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This paper examines the development of customer-based brand equity through communication consistency in a luxury hotel segment. Communication consistency is considered as a basic principle of the integrated marketing communications (IMC) approach.

The empirical research was conducted among 223 guests during their stay in five-star deluxe luxury hotels in a Mediterranean country. Data are analyzed through the PLS technique and impact-asymmetry analysis.

Communication consistency is found to have a strong positive impact on all brand equity dimensions, especially on brand trust, brand image and perceived quality. The impact-asymmetry analysis further revealed negatively asymmetric relationships between communication consistency and six out of seven brand equity dimensions, except for affective commitment.

Some restrictions related to the measurement scales should be mitigated in future research.

Communication consistency is confirmed as a core management practice in luxury hotel business. Marketing professionals operating in this industry are therefore confronted with increasing challenges of efficient management of IMC.

This work addresses several research calls from the most recent marketing and hospitality literature. The analysis of the impact of communication consistency has extended our knowledge on the potential of IMC in creation of a strong brand. Obtained insights into the shape of the relationship between communication consistency and seven different brand equity dimensions help to better understand the process of brand equity building in a luxury hotel setting.

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.

The research is geared towards studying the impact of “GDP per capita (GDP)”, “energy consumption (EC)”, “human capital (HC)” and “trade openness (TO)” on India's ecological footprint (EF) from 1997–1998 to 2019–2020.

The autoregressive distributed lag model (ARDL) bound test was used to look at the short-run and long-term coefficients and the cointegration of the variables.

The results depicted a long-run connection between the variables. The long-run results found a favourable relationship between GDP, EC and EF, indicating that economic growth through heavy reliance on fossil fuels contributes to environmental unsustainability. An inverse relationship between HC, TO and EF was also observed, indicating that education fosters pro-environmental behaviour and leads to adopting cleaner technology that contributes to environmental sustainability.

The research substantiates India's pressing requirement for sustainable development, ensuring a harmonious balance between economic performance and environmental preservation. A carefully designed policy needs to be formulated to mitigate emissions stemming from growth in India. Policymakers are urged to implement measures that promote ecologically friendly tools, utilities and transportation to curb long-term environmental degradation.

The study is novel, incorporating an exhaustive review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study further examines how India's EF is affected by its HC; the preceding literature has yet to discuss much about the connection between HC and the environment. Finally, the study employed advanced econometric techniques, namely the cointegration technique and ARDL model, to find the relationship between EF, GDP, HC, EC and TO.

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 study aims to present and empirically examines an expanded service model that incorporates green hotel practices together with a multidimensional/higher-order measurement model of service quality, as well as perceived value and satisfaction, to examine the relationships among these variables and hotel consumers’ loyalty/behavioral intentions (BI).

The model was examined using partial least squares structural equation modeling (PLS-SEM) using data gathered in August 2018 from 200 surveys completed by UK subjects who stayed at upscale European hotels.

The results of PLS-SEM found that hotel service quality has a direct and positive effect on perceived value, satisfaction and BI. There is also an indirect effect of service quality on BI through perceived value and satisfaction, while green practices only had a direct effect on perceived value, not satisfaction or BI.

This study offers new insights into the network of causal relationships among determinants of hotel consumers’ BI. The results offer hotel operators a better understanding of specific green practices and service quality attributes they can use to more favorably influence consumers’ intentions to revisit the property and recommend them through positive word-of-mouth.

This research is particularly relevant in today’s reality characterized by travelers’ growing concern for green issues and business’ responsibilities toward the environment. Moreover, unlike previous studies, this study assumes a multidimensional scheme for service quality, further enhancing the understanding of hotel consumers’ BI relationships.

The competitive nature of the hotel industry has given the impetus to practitioners and researchers to invest in Customer Retention strategies and research. Although numerous studies have investigated Customer Retention in the hotel industry, there is still uncertainty regarding Customer Retention in emerging markets. This study, therefore, adds to the existing knowledge by exploring Customer Retention in Ghana, an emerging market. The study adopted a cross-sectional survey of 873 customers of 56 hotels in four regional capitals located in the southern part of Ghana. Findings indicate that service Tangible and Reliability indirectly relates hotel Customer Retention in Ghana, an emerging market.

Tourism product designing is a process of integrating all the components of a tourism product, i.e., the 5 A's: Attraction, Accommodation, Accessibility, Amenities, and Activities. The lack of awareness and unaccountability of mass tourism has demanded the need for “Responsible and Accountable Tourism, calling for better places to live and visit.” However, due to the COVID-19 pandemic restrictions on mass travel, “Mother Earth” got the opportunity of environmental restoration. The tourism business was badly hit by the pandemic and created an introspection phase for the tourism stakeholders to control and check their activities for a better global recovery. The responsible tourism product development in this VUCA (volatile, uncertain, complex, ambiguous) world needs controlled planning and a duty-bound PPP (Public–Private Partnership) model. A scoping review was done to analyze the planning process of the responsible sustainable tourism product. It was observed that all the stakeholders involved in tourism product development need to understand their accountability. Local host communities and tourists visiting the destinations should be culturally and environmentally sensitive. They should be involved in decision-making during the tourism product development and be pride bearers. The responsibility of the various tourism agencies, organizations, and individuals toward minimizing the impact of carbon footprints is evident from various research.