Leveraging Data Analytics Frameworks for Strategic Planning and Operational Efficiency in Hospitality Development: Standardization and Ethical Considerations in AI and Machine Learning- A systemic review

Md Zubayer

doi:10.25163/business.1110215

Business and social sciences | Online ISSN 3067-8919

REVIEWS (Open Access)

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Leveraging Data Analytics Frameworks for Strategic Planning and Operational Efficiency in Hospitality Development: Standardization and Ethical Considerations in AI and Machine Learning- A systemic review

Md Zubayer Islam¹*

+ Author Affiliations

Business & Social Sciences 1 (1) 1-11 https://doi.org/10.25163/business.1110215

Submitted: 08 December 2022 Revised: 13 February 2023 Published: 16 February 2023

Abstract

Background: The hospitality market is rapidly transforming with new data analytics frameworks. Higher customer expectations, increased competition, and efficiencies are creating pathways toward strategies that promote service quality and customer satisfaction. Significance: Data analytics at all operational levels fosters innovation, enhances service quality, and allows the hospitality industry to practice ethical, data-driven decision-making to achieve future sustainable growth. Methods: The qualitative study of this research focuses on trends and case studies in the current hospitality landscape to evaluate data analytics frameworks and utilize AI intelligent technologies, predictive modeling, sentiment analysis, and automated decision-support systems. We also evaluate the future state of collecting multiple data streams, ethical considerations, and standardizations when working across sectors. Results: The findings also show clearly that data analytics can promote productive performance in strategic planning, operational performance and a higher level of customer satisfaction. However, they also identified several significant barriers to its use, namely the variance in standardization from numerous data sources, limits on interoperability among systems, and very significant ethical concerns around data privacy and fairness algorithmically. Conclusion: Data analytics will be critical for the hospitality industry around future innovation, sustainability and competitiveness. To support the transition of the hospitality industry to ethical frameworks based on standards, the industry must endorse and promote a universal and adaptable analytical framework, while maintaining a customer-centric and personalization strategy around collecting data.

Keywords: Forecasting analysis, Hospitality, Statistical Modeling, Sustainable Tourism, Data-Driven Hospitality

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