UID:
(DE-602)kobvindex_INTbi00005175
Format:
40 pages :
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illustrations ;
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21 × 29.7 cm.
Content:
AI-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 behavior
Note:
DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business & Management, Berlin International University of Applied Sciences, 2023.
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MACHINE-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 & Desire of Travel.20 4.3. Testing 2nd Hypothesis: Cautious Spending & Financial Factors21 4.4. Correlation of Revenge Spending & 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..........................................................35
Language:
English
,
Undetermined
Keywords:
Academic theses
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