Do online hotel rating schemes influence booking behaviors?

Travelers’ hotel booking behaviors increasingly depend on peers' opinions and online ratings. This study investigates the effects of online hotel ratings on travelers' attitudes toward the hotel and booking intentions, using a 2 × 2 experimental research design. The results suggest that online rating lists are more useful and credible when published by well-known online travel communities (e.g., TripAdvisor). More favorable attitudes toward a hotel and higher booking intentions emerge when the hotel appears in best hotels lists. Finally, for the entries on best hotels lists, better attitudes and higher booking intentions result if the list is published on a well-known online travel community (Tripadvisor), whereas for entries on a worst hotel list, attitudes and booking intentions decrease even further if the list appears on TripAdvisor.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

邮轮收益管理的舱位分配：基于EMSR-a和EMSR-b的比较分析

邮轮业被称为“漂浮在黄金水道上的黄金产业”,近几年达到8％左右的增长速度,成为现代旅游业中发展最迅速的行业。与航空和酒店业一样,邮轮业具有收益管理的所有行业特征,可以看作是典型的收益管理行业。文章基于北美邮轮市场的实际数据,对邮轮收益管理的需求分布特征和存量分配问题进行了研究。首先,以邮轮公司特定航线的需求数据为基础,将实际数据与多种概论分布进行比较,来挖掘邮轮舱位水平上总需求的分布规律。研究表明,正态分布和伽玛分布能较好地拟合邮轮舱位总需求的分布特征。其次,以正态分布为例,基于EMSR—a和EMSR—b两种舱位分配算法,以某次航行的实际数据为基准需求,对高总需求和低总需求下分别考虑（1）高价格舱位高需求和（2）低价格舱位高需求的4种需求状况进行了模拟,并对两种存量分配方法的分配效果进行了比较。结果表明,两种方法分配效果的优劣取决于实际的需求状况。     

Identifying critical hotel cancellations using artificial intelligence

Cancellations have a significant impact on the hotel and lodging industry because they directly affect income and are thus considered critical in revenue management. Specifically, cancellations made close to the time of service are the most damaging for hotels because they leave management with no time to react. The use of Personal Name Records (PNR) has led to new approaches in this field, however despite this novel research area there are no investigations focusing on forecasting for individual hotel cancellations made close to the time of service. With the aim of filling this gap, this research is intended to identify those individuals likely to make cancellations in a short-horizon of time using Artificial Intelligence (AI) techniques through PNR data. Promising results have been achieved with 80% accuracy for cancellations made 7 days in advance. By taking this approach, booking management systems, as well as cancellation policies may be optimised.     

Strategic consumer behavior in online hotel booking

This study investigates strategic consumer behavior in online hotel booking. Free cancellation policies enable consumers to rebook the hotel room at a later time should the price drop prior to the date of stay. A discrete choice experiment is used to infer consumer preferences for free cancellation and non-refundable rates under different scenarios. The study also examines the moderating role of the risk attitude of consumers. Risk-seeking consumers show preference for a free cancellation rate that increases with the availability of an automatic rebooking service. Although a higher booking window increases the utility of the free cancellation rate, such impact decreases as risk propensity increases. The identification of four distinct consumer segments provides clear implications for industry practitioners.     

Tourists’ preferences for hotel booking

This paper analyzes tourists’ preferences for hotel booking mode using a sample of 17,921 tourists visiting a Northern Spanish region during 2005–2016. Four different booking modes are considered: telephone, the internet, travel agency and other non-market-based intermediaries. We estimate a Finite Mixture Multinomial Logit Model that allows us to define three classes of tourists. Our results show that leisure tourists coming from distant locations and lodged at luxury hotels have higher likelihood of online hotel booking in class 1. Travel agencies are preferred by offseason tourists with longer stays, while those travelling by public transit modes and staying at luxury hotels opt for non-market-based intermediaries in class 2. In class 3, first-time tourists choose the internet, telephone is more prevalent among those staying at economy hotels and travel agencies are preferred among those travelling in the offseason and by public transit modes.     

BayesRec: Personalize search ranking with customer attribute-level willingness-to-pay using heterogeneous booking choice data

The ongoing travel restrictions owing to the COVID-19 shutdown continue to impact the hospitality sector. This pause on travel has led to a bigger regime shift in the customer's preferences for hotel selection. Therefore, this study aims to help understand customers’ evolving preferences by assessing the customers’ attribute-level willingness-to-pay (WTP) to tailor the booking experience. Furthermore, the study accounts for customers’ behavioral heterogeneity and decision choices while estimating WTP. The proposed approach utilizes multi-stage online modeling: (i) Segmentation to create behavioral customer cohorts; (ii) Hierarchical Bayesian modeling to estimate customer-level WTP; and (iii) Multi-criteria decision optimization to rank-optimize the hotel alternatives by leveraging the assessed attribute-level WTP. This continuous assessment provides personalized hotel recommendations and enhances customer’s booking experience, thereby allowing hotels to accelerate revenue recovery and aid in proactive demand generation for the next wave of travelers in a post-COVID economy.     

