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The purpose of this paper is to examine the hotel growth model including hotel brand, culture and life cycle phases of the Myrtle Beach, South Carolina, the fastest growing tourism destination in the United States.

Culture reflecting consuming behaviour of low-context innovators and high-context imitators is measured by the price elasticity of demand (PED). Hotel brand reflecting guests’ hotel class is measured by the income elasticity of demand. Autoregressive distributed lag has been conducted on the Smith Travel Research data in 33 years (1989–2022) to determine the relationship among hotel brand, culture and life cycles.

Skilled labour is the key to make hotels grow. Therefore, increase room rates when hotels possess skilled professionals and decrease room rates when hotels have no skilled professionals. During the rejuvenation in Myrtle Beach (1999–2003), hoteliers increased room rates for innovators due to skilled professionals to increase revenue. Otherwise, a decrease in room rates due to lack of skilled professionals would lead to increase revenue.

(1) Although Myrtle Beach is one of the fastest growing tourism destinations in the US, it has a relatively small geographic area relative to the country. (2) Data cover over one tourist life cycle, so the time span is relatively short. Hoteliers can forecast the number of guests in different culture by changing room rates.

To optimize revenue, hoteliers can select skilled labour in professional design hotel brands which could make an increase in demand for leisure transient guests no matter what room rates increase after COVID-19 pandemic.

The study has considered the applied ethical processes regarding revenue management that would maximize both revenue and customer satisfaction when it set up an increase in room rates to compensate for professional hotel room design or it decreases room rates for low-income imitators in exploration and development.

This research highlights that (1) skilled design in the luxury hotel brand is the key for the hotel growth and (2) there is a steady state of the growth model in the destination life cycle.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.

The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.

The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.

The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.

The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.

Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.

Considering the significance of the human resource management (HRM) and corporate social responsibility (CSR) relationship, the aim of this research is twofold: first is to measure the cultural differences between HRM, CSR and sustainable performance relationship (study 1) and second is to identify the how HRM instigates CSR and sustainable performance (study 2) in the hospitality industry of UK and Pakistan.

A mixed-method approach was used to collect the qualitative and quantitative data from upscale hotels. In Study 1, a multi-respondent and time-lagged strategy was employed to collect the data from 162 Pakistani and 290 UK upscale hotels. In Study 2, in-depth semi-structured interviews were conducted to understand the HRM–CSR–performance nexus.

The results of Study 1 highlight the significant cultural differences in the relationships of HRM–CSR–performance, while Study 2 explains that ethical culture, shared objectives, transparency, training and development, and economic incentives are the factors that push the employees to take part in CSR-related activities and attaining higher sustainable performance.

This study addresses the debate on the difference between cross-cultural studies related to implementing Western theories in shaping, developing and implementing business strategies, including CSR, HRM and sustainable performance in an Asian context.

The purpose of this research is to investigate the effects of service recovery experiences on customer engagement in negative word-of-mouth (WOM) in the hotel industry and explore the psychological motives and mediating mechanisms driving consumer behavior.

A scenario-based experimental design on Qualtrics was used, with a pre-test (N = 200). The main study data were collected using Amazon's Mechanical Turk platform.

Findings reveal that negative service experiences lead to higher engagement in negative WOM compared to positive and satisfactory recovery service experiences. Even well-executed recovery efforts may not completely eliminate negative WOM. The mediating role of emotional responses is substantiated, as heightened negative service experiences result in more intense negative emotional responses, leading to increased engagement in negative WOM.

The study emphasizes the importance of service recovery strategies and the need for businesses to consistently strive for exceptional service quality. It also highlights the complexity of customer reactions to service experiences, suggesting that further research is needed to explore the factors that minimize negative WOM across various service contexts.

The purpose of this paper is to investigate how previous literature has analyzed the role of augmented reality and virtual reality in the field of tourism, distinguishing between studies focused on one technology or the other as both have characteristics that profoundly differentiate them.

This study carries out a critical review to assess and synthesize the literature on augmented reality and virtual reality in tourism. Literature searches are conducted using various keywords, resulting in a selection of 84 articles (19 on augmented reality and 65 on virtual reality) from 39 indexed journals.

