Determinants of consumers’ choices in hotel online searches: A comparison of consideration and booking stages

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Determinants of consumers’ choices in hotel online searches: A comparison of consideration and booking stages ⁎

Xingbao (Simon) Hu , Yang Yang Department of Tourism and Hospitality Management, Temple University, Philadelphia, PA 19122, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Hotel booking Price information Review information Consideration set Stated choice experiment

Using a stated choice experiment, this study uncovers how hotel review- and price-related attributes affect consumers’ formation of consideration sets and hotel bookings based on online search results alongside a twostage model of consideration and choice. Empirical findings indicate that consumers’ evaluations of certain attributes vary across these stages. During the consideration stage, the listed hotel price, promotional discount, overall rating, review volume, and booking popularity are significant attributes for prospective guests, whereas price disparities across websites are not. Insignificant interaction effects among these determinants imply that in this stage, consumers employ fast-frugal heuristics and a noncompensatory strategy. During the booking stage, listed price, promotional discount, overall rating, and review volume inform consumers’ booking decisions. Furthermore, the interaction effects point to consumers’ adoption of a compensatory strategy in making a final booking decision. Finally, this study concludes with implications for hotel pricing and system optimization of online platforms.

1. Introduction Consumers’ decision-making processes have been thoroughly investigated in tourism and hospitality (Cohen et al., 2014). According to Shocker et al. (1991), the decision-making process is analogous to a funnel, in which consumers exclude unmatched options sequentially from an awareness set and a consideration set to construct a smaller choice set from which a final choice is made. Some scholars have noted that tourists’ decision making involves a two-stage process: a consideration set (i.e., evoked set) and final choice stage (Crompton and Ankomah, 1993). Consideration sets can influence consumers’ evaluations of available alternatives as well as ultimate choices (Shocker et al., 1991). Consumers construct consideration sets by repeatedly and successively examining associations between the outcome of a decisionmaking process, attributes of acceptable alternatives, and personal preferences (Smallman and Moore, 2010). In the online context of hotel selections, the intangibility of service products coupled with information asymmetry between consumers and hotels renders optimal hotel selection complicated. Accordingly, consumers require numerous external heuristic cues to facilitate the evaluation of alternative hotels when forming consideration sets and then making final choices. Recently, the proliferation of travel-related review websites (e.g., TripAdvisor) has dramatically alleviated the effort and costs associated

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with acquiring external heuristic cues. Prospective guests can now easily review common hotel attributes (e.g., hotel amenities) and electronic word-of-mouth (eWOM) (e.g., review text) via online travel agencies (OTAs) or review platforms. Today’s travelers have come to rely increasingly on online reviews to minimize the risks and uncertainty associated with purchase decisions (Kim et al., 2011). Whenever a traveler enters a keyword into the search box of a review platform, a list of available hotels is presented for consideration and screening. The resulting set of hotels can be viewed as the universal set from which a consideration set and final choice are derived (Pan et al., 2013). The consideration set eventually converts a hotel browser to a guest; additionally, hotels excluded from the consideration set are difficult to book (Ghose et al., 2014). In the web 2.0 era, the influences of online reviews on consumers’ considerations and choices have attracted growing interest in the tourism and hospitality literature over the past decade (Gavilan et al., 2018); however, few studies have examined interactive effects between hotel attributes, such as review- and price-related information. Theoretically, customers evaluate alternatives based on perceived utility/ value, particularly the trade-off between perceived benefits (partly implied by reviews) and costs (partly represented by price information) (Roberts and Lattin, 1991). Also, interaction effects have been supported by the compensatory model of decision making, which states

Corresponding author. E-mail addresses: [email protected] (X.S. Hu), [email protected] (Y. Yang).

https://doi.org/10.1016/j.ijhm.2019.102370 Received 21 October 2018; Received in revised form 4 July 2019; Accepted 26 August 2019 0278-4319/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Xingbao (Simon) Hu and Yang Yang, International Journal of Hospitality Management, https://doi.org/10.1016/j.ijhm.2019.102370

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study further specifies the model’s two stages (consideration and booking) to accommodate hotel consumers’ decision-making process. Notably, different decision rules may be employed across these stages because of the distinct purposes in each stage (Gensch, 1987). In the first stage, due to information overload caused by numerous alternatives, consumers are apt to quickly and intuitively screen out hotels that are inferior on prominent attributes by using “System 1” (e.g., a lower rating reflects poor service quality) (Kahneman, 2013). In the second stage, customers tend to deliberatively evaluate an alternative by using “System 2” to trade off various product attributes (Gensch, 1987). The adopted strategies determine how the relative importance of product or service attributes and customers’ preferences vary across the two stages (Um and Crompton, 1990). A host of studies in tourism and hospitality have emerged over the past decade to identify critical determinants of customers’ considerations or choices in an online context. Some researchers have tracked customers’ decision-making processes on simulated OTA webpages to identify the major hotel attributes customers value, such as by using eye-tracking technology (Noone and Robson, 2016; Park et al., 2017) or consumer experiments (Jones and Chen, 2011). Several primary hotel attributes have been uncovered, including hotel rank, image, accompanying text, hotel name, price, and the size of alternative hotels (Masiero et al., 2016). However, the decision rules guiding each stage remain underexplored, as do whether and how hotel attributes mutually affect consumers’ consideration sets and final choices. This study attempts to shed light on these topics via a stated choice experiment.

