The Impact of Same versus Different Price Presentation on Travel Choice and the Moderating Role of Childhood Socioeconomic Status

Abstract

When making travel decisions, consumers are frequently exposed to a multitude of options, including differing price levels for the same product or service across a range of online travel agencies. The current research investigates how the magnitude of price dispersion in online pricing can influence travelers’ product evaluations and purchase intentions. Specifically, we predict that travelers will prefer a hotel with no price dispersion to a hotel with different prices listed when the price difference is small, or narrow. However, when the price difference is more pronounced, or wide, travelers will prefer a hotel with price differences compared to a hotel with no price dispersion. Four experiments demonstrate that this effect is consistent across different contexts and categories. Additionally, based on life history theory, we argue that the relative preference for the same versus different price dispersion will be moderated by the travelers’ childhood socioeconomic status (SES).

Keywords

price strategy price difference price dispersion dynamic pricing childhood socioeconomic status

Introduction

Because of the widespread adoption of revenue management systems within the hospitality and travel industry, pricing strategists have enjoyed an evolution of insights and instruments toward maximizing travel and tourism profits. Additionally, because of the perishable nature of many hospitality and travel products, variations in pricing methods, such as dynamic pricing, have become commonplace. As a result, both consumers and commentators alike have observed an increased variability in online travel and tourism pricing. With the growing importance of online purchasing channels enabling an ease of dynamic pricing strategy implementation, this phenomenon is expected to increase exponentially (Abrate, Fraquelli, and Viglia 2012; Abrate and Viglia 2016). Travel organizations can much more efficiently adapt their prices depending on availability and competitive activity than ever before, resulting in positive outcomes for both the organization and consumers. However, dynamic pricing can also result in consumer perceptions of unfairness (Choi and Mattila 2005) if not managed appropriately.

When making travel decisions, consumers are frequently exposed to several options, including those featuring the same and different price levels across a range of online travel agencies (OTAs). In this context, consumers are presented with price comparisons involving both similarity and disparity between agencies. In this work, we focus on the situation where travelers compare between hotel or travel option with the same prices presented and another option featuring different price levels. The extant literature suggests mixed findings relative to consumers’ reactions to price dispersion. Some studies suggest that high levels of price dispersion lead to higher uncertainty (Biswas and Burman 2009; E. Y. I. Chen and Bei 2005). Conversely, other research demonstrates that travelers favor a hotel with wide price dominance dispersion compared to an option with narrow price dominance dispersion (J. Kim, Franklin, et al. 2020). In the present research, we extend existing knowledge about price dispersion in the tourism literature by focusing on the price presentation factors that influence consumers’ evaluations. Based on the importance of the attribute distance in the judgment, we argue that preference for the same (vs. different) price depends on the magnitude of difference in the difficult price condition. More specifically, we predict that travelers will prefer a hotel with no price dispersion to a hotel with different prices when the difference is small. However, when the price difference is wide, consumers will prefer a hotel with price differences compared to a hotel with no price dispersion. Additionally, based on life history theory, we argue that the relative preference for the same versus different price dispersion will be moderated by the travelers’ childhood socioeconomic status (SES).

This work contributes to theoretical and practical knowledge in several ways. First, these findings contribute to the pricing literature, in general, by expanding our understanding of the impact of price presentation of multiple options in the tourism domain. The findings in this work are significant as current research presents contradictory findings on the influence of price dispersion in an online setting. The results suggest that magnitude of the dispersion will directly influence individuals’ predispositions and attitudes toward the options. In addition, this research contributes to the tourism literature, specifically, by further investigating the impact of dynamic pricing in online travel choices. This study strengthens our comprehension of price dispersion by providing empirical evidence that price dispersion can directly influence reference points, resulting in changing the relative preference for multiple options. Finally, this article extends the literature on life history theory. Specifically, we improve the understanding of how childhood socioeconomic status (SES) influences the processing of price dispersion. Indeed, the effect of the price dispersion on preference for the hotel was only significant for consumers with lower childhood SES, whereas it did not matter for individuals with high childhood SES.

This study has notable managerial implications for hotels and online travel agencies as well as marketing managers regarding their dynamic pricing strategies. This study advances the understanding of price dispersion and its impact on consumers’ choices, offering important guidance to travel service providers seeking to increase the effectiveness of their pricing strategies. Specifically, we recommend monitoring the magnitude of price dispersion within different options and adjust prices. Furthermore, the findings suggest that travel and tourism companies should consider additional target consumers’ characteristics, particularly childhood SES, when employing pricing strategies to optimize their effectiveness.