Advance booking and hotel price variability online: Any opportunity for business customers?

Is it possible for business customers to effectively adjust their purchasing strategies, as a response to revenue management? We consider daily online best available rates for a panel of 357 hotels in Milan and Rome, up to an advance booking of 29 days.We analyse price trajectories, finding that dynamic pricing strategies with no established trend towards the arrival date are prevalent, with a predominance of decreasing trajectories for lower-scale hotels in Milan during fairs. We show that price levels are explained by a variety of structural determinants. We quantify the effects of advance booking, room quality, services, competition, seasonality and fairs, underlining their different importance on leisure and business destinations. Other features, such as breakfast and refunding options, appear to be used as marketing tools to differentiate rooms, keeping a low pace in price adjustment. Managerial implications are discussed, with reference to both corporate travel departments and hôteliers.     

The power of design: How does design affect consumers’ online hotel booking?

Emphasizing experiential appeals to consumers through design is most notable in the emerging lifestyle hotel segment of the lodging industry. This study identifies which lodging design mechanisms evoke consumer responses and provides a novel understanding of the relationships among consumer perception of hotel product/service design and booking intention by incorporating consumer emotional arousal and quality expectations in a theoretical model. The findings of this study suggest that aesthetics and symbolism in hotel design shape booking intention through emotional arousal and quality expectation. The functional dimension of design affects booking intention only through quality expectation. The findings will be particularly meaningful to the lodging industry because online booking requires both emotional and cognitive responses on the part of consumers.     

Time,Price, and Advanced Booking of Hotel Rooms

ABSTRACT

The effectiveness of revenue management systems has diminished in recent years due to the systems' inability to address the increasing complication of online deal-seeking behavior. To restore their efficiency, one must first understand the changes in advanced-booking behavior and their implications. This study expands the consumer booking model by addressing the impact of time-before-the-date-of-stay and exploring the implication for the hotel's pricing/marketing strategies. The findings underscore the urgent need for empirical research on timing by showing that the predictions of the advanced-booking model, and consequently the effectiveness of RM systems, depend on the actual patterns over time.     

The effect of online reviews on hotel booking intention: The role of reader-reviewer similarity

Due to the experiential nature of travel-related products, online reviews have become an increasingly popular information source in travel planning and have a profound effect on consumers’ buying decisions, particularly in hotel booking. On the basis of homophily and similarity-attraction theory, we posit that review valence is positively related to consumers’ hotel booking intentions, and expect this relationship to be moderated by surface- (demographic) and deep-level (preference) similarities. The findings from two experiments conducted in Germany and Macau indicate that review valence significantly affects hotel booking intention, and that reader-reviewer demographic similarity moderates this effect. This three-way interaction reveals a substituting moderation effect between demographic similarity and preference similarity. One practical implication is that travel websites should find methods of exposing users to reviews written by those with either similar demographic characteristics or preferences, which facilitate travelers’ decision-making processes.     

Application of revenue management practices to the theme park industry

Revenue management (RM) has been an essential strategy to maximize revenue for many capacity-limited service industries. Considering the common industry characteristics of traditional RM industries, the nature of the theme park industry suggests potential for enhancing revenue by exercising a variety of RM techniques. This study suggests practices for theme park operators for successful RM application. In addition, this study examines how customers perceive RM practice in the theme park industry compared to a traditional RM industry, hotel industry. The findings indicate that customers seem to perceive RM practice in the theme park industry as relatively fair practices as similarly perceived for the hotel industry. The findings are encouraging for the theme park industry because a relatively similar level of its customers’ perceived fairness of the RM practice compared to the hotel industry suggests that adoption and implementation of the RM practice has great potential to become successful as it has been in traditional RM industries, such as hotels.     

An investigation of multiple devices and information sources used in the hotel booking process

With the growing ownership of multiple technology devices, and the contribution of mobile travel bookings to the overall travel market, it is critical to investigate the hotel searching and booking process for different device users. This research investigates four categories of device users via a survey with 383 respondents, in the context of search behaviour and information sources used. The results reveal that search engines and family and friends are the most frequently used information sources while the personal computer (PC) is the most used device for both searching and booking. However, there is a significant difference in how these device users engage with information sources, specifically online travel agents (OTAs) and search engines. Furthermore, device users favour one device and are unlikely to switch devices during the search process.     

The effects of choice set size and information filtering mechanisms on online hotel booking

Online booking is one of the most popular ways of making reservations for hotel guests. Thus, hoteliers are paying increasing attention to hotel website information presentation and design. The purpose of this study, then, is to examine the joint influence of choice set size and information filtering mechanisms on consumers’ decision confidence towards online hotel booking. Choice set size was operationalized through 3-, 9-, and 30-hotel room choice sets. Through experimental design, this study shows that the presence of an information filtering mechanism reduces consumers’ perceptions of choice overload with a large number of choices (30 choices), whereas its impact is attenuated with smaller choice sets (3 and 9 choices). In addition, choice overload mediates the impact of choice set size on decision confidence. Theoretical contribution and managerial implications are also discussed.     