that low-utility/value attributes of a product can be compensated by other attributes’ utility/value (Roberts and Lattin, 1991). In other words, hotel attributes may affect consumers’ considerations/choices in an interactive and dynamic manner, a phenomenon that remains underexamined in hospitality research. Relatedly, most literature has neglected to investigate the multistage decision-making process of hotel booking. In a two-stage decision process, distinct purposes lead decision makers to adopt unique decision rules in each stage (Gensch, 1987). For example, in the first stage, consumers may focus on salient attributes (e.g., online rating valence) (Gigerenzer, 2008) using various heuristic cues before performing in the second stage more detailed trading off of various attributes in the reduced set of alternatives (Dawes and Brown, 2005). Thus, consumers may assign varying weights to assorted hotel attributes across these stages. A better understanding of decision-making rules can help hoteliers stand out from competitors to enhance exposure and conversion rates via online distribution channels. To fill the above-mentioned research gaps, this study proposes to (1) uncover the major hotel attributes shaping consumers’ consideration sets and final hotel bookings based on search results on online review platforms; and (2) compare the varying effects of these factors across the two stages. Specifically, this study seeks to answer four questions: (1) What are the key factors affecting consumers’ consideration set formation? (2) Sequentially, what are the key factors driving consumers’ final choices? (3) Do differences exist in the roles of these factors across the two stages? (4) What decision rules do consumers leverage in each stage? This study contributes to the literature in at least three major ways. First, we distinguish decision-making rules across two stages, which previous studies have tended to overlook. Ignoring multi-stage decision making can lead to substantial biases in model conceptualization, model results, and practical implications. Second, we investigate the interplay of review- and price-related hotel attributes in shaping behavioral outcomes. Studies have often examined these attributes for reputation management and revenue management purposes, respectively. However, this study represents a pioneering effort to investigate the interactions of these two types of factors. Last but not least, we incorporate two factors that are popular in industrial practices but largely untapped in academia (i.e., booking popularity and cross-platform price disparity).

2.2. Price-related factors and consideration/booking 2.2.1. Price and consideration/booking As posited by economic demand theory, the price of a product is inversely associated with demand for a related good or service; that is, demand declines as price increases (Mortensen, 1973). Room price is a critical predictor of the demand for hotel and travel services (Song et al., 2011). Furthermore, according to prospect theory, consumers tend to make purchase decisions based on the perceived value of losses and gains and often appraise value using cues such as price, brand, or eWOM (Ye et al., 2014). Perceived value involves a trade-off between customers’ perceived costs and benefits (Zeithaml, 1988) and has a pronounced effect on customers’ considerations and purchase intentions (Hutchinson et al., 2009). Perceived costs refer to what consumers sacrifice to acquire a product or service (Nasution and Mavondo, 2008). Monetary costs are primarily measured by the price paid for consumption, whereas non-monetary costs encompass time, information searches, and other consumption-related efforts (Cronin et al., 2000). The higher the perceived costs, the lower the perceived value of a product or service (Woodruff, 1997). Only when perceived benefits exceed perceived costs will consumers add a hotel to their consideration set or book a certain hotel outright. Room price has been proven to influence guests’ value evaluations and purchase intentions (Ye et al., 2014). Therefore, we propose the following:

2. Literature review and hypotheses 2.1. Two-stage decision-making model An essential step in the decision-making process is the evaluation of alternatives (Kollat et al., 1970). Because it is challenging to process multiple alternatives simultaneously, consumers tend to narrow their awareness set into a more manageable consideration set that includes a smaller subset of goal-satisfying alternatives (Turley and LeBlanc, 1993). Based on this consideration set, consumers then arrive at a final choice by trading off between various product attributes to exclude undesirable alternatives (Um and Crompton, 1990). Some scholars have conceptualized these stages into a simplified two-stage model: a consideration set and a final choice (Gensch, 1987). The first stage condenses the set of alternatives, whereas the second stage aims to identify an optimal alternative (Roberts and Lattin, 1991). Compared with the single-stage consideration or choice model, this two-stage model features stronger predictive power (Roberts and Lattin, 1991) and better simulation of consumers’ choice making, allowing marketers to track the dynamic roles of these attributes across consumers’ path to purchase (Li et al., 2017). The model also assists marketers in appraising the effectiveness of the marketing mix on consumers’ considerations and choices (Roberts and Lattin, 1991). Although this model has been applied in studies of tourists’ destination choices (Li et al., 2017; Um and Crompton, 1990), its use in hospitality is limited. Accordingly, this

H1a. A lower room price increases the likelihood that a hotel will be considered. H1b. A lower room price increases the likelihood that a hotel will be booked.

