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2008 47: 35 originally published online 14 January 2008Journal of Travel ResearchChih-Chien Chen and Zvi Schwartz

Timing Matters: Travelers' Advanced-Booking Expectations and Decisions  

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Timing Matters: Travelers’ Advanced-BookingExpectations and Decisions

CHIH-CHIEN CHEN AND ZVI SCHWARTZ

while leisure travelers are more price sensitive and are will-ing to book earlier if they think it will help secure a better(lower) price (Capiez and Kaya 2004; Greenberg 1985;Orkin 1990; Relihan 1989). The second school of thoughtsuggests that, regardless of the different market segments(businesses or leisure), a customer might increase his or herwillingness to pay as time nears the date of stay because ofthe customer’s shift in perceptions and expectations. Forexample, Schwartz (1998, 2000) suggested that willingnessto book a hotel room increases over time because the trav-eler’s perceived risk of a sellout intensifies as the date ofstay approaches. This explanation is grounded in the theoryof product scarcity, as the connection between perceivedscarcity and consumer demand and willingness to pay iswell established in the marketing literature (The Economist2002; Wu and Hsing 2006).

Timing is as important for prospective guests who seekto reserve a hotel room as it is for the hotels. While it is wellestablished in the marketing, psychology, and behavioraleconomics literatures that customers’ product evaluationsand purchase decisions are time dependent (Lynch andZauberman 2006), it is argued here that with the special caseof advanced booking in uncertain environments, and withthe rapid advances of online booking activities, timing isoften of even greater importance in travelers’ decisions.Online booking has gained tremendous popularity in recentyears, changing the culture of advanced-booking practices.As a convenient search tool (Bai et al. 2004; Carvell andQuan 2005; Jang 2004; Oh 2000), the Internet has reducedthe information gap that once existed between consumersand hotels and has promoted deal-seeking behavior, in whichprospective guests attempt to find the best deal by searchingand booking at what they believe to be the optimal time. Inmany cases, this emerging deal-seeking behavior impairsthe effectiveness of traditional revenue management mecha-nisms. Recent studies demonstrated how the Internet’s wealthof information, and the resultant shrinking of the information

Chih-Chien Chen is a doctoral student and Zvi Schwartz, PhD, isan assistant professor in the Department of Recreation, Sport andTourism at the University of Illinois at Urbana-Champaign. This arti-cle is based on Chih-Chien Chen’s doctoral dissertation. Ms. Chen isgrateful to her dissertation committee members (William R. McKinney,Zvi Schwartz, Patrick T. Vargas, and Bruce E. Wicks).

Journal of Travel Research, Vol. 47, August 2008, 35-42DOI: 10.1177/0047287507312413© 2008 Sage Publications

Dynamic revenue management models often price discriminate based on time. The literature concerning changesin travelers’propensity to book a room over time, however, isscant, and industry practices provide few and somewhat con-tradictory hints about the phenomenon. To facilitate efficientand dynamic revenue management polices, this study furtherexplores how and why customers’propensity to book changesover time. Specifically, this study empirically tests how theexpectations of advanced-booking customers change in con-nection with the likelihood of being offered a better deal andthe sellout risk as the date of stay nears. The results indicatethat timing matters and that the expectations’ change patternis more complex than anticipated. These findings underscorethe importance of the timing element and call attention to theneed for more empirical research on the role of timing intravelers’ advanced-booking decisions.

Keywords: hotel booking; sellout risk; better deal; revenue management; tourism

Timing plays an important role in the advanced bookingof hotel rooms as both hotels and their customers make pric-ing and reservation decisions that are time dependent. Profit-maximizing hotels that practice revenue management adjustoptimal rates and room allocation as the date of stay nears.With dynamic pricing policies, hotels change room ratesover time to reflect demand variability such as seasonal fluc-tuations and observed daily demand changes. That is, in anattempt to maximize yield, hotels increase or decrease therates they quote for a specific date of stay, responding tochanges in observed and predicted demand. Equally impor-tant is the notion that timing is a fundamental factor in pricediscrimination. Revenue management models often pricediscriminate based on time (Talluri and van Ryzin 2005), asroom rates are adjusted to capitalize on prospective cus-tomers’ shifting willingness to pay (Schwartz 1998, 2000).That is, many traditional price discrimination revenue man-agement models assume that the distribution of customers’reservation price changes over time (Zhao and Zheng 2000)and, more specifically, that as time nears the date of stay thecustomer’s willingness to pay increases (Raeside 1997).Studies have offered two explanations for this assumedincrease in willingness to book over time. The first explana-tion stipulates that as the date of stay approaches, the pro-portion of business travelers increases. These businesstravelers tend to book close to their date of travel, are lessprice sensitive, and thus, on average, have a higher willing-ness to pay for immediate availability at the last minute,