Literature Review

Travel and Tourism Pricing Literature

In an increasingly competitive travel and tourism marketplace, operators are adopting new pricing and price comparison mechanisms as a means to overcome challenges within this complex industry (Christodoulidou et al. 2007; Kracht and Wang 2010; Stangl, Inversini, and Schegg 2016). These challenges include quickly evolving pricing systems and strategies (Abrate and Viglia 2016) and an increasingly multifaceted distribution environment (W. G. Kim et al. 2020). Additionally, as empirical insights into pricing-setting and price presentation tactics become increasingly complex, operators within the travel and tourism sector have a range of pricing influences to consider (Jeong and Crompton 2018; Khandeparkar, Maheshwari, and Motiani 2020).

An increased understanding of the dynamics of the external, informational cue that pricing and price presentation represents to travel and tourism consumers has been increasingly investigated as a result of the exponential growth of online distribution channels (J. Kim et al. 2014) and dynamic pricing model adoption (Bilotkach, Gaggero, and Piga 2015). Additionally, in the travel and hospitality sector, consumers are more likely to consider pricing as an external informational cue in the absence of other objective, diagnostic information—such as prior experience with a travel or hospitality product/service (Ainscough 2005; Murray 1991; Zeithaml 1981). While a multitude of factors contribute to consumer decision making in travel and tourism services, price (or pricing) persists as one of the most critical (Lockyer 2005). A breadth of tourism and hospitality research has demonstrated that pricing, as an external cue, can influence both consumers’ initial decision making, in terms of the willingness to reserve a hotel room online (Noone and Mattila 2009), perceived risk reduction (Maser and Weiermair 1998), and posttravel service quality evaluations (Ainscough 2005; D. Kim and Jang 2013), such as satisfaction (Oh 1999; Mattila and O’Neill 2003; Voss, Parasuraman, and Grewal 1998) and value for money (Chua et al. 2015).

Owing in part to the exponential growth of online distribution channels, the development and flexibility in the variety of means to present price and price-related information (as well as interactions between these broad characteristics and factors) has resulted in much empirical attention (Carlson, Bearden, and Hardesty 2007). The multifaceted nature of price presentation factors as an external cue are reported in a meta-analysis by Krishna and colleagues (2002), with their findings suggesting that consumers’ perceptions of price are significantly influenced by many different pricing presentation characteristics. This multiplex role of price as a diagnostic inference in consumer decisions is highlighted by Völckner (2008) when considering the price-sacrifice effect and the price-information effect. Based on classical economics theory, the price-sacrifice effect represents an economic cost in the purchase decision (Völckner 2008). Consumer evaluations of the price are based on a more transactional view of the objective benefits of the product in an effort to diagnose how much money can, or should, be sacrificed to obtain the product (Erickson and Johansson 1985).

By extension, the price-sacrifice effect suggests a relationship between price (or price level) and the probability of purchase—the greater the price (or sacrifice), the lower the likelihood of purchase (Bornemann and Homburg 2011). More specifically to the tourism domain, Chua et al. (2015) demonstrate that this effect can persist in vacationer’s posttravel estimations of perceived value, when a higher perceived price of a cruise package negatively impacted the overall perceived value of the travel product.

The dual role of pricing in consumer evaluations of a travel product is highlighted in the price-information effect, when a high price serves to act as an indicator of quality to consumers (Bornemann and Homburg 2011), and vice versa (Ainscough 2005). This effect is demonstrated when, even in the absence of objectively distinct criteria or characteristics, consumers select a more expensive product or service option (Völckner 2008). When investigating travel package service quality, Ainscough (2005) found that the three main categories constituent of consumers’ perceptions of service quality were pricing, agent factors, and travel package brand. However, while all factors contributed to estimations of service quality, the role of pricing in influencing traveler’s willingness to purchase was more pronounced. Understanding this dual role of pricing in consumer evaluations and decision making is fundamental to sound pricing strategy design and deployment (Zeithaml 1981; Chang, Chang, and Su 2015).

Another key research stream investigating the role pricing plays in consumers’ travel or tourism decision making is situational factors as contributing to pricing level, or overall estimations of price attractiveness or appropriateness. This multifaceted relationship between booking accommodation and price (or pricing level) was investigated by Lockyer (2005) and included estimations of a number of different situational factors, including the availability of rooms (or alternatives) and physical hotel characteristics or amenities. Similarly, Abrate and Viglia (2016) categorized situational factors as contributing to pricing level as either tangible (physical hotel characteristics), reputational (previous guests’ ratings or reviews), or contextual (attractiveness of the city/region/country), with Wang and Nicolau (2017) extending these findings to include the level of competitiveness in the focal marketplace. The complex and multilayered nature of these additional pricing factors in consumer evaluations and decision making invites further investigation into other potential factors (and their interrelatedness) in a travel or tourism context.

