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This paper aims to explore the international marina industry, encompassing both marina customers and managers. It also aims to put forth the argument that marinas as business entities can create and sustain competitive advantage by maximizing the advantages that stem from their superior resources and core competences.

To ensure proper representation for the marina managers worldwide, questionnaires were sent to 200 managers of various marinas on five continents, in order to receive a representative sample. The questionnaires were distributed for the most part via e‐mail (where an address was available), some by fax and additional questionnaires were distributed at the international ICOMIA conference for marina managers held in Sydney, Australia. Overall, 138 marina managers replied. The statistical analysis applied in the study is a structural equations analysis, which is known in the literature as covariance structure modeling and structural equations modeling (SEM).

The correlation between government intervention and occupancy is a negative correlation, meaning that the greater government intervention, the lower occupancy rate. The correlation between crowding and occupancy is a positive one. The remaining correlations are not significant. This shows that it is not possible to claim a relationship between the occupancy index and the other variables examined: view, services, level of security/safety, environmental protection, distance from competitors, proximity to customer/city and local community.

The study offers a method of classifying variables according to which marinas can be characterized. Second, these criteria are placed in clusters viewed by the marina managers as being related to one another, adding structure to the process of classification, which is similar to the “conceptual map” that exists in the minds of the marina managers.

This research provides corroborative empirical evidence to the hypothesis that occupancy can be used as a proxy for marina's profitability. Finally, the model provides tools for strategic planning and ongoing management of an existing marina and/or for the establishing of a new marina.

The purpose of this paper is to identify, assess, rank, and compare the most influencing factors in three-, four-, and five-star hotels in the Eastern Province in Saudi Arabia.

A combination of literature and interviewing of ten local experts have resulted in the identification of 46 factors. Through a web-based questionnaire survey, the identified factors were ranked. A total of 23 responses were gathered from the full population of the 47 hotels (14 five-star, 15 four-star and 18 three-star hotels) in the Eastern Province of Saudi Arabia. A reliable overall response rate of 50 percent was achieved.

The most important factors affecting the operating and maintenance cost of five-star hotels are “climatic conditions” followed by “availability of the materials,” “annual energy consumption rate,” and “efficient energy consuming equipment.” Regarding four-star hotels, those factors are “star rating of the hotel” followed by “customer satisfaction,” “occupancy rate,” and “efficient energy consuming equipment.” Finally, the top-ranked factors for three-star hotels are “climatic conditions” followed by “floor area,” “glass and non-glass façade,” and “occupancy rate.”

The most important factors affecting the operating and maintenance cost of five-star hotels are “climatic conditions” followed by “availability of the materials,” “annual energy consumption rate,” and “efficient energy consuming equipment.” Regarding four-star hotels, those factors are “star rating of the hotel” chased by “customer satisfaction,” “occupancy rate,” and “efficient energy consuming equipment.” Finally, the top-ranked factors for three-star hotels are “climatic conditions” followed by “floor area,” “glass and non-glass façade,” and “occupancy rate.” The shared concern point between the respondents of all three groups of hotels is relevant to the energy department, which keeps the facility running.

This paper is original in the sense that the areas of knowledge and practice covered in the identified factors were distributed and not available in one source. The factors are derived from personal interviews with the selected operation and maintenance/engineering managers of all three classes of hotels, and from the relevant literature.

This paper aims to explore the effects generated by the Milan World Expo 2015 on both firm performance and seasonality structure. It aims to answer the following research question: Did the Milan Expo 2015 influence only hotel results without changing seasonal patterns, or was this mega event able to reconfigure seasonal periods?

The present analysis is based on Smith Travel Research (STR) data. This source offers daily data on a large sample of Milan hotels (approximately 80 per cent of the total), representing more than 30,000 rooms. The empirical data relate to a period of 12 years, 11 of which are focused on the pre-event period (2004-2014), while 2015 is centered on the Milan Expo. This data comprise 4,383 daily observations. For each day, three operating measures were analyzed: occupancy, average daily rate (ADR) and revenue per available room (RevPAR).

The empirical findings fully support the first hypothesis: the four seasonal periods built around the main market segments are relevant lenses for understanding Milan’s demand structure before Expo 2015. The findings also support the second hypothesis relating to the effects generated by the event: Expo 2015 was able to improve hotel performance during the four seasonal periods analyzed. The most fragile seasonality registered the highest rise. Finally, the last two hypotheses to be investigated are as follows: did the Milan Expo 2015 simply improve hotel performance, without changing the underlying seasonal patterns (H3), or did this event reconfigure the demand structure (H4)? The analyses carried out lend more support to the fourth hypothesis, suggesting that new seasonal patterns emerged during Expo 2015.

This paper explores the impact of a mega event on seasonal patterns of hotel performance metrics. At least three original aspects are introduced. First, to analyze the Milan demand variation, a market segment approach that proposes an innovative seasonal matrix is developed. This is based on the three main client groups attracted by the destination. Second, the effects generated by the Expo are measured with consideration given to the four seasonal periods. Third, based on graphical and statistical analysis, the paper confirms that new seasonal patterns emerged during the Expo.

Much research regarding social media posts and relevancy has resulted in mixed findings. Furthermore, the mediating role of relevancy has not previously been examined. This paper aims to examine the correlating relationship between types of posts made by hotels and the resulting occupancy rates. Then, the mediating role of relevancy is examined and ways that posts can increase/decrease relevancy of the post to potential hotel users.

Within the context of the hotel industry, three studies were conducted – one including hotel occupancy data from a corporate chain – to examine the impact of social media posts on relevancy and intentions to stay at the hotel. Experimental studies were conducted to explain the results of the real-world hotel data.

