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This study aims to examine the impact of a mega-sporting event “2014 FIFA World Cup” on hotel pricing strategies and performance.

The present project examines the host regions’ response to the 2014 FIFA World Cup which was established by the variance in the main hotel key performance indexes: occupancy, average daily rate, revenue per available room (RevPAR) and supply. Using data gathered from STR, this research distinctly shows how the Brazilian host regions reacted to the World Cup.

Results suggest that the key performance indicators of Brazil’s lodging sector reacted differently to the World Cup. Although all hosting cities experienced significant RevPAR growth because of the increase in hotel room rates during the event, the supply and occupancy performed differed from each city.

Research is limited to the case of hotel performance at the country level for mega-events. The study focused on the reaction of revenue managers in the Latin America context. Other contexts may generate different results.

The study helps revenue managers to examine how the FIFA World Cup travel demand affected pricing strategies and revenue management practices in the Brazilian hotel sector in areas undergoing seasonal growths in overnight tourism. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

This study reveals that the benefits brought by a mega-event are not always translated into strong hotel revenue performance. This study highlights an important but understudied research area of revenue management pricing strategies and the effect of mega-sporting events in the hotel sector. This study contributes to the literature as one of the few investigations to benefit hotel pricing strategies and overall revenue performance.

This study is one of the few studies about exploring the reaction of revenue managers during the execution of a mega-sporting event. The value of the present study lies in the fact that the authors extend previous studies examining the impact of the most important sporting event in the hotel industry at the country-level perspective. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

This study aims to analyze the tradeoff between two potential marketing strategies for service providers, namely, market segmentation on the basis of reservation lead time and cooperation with third parties.

This paper proposes an optimization model to describe market segmentation strategy after cooperating with third parties by taking hotels for example.

The results show that the profitability of adopting two strategies simultaneously is lower than that with market segmentation alone under some cases, which is relevant with attributes of travel agencies, such as switch rate and market share.

This study indicates that cooperation with third parties has a negative impact on profitability of hotels using market segmentation in some cases. However, randomness of demand, customer loyalty and existence of cancellation should be considered in further research.

In an e-commerce era, hotels with market segmentation based on reservation lead time, are not required to cooperate with third parties under a number of situations (e.g. high switch rates and small market sizes of travel agencies). In addition, hotels should revise the segmentation strategy based on the change rate of potential demand of individual customers. Furthermore, hotels should enhance customer loyalty, strengthen cooperation with travel agencies that possess large market shares or small switch rates.

The study preliminarily formulates the optimal market segmentation strategy on the basis of reservation lead time after cooperating with third parties, which contributes to the revenue management.

The objectives of this study are to investigate the relationship between hotel room demand and room rates, and to find a viable solution for the optimal room rate that maximizes the total profit.

There are various studies in the literature on how room rates affect profitability, and how the optimal room rate that maximizes the total revenue can be determined. Most of these studies assume an algebraic relationship between room rates and room demand, and obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts the alternative approach of using a model with a demand function that has been shown to be a superior causal forecasting model in some markets, and develops a new method to optimize the total profit.

The traditional method of applying calculus to the profit function based on a causal forecasting model leads to unrealistic solutions. This gives rise to the paradox that, on the one hand, there is a superior causal forecasting model based on room rates, but on the other hand, the traditional method does not yield a realistic solution for room rate optimization. This study analyzes the underlying causes of this paradox and proposes a method to resolve it.

The findings can be used by hotels to fine‐tune the room rates determined by conventional methods to arrive at a realistic and definitive value for the optimal room rate.

This study highlights the problems that arise with the traditional method of applying calculus to revenue and profit optimization and proposes a new method to resolve it.

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

This paper aims to develop overbooking models to determine the optimal number of overbooking for hotels having one and two different types of rooms.

This paper presents mathematical modeling to find the optimal solutions of overbooking for stochastic cancellation.

The authors prove that for hotels with only one type of room, there exists a closed form solution to guarantee the optimal number of overbooking, depending on the cost of walking customers to other hotels, the cost of unsold rooms and cancellation distribution observed in the past. For hotels with two types of room, they prove the convexity structure and identify equations to seek the number of overbooking for low-price and high-price rooms. The authors also provide key comparative statics on how model parameters impact the optimal decisions under different scenarios.