2.2.2. Promotional discount and consideration/booking Growing competition has inspired many hotels to solicit prospective customers by adopting promotional strategies, especially during the offseason. Promotions appear to play vital roles in shaping hotel guests’ attitudes and choices (Lee et al., 2015). As noted earlier, hotel guests tend to evaluate the potential value of products by weighing the tradeoff between perceived costs and benefits (Zeithaml, 1988). They prefer hotels that offer extensive benefits at low costs. Hotel promotions can 2

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H4a. The valence of an overall hotel rating is positively associated with the likelihood that a hotel will be considered.

generate additional benefits by providing various discounts (e.g., price discounts) (Lichtenstein et al., 1997). The theory of planned behavior asserts that customers’ behavioral intentions and actual behaviors can be predicted by their attitudes towards a given product (Han, 2015). Thus, because of increased perceived value and positive connotations, a price discount may enhance the probability that a customer will consider and ultimately book a hotel. Chang (2017) identified a clear link between promotions and purchase intention. Christou (2011) pointed out that consumers’ preferences for reward points and price discounts exerted significant impacts on their booking intentions. Overall, price discounts may comprise a key factor when a customer contemplates which hotel to consider or book online. Hence, the second set of hypotheses is as follows:

H4b. The valence of an overall hotel rating is positively associated with the likelihood that a hotel will be booked.

2.3.2. Review volume Review volume usually refers to the aggregate number of reviews posted by previous customers. It reflects product popularity and can reduce customers’ perceived uncertainty when evaluating a product (Zhang et al., 2010). Yet a high review valence alone is insufficient to persuade consumers of high service quality due to suspected fake reviews (Mayzlin et al., 2014), especially if the review volume is relatively small. Consumers tend to prefer hotels with a greater volume of online reviews because more reviews imply stronger hotel popularity and trustworthiness (Gavilan et al., 2018). Per the theory of herding behavior, individuals tend to mimic the behavior of others in similar situations when making decisions to minimize uncertainty and risk (Shen et al., 2016). Accordingly, a larger review volume indicates that more consumers have chosen that hotel; thus, consumers are more likely to follow predecessors’ actions (i.e., considering or booking a hotel). Tsao et al. (2015) identified a positive association between the volume of positive reviews and hotel customers’ booking intentions. Hence, we propose that

H2a. The extent of a promotional discount positively affects the likelihood that a hotel will be considered. H2b. The extent of a promotional discount positively affects the likelihood that a hotel will be booked.

2.2.3. Price disparities across platforms and consideration/booking Unlike individual OTAs, some review websites present a range of prices retrieved from multiple OTAs. Toh et al. (2011) stated that most participants checked several websites for the lowest price before making final choices. As the reference effect suggests, consumers evaluate a product’s value by comparing actual prices to reference prices (Hardie et al., 1993). Consumers favor a hotel if its actual price is lower than their internal price tolerance because they perceive the hotel to be a better value (Liang and Chen, 2012). According to the definition of perceived value (Zeithaml, 1988), consumers tend to presume lower transaction costs if the hotel price one OTA is lower than that on others. The discrepancy between benefits and costs then rises, leading to more alluring perceived value and promoting consideration/booking intention (Tanford et al., 2012). Essentially, the wider the price disparities across platforms, the more positive customers’ value perceptions and the more likely a hotel is to be considered/chosen. Hence, the third hypothesis set is as follows:

H5a. The volume of online hotel reviews is positively associated with the likelihood that a hotel will be considered. H5b. The volume of online hotel reviews is positively associated with the likelihood that a hotel will be booked.

2.4. Booking popularity and consideration/booking Hotel guests rely heavily on heuristic cues to conserve cognitive effort and time when making decisions due to the abstract nature of hotel products (Xie et al., 2014). On OTAs (e.g., Travelocity.com), one critical cue is recent booking times, a metric representing a hotel’s short-term booking popularity (Park et al., 2017). Similar to review volume, a larger number of recent bookings signifies greater popularity and trustworthiness of a hotel. According to the theory of herding behavior, consumers are more likely to imitate predecessors’ behavior in considering/booking a hotel with more recent booking times due to presumed lower uncertainty and stronger trustworthiness of that hotel (Gao et al., 2017). Additionally, a hotel’s number of available rooms per day, especially those on sale, is often limited. Hence, the more times a hotel has recently been booked, the fewer rooms are available; dwindling inventory promotes scarcity around a hotel’s rooms (especially those at discounted prices) and hinders customers’ freedom of choice. In line with reactance theory in social psychology, individuals are more motivated to obtain a product when their freedom to choose is constrained by induced psychological reactance, a motivational state intended to safeguard behavioral freedom (Clee and Wicklund, 1980). Put simply, product scarcity or low availability increases a product’s perceived value (Eisend, 2008), makes it more appealing to consumers (Clee and Wicklund, 1980), and influences consumers’ purchase intentions (Eisend, 2008). Consumers may be more inclined to consider/ book a hotel with greater booking popularity due to less freedom of choice or scarcity around that hotel’s rooms. Therefore, we propose that

H3a. Price disparities across OTAs positively influence the likelihood that a hotel will be considered. H3b. Price disparities across OTAs positively influence the likelihood that a hotel will be booked.