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gap, can affect customers’ booking decisions and, conse-quently, hotel revenues. For example, Chen and Schwartz(2006) showed that an online room selection feature, whichprovides information about current occupancies to prospec-tive customers, affects the customers’ propensity to book aroom. Similarly, Chen and Schwartz (forthcoming) demon-strated that prospective guests’ perceptions and their propen-sity to book a room are affected by the pattern of quotedroom rates they observe over a period of time. Differentaspects of this advanced-booking, deal-seeking behavior,and the issue of timing, were addressed by Carvell and Quan(2005). The authors suggested that a low-price-guaranteestrategy can be used to reduce customers’ incentives tosearch for a better deal and can encourage early booking byreducing price uncertainty.

A dominant characteristic of this emerging Internet-induced deal-seeking behavior is that cost-minimizing travel-ers who seek to pay the lowest room rate search for a gooddeal over a period because they know that, because of rev-enue management practices, hotels change their prices astime nears the date of stay and that the rates can increase ordecrease depending on the demand and predicted occupancy.At any time during their search for the best deal, customerscan choose to make a reservation and pay the quoted roomrate, risking the possibility that a better price will be offeredin the future. Alternatively, they can wait for a lower roomrate and risk that rates will only increase as the date of staydraws closer or that the hotel will sell out before they placetheir reservation. Customers, thus, are likely to change theirwillingness to pay because conditions (price and room avail-ability) change as the date of stay nears. A crucial questionfor these deal-seeking customers is, when is the optimal timeto stop searching for a better deal and reserve the room?

Despite the online-booking revolution and the decreas-ing information gap, customers still face considerable uncer-tainty when it comes to answering this question (Carvell andQuan 2005). Interestingly, rather than providing a clearanswer to the optimal timing question, industry practicesinstead might be confusing to consumers who attempt todetermine the optimal time to book. For example, travel tipsposted on Travelocity.com (Ziff 2003) suggest that rates arecheapest when the room is reserved a month or two ahead oftime. Likewise, some hotels and airline companies encour-age their customers to book at least 14 days prior to thearrival date to get the greatest savings.1 On the other hand,many hotels offer remarkably low last-minute (Internet)deals that can be booked no earlier than 14 days before thecheck-in date (O’Neill 2006; Ziff 2006). Recent develop-ments in the airline industry underscore the relevance of thetravelers’ optimal booking timing dilemma. Several mediaoutlets have released reports about a new type of online sitefor travel (e.g., Farecast.com). These sites not only serve asairfare search engines but also predict how much the price ofan airline ticket will rise or fall during the coming days(Darlin 2006; Travel Tech 2006). That is, these sites areonline predictors of what a plane seat will cost at a certaintime and assist deal-seeking travelers in their attempts toidentify the optimal time to book.

In addition, it is important to note that the two advanced-reservation decision processes (hotel revenue managementpolicies and consumers’ advanced-booking behavior) areintertwined; travelers’ booking decisions are affected byhotel pricing decisions, while hotel pricing decisions take

into account customers’booking decisions. These interrelationsof the two decision processes suggest that the impact of timemight be quite profound, underscoring the need to fullyunderstand timing as an element in revenue management set-tings. Unfortunately, despite its evident importance, little isknown about the issue of timing, as research and publicationson the topic are scarce. This study aims to add to the litera-ture by focusing on the role of timing in advanced-bookingdecisions. Specifically, it aims to uncover the manner inwhich the travelers’ perceptions, which shape their propen-sity to book, might change as time nears the date of stay.

