Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality
The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, t...
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MDPI AG
2025-01-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/2/32 |
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| author | Avisek Kundu Seeboli Ghosh Kundu Santosh Kumar Sahu Nitesh Dhar Badgayan |
| author_facet | Avisek Kundu Seeboli Ghosh Kundu Santosh Kumar Sahu Nitesh Dhar Badgayan |
| author_sort | Avisek Kundu |
| collection | DOAJ |
| description | The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements. |
| format | Article |
| id | doaj-art-c6efd9bbecda481ea32a14a509d3a86d |
| institution | DOAJ |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-c6efd9bbecda481ea32a14a509d3a86d2025-08-20T02:44:38ZengMDPI AGComputers2073-431X2025-01-011423210.3390/computers14020032Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in HospitalityAvisek Kundu0Seeboli Ghosh Kundu1Santosh Kumar Sahu2Nitesh Dhar Badgayan3Technology Consulting (Data Science, ML & AI), Ernst & Young LLP, Gurgaon 122002, IndiaSymbiosis Centre for Management Studies, Bengaluru Campus, Symbiosis International (Deemed University), Pune 560100, IndiaSchool of Mechanical Engineering, VIT-AP University, Besides A.P. Secretariat, Amaravati 522237, IndiaKPMG, Mumbai 400011, IndiaThe importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements.https://www.mdpi.com/2073-431X/14/2/32service qualitySERVQUAL modelCatBoostAzure Automated Machine Learning |
| spellingShingle | Avisek Kundu Seeboli Ghosh Kundu Santosh Kumar Sahu Nitesh Dhar Badgayan Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality Computers service quality SERVQUAL model CatBoost Azure Automated Machine Learning |
| title | Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality |
| title_full | Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality |
| title_fullStr | Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality |
| title_full_unstemmed | Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality |
| title_short | Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality |
| title_sort | leveraging azure automated machine learning and catboost gradient boosting algorithm for service quality prediction in hospitality |
| topic | service quality SERVQUAL model CatBoost Azure Automated Machine Learning |
| url | https://www.mdpi.com/2073-431X/14/2/32 |
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