2.3. Review-related factors and consideration/booking 2.3.1. Valence of overall rating Whereas price-sensitive consumers focus on hotels’ prices and promotions, quality-focused consumers may place more weight on hotel quality based on the valence of an overall hotel rating (i.e., a 5- or 10point scale). As an external heuristic cue, a higher overall rating denotes better service quality and average satisfaction according to prior consumers (Nieto-García et al., 2017). Rational choice theory postulates that consumers tend to choose a higher-quality product over a lowerquality one to maximize potential benefits when making purchase decisions (Allen, 2002). It is therefore reasonable to assume that a higher overall rating will more easily persuade potential guests to consider or purchase recommended services by endorsing service quality (Liu, 2006). Noone and McGuire (2013) reported that review valence and hotel price significantly influenced consumers’ value perceptions in prepurchase decisions, and review valence had a stronger effect on hotel choices compared to other attributes. Ye et al. (2009) discovered that a 10% improvement in review ratings elicited a 4.4% boost in hotel sales. Given these findings, the valence of online hotel reviews likely informs consumers’ considerations and choices of hotels. Therefore, we hypothesize the following:

H6a. Booking popularity is positively associated with the likelihood that a hotel will be considered. H6b. Booking popularity is positively associated with the likelihood that a hotel will be booked.

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popularity). Table 1 presents the descriptions of selected attributes (i.e., variables) and corresponding levels, which were combined to generate hotel profiles consisting of varied combinations of these attributes. Selections of room price, promotional discount, overall rating valence, and review volume were based on previous studies; price disparity and recent booking popularity were chosen because they are widely adopted in practice but underexamined empirically. price: Three levels were employed for this attribute: $80, $180, and $280. These levels were chosen to ensure that the prices covered budget hotels through five-star hotels (Jones and Chen, 2011). promotion: Three levels of percentage discounts were selected for this attribute: no discount, 10% off, and 20% off. We selected 20% as the deepest discount because promotions beyond 20% off a product’s original price adversely affect consumers’ preferences (DelVecchio et al., 2006). If a price discount seems excessive, consumers may intuitively associate a deeper price cut with lower hotel service quality (Nusair et al., 2010). Each price discount was then converted into a corresponding price difference in dollars between a hotel’s regular and discounted price. valence: We included three levels for this attribute, similar to Noone and McGuire (2013) and Sparks and Browning (2011). Melián-González et al. (2013) reported that more than 70% of ratings were 4 or 5 on TripAdvisor, whereas only 15% were 1 or 2, demonstrating a rightskewed distribution (Xie et al., 2014). Accordingly, instead of using 2.5, we adopted 3.5 as the neutral rating, 2 as terrible, and 5 as excellent. volume: We selected two levels for this attribute: 50 vs. 1000. A small difference in volume may not influence customers’ perceptions substantially; therefore, we broadened the gap purposefully, corresponding to a similar operation by Gavilan et al. (2018). price_diff: This attribute was split into two levels: 1 = price parity and 2 = price disparity. In this study, a price disparity is defined as the price difference across multiple OTAs (Choi et al., 2009), which differs from the traditionally defined price difference across hotels. booking_popularity: This variable represents the booking times of a hotel within the recent two days. Three levels were employed for this

2.5. Interactions between price- and review-related factors Given distinct objectives and the variable complexity of information processing in each stage, consumers may evaluate relevant cues differently by stage (Um and Crompton, 1990). Consumers’ adoption of a compensatory strategy or a noncompensatory decision strategy is contingent on decision complexity (Payne et al., 1988). In the booking stage, consumers tend to select an optimal hotel via systematic comparisons of alternatives. Customers are likely to use a compensatory strategy in this case, wherein low-utility product attributes can be compensated by the utility of other attributes (Roberts and Lattin, 1991). This strategy involves an in-depth trade-off among hotel attributes; therefore, consumers consider these attributes in an interactive manner. Noone and McGuire (2013) demonstrated that price- and review-related factors cooperate to influence consumers’ decision making. Lockyer (2005) pointed out that the anticipated negative effect of price relies on its connection to other ‘trigger points.’ As such, the effects of hotel attributes must not be modeled in isolation. We propose that H7a. An interaction effect exists between review- and price-related factors in determining the likelihood that a hotel will be considered. H7b. An interaction effect exists between review- and price-related factors in determining the likelihood that a hotel will be booked. In sum, Fig. 1 delineates the conceptual research model. 3. Methodology 3.1. Experimental design and data collection 3.1.1. Experimental design This study employs a stated choice experiment, namely a 3 × 3 × 2 × 3 × 2 × 3 factorial design based on six selected attributes/factors (room price, promotional discount, price disparity, overall rating valence, online review volume, and recent booking

Fig. 1. Research model. 4

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Table 1 Variable definitions in the stated choice experiment. Variable

Description

Level

price promotion price_diff valence volume booking_popularity

Hotel listed price on the website of interest. Promotional discount listed on the website of interest. Price disparity across different booking websites. The valence of overall rating (on a 5-point scale) on the website of interest. Hotel review volume on the website of interest. Recent booking popularity indicated on the website of interest.

