{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:12:19Z","timestamp":1777569139740,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2014reliability, assurance, tangibility, empathy, and responsiveness\u2014provides 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.<\/jats:p>","DOI":"10.3390\/computers14020032","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T03:40:17Z","timestamp":1737517217000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality"],"prefix":"10.3390","volume":"14","author":[{"given":"Avisek","family":"Kundu","sequence":"first","affiliation":[{"name":"Technology Consulting (Data Science, ML & AI), Ernst & Young LLP, Gurgaon 122002, India"},{"name":"Department of Operations and IT, IBS, Hyderabad (A Constituent of ICFAI Foundation for Higher Education), Hyderabad 501203, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7087-2774","authenticated-orcid":false,"given":"Seeboli Ghosh","family":"Kundu","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Management Studies, Bengaluru Campus, Symbiosis International (Deemed University), Pune 560100, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6729-0415","authenticated-orcid":false,"given":"Santosh Kumar","family":"Sahu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, VIT-AP University, Besides A.P. Secretariat, Amaravati 522237, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitesh Dhar","family":"Badgayan","sequence":"additional","affiliation":[{"name":"KPMG, Mumbai 400011, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rane, N.L., Mallick, S.K., Kaya, O., and Rane, J. (2024). Tools and frameworks for machine learning and deep learning: A review. Applied Machine Learning and Deep Learning: Architectures and Techniques, Deep Science Publishing.","DOI":"10.70593\/978-81-981271-4-3_4"},{"key":"ref_2","first-page":"52","article-title":"AutoML: A systematic review on automated machine learning with neural architecture search","volume":"2","author":"Salehin","year":"2024","journal-title":"J. Inf. 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