The research findings demonstrate an increasing scholarly focus on exploring the application of virtual reality and augmented reality within the realm of tourism. These results highlight a noticeable progression in recent years with respect to different matters, such as methodologies, used theories or considered variables, among others. Based on these results, it is proposed a future research agenda that seeks to establish a cohesive framework and drive the development of both augmented reality and virtual reality research in tourism.

By conducting an individual and comparative review of the literature on the application of augmented reality and virtual reality in tourism, this research helps elucidate the specific lines of research for each technology while providing a better understanding of how each technology can be used to generate effective tourist experiences.

El objetivo de este artículo es investigar cómo la literatura previa ha analizado el papel de la realidad aumentada y la realidad virtual en el ámbito del turismo, distinguiendo entre estudios centrados en una u otra tecnología ya que ambas tienen características que las diferencian profundamente.

Este estudio realiza una revisión crítica para evaluar y sintetizar la literatura sobre realidad aumentada y realidad virtual en turismo. Se realizan búsquedas de literatura utilizando diversas palabras clave, que dan como resultado la selección de 84 artículos (19 sobre realidad aumentada y 65 sobre realidad virtual) procedentes de 39 revistas indexadas.

Los resultados de la investigación demuestran que existe un interés creciente en explorar la aplicación de la realidad virtual y la realidad aumentada en el ámbito del turismo. Asimismo, se pone de manifiesto una notable progresión en los últimos años con respecto a diferentes cuestiones, como las metodologías aplicadas, las teorías empleadas o las variables consideradas, entre otras. A partir de estos resultados, se propone una agenda de investigación futura que pretende establecer un marco cohesionado e impulsar el desarrollo de la investigación en el ámbito tanto de la realidad aumentada como de la realidad virtual en turismo.

Al realizar una revisión individual y comparativa de la literatura sobre la aplicación de la realidad aumentada y la realidad virtual en el turismo, esta investigación ayuda a esclarecer las líneas de investigación específicas de cada tecnología a la vez que proporciona una mejor comprensión de cómo cada tecnología puede ser utilizada para generar experiencias turísticas efectivas.

本文旨在研究以往的文献是如何分析增强现实和虚拟现实在旅游领域的作用的, 并对侧重于其中一种技术或另一种技术的研究加以区分, 因为这两种技术都有深刻区别于其他技术的特点。

本研究通过批判性综述来评估和归纳有关旅游业中增强现实和虚拟现实技术的文献。通过使用各种关键词进行文献检索, 从 39 种索引期刊中筛选出 84 篇文章（19 篇关于增强现实技术, 65 篇关于虚拟现实技术）。

研究结果表明, 学术界越来越重视探索虚拟现实和增强现实在旅游业中的应用。这些结果突显了近年来在方法论、采用的理论或考虑的变量等不同方面的明显进步。在这些成果的基础上, 提出了未来的研究议程, 旨在建立一个具有凝聚力的框架, 推动旅游业中增强现实和虚拟现实研究的发展。

本研究通过对增强现实和虚拟现实技术在旅游业中应用的文献进行单独和比较审查, 有助于阐明每种技术的具体研究方向, 同时让人们更好地了解如何使用每种技术来产生有效的旅游体验。

This study aims to investigate city branding as a post-pandemic COVID-19 outcome factor on brand satisfaction, brand experience, perceived risk and revisit intention. In addition, this research contributes to the discussion of post-COVID-19 city branding that needs to be considered in the development of future tourism marketing.

A quantitative approach was used with PLS-SEM statistical analysis and a 263-tourist sample. The study was conducted on tourists from Malang Regency in Indonesia by distributing questionnaires modified from previous studies in a similar context.

The results of this study found that there were significant influences of city brand personality on brand experience, brand satisfaction, brand experience on perceived risk, brand satisfaction on revisit intention and perceived risk on revisit intention. This study also presents the mediating role.

The study was only conducted on a small regency in Indonesia, and therefore the results cannot be generalized for other cities over the world.

The proposed study model suggests that stakeholders must seek to socialize services to potential tourists, so that tourists can understand the description of tourism activities that can be enjoyed during the COVID-19 pandemic and the way they travel in the future.

Understanding the determinant factors of city branding post-COVID-19 was valuable for developing marketing strategies to cope with intense competition among the city.

