Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text

There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report our experiences using two semi-automated methods based on textual data: rule-based classification and machine learning with curated keywords. We use those methods to classify Austrian nonprofit organizations based on the International Classification of Nonprofit Organizations. Those methods can provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, long high-quality texts describing NPOs' activities are mostly unavailable, and human labor resources are limited. We find that in such a setting, rule-based classification performs about as well as manual human coding in terms of precision and sensitivity, while being much more labor-saving. Hence, we share our insights on how to efficiently implement such a rule-based approach. To address scholars with a background in data analytics as well as those without, we provide non-technical explanations and open-source sample code that is free to use and adapt

In the Shadow of World Polity: Spatial Narratives of Civil Society Organizations

World Polity Theory has found broad acceptance as an explanation for the worldwide spread of rationalist ideas and modern models of actorhood in and through civil society. This theory states that modern actorhood is about the representation of legitimated principals, which in many cases are abstract principles such as global notions of human rights or environmental sustainability. In our study, we add on to this by analyzing spatial narratives of CSOs located in Austria's largest metropolitan region. We identify six narratives: lococentric, home/alien, world polity, world society, glocalization and earthly/metaphysical world. We find that these narratives form a spectrum whose focus ranges from the local to the global to the metaphysical level. World polity theory is able to explain the middle of this spectrum, but has been insensitive to its outer sections, which in the case of the lococentric narrative make up a major part of what is going on in civil society. We thus show that there are remarkably large spaces for the development of CSO identities that are hardly affected by global isomorphism

Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semiautomated Methods Based on Text

In this research note we discuss the two basic computational methods available for categorizing nonprofit organizations (NPOs) according to their field of activity based on textual information about these organizations: (1) rule-based categorization and (2) pattern recognition by using machine learning techniques. These methods provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, and that manual categorization is not feasible for large samples. We explain both methods and report our experience in using them to categorize Austrian nonprofit associations on the basis of the International Classification of Non-Profit Organizations (ICNPO). Since we have found that rule-based categorization works much better for this task than machine learning, we provide detailed recommendations for implementing a rule-based approach. We address scholars with a background in data analytics as well as those without, by providing non-technical explanations as well as open-source sample code that is free to use and adapt

Predicting Properties of Oxide Glasses Using Informed Neural Networks

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young's modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models.Comment: 25 page

Mycocerosic acid synthase exemplifies the architecture of reducing polyketide synthases

Polyketide synthases (PKSs) are biosynthetic factories that produce natural products with important biological and pharmacological activities1, 2, 3. Their exceptional product diversity is encoded in a modular architecture. Modular PKSs (modPKSs) catalyse reactions colinear to the order of modules in an assembly line3, whereas iterative PKSs (iPKSs) use a single module iteratively as exemplified by fungal iPKSs (fiPKSs)3. However, in some cases non-colinear iterative action is also observed for modPKSs modules and is controlled by the assembly line environment4, 5. PKSs feature a structural and functional separation into a condensing and a modifying region as observed for fatty acid synthases6. Despite the outstanding relevance of PKSs, the detailed organization of PKSs with complete fully reducing modifying regions remains elusive. Here we report a hybrid crystal structure of Mycobacterium smegmatis mycocerosic acid synthase based on structures of its condensing and modifying regions. Mycocerosic acid synthase is a fully reducing iPKS, closely related to modPKSs, and the prototype of mycobacterial mycocerosic acid synthase-like7, 8 PKSs. It is involved in the biosynthesis of C20–C28 branched-chain fatty acids, which are important virulence factors of mycobacteria9. Our structural data reveal a dimeric linker-based organization of the modifying region and visualize dynamics and conformational coupling in PKSs. On the basis of comparative small-angle X-ray scattering, the observed modifying region architecture may be common also in modPKSs. The linker-based organization provides a rationale for the characteristic variability of PKS modules as a main contributor to product diversity. The comprehensive architectural model enables functional dissection and re-engineering of PKSs

How to raise technology acceptance: User experience characteristics as technology-inherent determinants

