Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes
A trend of purchasing a lakeside, seaside, or forest vacation home has been raised in China. However, such purchase behavior has received limited attention from the research community in emerging markets. This study aims at investigating the factors behind vacation home purchase behavior and helping...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145830 |
| _version_ | 1811692085817901056 |
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| author | Li, Fan Katsumata, Sotaro Lee, Ching-Hung Ye, Qiongwei Dahana, Wirawan Dony Tu, Rungting Li, Xi |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Li, Fan Katsumata, Sotaro Lee, Ching-Hung Ye, Qiongwei Dahana, Wirawan Dony Tu, Rungting Li, Xi |
| author_sort | Li, Fan |
| collection | NTU |
| description | A trend of purchasing a lakeside, seaside, or forest vacation home has been raised in China. However, such purchase behavior has received limited attention from the research community in emerging markets. This study aims at investigating the factors behind vacation home purchase behavior and helping identify potential buyers. Specifically, factors, such as air quality, enduring involvement, place attachment, and destination familiarity, are examined via a proposed integrative model, which links these factors to purchase intention. The total number of potential buyers of vacation homes is increasing but remains small, compared to the whole consumers’ population, resulting in imbalanced purchase behavior data when validating a model. To address this problem, this study proposes an autoencoder-enabled and $k$ -means clustering-based (AKMC) method to identify potential buyers. The proposed methods tested on a dataset of 309 samples, collected through a questionnaire-based survey, and achieves a model accuracy of 82% in identifying potential buyers, outperforming other traditional machine learning methods, such as decision trees and support vector machines. This study also provides explainable results for the vacation home purchase behavior and a decision-making tool to identify potential buyers. |
| first_indexed | 2024-10-01T06:30:11Z |
| format | Journal Article |
| id | ntu-10356/145830 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:30:11Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1458302023-03-04T17:24:51Z Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes Li, Fan Katsumata, Sotaro Lee, Ching-Hung Ye, Qiongwei Dahana, Wirawan Dony Tu, Rungting Li, Xi School of Mechanical and Aerospace Engineering Fraunhofer Singapore Engineering::Electrical and electronic engineering Enduring Involvement Identification A trend of purchasing a lakeside, seaside, or forest vacation home has been raised in China. However, such purchase behavior has received limited attention from the research community in emerging markets. This study aims at investigating the factors behind vacation home purchase behavior and helping identify potential buyers. Specifically, factors, such as air quality, enduring involvement, place attachment, and destination familiarity, are examined via a proposed integrative model, which links these factors to purchase intention. The total number of potential buyers of vacation homes is increasing but remains small, compared to the whole consumers’ population, resulting in imbalanced purchase behavior data when validating a model. To address this problem, this study proposes an autoencoder-enabled and $k$ -means clustering-based (AKMC) method to identify potential buyers. The proposed methods tested on a dataset of 309 samples, collected through a questionnaire-based survey, and achieves a model accuracy of 82% in identifying potential buyers, outperforming other traditional machine learning methods, such as decision trees and support vector machines. This study also provides explainable results for the vacation home purchase behavior and a decision-making tool to identify potential buyers. Published version 2021-01-11T06:22:28Z 2021-01-11T06:22:28Z 2020 Journal Article Li, F., Katsumata, S., Lee, C.-H., Ye, Q., Dahana, W. D., Tu, R., & Li, X. (2020). Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes. IEEE Access, 8, 212383-212395. doi:10.1109/ACCESS.2020.3037920 2169-3536 https://hdl.handle.net/10356/145830 10.1109/ACCESS.2020.3037920 8 212383 212395 en IEEE Access © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Enduring Involvement Identification Li, Fan Katsumata, Sotaro Lee, Ching-Hung Ye, Qiongwei Dahana, Wirawan Dony Tu, Rungting Li, Xi Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title | Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title_full | Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title_fullStr | Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title_full_unstemmed | Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title_short | Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes |
| title_sort | autoencoder enabled potential buyer identification and purchase intention model of vacation homes |
| topic | Engineering::Electrical and electronic engineering Enduring Involvement Identification |
| url | https://hdl.handle.net/10356/145830 |
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