Fakultät Kommunikation und Umwelt
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Keywords
- Basketball free throw (1)
- Donation intention (1)
- Donor motivation (1)
- External visual imagery (1)
- Internal visual imagery (1)
- Mental practice (1)
- Motor self-efficacy (1)
- Natural language processing (1)
- Plasma donation (1)
- Self-efficacy (1)
Institute
Purpose: The present study aims to investigate the acute effects of mental practice on novices’ basketball free throw performance and their motor self-efficacy. Moreover, it is examined whether internal visual imagery combined with kinesthetic elements enhances performance more than external visual imagery.
Methods: In order to test the research questions, a sample of 61 participants who are currently not participating in any type of basketball program were assigned to one of the following three conditions with the help of the matched sample method: The internal visual imagery group with kinesthetic elements, the external visual imagery group, and the control group. The experimental interventions with a duration of 15 minutes (mental imagery internal or external or mathematic exercises) followed by physical practice were repeated three times in a row. Consequently, the basketball free throw enhancement was measured via the shot accuracy. The results were compared by using an ANOVA with repeated measurements. Changes in the participants’ motor self-efficacy were assessed via a pre- and a post-test of the MOSI (Wilhelm and Büsch, 2006).
Results: Results showed no significant differences between the groups, neither in the performance increase nor in the self-efficacy.
Conclusion: The current study can serve as a reference point to avoid overestimating the effectiveness of mental practice. Findings are discussed in terms of possible explanations and hints for future research are given.
The urgent need for plasma and its components is increasing rapidly all over the world. This is due to demographic changes, which bring about an imbalance between eligible donors and possible future recipients. Hence, these changes pose a great challenge in meeting the demand for plasma and plasma-derived products in the future. As plasma cannot be synthesized, recipients depend on voluntary plasma donors. In order to ensure a sufficient supply of plasma considering the ongoing process of aging population, it is crucial to understand why people engage in donation behavior and which factors influence their decision to maintain the donor career.
The current study investigates the determinants of plasma donation intention using an extended version of the theory of planned behavior. As the study is conducted in cooperation with Octapharma Plasma GmbH, a subsidiary of one of the largest worldwide operating providers for plasma preparations, the analysis is based on the company’s donors only. By using an online questionnaire, data of N = 1153 plasma donors is surveyed in order to examine the hypotheses. Multiple hierarchical regressions reveal significant predictors for plasma donation intention for all donors combined and also for both first-time and repeat plasma donors. Across all donor stages, self-efficacy turns out to be the predominant predictor for future donation intention. Moreover, the satisfaction with the last donation influences all groups of plasma donors in their decision of career maintenance. Therefore, organizations should pay attention to these components, developing strategies to enhance both donors’ self-efficacy and donation satisfaction.
The German logistics company Schenker AG categorizes its customers into so-called vertical markets. For instance; category “Automotive” is assigned to car manufacturer BMW AG. The classification allows the company to evaluate its revenue and profits on different customer segments which, in turn, has an impact on Schenker’s strategic planning.
Until now the assignment is carried out manually which means that someone from sales department should perform some research on a customer’s public profile whenever a new customer is registered in the database. With the rapid growth of global trade in recent years and Schenker’s expansion to the Asian market with thousands of new customers the manual approach is no longer sustainable in a global market.
This thesis provides an alternative solution based on scraping customer data available in the web and classification of the extracted content. We deal with three significant difficulties: find web data related to a given customer name (we do have a company name but no homepage URL in Schenker database), extract a predictive portion of the data without introducing too much noise and, finally, set up a classification algorithm. Most importantly, the whole process needs to be implemented automatically.
For the classification task, we have identified two tree-based classification algorithms random forest and extreme gradient boosting (xgboost). Random forest performs better by using package ranger with an overall 52% accuracy and 87.8% multiclass area under the curve. On the other hand, xgboost takes less time to compute, but the accuracy is poor as compared to random forest.