Overbooking decision is one of important and complicated decision-makings, which is related directly to the yield of hotel revenue management. It is necessary for a hotel manager to observe cancellation pattern in the history to make a reliable decision. This paper presents a method that can help hotel manager make this decision in practice.

This paper is one of the first articles in the hotel industry that considers the marginal cost for each room unsold caused by no shows and the marginal cost for each walking guest in a comprehensive perspective.

This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy.

Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue.

All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues.

Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments.

It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

One of the important characteristics of the hotel business is uncertainty of lodging demand, which can jeopardize hotel operation and ultimately even threaten a hotel’s survival during an economic recession. The purpose of this paper is to propose an approach to determine optimal hotel investment issues under uncertain lodging demand.

Uncertainty of lodging demand is classified into two types: the impact of unexpected economic recession and the temporary imbalance between supply of hotel rooms and lodging demand. A jump-diffusion real option approach is proposed to analyze how these two types affect optimal investment timing and the potential value of new hotel projects. The case of hotel investment in Macao is used to illustrate the jump-diffusion real option approach.

The results of numerical analysis show that the uncertainty induced by temporary imbalance between supply of hotel rooms and lodging demand increases the threshold of investment and hotel value, while the uncertainty induced by unexpected economic recession has ambiguous effects on the value and optimal investment timing of new hotel projects.

The jump-diffusion real option approach increases managerial flexibility for managers when making investment decisions on new hotel projects, allowing greater value to be generated than is possible with the conventional discounted cash flow method.

The approach separates the impact of unexpected economic recession on lodging demand from that of “normal” fluctuations in lodging demand, and it considers the impact of both types of uncertainty on hotel investment.

The purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.

This quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.

Random-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.

This study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.

This empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.

This chapter examines the pricing, demand (occupancy), and revenue per available room (RevPAR) dynamics of European hotels for the period 2006–2007. The importance of understanding the pricing behavior of direct competitors is critical to effective strategy formulation and meaningful industry analysis. Nevertheless, existing demand studies miss a critical link to local market dynamics. This study offers an alternative approach to examining competitive set pricing behavior that yields insights into the inelasticity of lodging demand. The results of this study of over 3,000 European hotel observations reveal that hotels that offered average daily rates (ADRs) above those of their direct competitors had lower comparative occupancies but higher relative RevPARs. The observed pattern of demand and revenue behavior was consistent for hotels in all market segments from luxury to economy. Country-specific analyses reveal a similar pattern, with more volatility in the results for hotels in Spain and Italy. Overall, the results suggest that the best way for a hotel to have higher revenue performance than its competitive group is to maintain higher rates. The results of this study support the position that hotel operators who resist pressures to undercut competitor's prices may be better served with higher revenues.

The interviews were conducted with the respondents (the founder and Manager of De Cyber Hotel). Other data were obtained through the websites of the relevant businesses.

Siti Alia and her friends established De Cyber Hotel in January 2019. It was incorporated as a Malaysian private limited company in Cyberjaya, Selangor. Siti Alia was appointed as the hotel manager and was responsible for managing the hotel’s day-to-day operations and financial matters. Being a new budget hotel, competing with other established hotels was quite difficult. De Cyber Hotel used brochures and word-of-mouth for its promotion activities and mainly depended on walk-in guests. Siti Alia knew she had to take immediate action to ensure the hotel’s survival and could no longer rely on walk-in guests. Hence, to increase the occupancy and revenue rate, on 27 March 2019, De Cyber Hotel management decided to accept an offer from ABC Digital Booking to implement a digital booking mechanism and form a partnership for at least a year. ABC Digital Booking provided an online system to enable the listing and booking of budget accommodations and partnered with hotels to provide similar guest experiences across countries. After working and collaborating for 10 months with ABC Digital Booking, Siti Alia had to decide whether De Cyber Hotel should continue its alliance with ABC Digital Booking. Hence, she had to think thoroughly and consider the advantages and disadvantages, as well as the impact of her decision on the business.

Undergraduate Integrated Case Studies, Seminar in Management, Risk Management and Corporate Governance, Management Accounting, Financial Accounting, Strategic Management. Postgraduate Organizational Behaviour, Management Accounting and Controls, Strategic Management Accounting, Marketing Management, Hospitality Strategic Management, Entrepreneurship Development.