Impact of hotel website quality on online booking intentions: eTrust as a mediator

Growing competitiveness in the marketplace pushes hoteliers to fully take advantage of their websites as a marketing tool. This study proposed that a trusting relationship with customers could be developed by investing in hotel website development. A research model that incorporates hotel website quality, eTrust, and online booking intentions was put forward. The software AMOS 20.0 was adopted to analyze the proposed inter-variable relationships. Statistical results demonstrated that hotel website quality is a strong predictor of eTrust which then also mediates the relationship between website quality and consumers’ online booking intentions. Implications were offered for practitioners based on the results.     

An empirical study on the influence of economy hotel website quality on online booking intentions

This study investigates the potential theoretical relationships among economy hotel website quality, eTrust, and online booking intentions in China. A questionnaire survey was performed, and 298 samples were collected from the users of three economy hotel websites (7 days, Home Inns, and Hanting Hotel) in Guangzhou, China. Results of a detailed data analysis demonstrate the influence of the four dimensions of economy hotel website quality (usability, ease of use, entertainment, and complementarity) on eTrust and online booking intentions. Specifically, (1) the usability, entertainment, and complementarity of economy hotel websites significantly positively affected eTrust; on the contrary, the influence of ease of use on eTrust was insignificant; (2) complementarity showed a more positive influence than the three other attributes on online booking intentions; and (3) eTrust significantly positively influenced online booking intentions. These findings contribute to economy hotel online marketing in China by bridging the gap between the exploration of hotel website quality and its influence on online booking intentions.     

Advance booking across channels: The effects on dynamic pricing

This research analyzes the effects of advance booking and channel type on hotel rates. While this relationship has been addressed in the literature, most studies take a partial approach by focusing only on one distribution channel or one destination. This study fills this gap by analyzing the price dynamics for four channels and multiple destinations. The data set consists of 39,363 bookings for 1085 hotels over 27 consecutive months. We used two-stage least squares to solve potential endogeneity issues, and the results proved that distribution channel, hotel type and hotel size have an influence on the effect of advance booking on hotel rates. Critical managerial implications are discussed.     

An examination of the role of booking lead time in consumers’ reactions to online scarcity messages

Scarcity messaging is frequently employed by travel marketers to drive consumer conversion rate in the online sales environment. This study examines the effects of two types of scarcity messages, unit and option scarcity, on consumer booking intentions and explores the role of booking lead time on the scarcity messaging-booking intentions relationship in the context of the purchase of hotel accommodation. Findings suggest that when booking lead-time is long, unit scarcity messages will be more effective than option scarcity messages in influencing booking intentions as they are perceived as more credible, and induce higher perceptions of a sellout risk. When booking lead-time is short, consumers perceive no differences in the credibility of unit and option scarcity messages: both are equally effective in driving booking intentions. This research provides marketers with guidance on how and when to leverage scarcity appeals to drive consumer conversion rate.     

An introduction to helpful forecasting methods for hotel revenue management

Revenue management is a key tool for hotel managers’ decision-making process. Cutting-edge revenue management systems have been developed to support managers’ decisions and all have as an essential component an accurate forecasting module. This paper aims to introduce new time series forecasting models to be considered as a tool for forecasting daily hotel occupancies. These models were developed in a state space modelling framework which is capable of tackling seasonal complexities such as multiple seasonal periods and non-integer seasonality. An empirical study was carried out to illustrate how a practitioner may apply and compare the performance of different models when forecasting a hotel’s daily occupancy. Results showed that the trigonometric model based on the new modelling framework generally outperformed the majority of the other models. These findings are potentially useful to the entire revenue management community facing the challenge of accurately forecasting a hotel’s daily demand.     

On revenue management and the use of occupancy forecasting error measures

This study aims to draw the attention of the revenue management academic community to inherent problems in forecasting accuracy measurement, and to initiate a critical discussion about forecast quality assessment in hotels. An exhaustive, literature-based set of seventeen forecasting accuracy measures was applied to hotel daily occupancy forecasting data of 2043 pairs of computer and human forecast/actuals, across multiple forecasting horizons. The empirical analysis demonstrates endemic inconsistencies across the accuracy measures, and a plethora of theoretical and practical challenges with regard to total hotel, as well as customer segment level forecast accuracy assessment. The analysis illustrates the difficulty of interpreting conflicting results, as well as issues like level of data aggregation and multiple forecasting horizons. The paper concludes by briefly discussing a more comprehensive approach to hotel forecasting quality assessment framework and serves to warn hotel revenue management academics, practitioners and solution providers against the unconsidered use of accuracy measures.