Dynamic Pricing

A breadth of academic and editorial literature has detailed various pricing strategies and influencing factors such as strategy, cost, competition, and customer value (see Dholakia 2017; Schindler 2012). Table 1 summarizes research methods and key findings from the wider literature regarding dynamic pricing, and its impact on firm performance and customer behavior. Because of recent and ongoing developments in information technology, it has become easier for companies to monitor both customer demands and competitive activity in the marketplace. Thus, much research has focused on analyzing optimal pricing strategies in this changing marketing environment (Fisher, Gallino, and Li 2018; Ferreira, Lee, and Simchi-Levi 2016) and the impact of a company’s pricing on revenue maximization (Abrate, Fraquelli, and Viglia 2012; Abrate, Nicolau, and Viglia 2019; Anderson and Xie 2016; Caro and Gallien 2012; Sen 2013). Strategic consumers, those who anticipate the best purchase time based on various sources of information, have also become an important consideration in optimal pricing research (Cachon and Swinney 2011; Dellarocas, Zhang, and Awad 2007; Popescu and Wu 2007; Aviv, Wei, and Zhang 2019).

Table 1.

Literature Review Summary for Dynamic Pricing.

Article	Research Method and Design	Key Findings
Price optimization
Gallego and Van Ryzin (1994)	Analytic economic model	Fixed pricing is found to be optimal policy as opposed to dynamic pricing.
Y. Chen and Cui (2013)	Analytic model	Uniform pricing induced by consumers' concerns of fairness can help mitigate price competition and hence increase firm's profits.
Ferreira, Lee, and Simchi-Levi (2016)	Machine learning with historical data and field experiment	Multiproduct price optimization models based on machine learning improve revenue prediction under dynamic pricing.
Fisher, Gallino, and Li (2018)	Choice model with field experiment	Best response pricing algorithm incorporating consumer behavior, competitor actions, and supply parameters can improve revenue by 11% for the category under study.
Impact of dynamic pricing on firm performance
Popescu and Wu (2007)	Analytic model	Purely reactionary response to market price changes can result in suboptimal outcomes.
Caro and Gallien (2012)	Price forecasting model with field study	Proposed optimal pricing model increases clearance sales revenue by about 6%.
Sen (2013)	Numerical analysis	Dynamic pricing heuristics lead to near-optimal performance and can provide important gains over fixed price heuristics.
Bilotkach, Gaggero, and Piga (2015)	Empirical analyses with fare data, aviation data and passenger survey	Dynamic pricing can help all tickets be sold at the end of the booking window.
Abrate, Nicolau, and Viglia (2019)	Empirical analysis with OTA and hotel database	Higher dynamic pricing variability leads to higher hotel revenue.
Dynamic pricing and consumer behavior
Xia, Monroe, and Cox (2004)	Review of research	Conceptually integrates the theoretical foundations of fairness perceptions and summarize empirical findings on price fairness.
Haws and Bearden (2006)	Lab experiment	The potential negative effects associated with price differences from dynamic pricing practices outweigh benefits.
Papanastasiou and Savva (2017)	Analytic model	Social learning increases strategic purchases. With strong social learning, responsive dynamic pricing benefits both the firm and the consumer.
Viglia, Mauri, and Carricano (2018)	Lab and field experiment	Customers feel unfairness and decrease their reference price when competing hotels adjust their prices simultaneously. Independent dynamic pricing has little impact on customers’ reference price.
Aviv, Wei, and Zhang (2019)	Game-theoretic model	When the consumers are all strategic, the benefits of responsive pricing tend to worsen when there is a higher potential for learning. Quick response strategies—ones that embed both dynamic pricing and quick inventory replenishment during the sales season—and found that they are particularly significant under strategic consumer behavior.
Strategic (forward-looking) consumer
Dellarocas, Zhang, and Awad (2007)	Empirical modeling with online movie data	Online reviews help customers make more strategic decisions.
Aviv and Pazgal (2008)	Game theoretic model	Strategic customer behavior suppresses the benefits of price segmentation. However, when the level of consumer heterogeneity is small, the rate of decline is medium to high, and the seller can optimally choose the time of discount in advance, segmentation can be used quite effectively even with strategic consumers.
Cachon and Swinney (2011)	Analytic model	Quick response and enhanced design are combined to mitigate strategic behavior of customers.

With the advent of online pricing comparison sites (e.g., OTAs), competition-based dynamic pricing has become popular in various industries, including travel and hospitality markets (Abrate, Fraquelli, and Viglia 2012; Fisher, Gallino, and Li 2018; Papanastasiou and Savva 2017; Kannan and Kopalle 2001). Conventional dynamic pricing is mostly used to control perishable inventory or for seasonal clearance (Caro and Gallien 2012; Weatherford and Bodily 1992; Bilotkach, Gaggero, and Piga 2015). For instance, Caro and Gallien (2012) develop and implement a clearance pricing model for the fashion brand Zara. In the travel or hospitality industry, dynamic pricing ensures key items such as seats in an aircraft or rooms in a hotel are available throughout the booking window (Weatherford and Bodily 1992) or that all tickets are sold at the end of the booking window (Bilotkach, Gaggero, and Piga 2015).

