AI-Powered Mobile Feedback Systems for Real-Time Service Quality Improvement in Hotels.
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| Title: | AI-Powered Mobile Feedback Systems for Real-Time Service Quality Improvement in Hotels. |
|---|---|
| Authors: | Kumar, Anuj1,2 anuj.kumar@rushford.ch, Jain, Neetu3 neetu.jain@bharatividyapeeth.edu, H. M., Amila Ishanthi1 dba1037@rushford.eu |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 7, p111-122. 12p. |
| Subjects: | Sentiment analysis, Customer feedback, Hotel management, Quality of service, Decision support systems, Customer service management |
| Abstract: | The explosion in electronically submitted guest feedback has created new opportunities for managing hotel service quality. With online review platforms, smartphone apps, and messaging applications now generating unprecedented quantities of unstructured textual guest feedback about hotels, manually monitoring and responding to online feedback at scale is not feasible. Forward-thinking hotel operators are instead implementing artificial intelligence (AI)-enabled guest feedback platforms to inform operations in real time. This paper reports a review of academic literature, industry articles, and hotel case studies describing the use of AI to analyze guest feedback within hotels. Thematic analysis was conducted to identify AI techniques that are commonly used to analyze guest feedback, implementation themes, and reported results. Results show that AI is most used in feedback systems to perform sentiment analysis, aspect-based sentiment analysis, topic extraction, or automated/semi-automated responses. Reported uses include enabling faster service failure detection, targeted service recovery, and increased responsiveness with real-time and mobile feedback channels. Articles also discussed key contextual factors that affect implementation success, such as language, cultural variations in guest expression, and the receiving hotel's preparedness. This paper contributes to the hospitality literature by centring conversations about AI use in guest feedback analysis on management and operational issues rather than algorithm accuracy. Managers can use these findings to understand how other organizations have leveraged AI-enabled guest feedback systems as decision-support technology rather than automation technology. These findings support the design of mobile feedback systems as operational decision-support tools rather than automation replacements. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192950826 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: AI-Powered Mobile Feedback Systems for Real-Time Service Quality Improvement in Hotels. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Anuj%22">Kumar, Anuj</searchLink><relatesTo>1,2</relatesTo><i> anuj.kumar@rushford.ch</i><br /><searchLink fieldCode="AR" term="%22Jain%2C+Neetu%22">Jain, Neetu</searchLink><relatesTo>3</relatesTo><i> neetu.jain@bharatividyapeeth.edu</i><br /><searchLink fieldCode="AR" term="%22H%2E+M%2E%2C+Amila+Ishanthi%22">H. M., Amila Ishanthi</searchLink><relatesTo>1</relatesTo><i> dba1037@rushford.eu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 7, p111-122. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+feedback%22">Customer feedback</searchLink><br /><searchLink fieldCode="DE" term="%22Hotel+management%22">Hotel management</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+service+management%22">Customer service management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The explosion in electronically submitted guest feedback has created new opportunities for managing hotel service quality. With online review platforms, smartphone apps, and messaging applications now generating unprecedented quantities of unstructured textual guest feedback about hotels, manually monitoring and responding to online feedback at scale is not feasible. Forward-thinking hotel operators are instead implementing artificial intelligence (AI)-enabled guest feedback platforms to inform operations in real time. This paper reports a review of academic literature, industry articles, and hotel case studies describing the use of AI to analyze guest feedback within hotels. Thematic analysis was conducted to identify AI techniques that are commonly used to analyze guest feedback, implementation themes, and reported results. Results show that AI is most used in feedback systems to perform sentiment analysis, aspect-based sentiment analysis, topic extraction, or automated/semi-automated responses. Reported uses include enabling faster service failure detection, targeted service recovery, and increased responsiveness with real-time and mobile feedback channels. Articles also discussed key contextual factors that affect implementation success, such as language, cultural variations in guest expression, and the receiving hotel's preparedness. This paper contributes to the hospitality literature by centring conversations about AI use in guest feedback analysis on management and operational issues rather than algorithm accuracy. Managers can use these findings to understand how other organizations have leveraged AI-enabled guest feedback systems as decision-support technology rather than automation technology. These findings support the design of mobile feedback systems as operational decision-support tools rather than automation replacements. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i07.61083 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 111 Subjects: – SubjectFull: Sentiment analysis Type: general – SubjectFull: Customer feedback Type: general – SubjectFull: Hotel management Type: general – SubjectFull: Quality of service Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Customer service management Type: general Titles: – TitleFull: AI-Powered Mobile Feedback Systems for Real-Time Service Quality Improvement in Hotels. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Anuj – PersonEntity: Name: NameFull: Jain, Neetu – PersonEntity: Name: NameFull: H. M., Amila Ishanthi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 7 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
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