This study emphasizes the determinants of COVID-19 perceived risk and revisit intentions as explained in the tourism marketing literature by considering the role of brand satisfaction, brand experience and city brand personality which significantly contribute to build the city competitiveness. Therefore, various creative strategies should be implemented to promote the city as well as escalate tourist visits without ignoring the pandemic’s risks.

This study aims to create actionable guidelines for pricing decision-making by employing game a theory matrix to forecast the correlation between the average daily rate and the latest ambiance of hotels.

Utilizing a vector error correction model, the research employs game theory to assess the influence of the average daily rate on the hotel's newest atmosphere during both peak season (April–September) and valley season (October–March).

Findings indicate that during the peak season, when the average daily rate rises in resorts and falls in suburban areas, the hotel’s newest atmosphere is at its best in both types of accommodations. During the off-peak season, the hotel’s newest atmosphere is achieved when both resorts and suburban accommodations increase their average daily rates.

There are two study constraints. One is the assumption that hotel guests in both parties prefer not to change hotels, but in fact they would. Two is a limited sample of two resort and suburban markets.

This suggests that the hotel’s newest atmosphere can draw both leisure and business travelers to suburban areas during the low season and more leisure travelers to resorts during the high season.

The study’s findings have implications for revenue related to the hotel’s newest atmosphere and cleanliness for both suburban and resort hotels, particularly when promoting tourism collaboratively.

The study provides valuable insights for hotel managers in analyzing pricing strategies using matrices.

Corporate social responsibility (CSR) activities of companies have been reported to generate favorable consumer attitudes towards the business, which in turn, encourages positive consumer behavior. But the application of this conclusion in the mobile commerce domain remains limited. Drawing on social exchange theory, the authors examined the mediating role of trusting beliefs and satisfaction in the relationship between CSR and purchase intention in mobile commerce.

Using nonprobability convenience sampling, the authors administered a cross-sectional survey with 314 students from Keimyung University in Korea. Initially, SPSS-Amos was utilized to run confirmatory factor analysis. The proposed hypotheses were tested using structural equation modeling.

This study showed that two components of trusting beliefs, namely benevolence and competence, had a positive effect on the association between CSR and the purchase intention on mobile shopping applications. This relationship was partially mediated by trusting beliefs. Further findings demonstrated that the impact of CSR on purchase intention was also partially mediated by satisfaction.

The authors' contribution includes extending the social exchange theory to the mobile commerce setting by establishing mechanisms that explain the ways CSR influences purchase intention in the mobile commerce framework. The authors integrated trusting beliefs and satisfaction in the CRS chain link with purchase intention. Additionally, the authors examined the individual effects of three trusting beliefs components. Based on the results, the authors proposed suggestions for the mobile shopping application business on the methods they can implement to boost the outcomes of their CSR activities.

:過去的研究結果、均顯示公司的企業社會責任行為會產生對公司良好的消費者態度,而這消費者態度,則進而促進積極的消費行為; 唯這結論的應用、在移動商務的領域內仍是有限的。我們利用社會交換理論、探討了信任的信念和滿意在移動商務裡的企業社會責任與購買意圖之間的關係上的中介角色。

:透過使用非機率便利抽樣的研究法,我們對來自南韓啟明大學314名學生進行了一個橫向調查研究。最初,我們使用結構方程模型 (SPSS-AMOS) 來進行驗證性因素分析。又用結構方程模型 (SEM) 來為提出的假設進行測試。

:研究結果顯示,信任的信念的兩個組成部分,即仁愛和能力,均於移動購物應用上對企業社會責任與購買意圖之間的關聯有積極正面的影響。這關聯部分上為信任的信念所調節。研究結果更表明了企業社會責任對購買意圖的影響、亦是部分上為滿意所調節的。

:本研究的貢獻、包括藉著建立一個可解釋企業社會責任、如何在移動商務的框架裡影響購買意圖的機制,去把社會交換理論擴展至移動商務的環境。我們在企業社會責任與購買意圖一連串的關聯裡、融入了信任的信念和滿意。而且,我們探討了三個信任的信念的組成部分各自所帶來的影響。我們根據研究的結果,為移動購物應用之商業機構,建議有效的方法,去增強其企業社會責任行為的效果。