Fakultät Kommunikation und Umwelt
Refine
Year of publication
- 2018 (3) (remove)
Document Type
- Master Thesis (2)
- Bachelor Thesis (1)
Language
- English (3)
Has Fulltext
- no (3) (remove)
Is part of the Bibliography
- no (3)
Keywords
- Donation intention (1)
- Donor motivation (1)
- FinTech (1)
- Natural language processing (1)
- Plasma donation (1)
- Self-efficacy (1)
- Text classification (1)
- Theory of planned behavior (1)
Institute
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 development of technologies that created FinTechs have brought new possibilities for customers into life that provide a quick and convenient handling of operations by covering various segments of the financial industry. However, the innovations also implicate drawbacks: Traditional banks may be threatened due to a loss of customers. This thesis evaluates to which extent this case can be measured by executing quantitative research methods as well as qualitative research methods for the purpose of gaining information from a bank’s perspective as well as opinions from customers. The result of this empirical study is that there is a certain correlation between the development of FinTechs and a threat caused by FinTechs although the latter is rather weak. Furthermore, this thesis indicates the knowledge of customers regarding FinTechs and their willingness to face said innovations.
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.