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Building a Machine Learning Model to Forecast Consumer Revenge Spending Behavior in the Post COVID-19 Travel Industry

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dc.contributor.advisorArtinger, Florian
dc.contributor.advisorVillegas, Erick Behar
dc.contributor.authorWing Lam Kong
dc.contributor.departmentFaculty of Business Administration
dc.contributor.otherBerlin International University of Applied Sciences
dc.date.accessioned2024-01-15
dc.date.accessioned2025-11-28T13:27:13Z
dc.date.available2025-11-28T13:27:13Z
dc.date.issued2023
dc.description.abstractAI-GENERATED ABSTRACT: Abstract: This paper aims to build a machine learning model to forecast consumer revenge spending behavior in the post Covid-19 travel industry. Covid-19 has created a new phenomenon "Revenge Spending", where consumers spend excessively in order to compensate for the negative emotion and constraint experienced during the pandemic. This study utilized travel related factors like travel intentions and financial variables to train the machine learning models which included Logistic Regression, Random Forest and Decision Trees. To evaluate which predictive model performs the best in predicting consumer revenge spending behavior in post pandemic travel, cross-validation techniques, accuracy, precision, recall, F1-score, and AUC-ROC metrics were used. The findings of the study bring a meaningful understanding of consumer revenge spending behavior in travel and offer some insight on the key features that are influencing this behavior. Keywords: consumer revenge spending, post Covid-19, travel industry, machine learning, Logistic Regression, Random Forest, Decision Trees, predictive modeling, evaluation metrics, travel behavioren
dc.description.degreeBA
dc.description.tableofcontentsMACHINE-GENERATED CONTENTS NOTE: Table of Contents abstract..........................................................................2 1. Introduction.................................................................2 2. Literature Review..........................................................3 2.1. Definition of Consumer Revenge Spending Behavior.............3 2.2. Consumer Revenge Spending Behavior in Travel.................4 2.3. the Significance of Understanding Change in Consumer Behavior in the Post Covid-19 Travel Industry...................4 2.4. Utilizing Machine Learning Techniques to Predict Consumer Behavior.................................................................5 2.5. Machine Learning Approaches: Strengths and Limitations.....6 2.6. Evaluation Metrics.....................................................8 2.7. Research Gaps and Limitation......................................8 2.8. Hypotheses Development............................................9 3. Method...................................................................10 3.1. Survey Design and Approach......................................11 3.2. Data Collection Process.............................................12 3.3. Data Pre-processing...................................................12 3.4. Machine Learning Methods.........................................13 4. Data Analysis..............................................................13 4.1. Overview of the Dataset and Data Exploration.....................13 4.2. Testing 1st Hypothesis: Revenge Spending and Desire of Travel.20 4.3. Testing 2nd Hypothesis: Cautious Spending and Financial Factors21 4.4. Correlation of Revenge Spending and All Variables................22 4.5. Feature Selection......................................................23 5. Results...................................................................25 5.1. Model Performance and Comparison...............................25 5.2. Cross-validation.......................................................26 5.3. Testing Final Hypothesis.............................................27 6. Discussion.................................................................28 6.1. Theoretical Implications..............................................28 6.2. Practical Implications................................................28 6.3. Limitations and Future Research...................................29 7. Conclusion.................................................................29 references...................................................................31 appendix 1 - List of Variables - Explanation..............................34 appendix 2 - Survey..........................................................35en
dc.identifier.urihttps://repository.berlin-international.de/handle/123456789/765
dc.language.isoeng
dc.subjectDecision Trees
dc.subjectLogistic Regression
dc.subjectMachine Learning
dc.subjectPost Covid-19
dc.subjectPredictive Modeling
dc.subjectRandom Forest
dc.subjectTravel Behavior
dc.subjectTravel Industry
dc.subjectConsumer Revenge Spending
dc.titleBuilding a Machine Learning Model to Forecast Consumer Revenge Spending Behavior in the Post COVID-19 Travel Industry
dc.typeThesis

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