$80; $180; $280 no discount; 10% off; 20% off 1 = parity; 2 = disparity 2; 3.5; 5 50; 1,000 no booking; 5 bookings; 15 bookings

facilities existed among hotels at the same price level.

attribute: 0 vs. 5 vs. 15 times. This attribute aligns well with features of the stated choice experiment, particularly in terms of how newly added attributes shape consumers’ preferences (Fujii and Gärling, 2003). It is challenging to incorporate all factors in a consumer’s decisionmaking process. Therefore, we tested selected factors by adopting a research program based on relevant theoretical foundations (Gavilan et al., 2018). The process consumers undergo when constructing a consideration set to reach a final choice is a “reasonable representation of the results of individual-specific and situation-specific judgments” (Hauser and Wernerfelt, 1990, p. 398). To reduce participants’ workload and minimize fatigue, we randomly selected 22 hotels from 324 hotel profiles generated based on combinations of the six factors. We chose 22 hotels, a manageable number requiring less effort when scrolling through a webpage (Ert and Fleischer, 2016), while incorporating as many hotel profiles as possible. This operation complements findings from Park and Jang (2013), wherein choice overload heightened tourists’ likelihood of not making a choice at all and feeling choice-related remorse when presented with more than 22 options. Subsequently, a list of the 22 hotel profiles was arranged as search results on the first webpage of a review site to approximate consumers’ actual online decision making (see example in Fig. 2). According to Hinckley (2015), nearly half of online consumers do not go beyond the first page of search results; therefore, the design in this study ensured the survey effectively simulated customers’ real consideration and choice behaviors. To control for the effects of hotel photos, we used a default image as the profile image for each of the 22 hotels (Fig. 2). In the experiment, hotels were ranked based on best value, a default search result filter adopted by most OTAs and review sites (e.g., TripAdvisor) to rank retrieved hotels. In the hypothesized scenario, participants were asked to imagine they were taking a trip next weekend to a famous tourist destination, City A in the U.S. City A hosts 400 hotels, and participants were instructed to search for an appropriate hotel on review websites (e.g., TripAdvisor). A list of hotels was presented as the first page of search results. Participants were required to select hotels they would consider/choose among 22 alternatives. To control for confounding factors, participants were informed at the beginning of the experiment that complimentary Wi-Fi, free parking, and breakfast were included in the total room rates, and no differences in

3.1.2. Data collection The experiment targeted adult U.S. citizens who had previously booked hotels online. To test the efficiency of the experimental design, a pilot test was conducted in the U.S. via Amazon Mechanical Turk, an experiment and survey platform whose reliability has been verified by scholars across various domains (Buhrmester et al., 2011; Goodman et al., 2013). Based on the results of the pilot test, we renamed each hotel with three randomized letters instead of a number to avoid confusion and presented 22 alternatives randomly rather than in a fixed order to eliminate potential biases (Tsao et al., 2015). The formal experiment was conducted in July 2017 in the U.S. via Amazon Mechanical Turk. The questionnaire consisted of two parts: the first asked participants to evaluate 22 potential hotels based on six attributes; the second requested participants’ sociodemographic information. In the consideration stage, participants were instructed to read a hypothetical scenario and then select at least three hotels they would consider out of the 22 available hotels to build a consideration set (Vermeulen and Seegers, 2009). At the booking stage, from the formed consideration set, participants were further required to choose the ‘best’ hotel they would decide to book as their final choice. In total, 324 respondents participated in the experiment, resulting in 310 valid cases yielding 6820 (22 × 310) observations. 3.2. Discrete choice modeling This study uses an alternative-specific conditional logit (ASC-Logit) model to investigate factors influencing consumers’ hotel consideration and booking choices based on a stated choice experiment (Liu, 2015). The model requires multiple observations for each case (i.e., each individual), where each observation represents a potential alternative. In the ASC-Logit model, alternative-specific variables vary across cases and alternatives; case-specific variables vary across cases only. In this model, we included a set of unordered alternatives indexed by 1, 2, …, J. Let yij, j = 1, 2, …, J be an indicator variable for the alternative chosen by the ith individual (i.e., case) (StataCorp, 2017). Here, yij = 1 if individual i chose alternative j, and yij = 0 otherwise. Assume we