Many studies find that dynamic pricing has a positive effect on firm performance (Su 2007; Aviv and Pazgal 2008; Araman and Caldentey 2009; Sweeting 2012; Sen 2013) while others argue that fixed pricing is more beneficial (Gallego and Van Ryzin 1994; Xia, Monroe, and Cox 2004). Moreover, customers are strategic in that they remember historical price changes and expect a better price on their next purchase (Cachon and Swinney 2011; Liu and van Ryzin 2008; Kopalle, Rao, and Assunção 1996). Such strategic behavior has become more predominant because of the development of OTAs and social learning from online reviews (Dellarocas, Zhang, and Awad 2007). Customers also consider fairness of pricing with dynamic pricing because they realize that they pay a different price for the same item (Viglia, Mauri, and Carricano 2018; Fosfuri, Giarratana, and Roca 2015; Y. Chen and Cui 2013; Haws and Bearden 2006; Xia, Monroe, and Cox 2004). For example, Viglia, Mauri, and Carricano (2018) found that customers feel unfairness and decrease their reference price when competing hotels adjust their prices simultaneously. Thus, companies should carefully design their optimal pricing strategies to account for those customers (Viglia, Mauri, and Carricano 2018; Popescu and Wu 2007; Aviv and Pazgal 2008) as purely reactionary responses to market price changes can result in suboptimal outcomes (Fisher, Gallino, and Li 2018; Popescu and Wu 2007; Aviv, Wei, and Zhang 2019). Fisher, Gallino, and Li (2018), for example, demonstrate that an explicit, algorithmic sensitivity to different factors (such as consumer behavior, competitor reactions, and supply parameters) can improve revenue by 11% for the category under study.

While dynamic pricing is most frequently adopted by discrete hotel or airline companies, some OTAs (e.g., Orbitz) have also adopted dynamic pricing in order to manage their inventory. Merchant model OTAs like Orbitz purchase rooms from the hotel at a wholesale price and sell them to their customers, whereas agency model OTAs (e.g., Booking.com) only list hotels as an agency and do not hold inventory (Ye, Zhang, and Li 2018). Therefore, the price of rooms listed in merchant model OTAs tend to deviate from standard price set by the hotel.

Price Dispersion

The exponential growth of online channels has transformed the design and deployment of travel and tourism pricing strategies. Because OTAs allow service providers to reach consumers and exert significant influence on travelers’ information search, they have gained power in controlling commissions and relationships with service providers like hotels (Li, Yang, and Pan 2015). At the same time, OTAs do not have products or services to offer without the service providers, creating a love–hate relationship between service providers and OTAs (Nicolau and Sharma 2019; Sharma and Nicolau 2019). As OTAs strengthen their position with the advancement of the Internet and other digital channels, price disparity, which refers to a price discriminating strategy of setting different rates across distribution channels, becomes inevitable in the multichannel environment and has been the center of debate between service providers and OTAs (Demirciftci et al. 2010).

Importantly, the price dispersion resulting from rate disparity affects consumers’ decision making and choice behavior. When judging prices, consumers often rely on reference prices, which form based on either prior experiences or a range of prices across distribution channels (Melis and Piga 2017). Particularly, because of the growth of OTAs and popularity of dynamic pricing strategies, consumers frequently face price variability. When consumers are aware that price variation exists across distribution channels, they are increasingly willing to search for better prices (W. G. Kim et al. 2020). Also, as consumers can spontaneously compare prices and easily search for the best rate, price disparity contributes to greater price elasticity among travelers (Ivanov 2014). Furthermore, research indicates that consumers tend to perceive price disparity as less justifiable and unfair (Nicolau and Sharma 2019).

