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Creative intensive processes
(2023)
Creativity – developing something new and useful – is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process – in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.
Creative thinking is an indispensable cognitive skill that is becoming increasingly important. In the present research, we tested the impact of games on creativity and emotions in a between-subject online experiment with four conditions (N = 658). (1) participants played a simple puzzle game that allowed many solutions (priming divergent thinking); (2) participants played a short game that required one fitting solution (priming convergent thinking); (3) participants performed mental arithmetic; (4) passive control condition. Results show that divergent and convergent creativity were higher after playing games and lower after mental arithmetic. Positive emotions did not function as a mediator, even though they were also heightened after playing the games and lower after mental arithmetic. However, contrary to previous research, we found no direct effect of emotions, creative self-efficacy, and growth- vs. fixed on creative performance. We discuss practical implications for digital learning and application settings.
Creative thinking is an indispensable cognitive skill that is becoming increasingly important. In the present research, we tested the impact of games on creativity and emotions in a between-subject online experiment with four conditions (N = 658). (1) participants played a simple puzzle game that allowed many solutions (priming divergent thinking); (2) participants played a short game that required one fitting solution (priming convergent thinking); (3) participants performed mental arithmetic; (4) passive control condition. Results show that divergent and convergent creativity were higher after playing games and lower after mental arithmetic. Positive emotions did not function as a mediator, even though they were also heightened after playing the games and lower after mental arithmetic. However, contrary to previous research, we found no direct effect of emotions, creative self-efficacy, and growth- vs. fixed on creative performance. We discuss practical implications for digital learning and application settings.
Many prediction tasks can be done based on users’ trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners’ needs based on their creative performances.
Many prediction tasks can be done based on users’ trace data. In this paper, we explored convergent thinking as a personality-related attribute and its relation to features gathered in interactive and non-interactive tasks of an online course. This is an under-utilized attribute that could be used for adapting online courses according to the creativity level to enhance the motivation of learners. Therefore, we used the logfile data of a 60 minutes Moodle course with N=128 learners, combined with the Remote Associates Test (RAT). We explored the trace data and found a weak correlation between interactive tasks and the RAT score, which was the highest considering the overall dataset. We trained a Random Forest Regressor to predict convergent thinking based on the trace data and analyzed the feature importance. The result has shown that the interactive tasks have the highest importance in prediction, but the accuracy is very low. We discuss the potential for personalizing online courses and address further steps to improve the applicability.