Fig. 2. An example of a hotel profile. 5

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affected customers’ consideration of hotels in the search results. The coefficients possessed convergent signs: four were positive (i.e., favorable) but negative for the listed price (i.e., unfavorable). Therefore, H1a, H2a, H4a, H5a, and H6a were each supported, and H3a was rejected. The probability that consumers would consider a hotel rose as the listed price declined and the other four significant determinants increased. We further tested H7a by including interaction terms between price- and review-related factors. Table 3 lists the interaction terms with the major review-related factor (valence) and major pricerelated factor (price) in Models 2–5; none were statistically significant, suggesting fast-frugal heuristics and a noncompensatory decision strategy in this stage (i.e., no trade-off among hotel attributes). The Bayesian information criterion (BIC) of Model 1 was the lowest among the five models, indicating that the model had the best goodness-of-fit. The estimates of other interaction terms are available upon request, and they were all statistically insignificant; hence, H7a was rejected. Table 4 shows the estimation results of ASC-Logit models at the choice stage (i.e., booking), during which consumers were expected to exhibit diverse concerns and decision rules in the trade-off between alternative hotels. At this stage, consumers refined and chose the bestvalue hotel from their consideration sets. According to the results of Model 6, several factors (valence and volume, listed price, and promotional discount) significantly affected consumers’ choices; by contrast, price disparity and booking popularity did not exert significant impacts. A comparison of the coefficient magnitudes in each stage revealed that some increased, whereas others diminished from Model 1 to Model 6. For example, the coefficient of listed price changed from -0.0113 to -0.00713, and that of valence changed from 1.463 to 0.996. Conversely, the coefficient of volume increased from 0.000466 to 0.00142, and that of promotional discount increased from 0.133 to 0.770. These changes imply that compared to the first stage (consideration set formation), review valence and listed price became less important at the booking stage, whereas review volume and promotion information became more influential for consumers at this point. In other words, consumers referred to more prominent factors, such as the listed price and valence of overall ratings, to screen out alternatives to form a consideration set in the first stage. Comparatively, in the booking stage, review volume and promotional discount each played a more important role relative to the first stage. In Models 7–10 in Table 4, we introduced the interactions between price- and review-related factors. Unlike the results of their counterparts in Table 3 for the consideration stage, most interaction terms were estimated to be statistically significant. The BICs of Models 7, 8, and 10 are lower than that of Model 6, highlighting that these three models with interactions have better goodness-of-fit than the basic model (Shmueli, 2010). Consumers tended to employ a compensatory strategy in the second stage to evaluate alternatives when finally booking a hotel. More specifically, in Model 7, the interaction term between valence and price was estimated to be negative and significant, while the main effect of valence remained significantly positive. Thus, the positive effect of rating valence appeared more salient for lower-priced hotels than mid-priced hotels (Fig. 3.a), presumably because the listed price also served as a signal of hotel quality: when the price was low, customers turned to other quality signals like the valence of overall ratings to reduce uncertainty (Manes and Tchetchik, 2018). Interestingly, after a particular price point (around USD 200), the effect of valence became insignificant for these higher-priced hotels as their 95% CIs included zero. A possible reason is that expensive hotels are typically characterized by high online ratings, and consumers do not particularly focus on valence when making final choices. The same rationale can be applied to explain the negative and significant interaction between price and volume in Model 10, as volume functioned as another important quality-signaling metric (Fig. 3.c). In Model 8, the positive and significant interaction between valence and promotional discount implies that the effectiveness of a promotion relies on the valence of overall rating (Fig. 3.b). Specifically, a promotion became

Table 2 Descriptive statistics of the sample. Variable

Level

Frequency (%)

Gender

Male Female Prefer not to say

59.35 39.68 0.97

Age

18–24 25–34 35–44 45–54 55–64 65 and above

11.29 47.42 25.81 8.06 6.45 0.97

Race

American Indian or Alaskan Native Asian Black or African American Hispanic or Latino Native Hawaiian or other Pacific Islander White Other

9.35 9.35 5.81 10.32 0.32

Education

High school graduate Some college, no degree Associate's degree Bachelor's degree Graduate or professional degree

11.61 24.52 14.84 41.61 7.42

Annual household income

< $25,000 $25,000–$34,999 $35,000–$49,999 $50,000– $74,999 $75,000–$99,999 $100,000–$149,999 $150,000–$199,999 $200,000 and above

17.42 17.74 16.77 6.45 11.29 7.42 1.94 0.97

72.26 1.61

have p alternative-specific variables resulting in a J × p matrix, Xi, for case i. Further, assume we have q case-specific variables resulting in an i × q vector zi for case i. The random utility model can then be expressed as

μi = Xi β + (Zi A)′ + εi where β is a p × 1 vector of alternative-specific regression coefficients, and A = (α1, …, αJ) is a q × J matrix of case-specific regression coefficients. The elements of the J × 1 vector εi are independent Type I (Gumbel-type) extreme-value random variables with a mean γ and variance π 2/6. We must fix one j to the constant vector to normalize the location. We set k = 0, where k is specified by the base alternative option. The vector μi quantifies the utility an individual gains from J alternatives. This model is consistent with random utility theory, and the alternative chosen by individual i is that which maximizes expected utility (StataCorp, 2017). 3.3. Descriptive statistics Table 2 presents the descriptive statistics of the sample. Most participants were between 18 and 44 years old (84.52%), more than half were male (59.35%) and white (72.26%), and approximately half held a bachelor's degree or above (49.03%). Over half (51.93%) of participants earned an annual household income of less than $49,999. 4. Empirical results Table 3 displays the estimation results of ASC-Logit models for selections in the consideration set using all respondents. In Model 1, only the main effects of the proposed factors were included. Five coefficients, excluding that of price disparity (price_dif), were statistically significant, indicating that listed price, promotional discount, valence of overall rating, review volume, and booking popularity significantly 6