The growth in Internet retailing, particularly with the emergence of OTAs, has significantly reduced the time and energy required of consumers in the comparison of different hotel prices through various online retailers (Baye, Morgan, and Scholten 2004). With an almost instant ability to search and compare prices online, some research has suggested that one positive outcome of these changes would be that of lower levels of price dispersion between online vendors (Bakos 1997; Jiang 2002). In an online hotel pricing context, J. Kim, Franklin, et al. (2020, p. 706) define price dispersion as “the variation of price between the highest and lowest prices across different sellers for the same goods and services” and echo the sentiments of other researchers who suggest this phenomenon, while manifest initially through more traditional offline pricing strategies, can still be observed online (Clemons, Hann, and Hitt 2002; W. G. Kim 2014; Pan, Ratchford, and Shankar 2002). Investigations into the degree of online price dispersion reveals mixed results, with some researchers demonstrating lower degrees of price dispersion in online markets (Brown and Goolsbee 2002; Morton, Zettelmeyer and Silva-Risso 2001), and others establishing the opposite (Bailey 1998; Clay et al. 2002). While previous empirical findings regarding the degree of online price dispersion within the travel and tourism marketplace remains mixed, what is clearer is the incidence, and persistence, of price dispersion across many different online travel and tourism product categories (Ancarani and Shankar 2004; Sengupta and Wiggins 2014). In spite of the ease, access, and transparency of online information available to consumers, online price dispersion still persists in the marketplace, even if it is less pronounced than that of the traditional offline marketplace (Baye and Morgan 2001). Thus, an understanding of the effects of this online information on consumer decision making, its interrelatedness with other factors, and an understanding of price dispersion from different perspectives (Pan, Ratchford, and Shankar 2004) is becoming increasingly important to online travel retailers (J. Kim, Franklin, et al. 2020).

A number of empirical studies have investigated the persistence of online price dispersion and offer some explanation. One potential theoretically driven explanation is that of online retailers adopting a random pricing strategy (P. Chen and Hitt 2003; Smith 2001), whereby online retailers randomize the presentation of prices to consumers based on their awareness or knowledge of other, competitive online retailers. Thus, price dispersion is manifest across these different online sellers (Pan, Ratchford, and Shankar 2002). However, Baylis and Perloff (2002) suggest that contrary to many offline retail pricing strategies, the incidence of online random pricing strategies is limited, at best. Nevertheless, a random pricing strategy does share some similarity to that of dynamic pricing strategies, which may offer some additional, theoretically driven explanation for price dispersion. A dynamic pricing strategy provides the means and mechanisms to actively adjust the pricing of products in the marketplace based on specific (and sometimes intimate, individual-level) customer data. These data are often of a very granular level and can include information on purchasing habits and behavior (Huang, Chang, and Chen 2005; Kannan and Kopalle 2001). Dynamic pricing models have become increasingly easier to design and deploy in the marketplace because of the increased development of mechanisms that collect customer and competitor information, as well as actively monitor and adjust online pricing and price presentation (Dolan 2000; Huang et al. 2005; Kannan and Kopalle 2001).

Another potential theoretical explanation for the persistence of price dispersion in online marketplaces is that of product or service differentiation; a critical consideration relative to the price-performance relationship of a product. In a hotel pricing context, the findings of Voss, Parasuraman, and Grewal (1998) suggest that when a congruence of price and performance is realized (e.g., high price/high quality and low price/low quality) at an individual level, guests’ evaluations of a hotel are more positive and vice versa. However, this product or service differentiation and price dispersion relationship is not always supported when, for example, a lower-priced product yields better service and subsequent guest evaluations (Baylis and Perloff 2002; Pan, Ratchford, and Shankar 2002).

Another potential explanation for the persistence of online price dispersion is the uncertainty consumers associate with descriptive information about the online products or services (e.g., currency displayed). This uncertainty can serve to influence consumers’ perceptions of the listed prices, particularly if the descriptive information is unfamiliar or requires additional insight (such as calculating exchange rates) or perception (E. Y. I. Chen and Bei 2005), thus increasing their search efforts (Biswas 2004). While studies investigating the effects of online price dispersion are limited (Wu et al. 2015), some previous research also suggests that high price dispersion can result in perceptions of transaction risk (Biswas and Burman 2009; J. Kim, Franklin, et al. 2020).

A review of current research suggests a relatively significant shortcoming in the understanding of online price dispersion and the potential effects this information has on travel and tourism consumers’ decision making. As such, we investigate specific traveler preferences in an online retail context representative of differing levels of price dispersion.

Research Hypotheses

Main Predictions: The Role of Price Dispersion in Preference Formulation

Consumers often form a perceived value of an offering according to external reference prices (Biswas and Blair 1991; Urbany, Bearden, and Weilbaker 1988). Research shows that exposure to external reference prices may shift one’s internal reference prices and affect the attractiveness of an offer as well as the probability of purchase (Hinterhuber 2015; J. Kim, Franklin, et al. 2020). In an OTA context, listed prices of a hotel room, for example, could serve as reference prices and affect consumers’ perceived value and purchases (J. Kim, Franklin, et al. 2020). Indeed, J. Kim, Franklin, et al. (2020) have empirically shown that travelers prefer a target option more when the price dispersion of a choice set is larger, even when the lowest price is constant. Based on the importance of the dominance relationship among various price options in the price comparison situation, they found that travelers preferred a hotel with wide price dominance dispersion (e.g., price range: $180–$260) to a hotel with narrow price dominance dispersion (e.g., price range: $180–$199).

