This study presents a comprehensive analysis of Customer Sentiment Intelligence (CSI) applied to hospitality management through a systematic examination of 20,491 TripAdvisor hotel reviews. The research addresses three critical business intelligence questions regarding the relationship between sentiment patterns and numerical ratings, operational aspects driving customer satisfaction, and strategic applications of text mining for competitive positioning. The methodology employed lexicon-based sentiment analysis calibrated for hospitality terminology, complemented by aspect-based performance evaluation across six strategic operational dimensions: Guest Experience Management, Location Advantage, Service Excellence, Room Quality Standards, Facilities Portfolio, and Value Proposition. Results demonstrate a strong correlation between sentiment language patterns and numerical customer ratings, validating sentiment analysis as a reliable complement to traditional satisfaction measurement approaches. The overall sentiment distribution reveals 86.4% positive sentiment, 7.8% neutral sentiment, and 5.8% negative sentiment across the dataset. Guest Experience Management emerged as the highest-performing operational aspect with 94.2% customer satisfaction rates, while Value Proposition represented the primary improvement opportunity at 78.9% satisfaction rates. The keyword frequency analysis identified service personnel, cleanliness standards, and location advantages as the most frequently discussed business elements, indicating customer evaluation priorities focus on fundamental service delivery components. The aspect-based performance analysis reveals substantial optimization potential, with a 15.3 percentage point performance difference between the highest and lowest performing operational dimensions. These findings provide hotel managers with data-driven frameworks for strategic resource allocation and operational improvement initiatives. The research establishes practical methodologies for implementing Customer Sentiment Intelligence as a strategic management tool that enables proactive customer satisfaction management and competitive advantage development through systematic analysis of unstructured customer feedback.

Yapay zekâ teknolojileri, konaklama sektöründe operasyonel süreçleri dönüştürmekte ve çalışanların algı, tutum ve örgütsel uyum dinamikleri üzerinde etkiler yaratmaktadır. Dijital dönüşüm sürecinde bu teknolojilere yönelik tutumlar, jenerasyon farklılıkları (X, Y ve Z) ile cinsiyet, eğitim ve gelir gibi demografik değişkenler çerçevesinde farklılaşabilmektedir. Bu çalışma, konaklama işletmelerinde çalışan bireylerin yapay zekâya yönelik tutumlarını, jenerasyon farklılıklarını (X, Y ve Z) merkezine alarak ve diğer demografik değişkenlerle ilişkili biçimde incelemeyi amaçlamaktadır. Nicel veri, Kuşadası destinasyonunda faaliyet gösteren konaklama işletmelerinde çalışan 446 kişiden anket tekniğiyle toplanmıştır. Nitel veri ise X, Y ve Z jenerasyonlarını temsilen toplam 30 katılımcıya yöneltilen açık uçlu sorular aracılığıyla elde edilmiştir. Nicel analiz sonuçları, yapay zekâya yönelik tutumların jenerasyonlar ve temel demografik değişkenler açısından istatistiksel olarak anlamlı biçimde farklılaşmadığını göstermektedir. Buna karşılık nitel bulgular, jenerasyonlar arasında algı, beklenti ve endişe düzeylerinde belirgin ayrışmalar bulunduğunu, özellikle güven, kontrol algısı ve insan etkileşiminin bu farklılaşmada belirleyici rol oynadığını ortaya koymaktadır. Çalışma, turizm sektöründe yapay zekâ uygulamalarının başarısının teknik yeterlilikten ziyade, insan odaklı tasarım ve jenerasyon temelli uyum stratejileriyle birlikte ele alınması gerektiğini vurgulamaktadır.

Po?et osob se zdravotn?m posti?en?m se zvy?uje v d?sledku st?rnut? populace, chronick?ch zdravotn?ch onemocn?n? a environment?ln?ch faktor?, jako jsou nehody, p??rodn? katastrofy nebo konflikty. Osoby se zdravotn?m posti?en?m jsou cennou sou...

This study explores the relationships among customer experience perceptions, customer satisfaction, revisit intention, and recommendation behaviour in accommodation businesses operating in Istanbul. It also examines whether perceptions of customer experience vary significantly across demographic characteristics. The research employed a quantitative approach, and data were collected via a structured questionnaire from 605 participants. The analyses were conducted using regression analysis, t-tests, and one-way ANOVA. The results reveal a strong, statistically significant positive relationship between customer experience and customer satisfaction, with high explanatory power. Similarly, customer experience was found to be a key predictor of both revisit intention and recommendation behaviour. These findings indicate that customer experience plays a critical role in shaping both emotional and behavioural outcomes. Moreover, the study identifies significant differences in experience perceptions across demographic groups. Generation Z participants reported more positive experiences than participants in other age groups. Male respondents also had a more favourable perception than female respondents. The highest experience scores were observed among participants with a high school education or lower, while the lowest were found in the associate degree group. In terms of income, those who described their financial situation as "poor" reported lower experience perceptions, whereas those in the "very poor" category surprisingly reported higher scores. In conclusion, the findings highlight the strategic importance of customer experience in the hospitality industry. Beyond increasing satisfaction, customer experience also drives loyalty and positive word-of-mouth. In Istanbul's accommodation sector, these results suggest that a well-designed customer experience strategy can significantly influence customer behaviour and strengthen a competitive position.

As blockchain-based metaverse platforms evolve into mature digital economies, understanding user behavior becomes essential for optimizing engagement and sustaining economic activity. This study introduces the metaverse engagement ladder, a novel behavioral framework designed to map user progression across three distinct tiers: new, established, and veteran participants. Drawing on a dataset of 78,600 blockchain transactions from five global regions, the study applies behavioral analytics to examine login frequency, session duration, and transactional patterns. The results reveal that user engagement follows a predictable lifecycle, with conversion rates reaching 70.2% during the established phase and declining to 0% at the veteran stage as users transition to management activities. Cross-regional comparisons indicate behavioral consistency across continents, with coefficients of variation below 1.6% for key metrics. The study identifies a behavioral sweet spot combining 45-90 minute sessions with 3-5 daily logins that produces 61.1% conversion rates, compared to 30.4% for shorter sessions and 0% for extended sessions. Statistical analysis using one-way analysis of variance confirms significant differences in engagement patterns across user tiers (F = 2847.3, p < 0.001), while correlation analysis reveals strong positive relationships between login frequency and session duration (r = 0.89, p < 0.001). Independent cluster validation confirms the three-tier structure with silhouette coefficient of 0.71. Grounded in theories of digital engagement, technology acceptance, and behavioral economics, this research provides empirical benchmarks for platform design, user experience optimization, and tier-specific security strategies.