Numerous studies have examined issues related to travel-ers’ perceptions, decisions, behaviors, and time. For example, arecent study by Crouch et al. (2007) looked at tourists’ behav-iors in the context of how tourism competes for a share of thehousehold’s discretionary use of its financial resources, andRoehl and Fesenmaier (1992) showed that three travelers’ riskperceptions (physical well-being and equipment risk, vacationrisk, and destination specific risk) affect their travel decisionsand behavior. Many of these studies have examined the processof making the travel decisions and, in particular, issues relatedto information search. For example, Fodness and Murray(1998) studied tourists’ information search behavior when plan-ning a vacation, Pennington-Gray and Vogt (2003) explored theeffect of welcome center location and residency on travelers’information search behavior, and Bieger and Laesser (2004)looked at information search behavior before and after a defi-nite trip decision. Time (the focus of this study) was examinedby Pearce and Schott (2005), who studied domestic and inter-national visitors’ use of distribution channels for informationsearch. The authors concluded that timing affects travelers’channel selection when booking accommodations.

This study’s main contribution is that it is the first attemptto systematically explore the role of time in advanced-bookingdecisions by empirically investigating how travelers’ percep-tions of the likelihood of the hotel selling out and the likeli-hood of finding a better deal change over time. The findings ofthis article support the theoretical assertion, made in the liter-ature, that these perceptions are not necessarily stationary.They also indicate that at least some of these perceptions mightchange in a particular manner over time. Furthermore, thestudy adds to the existing literature by demonstrating that thechange of patterns over time is more complex than anticipatedand, consequently, that more empirical studies are needed.

The article continues as follows. The theoretical back-ground is outlined in the next section, and the role of timingis discussed using the generic advanced-booking decisionmodel framework. The two hypotheses regarding the role oftiming in shaping consumer expectations about the risk of asellout and the likelihood of getting a better deal are listedin the third section. The forth section discusses the method.In the fifth section, the results are outlined and analyzed andthe patterns of change in perceptions over time are illus-trated. The conclusion, limitations, and venues for futureresearch are discussed at the end of the article.

THEORETICAL BACKGROUND

The generic advanced-booking decision model (Schwartz2000, 2006) provides the theoretical foundation for thisstudy as it frames human behavior and decision elements inan advanced-booking setting. The model states that, at any

36 AUGUST 2008

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point during their search (after narrowing down the consid-eration set to a single preferred hotel), prospective travelershave four different generic decision options as they respondto a price quoted by that hotel: book the hotel room (i.e.,book), book a room and keep searching for a better dealoffered in the future (i.e., book and search), not book andkeep waiting for a better deal to be offered (i.e., search), ordisregard that hotel and consider alternatives, such as anotherhotel (i.e., others). Customers choose an action such thattheir expected utility is maximized. The model assumes risk-neutral decision makers, whose utility is the difference betweenthe travelers’ reservation price (the highest price they arewilling to pay) and the expected cost associated with thestrategic booking decision. As shown in Figure 1, the fourgeneric options are placed on an expected utility–rate plane,in which each strategic switching point, P*, P**, and P***,is the quoted room rate at which travelers change their deci-sion (e.g., from book to book and search).

The prices at these three switch points are calculated asthe intersection of two utility lines, and they are given by thefollowing equations,

where S is the search cost per period, n denotes the expectednumber of search periods, D is the discount the consumerexpects the hotel will offer in the future, Pv denotes the prob-ability that the hotel will be fully booked (sold-out) duringthe search period, Pd is the probability that a discounted ratewill be offered after n periods of search, and F is the penaltyfor canceling a guaranteed reservation.

Obviously, the hotel prefers that prospective customersrespond to any quoted rate by booking. A less desirable out-come is the book and search strategy, followed by the search

and other strategies. Hotels can apply various elements oftheir marketing mix to shift the switch points to the right. Bypositioning these switch points at higher room rates throughmarketing actions, the hotel ensures that more people willrespond to any given rate with a more desirable action,increasing revenues for the hotel.