Mlekus L, Bentler D, Paruzel A, Kato-Beiderwieden A-L, Maier GW. How to raise technology acceptance: User experience characteristics as technology-inherent determinants. Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO). 2020;51(3):273-283.This paper in the journal Gruppe. Interaktion. Organisation. (GIO) presents a study that investigated user experience characteristics as determinants of technology acceptance. Organizations planning to implement new technologies are confronted with the challenge to ensure user acceptance. Barely accepted technologies are used less often, result in lower job satisfaction, and ultimately lead to performance losses. The technology acceptance model (Venkatesh and Bala 2008) incorporates determinants of information technology use. The model’s predictors have a strong focus on interindividual user characteristics (such as computer self-efficacy) and the job context (e.g., voluntariness). Yet, what is lacking in the model, are characteristics of the technology itself that can be used as starting points to design better technologies. To bridge this gap, we introduce the User Experience Technology Acceptance Model, and provide a first test of this model. In our online survey (N = 281), we investigated how technological determinants, more specifically user experience characteristics, affected technology acceptance. Except for two paths of our proposed model, all path coefficients were significant with small to large effect sizes (f² = 0.02 – 0.66). User experience predictors resulted in 60.6% of explained variance in perceived ease of use, 38.2% of explained variance in perceived usefulness, and 25.8% of explained variance in behavioral intention. Our results provide mostly support for our extension of the technology acceptance model. The technology-inherent characteristics output quality, perspicuity, dependability, and novelty were significant predictors of technology acceptance. We discuss theoretical and practical implications with the focus on technology designers, change managers, and users.Dieser Beitrag in der Zeitschrift Gruppe. Interaktion. Organisation. (GIO) stellt eine Studie vor, in der User Experience-Merkmale als Einflussfaktoren auf Technologieakzeptanz untersucht wurden. Bei der Einführung neuer Technologien sehen sich Unternehmen vor der Herausforderung, dass Benutzer diese akzeptieren. Wenig akzeptierte Technologien werden seltener eingesetzt, stehen in Verbindung mit einer geringeren Arbeitszufriedenheit und wirken sich schlecht auf die Leistung aus. Das Technology Acceptance Model (Venkatesh und Bala 2008) umfasst Faktoren, die die Nutzung von Informationstechnologien vorhersagen. Diese beinhalten vornehmlich interindividuelle Benutzermerkmale (z. B. Computer-Selbstwirksamkeit) und den beruflichen Kontext (z. B. Freiwilligkeit). Was jedoch im Modell fehlt, sind Merkmale der Technologie selbst, die als Ausgangspunkt für das Design besserer Technologien dienen können. Um diese Lücke zu schließen, präsentieren und testen wir das User Experience Technology Acceptance Model. In unserer Online-Umfrage (N = 281) haben wir untersucht, wie technologische Faktoren, insbesondere User Experience-Merkmale, die Technologieakzeptanz beeinflussen. Mit Ausnahme von zwei Pfaden waren alle Pfadkoeffizienten unseres Modells bei kleinen bis großen Effektstärken (f² = 0,02 - 0,66) signifikant. Die User Experience-Prädiktoren klärten 60,6% der Varianz der wahrgenommenen Einfachheit der Nutzung, 38,2% der Varianz der wahrgenommenen Nützlichkeit und 25,8% der Varianz der Nutzungsabsicht auf. Unsere Ergebnisse unterstützen größtenteils die Erweiterung des Technology Acceptance Models. Die technologiebezogenen Merkmale Output-Qualität, Durchschaubarkeit, Zuverlässigkeit und Neuartigkeit waren signifikante Prädiktoren für die Technologieakzeptanz. Wir diskutieren theoretische und praktische Implikationen mit dem Fokus auf Technologiegestaltern, Change-Managern und Anwendern

Theoretical determination of surface roughness during high-speed milling and grinding

Аналитически установлено, что высокоскоростное фрезерование располагает значительными технологическими возможностями с точки зрения уменьшения шероховатости поверхности при одновременном увеличении производительности обработки. Установлено также, что при шлифовании уменьшение шероховатости поверхности связано с уменьшением производительности. Наиболее прогрессивным методом шлифования, обеспечивающим одновременно увеличение производительности и уменьшение шероховатости поверхности, является глубинное шлифование с небольшой скоростью детали, которое характеризуется меньшей производительностью по сравнению с высокоскоростным фрезерованием.The paper presents the results of theoretical studies of the surface roughness during milling and grinding. It is shown that high-speed milling has significant technological capabilities in terms of reducing surface roughness, because cutting data parameters are included in the calculated dependencies obtained to determine surface roughness with higher degrees than during grinding. This applies in particular to the speed of rotation of the cutter. Therefore, with its increase, it becomes possible to significantly reduce the surface roughness while increasing the processing capacity, which opens up broad prospects for the practical use of high-speed milling. It is established that during grinding, a decrease in surface roughness is associated with a decrease in productivity, and this reduces the efficiency of processing. The most progressive method of grinding, providing b oth an increase in productivity and a reduction in surface roughness, is deep-grinding at a low speed of the part. However, it is characterized by lower productivity in comparison with high-speed milling