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Table 3 Estimation results of models for consideration.

valence price volume promotion price_diff booking_popularity valence × price valence × promotion valence × price_diff price × volume N AIC BIC

Model 1 consideration

Model 2 consideration

Model 3 consideration

Model 4 consideration

Model 5 consideration

1.463***(0.077) −0.0113*** (0.001) 0.000466*** (0.000) 0.133*** (0.051) 0.0471(0.094) 0.0147** (0.006)

1.577*** (0.143) −0.00765**(0.004) 0.000434*** (0.000) 0.152*** (0.055) 0.0245(0.097) 0.0169** (0.007) −0.000790(0.001)

1.363*** (0.192) −0.0114*** (0.001) 0.000407*** (0.000) −0.0908(0.386) 0.00273(0.123) 0.0147** (0.006)

1.256*** (0.179) −0.0113*** (0.001) 0.000549*** (0.000) 0.0972*(0.058) −0.609(0.541) 0.0191*** (0.007)

1.467*** (0.078) −0.0116*** (0.001) 0.000379*(0.000) 0.128** (0.051) 0.0437(0.095) 0.0131* (0.007)

0.0521(0.090) 0.154(0.125) 6820 2779.1 2820.1

6820 2780.1 2827.9

6820 2780.7 2828.5

6820 2780.0 2827.8

0.000000577(0.000) 6820 2780.9 2828.7

Robust standard errors are presented in parentheses. BIC: Bayesian information criterions; AIC: Akaike information criterions. BIC: Bayesian information criterions; AIC: Akaike information criterions. *** Indicates significance at the 0.01 level. ** Indicates significance at the 0.05 level. * Indicates significance at the 0.10 level. Table 4 Estimation results of models for booking.

valence price volume promotion price_diff booking_popularity valence × price valence × promotion valence × price_diff price × volume N AIC BIC

Model 6 booking

Model 7 booking

Model 8 booking

Model 9 booking

Model 10 booking

0.996*** (0.363) −0.00713*** (0.002) 0.00142*** (0.000) 0.770*** (0.192) 0.104(0.379) 0.0479(0.031)

2.367***(0.872) 0.0366(0.023) 0.000945(0.001) 0.901*** (0.275) −0.257(0.578) 0.0610(0.038) −0.00910** (0.005)

−1.426**(0.569) −0.00794*** (0.002) 0.0000651(0.001) −4.947*** (1.586) −0.925** (0.429) 0.0358(0.027)

2.314(1.544) −0.00693*** (0.002) 0.000875(0.001) 0.902*** (0.248) 4.386(4.316) 0.0332(0.031)

0.846*** (0.318) 0.00317(0.003) 0.00,407*** (0.001) 0.645*** (0.153) 0.709* (0.396) 0.115***(0.030)

1.234*** (0.343) −0.959(0.973) 1163 534.0 564.4

1163 524.1 559.5

1163 510.5 545.9

1163 532.6 568.0

−0.0000164*** (0.000) 1163 510.5 545.9

Robust standard errors are presented in parentheses. BIC: Bayesian information criterions; AIC: Akaike information criterions. *** Indicates significance at the 0.01 level. ** Indicates significance at the 0.05 level. * Indicates significance at the 0.10 level.

displayed in online search results shaped consumers’ consideration sets and booking choices. Results show that the roles of focal hotel attributes vary across the consideration set and final booking stages. Most importantly, interaction analyses illustrated that consumers adopted a noncompensatory strategy when forming a consideration set but a compensatory strategy in the booking stage.

effective for hotels with an online aggregate rating of four ( = 0.495/ 0.123) or above. Our results confirm that customers employed a compensatory strategy when evaluating alternatives to reach a booking choice from their consideration sets.

5. Discussion and conclusion Using a stated choice experiment, this study explored which factors

Fig. 3. Plots of interaction effects. 7

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granular insights into consumers’ decision-making process and online hotel marketing.

