Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-19094
Publication type: | Bachelor thesis |
Title: | Preventing corporate turnarounds : developing a conceptual EWS framework for the prediction of turnaround situations |
Authors: | Oehninger, Ramon |
Advisors / Reviewers: | Scherrer, Felix |
DOI: | 10.21256/zhaw-19094 |
Extent: | 96 |
Issue Date: | 2019 |
Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
Publisher / Ed. Institution: | Winterthur |
Language: | English |
Subject (DDC): | 658: General Management |
Abstract: | Currently, corporate crises are seen as an inevitability and an unavoidable part of the corporate life cycle. Therefore, the early recognition of crises has not been a focal point of business research in the past decades. The development of a conceptual framework would not only be beneficial for the company and the management, by predicting imminent crisis situations, but also for the employees, who would no longer be part of extreme downsizing campaigns. Therefore, the research hypothesis focused on challenging the existing consensus. By stating that a reliable framework would be able to prevent corporate turnarounds, the objective of this thesis was given. The created framework, consisting several analytical methods, including a multiple discriminant analysis and a logistic regression, was applied to several cases of corporate crises. The inclusion of companies operating in different industries in the analysis sample ensured the cross-industry predictive capabilities of the developed framework. The analysis relied on audited, publicly available financial information from annual reports. By focusing on the existing errors in crisis recognition, it was ensured that the developed framework creates added value for company executives. Overall, it was concluded that the created EWS model is a step forward regarding crisis recognition and possibly serves as the foundation for a more extensive framework in the future. By complementing the created model with qualitative factors, the cause-analysis of looming crises could be facilitated. Another way of improving on the conceptual framework would be the analysis of industry-specific scales, which would facilitate its interpretation. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/19094 |
License (according to publishing contract): | CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International |
Departement: | School of Management and Law |
Appears in collections: | BSc International Management |
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Bachelor Thesis_Ramon Oehninger.pdf | 1.46 MB | Adobe PDF | View/Open |
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Oehninger, R. (2019). Preventing corporate turnarounds : developing a conceptual EWS framework for the prediction of turnaround situations [Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften]. https://doi.org/10.21256/zhaw-19094
Oehninger, R. (2019) Preventing corporate turnarounds : developing a conceptual EWS framework for the prediction of turnaround situations. Bachelor’s thesis. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-19094.
R. Oehninger, “Preventing corporate turnarounds : developing a conceptual EWS framework for the prediction of turnaround situations,” Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, Winterthur, 2019. doi: 10.21256/zhaw-19094.
OEHNINGER, Ramon, 2019. Preventing corporate turnarounds : developing a conceptual EWS framework for the prediction of turnaround situations. Bachelor’s thesis. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Oehninger, Ramon. 2019. “Preventing Corporate Turnarounds : Developing a Conceptual EWS Framework for the Prediction of Turnaround Situations.” Bachelor’s thesis, Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-19094.
Oehninger, Ramon. Preventing Corporate Turnarounds : Developing a Conceptual EWS Framework for the Prediction of Turnaround Situations. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2019, https://doi.org/10.21256/zhaw-19094.
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