@article{Bernecker2014, author = {Bernecker, Andreas}, title = {Divided Government and the Adoption of Economic Reforms}, series = {CESifo DICE Report - Journal for Institutional Comparison}, volume = {12}, journal = {CESifo DICE Report - Journal for Institutional Comparison}, number = {4}, publisher = {Ifo Institute for Economic Research}, address = {M{\"u}nchen}, issn = {1612-0663}, pages = {47 -- 52}, year = {2014}, language = {en} } @article{GoergensGreubelMoosdorf2013, author = {G{\"o}rgens, Stefan and Greubel, Steffen and Moosdorf, Andreas}, title = {How to mobilize 20,000 people: Perspectives on retail and consumer goods}, pages = {52 -- 58}, year = {2013}, language = {en} } @article{Moosdorf2009, author = {Moosdorf, Andreas}, title = {It's not just the Talent, it's the Knowledge Transfer Method}, series = {GC Ticker}, journal = {GC Ticker}, number = {1}, pages = {16 -- 16}, year = {2009}, language = {en} } @article{EmhardtJarodzkaBrandGruweletal.2020, author = {Emhardt, Selina and Jarodzka, Halszka and Brand-Gruwel, Saskia and Drumm, Christian and Gog, Tamara van}, title = {Introducing eye movement modeling examples for programming education and the role of teacher's didactic guidance}, series = {ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications}, journal = {ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications}, number = {Art. 52}, publisher = {ACM}, address = {New York}, doi = {10.1145/3379156.3391978}, pages = {1 -- 4}, year = {2020}, abstract = {In this article, we introduce how eye-tracking technology might become a promising tool to teach programming skills, such as debugging with 'Eye Movement Modeling Examples' (EMME). EMME are tutorial videos that visualize an expert's (e.g., a programming teacher's) eye movements during task performance to guide students' attention, e.g., as a moving dot or circle. We first introduce the general idea behind the EMME method and present studies that showed first promising results regarding the benefits of EMME to support programming education. However, we argue that the instructional design of EMME varies notably across them, as evidence-based guidelines on how to create effective EMME are often lacking. As an example, we present our ongoing research on the effects of different ways to instruct the EMME model prior to video creation. Finally, we highlight open questions for future investigations that could help improving the design of EMME for (programming) education.}, language = {en} } @article{DrummEmhardtKoketal.2020, author = {Drumm, Christian and Emhardt, Selina N. and Kok, Ellen M. and Jarodzka, Halzka and Brand-Gruwel, Saskia and van Gog, Tamara}, title = {How Experts Adapt Their Gaze Behavior When Modeling a Task to Novices}, series = {Cognitive science}, volume = {44}, journal = {Cognitive science}, number = {9}, publisher = {Wiley}, address = {Weinheim}, issn = {1551-6709}, doi = {10.1111/cogs.12893}, pages = {26}, year = {2020}, abstract = {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.}, language = {en} } @article{KlettkeHomburgGell2015, author = {Klettke, Tanja and Homburg, Carsten and Gell, Sebastian}, title = {How to measure analyst forecast effort}, series = {European Accounting Review}, volume = {24}, journal = {European Accounting Review}, number = {1}, publisher = {Taylor \& Francis}, address = {London}, issn = {0963-8180}, doi = {10.1080/09638180.2014.909291}, pages = {129 -- 146}, year = {2015}, abstract = {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.}, language = {en} } @article{EmhardtJarodzkaBrandGruweletal.2022, author = {Emhardt, Selina N. and Jarodzka, Halszka and Brand-Gruwel, Saskia and Drumm, Christian and Niehorster, Diederick C. and van Gog, Tamara}, title = {What is my teacher talking about? Effects of displaying the teacher's gaze and mouse cursor cues in video lectures on students' learning}, series = {Journal of Cognitive Psychology}, journal = {Journal of Cognitive Psychology}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {2044-5911}, doi = {10.1080/20445911.2022.2080831}, pages = {1 -- 19}, year = {2022}, abstract = {Eye movement modelling examples (EMME) are instructional videos that display a teacher's eye movements as "gaze cursor" (e.g. a moving dot) superimposed on the learning task. This study investigated if previous findings on the beneficial effects of EMME would extend to online lecture videos and compared the effects of displaying the teacher's gaze cursor with displaying the more traditional mouse cursor as a tool to guide learners' attention. Novices (N = 124) studied a pre-recorded video lecture on how to model business processes in a 2 (mouse cursor absent/present) × 2 (gaze cursor absent/present) between-subjects design. Unexpectedly, we did not find significant effects of the presence of gaze or mouse cursors on mental effort and learning. However, participants who watched videos with the gaze cursor found it easier to follow the teacher. Overall, participants responded positively to the gaze cursor, especially when the mouse cursor was not displayed in the video.}, language = {en} } @article{EggertAlberts2020, author = {Eggert, Mathias and Alberts, Jens}, title = {Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda}, series = {Business Research}, volume = {2020}, journal = {Business Research}, number = {13}, publisher = {Springer}, address = {Heidelberg}, issn = {2198-2627}, doi = {10.1007/s40685-020-00108-y}, pages = {685 -- 739}, year = {2020}, abstract = {Researching the field of business intelligence and analytics (BI \& A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI \& A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI \& A research of the current decade, focusing on the third wave of BI \& A. By this means, the paper's contribution is fourfold. First, a systematically developed taxonomy for BI \& A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI \& A 3.0. Third, the results foster discussions on the predicted and observed developments in BI \& A research of the past decade. Fourth, research gaps of the third wave of BI \& A research are disclosed and concluded in a research agenda.}, language = {en} } @article{Pietsch2015, author = {Pietsch, Wolfram}, title = {Augmenting voice of the customer analysis by analysis of belief}, series = {QFD-Forum}, journal = {QFD-Forum}, number = {30}, issn = {1431-6951}, pages = {1 -- 5}, year = {2015}, language = {en} } @article{BerneckerBoyerGathmann2021, author = {Bernecker, Andreas and Boyer, Pierre C. and Gathmann, Christina}, title = {The Role of Electoral Incentives for Policy Innovation: Evidence from the US Welfare Reform}, series = {American Economic Journal: Economic Policy}, volume = {13}, journal = {American Economic Journal: Economic Policy}, number = {2}, publisher = {American Economic Association}, address = {Nashville, Tenn.}, issn = {1945-774X}, doi = {10.1257/pol.20190690}, pages = {26 -- 57}, year = {2021}, language = {en} }