Exploring the Linkage of Spatial Indicators from Remote Sensing Data with Survey Data: The Case of the Socio-Economic Panel (SOEP) and 3D City Models

This paper demonstrates the spatial evaluation of survey data from the German Socio-Economic Panel (SOEP) study using geo-coordinates and spatially relevant indicators from remote sensing data. By geocoding the addresses of survey households with block-level geographic precision (while preventing their identification by name and guaranteeingtheir complete anonymity), data on SOEP respondents can now be analyzed in a specific spatial context. In the past, regional analyses of SOEP based on official regional indicators (e.g., the unemployment rate) always had only very imprecise spatial information to work with. This limitation has now been overcome with the geocoded respondents' information. Within a protected unit of the fieldwork organization responsible for SOEP (TNS Infratest, Munich), the addresses of survey households can now be used to generate a variable describing the location of the household with block-level precision. At DIW Berlin, this additional variable is fed into a special computer infrastructure with multiple security layers that makes the socio-economic analysis possible. This paper demonstrates the use of this geographicallocation and remote sensing data to check respondents' subjective assessments of the location of their residence, anddiscusses the analytical potential of linking remote sensing data and survey data.Remote sensing data, social sciences, behavioral sciences, multi-disciplinarity, SOEP

Using Process Mining to Support Theorizing About Change in Organizations

Process mining refers to a family of algorithms used to computationally reconstruct, analyze and visualize business processes through event log data. While process mining is commonly associated with the improvement of business processes, we argue that it can be used as a method to support theorizing about change in organizations. Central to our argument is that process mining algorithms can support inductive as well as deductive theorizing. Process mining algorithms can extend established theorizing in a number of ways and in relation to different research agendas and phenomena. We illustrate our argument in relation to two types of change: endogenous change that evolves over time and exogenous change that follows a purposeful intervention. Drawing on the discourse of routine dynamics, we propose how different process mining features can reveal new insights about the dynamics of organizational routines

Recognition of two distinct elements in the RNA substrate by the RNA-binding domain of the T. thermophilus DEAD box helicase Hera

DEAD box helicases catalyze the ATP-dependent destabilization of RNA duplexes. Whereas duplex separation is mediated by the helicase core shared by all members of the family, flanking domains often contribute to binding of the RNA substrate. The Thermus thermophilus DEAD-box helicase Hera (for “heat-resistant RNA-binding ATPase”) contains a C-terminal RNA-binding domain (RBD). We have analyzed RNA binding to the Hera RBD by a combination of mutational analyses, nuclear magnetic resonance and X-ray crystallography, and identify residues on helix α1 and the C-terminus as the main determinants for high-affinity RNA binding. A crystal structure of the RBD in complex with a single-stranded RNA resolves the RNA–protein interactions in the RBD core region around helix α1. Differences in RNA binding to the Hera RBD and to the structurally similar RBD of the Bacillus subtilis DEAD box helicase YxiN illustrate the versatility of RNA recognition motifs as RNA-binding platforms. Comparison of chemical shift perturbation patterns elicited by different RNAs, and the effect of sequence changes in the RNA on binding and unwinding show that the RBD binds a single-stranded RNA region at the core and simultaneously contacts double-stranded RNA through its C-terminal tail. The helicase core then unwinds an adjacent RNA duplex. Overall, the mode of RNA binding by Hera is consistent with a possible function as a general RNA chaperone

Generating impactful situated explanations through digital trace data

Progressively, information systems (IS) researchers draw on digital trace data to capture the emergent dynamics of today’s digitalized world. Digital trace data enable researchers to generate highly context-specific insights into the features and dynamics of socio-technical phenomena. We suggest how IS researchers can use digital trace data to develop situated explanations, that is, explanations that capture the idiosyncratic features of real-world problems in order to generate impactful solutions to these problems. We outline five key principles to build situated explanations based on digital trace data. We make several suggestions on how the information system field can adjust its research and publication practices to embrace the development and dissemination of situated explanations.Einstein Foundation BerlinGerman Federal Ministry of Education and ResearchEuropean Union through the Erasmus+ programHilti Family Foundation LiechtensteinPeer Reviewe

The connection between process complexity of event sequences and models discovered by process mining

Process mining is a research area focusing on the design of algorithms that can auto-matically provide insights into business processes. Among the most popular algorithms are those for automated process discovery, which have the ultimate goal to generate a process model that summarizes the behavior recorded in an event log. Past research had the aim to improve process discovery algorithms irrespective of the characteristics of the input log. In this paper, we take a step back and investigate the connection between measures capturing characteristics of the input event log and the quality of the discov-ered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and ana-lyze this set of complexity measures on an extensive collection of event logs and corre-sponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process mod-els, demonstrating the potential of using complexity measures as predictors of process model quality. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied

Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP)

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.SOEP, Geographically Referenced Data, Feasibility Study, Air Pollution, EURAD-IM, Physical Health

Towards Routines Mining – Designing and Implementing the Argos Miner, a Design Science Artifact for Studying Routine Dynamics with Process Mining

Digital artifacts increasingly support actors in carrying out organizational routines. These artifacts leave digital trace data, that is, time-stamped data about what actions actors performed. While extant research on routines largely builds on qualitative methods, the increasing ubiquitousness and prevalence of trace data enable novel methodological opportunities. However, several challenges currently hinder the adoption of trace data in empirical research on routines in general and their dynamics in particular. Promising approaches such as process mining are neither designed for nor sensitive to the concept of routines. In this paper, we follow a design science research approach to develop the first iteration of an artifact, which we coin Argos Miner. This artifact is based on process mining algorithms and overcomes challenges inherent in adopting process mining in routine dynamics research. It enables scholars to capture reality in flight by analyzing routine dynamics using a computational, mixed-methods approach

DEVELOPMENT OF A MEASUREMENT SCALE FOR BUSINESS PROCESS STANDARDIZATION

Process-oriented companies face the dichotomy of process standardization versus process diversity. On the one hand, multinational companies try to realize returns of scale by standardization. On the other hand, markets require businesses to adapt to local needs and government regulations. As of to-day, there is no framework available to measure the degree of process standardization. This is both a problem for companies that want to assess their degree of standardization as well as for research that aims to investigate standardization and its connection with other concepts. In this paper, we address this research gap from the perspective of scale development. We utilize a well-acknowledged method for devising a measurement instrument to specifically and directly measure the degree of standardiza-tion in business processes. Various application scenarios and future research areas are pointed out