The findings show that relevancy is an important mediator in linking social media posts to service performance. A locally (vs nationally) themed post can decrease both the relevancy of a post and the viewer’s intentions to stay at a hotel. This relationship, however, can be weakened if a picture is included with the post, as a visual may increase self-identification with a post.

These results have important theoretical and practical implications as social media managers attempt to find the best ways to communicate to their customers and followers. Specifically, there are lower and upper limits to how many times a hotel should be posting to social media. The data also show many hotels post about local events, such as school fundraisers or a job fair, that can be harmful to stay intentions, likely due to the irrelevant nature of local posts to customers who are likely to stay in a hotel. National posts are seen as more relevant and likely to increase stay intentions, and the inclusion of a picture can help local posts seem more relevant.

This paper aims to improve the accuracy of hotel daily occupancy forecasts – an essential element in the revenue management cycle – by proposing and testing a novel approach. The authors add the hotel competitive-set’s predicted occupancy as an input of the individual property forecast and, using a recursive approach, demonstrate that there is a potential for significant reduction in the forecasting error.

The paper outlines the theoretical justification and the mechanism for this new approach. It applies a simulation for exploring the potential to improve the accuracy of the hotel’s daily occupancy forecasts, as well as analysis of data from a field study of two hotel clusters’ daily forecasts to provide empirical support to the procedure’s viability.

The results provide strong support to the notion that the accuracy could be enhanced. Incorporating the competitive set prediction by using either a genetic algorithm or the simple linear regression model improves the accuracy of the forecast using either the absolute or the absolute percentage as the error measure.

The proliferation of data sharing practices in the hotel industry reveals that the timely data sharing-aggregation-dissemination mechanism required for implementing this forecasting paradigm is feasible.

Given the crucial role of accurate forecasts in revenue management and recent changes in the hotels’ operating environment which made it harder to achieve or maintain high levels of accuracy, this study’s proposed novel approach has the potential to make a unique contribution in the realm of forecasting daily occupancies.

Using a unique data set, the purpose of this paper is to test the hypothesis that tenants pay increased accommodation costs for space in energy efficient office property.

The authors obtain lease contracts for office space in central Sydney, Australia. Empirical data on annual gross face rent and contract terms from each lease are combined with building characteristics and measured energy performance at the time of lease. Hedonic regression isolates the effect of energy performance on gross face rent.

No significant price differentials emerged as a function of energy performance, leading to a conclusion that tenants are not willing to pay for energy efficiency. Six factors – tenancy floor level, submarket location, proximity to transit, market fixed effects, building quality specification and, surprisingly, outgoings liability – consistently explain over 85 per cent of gross face rent prices in Sydney.

Rent premiums from an asset owner's perspective could emerge as a result of occupancy premiums, market timing or agent bias combined with statistically insignificant rental price differentials.

Tenants are likely indifferent to energy costs because the paper demonstrates that energy efficiency lacks financial salience and legal obligation in Sydney. This means that split incentives between owner and tenant are not a substantial barrier to energy efficiency investment in this market.

This study is the first to thoroughly examine energy efficiency rent price premiums at the tenancy scale in response to disclosure of measured performance. It also presents evidence against the common assumption that rent premiums at the asset scale reflect tenant willingness to pay for energy efficiency.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

The purpose of this paper is to analyse whether hotels that use a revenue management system (RMS) outperform non-RMS-users in a context of decreasing demand.

A database of chain hotels with a rating of three or more stars was used to estimate MANOVA and ANOVA models to analyse the role of RMSs in hotel performance.

In a context of strong competition in prices and surplus capacity, the findings suggest that RMSs have been more effective in improving occupancy than in achieving higher rates. Also, the use of RMSs did not have a significant impact on hotel labour productivity.

Managers may believe that they have adopted an RMS when, in fact, they have not fully done so. In addition, establishment-level unobserved heterogeneity, such as the quality of management or unobserved quality of service, cannot be fully controlled because of the nature of the data used. The main implication of this paper is that the potential of RMSs as revenue enhancer might be influenced by unstable market and economic conditions. However, the absence of significant effects on RevPAR performance might be also the result of firms’ adopting inadequate RM strategies. Further research could investigate whether the findings are context-specific or whether firms are failing to implement effective RMSs for other reasons.

The approach used in this paper is new to the literature, given that it uses statistical methods to analyse the impact of implementing an RMS on hotel performance under specific economic conditions and using alternative indicators.

The purpose of this study is to examine how the lodging market and the state economy affected by Hurricane Sandy have recovered from the damages sustained. Specifically, this study examines and predicts the influence of revenue management key performance indicators (KPIs) on recovery and lodging revenue in the affected states and the states’ economies. These KPIs include average daily rate (ADR), occupancy and revenue per available room (RevPAR).

Secondary financial data were collected for the states most damaged by Hurricane Sandy. Subsequently, pooled Ordinary Least Square (OLS) regression was conducted combining time and non-time dependent variables based on the states and radius from the landfall.

The results indicate that although the lodging market and the state economies have recovered since the onslaught of Hurricane Sandy, certain KPIs still need to improve.

Managerial implications are suggested in terms of dynamic pricing, market-based recovery, the KPIs, federal aid and facility management.

Despite its importance, research on the effects of climate change in the hospitality context has not actively progressed after Hurricane Katrina. Time and non-time dependent variables are combined in this analysis to gain a richer understanding of the impacts and recovery of KPIs on the revenue in the lodging market and the revenue on states’ economies. Additional analysis based on the radius from the landfall of the hurricane was performed to examine the impact and recovery based on geographical proximity.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.