In a recent study, Schwartz (forthcoming) expanded thistheoretical model to include timing, arguing that the genericadvanced-booking decision model could not be assumed tobe static over time. Instead, the model should reflect thedynamic nature of some of its variables. More specifically,the article suggested that some of the model’s elements (e.g.,consumers’ perceptions of the likelihood of the hotel sellingout) change as the date of stay nears. Accordingly, the modeldemonstrated how, when the time before the date of stay ele-ment was included in the equations, the model’s predictionsabout the patterns of the strategic-booking decision change.Similarly, the switch points described above were modifiedto include the time element, t, as follows:

Holding all other factors constant, this modified model pre-dicted that the booking decision depends, at least to someextent, on time. That is, when one or some of the model’s ele-ments change over time, the thresholds change and, conse-quently, the traveler’s booking strategy changes as well. Forexample, a traveler who at one point in time will select “book”as his or her response to a specific quoted room rate, at anotherpoint in time might choose book and search in response to thesame quoted rate because the threshold (P*) moved to the left.The suggestions made by the theoretical model underscored theneed for empirical research on the role of time in advanced-booking settings. It was argued that it is especially important todetermine (empirically) how the model elements evolve overtime because the way these elements change affects the book-ing decision. Equipped with a better understanding of thesetime-related shifts in consumers’ perceptions and propensity tobook, hotels can design and implement better and more effec-tive revenue management policies.

Hence, this article explores the role of timing on con-sumers’ advanced-booking decisions by measuring the waysin which timing affects two of the model elements, Pv and Pd.Figure 2 outlines the conceptual model for this investigation.Note that the solid arrows refer to the relations studied in thisarticle (Hypothesis 1 [H1] and Hypothesis 2 [H2]), while thedotted arrows represent relations that were empirically estab-lished in previous research. Schwartz (2000) showed thattime (days before the date of stay) affects advanced-bookingconsumers’ propensity to book, while Chen and Schwartz(forthcoming) demonstrated that consumers’ perceptions ofthe likelihood of the hotel selling out and of the likelihood ofgetting a better deal affect their propensity to book.

As discussed in the introduction, advanced-booking trav-elers face uncertainty, and, compared to service providers,

P***0=− ð1+PvðtÞÞðPb +Ra −RbÞ+ S · nðtÞð1+D ·PdðtÞÞðPvðtÞ− 1Þ

P**0=− PvðtÞðPb +Ra −RbÞF ·PdðtÞðPvðtÞ− 1Þ−PvðtÞ

P*0=− SnðtÞðD−FÞPdðtÞðPvðtÞ− 1Þ

P*** =− ð−1+PvÞðPb +Ra −RbÞ+ S · n

ð−1+D ·PdÞðPv − 1Þ

P** =− PvðPb +Ra −RbÞF ·PdðPv − 1Þ−Pv

P* =− Sn

ðD−FÞPdðPv − 1Þ

JOURNAL OF TRAVEL RESEARCH 37

Expected Utility, U

Room Rate, P

Book decision

Book & Search decision

Search decision

Other

P∗ P∗∗ P∗∗∗

U∗

U∗∗

U∗∗∗

FIGURE 1RATES AND OPTIMAL ZONES OF THE FOUR GENERIC

ADVANCED RESERVATION STRATEGIES

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they are often ill informed about price and availabilitychanges over time. In the absence of full information, timebefore the day of consumption serves as a cue of its own intheir booking decisions. Low prices were traditionally asso-ciated with early bookings, and many revenue managementpolicies were based on timing, in which the earlier the reser-vation was made the more likely the customer was to pay alow price (Raeside 1997; Ziff 2003).

Travelers are well aware of this relationship betweenprices and time, and they are often reminded about thistime–price relationship by the industry. For example, Glab(2004) stated that “conventional wisdom maintains that theearlier you book, the better the price you’ll get,” Perkins(2005) asserted that early booking discounts offered by vaca-tion package operators tend to sell out early, and GalaxSeaCruises (2006) claimed that “late bookers often miss out onthe best cruises deals.” On the other hand, the popularity ofthe “last-minute deal” has increased considerably in recentyears. If not fully booked, hotels and other travel-related busi-nesses tend to offer incredibly good last-minute deals (Capiezand Kaya 2004; O’Neill 2006; Woods 1996). This pricing pat-tern is well summarized by Chatzky (2004), who stated,

None of which means, however, that you shouldn’tbe looking for the best deal (once you’ve determinedthe right cruise style for you). Here’s how to find it:Book early (or late). . . . Signing up six to eightmonths in advance can cut the price in half. If that’snot an option, consider waiting until less than amonth before the cruise you want sets sail. At thatpoint, the cruise lines want to fill cabins, and they’llstart dropping prices.

