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This study aims to explore the quality of user-generated content regarding readability, polarity, word length and diversity, as well as its implications for guest satisfaction in Las Vegas luxury gaming resorts.

This study examined 12,940 textual customer reviews from six luxury hotels in luxury gaming destination resorts via Google Travel gathered from SCTM 3 (Smart Crawling and Text Mining). Moreover, the regression analysis identified the relationship between the variables in the textual customer reviews and the customer’s overall satisfaction.

A key finding of this study revealed that word length moderates the relationship between readability and overall customer satisfaction negatively, whereas it positively moderates the path from sentiment polarity and diversity to overall customer satisfaction.

This study contributes to the relationship between technical aspects of online reviews. The adopted methodology allows us to precisely identify the essential attributes that influence customer satisfaction through textual reviews. Further, the study explores the quality of user-generated content, addressing aspects such as readability, polarity, diversity and word length, providing a unique perspective on how these specific elements directly impact customer satisfaction in this context of hotels in luxury in Las Vegas.

本研究探讨了用户生成内容的可读性、情感倾向、词长和多样性等方面的质量, 以及这些因素对拉斯维加斯豪华博彩度假村顾客满意度的影响。

本研究通过 SCTM 3（智能爬虫与文本挖掘）收集了谷歌旅行上的六家豪华酒店的12,940 条客户评论文本。此外, 回归分析确定了文本客户评论中的变量与客户整体满意度之间的关系。

本研究的一个关键发现是, 词长在可读性与整体顾客满意度之间的关系中起到负面调节作用, 而在情感倾向和多样性与整体顾客满意度之间的路径中起到正面调节作用。

本研究对在线评论的技术方面之间的关系作出了贡献。采用的方法使我们能够精确识别通过文本评论影响顾客满意度的关键属性。此外, 本研究探讨了用户生成内容的质量, 涉及可读性、情感倾向、多样性和词长等方面, 提供了独特视角, 揭示了这些具体元素如何直接影响拉斯维加斯豪华酒店顾客满意度。

This research aims to analyze the customer satisfaction associated with experiences from 14 ecological hotels in Ecuador by exploring online guest reviews and classifying the most influential factors.

This study applied big data exploration, semantic network analysis, EFA and linear regression. It processed 22,629 online reviews from Google/travel, extracting 100 words with the highest frequency. In addition, CONCOR analysis built a comprehensive structural model gathering essential keywords. Furthermore, exploratory factor analysis and regression were conducted to explore the elements that best express customer satisfaction in ecological hotels.

The words such as green, sustainable, recycle, environment and ecological were not found among the main attributes extracted. Nonetheless, the keywords obtained reflect customer satisfaction, revealing that green practices do not affect comfort and the guests' experience. CONCOR analysis displayed four categories associated with satisfaction: tangibles, experience, location and empathy. Then, EFA restructured and revealed the factors: facilities feature, assurance, reliability, location and experience. Lastly, the regression disclosed location, assurance and facilities features as the most significant factors for customer satisfaction in the 14 ecological hotels. The terms related to the hotel area, staff care and hotel amenities were decisive for guests.

This study demonstrated that employee courtesy and location are the keys to enhancing customer experience and satisfaction. Hotel managers must promote green attributes and practices to increase customer awareness through constant staff training and information disclosure in common areas.

These findings provide insight and empirical evidence for hoteliers to understand how and what guest perceive to be green practices. By identifying the main features or concepts associated with satisfaction in Ecuador's green hotels, hoteliers could address new strategies to respond to expectations, effectively satisfy customers and provide a superior experience.