Evaluation of the Role of Artificial Intelligence on Customer Satisfaction as a Competitive Advantage in the Hospitality Industry in the United States
Why this work is in the frame
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Bibliographic record
Abstract
The hotel sector is experiencing a radical change due to technology interaction in the service responsibilities. Due to this transformation, the pattern of service delivery according to human interaction changed to digital interaction such as artificial intelligence (AI). It brings out the opportunity for players in the hotel sector to consolidate their competitive advantage. Understanding how artificial intelligence impacts guest satisfaction in the attainment of competitive advantage in the hotel sector guides the business on how it would carry out its operations to ensure maximum satisfaction among the guests. The aim of the paper is to examine the artificial intelligence on customer satisfaction as a competitive advantage in the hospitality industry in the US. The study used a qualitative approach to collect information from 60 students from the Virginia Technical University who had visited the following luxury hotels: The Westin Georgetown, Washington D.C., Canopy by Hilton, Washington D.C., and InterContinental, Washington D.C.—The Wharf an IHG Hotel. From the study results, it was found that there is a positive significant relation between AI technologies (virtual reality, in-person customer experiences, business intelligence tools powered by machine learning, and chatbots and messaging tools) and customer satisfaction (customer patronage, expectations, perceived value, and perceived quality of the service). Businesses in the hotel industry can use AI to enhance guest experience and escalate their satisfaction and loyalty to the hotel making it to remain highly competitive in the sector.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it