Hence, it is hypothesized that the expectations of advanced-booking travelers, regarding offers of a better price in thefuture, change over time. The closer travelers are to the date ofstay, the lower their expectations of being offered a better rate.However, when it gets really close to the date of stay, and if thehotel is not sold-out, then customers have higher expectationsthat better rates will be offered. Formally, H1 is as follows:

H1: The consumer’s expectation of being offered a lowerroom rate in the future (ELR) is U shaped. It decreasesand then increases as time nears the date of stay.

Information that a hotel has some rooms available mightbe of more significance as the date of stay approaches.

Consumers are likely to assume that the higher the demandfor a hotel, the more likely it is to sell out and to sell out ear-lier. Hence, the closer one is to the date of stay, the strongerthe “signal” one gets by realizing that the hotel still hasrooms available. This vacancy information can indicate tothe prospective guests that the hotel is less likely to sell out.Note that this is unique to a situation in which consumershave updated information about the vacancy or sellout statusof the hotel. Without this information, it is expected thatconsumers will not update their expectations regarding thelikelihood of a sellout as the date of stay nears. In otherwords, the presence of demand information received at a dif-ferent time should change the distribution of travelers’ per-ceptions of the likelihood of a sellout risk. Therefore,Hypothesis 2 is as follows:

H2: The consumer’s expectation about the sellout risk(ESR) decreases monotonically as time nears the dateof stay.

METHOD

Subjects

A total of 159 graduate and undergraduate students enrolledin various classes at a Midwestern university were recruited asthe subjects for this study. Students fall in the age group of 15to 25, referred to by many as the “youth” tourist segment.According to various reports, this fast-growing tourism seg-ment is considerably large, with some estimates as high as 20%of annual international tourism (e.g., Mintel InternationalGroup Ltd. 2004)

Procedures

The task was explained before the test was administered.The study participants were assured that participation wasvoluntary and that the questionnaire was anonymous and con-fidential. Subjects who agreed to participate in the study werethen given a written, image-enhanced scenario (see the appen-dix). According to the scenario, a group of friends decided togo to Hawaii and booked flights for the upcoming trip. Theparticipants were told that they were asked by their group offriends to book hotel rooms for the entire group. They(according to the scenario) searched the Internet and found amid-scale, locally owned hotel that was ranked favorably bytravelers who use the Web site tripadvisor.com. The textdescribed the task, the hotel, its location, the date of stay, andthe quoted room rate. After reading the scenario, participantsanswered two questions estimating the likelihood of futureevents (the two dependent variables).

Variables

Independent variable. The independent variable wastiming, namely the number of days before the date of stay.That is, the different groups of subjects had various timehorizons for their planned trip. This independent variablehad seven levels: 2, 7, 14, 21, 30, 60, and 365 days beforethe date of stay. Subjects were randomly assigned to each ofthese seven treatments.

38 AUGUST 2008

FIGURE 2THE CONCEPTUAL MODEL AND HYPOTHESES

Propensity to Book

H1

H2

TIME

Expected Lowest Price

Estimated Sellout Risk

Hypotheses

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Dependent variables. The two dependent variables wereESR, the subjects’ assessments of the expected sellout risk(i.e., how likely the hotel was to sell out), and ELR, theirassessment of the likelihood of being offered a lower roomrate any time before their date of stay. Note that these twodependent variables are the two elements in the time-depen-dent threshold equations of the travelers’ advanced-bookingdecision model, P*’, P**’, and P***’, as described above.Pv(t) denotes the probability that the hotel will be fully booked(sold-out) during the search period and its relation with timet, while Pd(t) is the probability that a discounted rate will beoffered after n periods of search and its relation to time t.Both were measured as percentages on a scale of 0 to 100.

