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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorGrift, Yolanda
dc.contributor.authorAhmadi Jozdani, Sajad
dc.date.accessioned2024-07-24T23:04:08Z
dc.date.available2024-07-24T23:04:08Z
dc.date.issued2024
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/46869
dc.description.abstractThis study explores the importance of worker bodies in combination with 67 other features on firm performance using the data from the European Company Survey (ECS) 2019 dataset. The scope of this study is limited to the Germanic cluster of countries, including Austria, the Netherlands, and Germany. Firm performance was measured based on a subjective variable rated by the management of the establishments based on their profit-making situation. The main research question of the study is “What are the most influential factors on firm performance?”, and the sub-question is “How important is the role of worker bodies in predicting firm performance?”. We used Random Forest, LightGBM, and XGBoost models using both classification and regression approaches to find the feature importance and SHAP values of the features. The results showed that worker body existence is the least important factor across all other features, while changes in production level, employment status, and motivation of employees are the most important features. At a higher level, firm characteristics, skill, and training factors demonstrated the highest level of importance, whereas collaboration and external factors, such as product market strategy, had the lowest importance values. This study is of value to econometricians and management researchers as it gives them an integrated and holistic overview of multiple features while focusing on a subset of them in their fields of interest.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectIdentifying Key Predictors of Firm Performance: An Analysis Using Machine Learning Models
dc.titleIdentifying Key Predictors of Firm Performance: An Analysis Using Machine Learning Models
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsrandom forest, LightGBM, XGBoost, firm performance, feature importance, SHAP values, ECS2019
dc.subject.courseuuApplied Data Science
dc.thesis.id34872


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