In this study, we mainly compared between the same price and different price range. Specifically, we investigate the relative preference either (i) between the same price dispersion (e.g., all price: $119) and narrow price dispersion (e.g., price range: $119–$130) or (ii) between the same price dispersion and narrow price dispersion (e.g., price range: $119–$179).

For comparison between the same and large dispersion case, we mainly predict that travelers will prefer a travel option with wide price dispersion to an option with no price dispersion. Two theories support this argument. First, attraction or decoy effect (e.g., Huber, Payne, and Puto 1982; Park and Kim 2005) clearly shows that decision makers will make a choice on the basis of the dominance relationship among options. It will be easier to detect the dominance relationship between the cheapest option and other high-priced options when the price dispersion is larger. Second, another stream of research highlights the importance of the attribute distance in the judgment. Prior research has shown that subjective experience of “feeling right” because of ease in product comparison can lead individuals to use the feeling to infer their attitude toward a target object or confidence in their attitude (Cesario, Higgins, and Scholer 2008; Schwarz and Clore 1983). For example, Wadhwa and Zhang (2015) showed that rounded prices (e.g., the booking price is $10–$12) are easier to process than precise prices (e.g., the booking price is $10.35–$12.35) and thus tend to generate a positive experience of “feeling right.” In addition, Khan, Zhu, and Kalra (2011) found that low (high) construal levels tend to enhance comparative trade-offs. Low construal levels make the difference among options more salient, allowing decision makers to compare options easily. In sum, previous work suggests that the comparison could be easy when the distance between the two options is far, whereas it becomes uneasy when the two options are located close to each other.

For a comparison between the same and small dispersion cases, we predict the opposite pattern based on other related theories. First, even though the decoy option generally increases the choice share of the target by an asymmetrically dominant relation, a decoy also results in a repulsion effect (e.g., Frederick, Lee, and Baskin 2014; Spektor, Kellen, and Hotaling 2018), especially when the target and decoy options are located very closely. Therefore, for a small dispersion case, detecting dominance information would be difficult and may decrease preference for the target option. Second, similar to the previous argument, decision makers experience more difficulty or feel less confident when their comparison among options is uneasy. Furthermore, making a decision given a marginal difference may create uncertainty in the decision.

In sum, based on prior research suggesting that comparison of options located far from (close to) each other tends to be (un)easy, we propose that the magnitude of price dispersion will influence consumers’ preference for a target option. Here is the first set of hypotheses:

Hypothesis 1a: Travelers will prefer a hotel with no price dispersion to a hotel with narrow price dispersion in a price comparison situation.

Hypothesis 1b: Travelers will prefer a hotel with wide price dispersion to a hotel with no price dispersion in a price comparison situation.

Boundary Condition: Role of Childhood Socioeconomic Status

We hypothesize that childhood socioeconomic status (SES) would moderate the above prediction based on research on life history theory (LHT; Kaplan and Gangestad 2005; Stearns 1992), which was developed to explain variations in human behavior and decision making. Highlighting the importance of early-life experience, LHT (e.g., Ellis et al. 2009; Frankenhuis and de Weerth 2013; Griskevicius et al. 2013; Stephens, Markus, and Phillips 2014; Todd and Gigerenzer 2007) suggests that adverse childhood environments lead people to adapt faster life strategies (e.g., short-time orientation—showing impatience), whereas advantageous childhood experiences make people follow slower life strategy (e.g., long-time orientation). Importantly, because early-life experiences guide individuals’ patterns of responses to their environment, research indicates that childhood SES is even more predictive of adults’ behavior than current SES (Griskevicius et al. 2011; Mittal and Griskevicius 2014).

We posit that childhood SES would affect consumers’ response to price dispersion and their choices for two reasons. First, compared with travelers with high childhood SES, those with low childhood SES would be more sensitive to external cues or situations and thus would be more responsive to price dispersion. Research on poverty suggests that repeated exposure to resource scarcity draws individuals’ attention to immediate issues (Shah, Mullainathan, and Shafir 2012). Other research also suggests that resource scarcity during childhood cultivates a narrow scope of focus and draws people’s attention to immediate consequences (Haushofer and Fehr 2014; Wang, You, and Yang 2020). Regarding choice behavior, Carey and Markus (2016) found that people with relatively low SES tend to make choices based on the situation, whereas people with high SES choose options based on their own preferences instead of the circumstance. Following this prior work, we predict that local information processing and greater sensitivity to information cues will lead travelers with low (vs. high) childhood SES to more strongly respond to price dispersion.

