Award Date

May 2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

William F. Harrah College of Hospitality

First Committee Member

Amanda Belarmino

Second Committee Member

Billy Bai

Third Committee Member

Seyhmus Baloglu

Fourth Committee Member

Lisa Bendixen

Number of Pages

260

Abstract

In the rapidly evolving landscape of the hospitality industry, guest profitability stands as a cornerstone for sustainable success. As hotels strive to enhance the guest experience, understanding the intricate interplay between communication strategies and guest spend becomes paramount. This research utilized a mixed-methods approach, to explore the nuanced relationship between artificial intelligence (AI) chatbot communication and guests behavioral cross-selling intention. The study conducted semi-structured interviews with Hotel and AI Leaders to understand technology integration and applications in hotels and for cross-selling use cases. Choice-based conjoint analysis was simultaneously used to survey participants on willingness-to-pay (WTP) for hotel services offered via AI chatbot communications. This model empowers hotel leadership with the knowledge to adapt AI chatbot communication strategies in order to maximize profitability of their operations. By extending the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986) and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) this study looks to further unravel the intricate dynamics of revenue optimization in the context of AI powered text message interactions within the hospitality industry. The study thoroughly examined the advantages and disadvantages of cross-selling hotel goods and services through AI chatbots, including the types of products that could be effectively cross-sold, the key motivators and challenges of implementing this technology, and its impact on the guest experience. Additional qualitative findings were considered to provide deeper nuanced insights. The study also explored how AI chatbot message attributes influenced guests' WTP. The role of demographics for both interview and survey participants was carefully analyzed to identify key trends and patterns. The study concludes by discussing the theoretical and practical implications of these findings, reviewing limitations, and discussing recommendations for future research in this topic area.

Keywords

Artificial Intelligence (AI); Chatbot; Cross-selling; Mixed-methods research; Revenue management

Disciplines

Hospitality Administration and Management | Leisure Studies | Tourism and Travel

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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Available for download on Monday, May 15, 2028


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