Statistical Tests

A multivariate analysis of variance (MANOVA) wasconducted to determine the overall differences in the meanassessment between groups. MANOVA is an appropriatemethod when there are multiple dependent variables (ELRand ESR) and when multicollinearity might exist among thedependent variables. One-way ANOVA was used, testingwhether there was any difference in the mean assessment ofeach treatment. A post hoc test (Tukey’s honestly significantdifference [HSD]) was also conducted to see which groupwas significantly different on each perception. In addition, aset of two nonparametric tests were applied: the Kruskal-Wallis one-way ANOVA by ranks and a multiple compari-son procedure, suggested by Dunn (1964). The Kruskal-Wallistest is preferred over the median test when the data are mea-sured on at least an ordinal scale because it uses more infor-mation and, consequently, is usually more powerful. Themultiple comparison procedure is recommended in conjunc-tion with the Kruskal-Wallis one-way ANOVA by ranks(Daniel 1990).

RESULTS AND ANALYSIS

Table 1 shows the number of observations in each of theseven treatments (days before the date of stay), along withthe average and standard deviation values for the two depen-dent variables: the likelihood of being offered a better deal(ELR) and the sellout risk (ESR). Respondents who were

told that they had 1 year before the trip estimated the prob-ability of being offered a better deal during that coming yearto be 49.7%. Subjects who were told that they had 60 daysbefore the date of stay arrived said that the likelihood ofbeing offered a better deal before their date of stay was only37.5%. Thirty days before the date of stay, subjects esti-mated the likelihood to be 32.7%. The average responsecontinued to decline with fewer days before the date of stay,with the lowest value measured at 7 days before the date ofstay. The average increased considerably to 31.8% at 2 daysbefore the date of stay. Respondents who were told that theywere 365 days before the date of stay predicted the selloutrisk to be 66.7%. The estimated probability decreased to55.2% in the 60 days condition and to 49.2% in the 30 dayscondition. At 21 days before the date of stay, the predictedprobability increased to 65.7%, declined to 56.4% at the 14days condition, increased again to 62.0% a week before thedate of stay, and, finally, declined to 44.7% two days beforethe date of stay.

The MANOVA test Wilks’s Lambda was significant (p < .05), suggesting that subjects did differ overall in regardto their assessments of ELR and ESR based on the timing.Since the MANOVA tests were significant, they were fol-lowed by one-way ANOVAs. ANOVA at p = .04 suggestedthat the subjects’ estimations of the likelihood of beingoffered a lower room rate differed between the groups, indi-cating that the time before the date of stay affected thesubjects’ expectations regarding future room rates. ANOVAat p = .09 suggested that the subjects’ estimations of the sell-out risk differed between groups, indicating that timeaffected the subjects’ perceptions about the sellout risk. Apost hoc Tukey’s HSD at the alpha level of .05 indicates thatsubjects’ ELR showed a statistically significant differencewith the seven days and one year pair. The nonparametrictest seems to support these findings, with the Kruskal-Wallisone-way ANOVA by ranks test at p = .06 for the expectedbetter room rate variable and at p = .10 for the sellout risk.Dunn’s nonparametric multiple comparison procedure didnot identify any pair as significantly different. This is sur-prising given that the Kruskal-Wallis test was significant andcan be explained by the rather conservative approach of “exper-iment error wise” applied by the multiple comparison proce-dure (see Daniel 1990, p. 240), an approach that guards wellagainst error when H0 is true but is not as good in detectingexisting differences when the null H0 is false.

JOURNAL OF TRAVEL RESEARCH 39

TABLE 1

SUBJECTS’ EXPECTED LOWER RATE (ELR) AND EXPECTED SELLOUT RISK (ESR) BY DAYS BEFORE THE DATE OF STAY

ELR ESR

Days Before the Number of Number ofDate of Stay Observations Average (%)a SD Observations Average (%)b SD

2 29 31.8 28.4 27 44.7 28.77 20 20.0 24.1 20 62.0 25.714 23 25.9 25.8 26 56.4 30.421 23 26.2 22.2 23 65.7 25.630 29 32.7 27.2 30 49.2 33.160 18 37.5 29.2 18 55.2 30.9365 15 49.7 31.1 15 66.7 24.0

a. One-way ANOVA, p = .04.b. One-way ANOVA, p = .09.