Second, as discussed earlier, LHT predicts that the harshness and unpredictability in early-life environments vary the strategies and decisions individuals follow (Griskevicius et al. 2011). Building on an uncertainty management framework, Amir, Jordan, and Rand (2018) posit that early life adversity is likely to develop preferences that help to minimize downside costs of uncertainty. Given limited resources to compensate unexpected, adverse consequences, people with low childhood SES would prefer options minimizing the downside costs of uncertainty. Research on price dispersion suggests that large price variation enhances perceived transaction risk (Biswas and Burman 2009; J. Kim, Franklin, et al. 2020) and requires consumers’ additional search efforts, postponing the decision process (Biswas 2004). According to LHT, individuals with low childhood SES are likely to use faster strategies. Therefore, the increased transaction risk and information search would decrease preferences for options with price dispersion especially for travelers with low childhood SES.

Based on the above logic, our formal predictions are generated for the moderating role of the childhood SES:

Hypothesis 2: The effect of the same (vs. narrow) price dispersion on preference for the hotel is moderated by childhood SES.

More specifically,

Hypothesis 2a: For travelers with a relatively low childhood SES, the effect of the same (vs. narrow) price dispersion on preference for the hotel will be strong.

Hypothesis 2b: For travelers with a relatively high childhood SES, the effect of the same (vs. narrow) price dispersion on preference for the hotel will reduce.

We conducted three experiments to provide empirical evidence of the arguments above. The key aspect of this study is the scenario-based experimental method based on two prior studies by J. Kim and colleagues (e.g., J. Kim, Franklin, et al. 2020; J. Kim, Kim, and Kim 2018). Studies 1 and 2 will test the main prediction of hypothesis 1, and studies 3 and 4 will examine the moderating role of childhood SES (i.e., hypothesis 2). Participants were recruited via Amazon Mechanical Turk (studies 1, 3, and 4) and Dynata (study 2) in the United States. The participant profile is illustrated in Table 2. The period of the data collection was from February to early March 2020 (studies 1, 3, and 4) and September 2020 (study 2) (Please see general discussion for the implication of this research in terms of the COVID-19).

Table 2.

Profile of Participants.

Characteristic	Study 1(n = 230)Amazon Mturk	Study 2(n = 305)Dynata	Study 3(n = 202)Amazon Mturk	Study 4(n = 168)Amazon Mturk
Gender
Male	43.9	45.2	44.1	44.6
Female	56.1	54.8	55.9	55.4
Age
18–29	21.7	10.2	26.2	18.5
30–39	30.9	22.6	28.7	33.9
40–49	23.5	21.6	28.7	20.2
50–59	13.5	27.9	9.4	11.9
≥60	10.4	17.7	6.9	15.5
Race
White/Caucasian	74.8	75.7	72.8	74.4
African American	9.6	15.4	6.4	8.9
Hispanic	2.2	4.3	5.0	5.4
Asian	10.4	3.0	13.4	8.9
Others	3.1	1.7	2.5	2.4
Educational level
Did not complete high school	0.9	0.7	2.0	3.6
High school graduate or college level	36.5	52.1	27.2	31.0
College graduate (4 years)	41.7	33.1	48.0	43.4
Postgraduate degree	20.9	14.1	22.8	22.0
Family income
≤$30,000	15.7	17.9	17.3	16.7
$30,001–$60,000	29.6	25.6	30.2	32.1
$60,001–$90,000	22.6	23.5	21.8	20.2
$90,001–$120,000	15.7	14.9	19.3	11.3
≥$120,001	16.5	18.1	11.4	19.6
Childhood SES, mean (SD)	Not measured	Not measured	4.09 (1.55)	4.66 (1.53)
Current SES, mean (SD)	Not measured	Not measured	4.38 (1.43)	4.88 (1.37)

Note: Unless otherwise noted, values are percentages.

Study 1

The main objective of study 1 is to provide the first empirical evidence of our main hypothesis 1. Specifically, we predicted that travelers will prefer a hotel with no price dispersion to a hotel with narrow price dispersion in a price comparison situation and the opposite pattern for the decision between no price dispersion and wide price dispersion.

Method: Subjects, Design, and Procedure

Participants were 230 US adults (average age = 40.11 years, 56.1% female) from an online panel (i.e., Amazon Mechanical Turk) for nominal compensation. Participants were assigned to one of 2 (different hotel sets: Hotel A with a different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with a different price range) × 2 (level of price dispersion: narrow vs. wide) conditions in a between-subjects experimental design.