5.1. Discussion First, consumers’ preferences for hotel attributes shifted across the two stages. Results of the two-stage model unveil distinct patterns in consumers’ decision making along with dynamic roles of various hotel attributes. For example, some studies have framed the choice process as homogeneous and demonstrated positive effects of rating valence and price discounts on consumers’ choices (Park et al., 2017). In a departure from this line of thinking, our study uncovered a substitution effect of these two factors in the booking stage. Presumably, consumers’ perceived quality of a hotel is negatively associated with price promotions; that is, consumers view price as a quality cue per the conventional wisdom “you get what you pay for” (Erickson and Johansson, 1985). Consumers are thus more likely to question the credibility of a hotel’s rating if it has a better rating but a deep price discount. Consequently, rating valence and promotion discounts each diminish the likelihood of consumers booking that hotel. Future studies could seek to identify an appropriate amount of price promotion to encourage purchase behavior. Second, this study unveiled unique decision rules across the two stages: noncompensatory vs. compensatory. This result can be elucidated by the objectives of each stage. In the first stage, consumers strive to form a consideration set by ruling out unacceptable alternatives from available options; in the second, customers aim to select an optimal alternative from the consideration set (Li et al., 2017). Consumers in the first stage therefore focus on the most salient attributes by adopting a noncompensatory decision strategy (Li et al., 2017). Contrarily, in the booking stage, consumers trade off hotel attributes via a compensatory strategy (Payne et al., 1988). This study substantiates the use of these two decision rules and extends relevant work by Crompton and Ankomah (1993) and Payne et al. (1988) to hotel choice making. Lastly, price discounts do not necessarily stimulate hotel bookings and may even have a boomerang effect on hotels with low overall ratings. Specifically, in the booking stage, consumers consider multiple hotel attributes by using the compensatory strategy; price discounts and overall ratings counterintuitively undermine consumers’ likelihood of choosing a hotel. This unexpected finding may explain why some hotels’ attempts to solicit consumers fail when providing drastic price discounts. Notably, the detrimental effects of price discounts are more salient for hotels with lower online ratings (rating < = 3) than those with higher ratings. Our findings oppose the claim that low prices can help hotels maintain a sustainable market advantage (Lien et al., 2015) but echo results from Yang et al. (2016), who discovered that consumers were less likely to revisit a hotel planning to implement price promotions. Consumers habitually view prices as an indicator of hotel service quality. Customers thus consider promotional price cuts analogous to lower service quality, which may override the potential benefits of cost reduction. As a result, consumers are more likely to avoid a hotel offering promotional pricing as a way to minimize purchase risk.

5.3. Practical implications Foremost, our findings insinuate that hoteliers should consider their hotel's online ratings when specifying price promotions. The effectiveness of such promotions on consumers’ choices is highly contingent on valence (Fig. 3.b); accordingly, hotels with excellent online ratings are encouraged to adopt promotions to attract consumers and boost sales, especially during the off-season, which can be beneficial to maintaining hotels’ market share and financial status in the long term. Conversely, hotels with low ratings should prioritize improving online ratings or cultivating prestigious reputations by enhancing service quality and diversifying facilities rather than relying on unreasonable price promotions, which may be futile or even backfire in drawing customers. The higher the online ratings, the more likely hotels are to be included in customers’ consideration sets and subsequently booked. Second, per the interaction results, overall rating and review volume are particularly influential for low-priced hotels. Therefore, relative to high-priced hotels, inexpensive ones should focus on online reputation management by persuading satisfied customers to post feedback to accumulate reviews. An enhanced overall rating signifies better service quality, and a large review volume represents greater popularity, both of which can conjointly offset customers’ intuitive assumption that low-priced hotels are of low quality. Therefore, consumers may become more likely to choose these hotels. Third, given that consumers adopt different information processing strategies (noncompensatory vs. compensatory) across the two stages, we propose that online platforms optimize their recommendation systems based on choice stages while consumers search for information on the platform. Specifically, during consideration set formation, consumers focus on critical attributes but ignore less important features via a noncompensatory strategy. Booking platforms should seek to facilitate consumers’ ruling out of undesirable alternatives by avoiding meaningless information that may increase consumers’ cognitive effort in information processing. Additionally, key attributes may be underscored aesthetically, such as by using bigger fonts or bright colors. By contrast, in the choice stage, more information can expedite consumers’ choice decisions because consumers tend to trade off hotel attributes to identify an optimal hotel. 6. Limitations and future research The limitations of this study are twofold. First, the experiment was conducted using a hypothesized review website or price comparison site; findings may not be generalizable to other types of websites with a different style of information display. Future research can extend this study by examining the determinants of travelers’ considerations across various platforms. Second, the research design did not include all possible decision-related factors; results may have differed if more attributes were included. Additional attributes and stages (e.g., choice sets) can be incorporated into subsequent studies.

5.2. Theoretical implications By integrating a two-stage choice model and stated choice experiment, this study provides a more comprehensive understanding of hotel consumers’ choice-making process. First, our work demonstrated that consumers weighed hotel attributes differently across the consideration set and booking stages, although the degree of difference depends on attributes. Furthermore, the exhaustive list of alternative hotels used in this study approximated a reasonable number of options available on online platforms, yielding a more realistic simulation to elicit consumers’ preferences for hotel attributes across stages. Most importantly, being among the first efforts to examine the two-stage choice process in hospitality, this study unveiled systematic differences in decision rules in information processing between the two stages: consumers used a noncompensatory strategy in the first stage but a compensatory strategy in the second stage. Comparisons between these stages can provide

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