Refine
Year of publication
Institute
- Fachbereich Wirtschaftswissenschaften (1119) (remove)
Has Fulltext
- no (1119) (remove)
Document Type
- Article (700)
- Book (214)
- Part of a Book (71)
- Conference Proceeding (54)
- Other (44)
- Review (13)
- Working Paper (7)
- Contribution to a Periodical (5)
- Doctoral Thesis (5)
- Report (4)
Keywords
- Datenschutz (5)
- Datenschutzgrundverordnung (3)
- Datenschutzrecht (2)
- EU-Datenschutzgrundverordnung (2)
- Internationales Recht / Europarecht (2)
- rebound-effect (2)
- sustainability (2)
- 3. EU Legislativpaket (1)
- Active learning (1)
- Atomausstieg (1)
Gleichstellungsabrede – Gestaltungsmöglichkeiten und -notwendigkeiten für die betriebliche Praxis
(2007)
Goal Driven Business Modelling - Supporting Decision Making within Information System Development
(1995)
Grenzen und Chancen der Neuverteilung von Instandhaltungsaufgaben bei einem Massenstahlproduzenten
(1995)
Grenzenlose Liebe
(2008)
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Haftung im Vereinsrecht
(2008)
Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.
We introduce a new way to measure the forecast effort that analysts devote to their earnings forecasts by measuring the analyst's general effort for all covered firms. While the commonly applied effort measure is based on analyst behaviour for one firm, our measure considers analyst behaviour for all covered firms. Our general effort measure captures additional information about analyst effort and thus can identify accurate forecasts. We emphasise the importance of investigating analyst behaviour in a larger context and argue that analysts who generally devote substantial forecast effort are also likely to devote substantial effort to a specific firm, even if this effort might not be captured by a firm-specific measure. Empirical results reveal that analysts who devote higher general forecast effort issue more accurate forecasts. Additional investigations show that analysts' career prospects improve with higher general forecast effort. Our measure improves on existing methods as it has higher explanatory power regarding differences in forecast accuracy than the commonly applied effort measure. Additionally, it can address research questions that cannot be examined with a firm-specific measure. It provides a simple but comprehensive way to identify accurate analysts.