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The pace of booking is a critical element in the accuracy of revenue management (RM) systems. Anecdotal evidence suggests that booking windows exhibit persistent shifts due to a variety of macro and micro factors. The article outlines several causes and tests the impact of the shifts on forecasting accuracy.

A novel methodological approach is utilized to empirically shift hotel reservation windows into smaller increments. Forecasts are then estimated and tested on the incremental shifts with popular RM techniques characteristic of advance booking data. A random effects model assesses the impact of the shifts on forecast accuracy.

The results show that shifts in booking behavior can cause the accuracy of forecasting models to deteriorate. The findings stress the importance of considering these shifts in model estimation and evaluation.

The results demonstrate that changes in booking behavior can be detrimental to the accuracy of RM forecasting algorithms. It is recommended that revenue managers monitor booking window shifts when forecasting with advanced booking data.

This study is the first to systematically assess the impact of booking window shifts on forecasting accuracy. The demonstrated approach can be implemented in future research to assess model accuracy as booking behavior changes.

With the advent of the smart home, where connectivity is facilitated by the internet of things, the provision of guest technologies in hotel service delivery has acquired greater significance. This ubiquity of technology implies that hotels need to view their technological offerings as facilitating guest’s broader lifestyles, and not just services in isolated spaces. This study aims to examine the role of “home” as a socio-technological unit, and how customers’ ownership of technologies at home affects evaluations of guest technologies at hotels.

Data are collected from a sample of US lodging consumers using Amazon’s Mechanical Turk. Partial least squares, which is a component-based structural equation modeling technique with SmartPLS 3.2, is used to test the hypotheses and meet the study’s objectives.

The findings show that hotel guest technologies should be of a higher standard than those at home, for guests to be satisfied with them. This relationship was robust across all hotel types, and both leisure and business visitation. Also, satisfaction with guest technologies has a relatively stronger impact on customer satisfaction in mid-scale and economy hotels compared to that in upscale and luxury hotels.

By empirically validating “home” as a frame of reference in the evaluations of hospitality experiences, it opens up the potential for future research to study how home affects the evaluation of the hospitality experience as a whole.

Hotels need to identify viable technologies that have the potential to become mainstream, and be ahead of customers in the technology adoption curve.

This study is the first to look at home as a conceptual entity that is integral to hospitality using a socio-psychological lens, and evaluates its impact on evaluations of guest technologies at hotels.

Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE’s scale-free and intuitive interpretation characteristics.

The study proposes and tests a new forecasting accuracy measure for hospitality revenue management (RM). A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures’ (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER’s effectiveness is empirically validated by using a large set of hotel forecasts.

The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.

It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.

Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the “asymmetry of MAPE.”

The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE’s asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to “quantify” the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.

Coopetition is the simultaneous cooperation and competition among firms operating in a specific market. It is particularly relevant in tourism where many competing suppliers (hotels in this case) contribute to the facilitation and delivery of the tourism product, i.e. the destination. By engaging in cooperative arrangements, firms can increase the attractiveness and competitiveness of the tourism product and subsequently demand for individual firms. This study aims to explore the three types of benefits derived from cooperative relationships in the context of the hotel industry, as well as the link between coopetition and market performance.

This study adopts several scales from prior research to survey 475 hotels in the USA. Specifically, respondents were asked to evaluate their performance with regard to the three benefits of coopetition. The responses were used to model the benefits of coopetition as a higher-order construct in a two-stage partial least squares model. In the second stage, the higher-order construct was linked to perceived hotel performance and the respondents’ RevPAR index.

The results show that perceived benefits from coopetition are positively associated with hotel performance. Specifically, the model depicts positive links between the coopetition construct and the hotels’ perceived performance, as well as their RevPAR index. Interestingly, the results were not as strong for index performance and may be due to the relative nature of the measure.

This study supports the notion that coopetition alliances between hotels provide a viable avenue for performance growth. Specifically, managers should consider working together to allocate resources strategically to grow the pie. It is important that managers measure the benefits of cooperative relationships outside of competitive index scores as these metrics may be relative to the cooperative arrangement.

The study is the first to investigate the three benefits of coopetition in the context of the hotel industry. Specifically, it is the first to establish a positive link between firm coopetition and perceived performance in the hotel industry at the firm level.

This paper aims to demonstrate how hospitality management research could benefit from the propositional style of theorizing, and how this approach could expand the scope of research in the discipline.

Developing new theories could provide unique insights and broaden the scope of research in hospitality management. To illustrate the power of proposition-based theorizing, this methodology is applied to the hotel cancellation policies domain.

Using the proposition-based theorizing in the context of cancellation policies, this study provides several propositions that could have broad implications for future research.

The contribution of this paper is threefold. First, the potential benefit of the proposition-based theorizing in the revenue management context of cancellation policies is demonstrated. Second, the theoretical frameworks and insights from the product return policy literature that could enrich future studies on hotel cancellation policies are introduced. Finally, this study conjectures on these theories’ relevance to hotel cancellation policies and consequently on their potential contribution to the scholarly discourse.

This study's goal is to examine how an intensified room rate signal affects customers' perceptions, their propensity to book, and consequently hotels' revenue maximization.

Subjects were asked to assess quality, sellout risk and the likelihood of getting a better deal based on an advanced booking scenario, where the signal's intensity was manipulated through the level of uncertainty. Analysis was conducted using a GLM multivariate procedure and a polytomous universal model.

The results suggest that high quoted rates might affect customers' perceptions, their propensity to book, and consequently the hotel's revenues. Surprisingly, the impact of the high room rate signal on the sellout risk perception reversed its direction with the intensified signal.

The results imply that the maximal achievable revenue levels reported in a previous theoretical study ought to be re‐calculated because the assumptions used were not supported by the study's findings.

The study demonstrated yet again that price affects the booking decision beyond the monetary value. It sends a signal that affects several perceptions and shapes the consumers' booking decision. Moreover, the intensity of the signal might change the direction of the impact.

The study is a first attempt to examine price signal intensity and the impact of the signal's strength in an advanced booking environment. The theoretical and practical implications underscore the need to better understand the complex impact that price changes have on consumer reactions and consequently on the effectiveness of hotels' revenue management policies.

This study aims to explore booking behavior in an online experiment where the time before the date of stay was controlled for and where action and perceptions were recorded.

An initial pool of 302 students enrolled in various undergraduate and graduate courses in a Midwestern university were asked to participate in the study. Participants were assigned randomly to one of five groups. The treatments differed in the number of days participants had to complete the task of booking a room.

The findings suggest that a sharp increase in the participants' propensity to book occurs during the last week before the date of stay. In addition, the results of the binomial logistic regression provide strong support for the notion that time and the two assessed perceptions are strongly associated with the booking decisions of last minute deal seekers. Participants were more likely to book the closer it was to the date of stay if they thought the hotel was likely to sell out, and if they expected to be offered a better deal later in the process.

This study explored the role of time in hotel room booking decisions. Research suggests that two relevant trends are to be reckoned with when it comes to last minute bookings. First, a growing segment of the travel market is taking advantage of technological advances that facilitate effective deal‐seeking behavior. Second, these patterns of deal‐seeking behavior, and the manner in which consumers respond and adapt to revenue management policies in their attempt to book optimally, are not well understood yet. Accordingly, this study aimed to explore several aspects of last minute perceptions and behavior.

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.