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As can be seen in Figure 3, the expectations concerningthe sellout risk are consistently higher than the expectationsabout being quoted a lower room rate. In all tested conditions,ESR is higher than ELR by at least 17%. Both ESR and ELRseem to follow the same decreasing trend between 365 and 30days before the date of stay. However, the two predictionsseem to head in an opposite direction within the 30 daysahead to 2 days ahead range. The pattern of the subjects’assessments of the likelihood of being quoted a better dealbefore the date of stay seems to support the U shape of H1, asit decreased between 365 and 7 days before the date of stayand increased closer to the date of stay. The participants’ esti-mations of the future sellout risk did decrease but not monot-onically, as they fluctuated closer to the date of stay, withsome notable increases providing only partial support for H2.Similarly, the results of the statistical tests provide only par-tial support for the two hypotheses. On one hand, both para-metric and nonparametric tests indicate that time matters.That is, the null hypothesis that the two perceptions do notchange as time nears the date of stay was rejected by both theMANOVA test and the Kruskal-Wallis one-way ANOVA byranks test. On the other hand, the individual comparison tests(the post hoc Tukey’s HSD and Dunn’s multiple comparisonprocedure) did not provide enough evidence that the patternsfully comply with the hypothesized U shape for the ELR andthe monotonically decreasing pattern of ESR.

The findings of this study demonstrate that, as postulated bythe theoretical models, travelers’ perceptions and expectationsabout elements of their advanced-booking decisions change asthe date of travel nears. These time-dependent changes overtime appear to follow patterns that are more complicated thananticipated, are difficult to interpret, have considerable theoret-ical and marketing implications, and call for more theoreticaland empirical research, as outlined below.

Theoretical Implications

Research has shown that time determines consumer per-ceptions and decisions. For example, Lynch and Zauberman(2006) demonstrated that time affects consumers’ prefer-ences toward commodities, as they tend to overvalue thepresent compared with any other period. This study extends

the literature on timing by showing that time plays a possi-bly more important and more complicated role when itcomes to travelers’ advanced-booking decisions. Travelershave less information than do hotels about future room ratesand availability. Consequently, as empirically demonstratedin this study, time before the date of stay becomes an exoge-nous factor that shapes prospective customers’ perceptionsof these two elements of their advanced-booking decision.Interestingly, the patterns explored in this study suggest thatthe time-induced changes of these two perception elementsare more complex than anticipated. The two observed pat-terns of change over time are different, and they do not fullymatch the hypothesized shapes. Both expectations seemedto decrease between 365 days and a month before the dateof stay, but they followed a different path closer to the dateof stay. As discussed earlier, the observed pattern of thebetter-deal expectation supported the hypothesis that travel-ers’ expectations follow a U-shaped pattern. However, onecan only speculate at this stage about the reasons for the morecomplicated patterns of changes in the likelihood of selloutover time. In particular, the fluctuations between 30 days and2 days before the date of stay are difficult to explain.

In terms of the theoretical foundations, the findings ofthis study indicate that the element t affects the two predic-tors, namely Pv and Pd, in the analytical model and thereforeshould be incorporated in the analytical model, as proposedby Schwartz (forthcoming). However, to successfully incor-porate the timing impact in these equations, the precise formof the impact of t on Pv and Pd must be better understood.Hence, more empirical research is needed before the forma-tion of these perceptions can be fully understood, explained,and modeled. One possible venue is to sample more timepoints beyond the seven that were tested so far. With moreobservations over time, one is more likely to model a time-induced pattern of change in a manner that will accommo-date the advanced-booking decision modeling approach.

Marketing Implications

Clearly, the findings of this study have important impli-cations for pricing and revenue management policies. ESRand ELR are two elements that affect consumers’ propensityto book. If the two elements change as the date of stay nears,so will customers’ propensity to book.2 In addition, considerthe switch points, that is, the thresholds at which, in responseto a quoted rate, consumers switch from one strategy toanother (e.g., from book to book and search). The literature(Schwartz, forthcoming) suggests that these switch pointsare not stationary over time and might follow a complex pat-tern. The traditional approach to revenue managementassumed a simple monotonic change in willingness to pay asthe date of stay nears. This study shows that it is most likelythat the pattern of changes fluctuates over time. Note alsothat these fluctuating patterns call for more time-relatedinterventions. At times when the sellout expectations are lowand/or the expectations for a future better deal are high, thecustomers’ propensity to book is lower. Hence, at thesetimes, it is more beneficial for the hotel to attempt to changethese perceptions. For example, during these periods whenthe sellout risk perception is low, it might be useful to strate-gically release information about the number of unitsalready booked. As shown by Chen and Schwartz (2006),the customers’ propensity to book increases if they are told

