Finance Thorsten Hens wird Fintech-Verwaltungsrat. Die Zuger Jungfirma steht kurz vor dem Start ihres Vorzeigeprodukts.