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0%

10%

20%

30%

40%

50%

60%

70%

050100150200250300350

Days BTDSLikelihood of a better deal Likelihood of a sell out

FIGURE 3EXPECTED LOWER RATE AND EXPECTED SELLOUT

RISK PATTERN OF CHANGE AS TIME NEARS THEDATE OF STAY

Note: BTDS = before the date of stay.

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that many of the rooms are already reserved. Accordingly,communicating to prospective customers that only a few unitsare still available during that specific period could prove veryeffective in increasing their propensity to book.

Limitations and Future Research

A major limitation of this study is that while the MANOVA,ANOVA, and Kruskal-Wallis tests confirm that time affectstravelers’ perceptions about sellout risk and the likelihood ofgetting a better deal (which, in turn, affect their booking deci-sion), the study does not provide enough information aboutwhat the patterns look like and why. More research is needed,and, as indicated above, examining time points beyond theseven tested might prove useful. Specifically, it seems thatresearch that closely looks at the time span of 21 or fewer daysbefore the date of stay might be beneficial, as the patterns at thisrange, and in particular the ESR pattern, are unclear and diffi-cult to explain. A study that looks at the pattern as it changes byday increments might reveal more relevant information.

A second limitation has to do with the population tested.Students represent a relatively large youth travel segment, andit is believed that, as such, they suffice when it comes to show-ing that time matters. However, research with other travel seg-ments is needed to establish different patterns of time impact,as other travelers segments might exhibit different sensitivi-ties to time and different impacts on their perceptions.

As this study suggests, marketing and information effortsthat are used to manipulate travelers’ perceptions could bemore effective if the time-induced changes are fully acknowl-edged. While a previous study (Schwartz 2000) demonstratedthat as time draws closer to the check-in date, the customers’willingness to pay changes, only two points of time (i.e., 3 and14 days before the date of stay) were examined in that study.Future research that builds on this study and on Schwartz’s(2000) study can further investigate the changes in willingnessto pay with more time points.

A different venue for future research has to do with theadditional factors that are likely to work together with time. For example, the quoted room rate might interact with time.Moreover, other decision factors, such as a cancellation penaltyor occupancy information, might change over time. Studyingthese patterns of change over time is of great importance, asthey may further affect consumers’ booking decisions.

APPENDIX

SAMPLE SCENARIO AND QUESTIONS: SEVEN DAYSBEFORE THE DATE OF STAY

Consider the following scenario. Today is August 8 and your friendssuggested a week long trip to Hawaii before the fall semester begins.The group decided to leave in 1 week (August 15) and booked theairline tickets. Obviously you are very excited about the trip and vol-unteered to make the hotel reservations for the group. You searchedthe Internet and found the right place: The Passion Island Inn, a mid-scale, locally owned, hotel. The hotel ranked favorably by travelerswho use the website tripadvisor.com. It is highly recommended byguests who stayed there recently and who share similar interests withyou. All of The Passion Island Inn’s rooms are identical. The dailyroom rate quoted for your days of stay (August 15–August 22) is$107 per night.

Please answer the following 2 questions after carefully consideringall of the facts outlined in this scenario.I believe that the probability (chance) that the hotel will quote a ratelower than $107 per night during the period that starts now (August8) and ends in 7 days on my date of arrival to the hotel (August 15)is __________ %. (Please indicate a number between 0 and 100)I believe that the probability (chance) that the hotel will sell outduring the period that starts now (August 8) and ends in 7 days onmy date of arrival to the hotel (August 15) is _________ %. (Pleaseindicate a number between 0 and 100)

NOTES

1. See, for example, the reservation Web sites of Marriott Village inOrlando (http://marriott.com/property/specials/mesoffer.mi?marrOfferId=130820&marshaCode=MCOLX) and Southwest Airlines (http://www.southwest.com/).

2. This relationship was empirically established by Chen and Schwartz(forthcoming).

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