First, participants were asked to imagine that they were planning a vacation to Auckland, New Zealand, and that they were searching for a hotel online (comparing prices from different hotel reservation agents) and found two hotels. Then, after exposing to two hotels options (e.g., [Hotel A] VR Queens Street vs. [Hotel B] Haka Hotel Suites Auckland city), they were asked to select one hotel along a 7-point scale (1 = I definitely choose Hotel A; 7 = I definitely choose Hotel B). The price for each hotel was systematically manipulated in that all price from 4 different OTAs (i.e.., Expedia, Agoda, Booking.com, and Wotif) was the same $119 for the same price range condition, whereas the price was different from four different OTAs for the different price range condition. In addition, for different price range conditions, the price from the four OTAs was {$119, $130, $155, & $179} for the wide price dispersion, whereas the price was {$119, $122, $127, & $130} for the narrow price dispersion, as shown in Figure 1. The lowest price for all 4 experimental conditions was the same (i.e., $119). Finally, participants were thanked after completion of the question regarding the realism of this study (i.e., 1 = highly unrealistic, 7 = highly realistic) as well as the demographic questions about their gender and age.

Figure 1.

Stimuli from Study 1: Hotel A – Same price range & Hotel B – Narrow price range dispersion. Hotel A – Narrow price range & Hotel B – Same price range dispersion.

Results and Discussion

First, the realism perception was higher for this study (M = 5.66, SD = 1.21 vs. “4” [neutral point]; t (244) = 20.87, p<.001).

Second, in the main analysis, we conducted a 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) × 2 (level of price dispersion: narrow vs. wide) analysis of variance. No main effect was significant, but the interaction effect was significant, F(1, 226) = 10.95, p =.001, η² = .046, as shown in Figure 2. Planned contrast analysis indicated that for the narrow price dispersion conditions, the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (M = 4.45, SD = 2.06) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 3.73, SD = 1.96), F(1, 226) = 4.11, p =.044, η² = .018. On the other hand, for the wide price dispersion conditions, the results were of an opposite pattern. Specifically, the preference for Hotel B was lower when the price of Hotel B was the same [& Hotel A’s price was different] (M = 3.21, SD = 1.92) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 4.16, SD = 1.72), F(1, 226) = 7.01, p =.009, η² = .030.

Figure 2.

Results of Study 1: Preference for Hotel B [Haka Hotel Suites Auckland City].

This study shows that the level of price dispersion significantly influences hotel preference between the same (vs. different) price hotels. When the price dispersion was narrow, travelers prefer the hotel with the same price (over the different, but narrow, price range). The opposite was true when the price dispersion was wide.

Study 2

This study focuses on replicating the previous findings in order to augment the generalizability of the results of Study 1 that investigated hypothetical international travel scenarios. In this study, we investigated a travel situation within the United States to make it more realistic and avoid the fear, and potential confound, of traveling overseas via aircraft because of the current COVID-19 situation. Besides, participants had to make a choice between the two hotels in addition to indicating their preferences.

Method: Subjects, Design, and Procedure

Participants were 305 US adults (average age = 46.33, 54.8% female) from an online panel (i.e., Dynata) that features a purposive sample of frequent leisure travelers, who had booked a hotel room online for leisure travel in the last 12 months, for nominal compensation. Participants were selected to be representative of the US population, at large, in terms of income, age, and level of education. Participants were assigned to one of 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) × 2 (level of price dispersion: narrow vs. wide) conditions in a between-subjects experimental design.

The overall procedure of this study was quite similar to previous studies, except for a few modifications. First, participants were asked to imagine that they were planning a weekend away in Chicago for leisure and that they were searching for a hotel online (comparing prices from different hotel reservation agents) and found two hotels that met their requirements. The study only targeted participants who have booked a hotel online for leisure travel in the previous 12 months and came from neighboring states (i.e., Indiana, Michigan, and Wisconsin). Those factors were considered in order to make the situation as realistic as possible, despite the current COVID-19 situation (i.e., all participants lived within driving distance of Chicago, thus precluding the potential confound of having to consider air travel). Then, after being exposed to two hotels options (e.g., [Hotel A] Eurostars Magnificent Mile vs. [Hotel B] Cambria Hotel Magnificent Mile), they were asked to indicate their preference in terms of the hotels on a 7-point scale (1 = I definitely prefer Hotel A; 7 = I definitely prefer Hotel B) and choose their selected option (“Please select one hotel from the following two options”). The price for each hotel was presented in US dollars and systematically manipulated in that all prices from four different OTAs (i.e., Agoda, Expedia, Booking.com, and Hotels.com) were the same (i.e., $149) within the same price range condition, whereas the price was different between the four different OTAs in the different price range condition. Specifically, within the different price range conditions, the price from four different OTAs was {$149, $160, $185, & $209} for the wide price dispersion, whereas the price was {$149, $152, $157, & $160} for the narrow price dispersion, as shown in Figure 3. Finally, participants completed demographic questions (including age, gender, education, ethnicity and household income) and were thanked.

Figure 3.

Stimuli from Study 2: Hotel A – Same price range & Hotel B – Wide price range dispersion. Hotel A – Narrow price range dispersion & Hotel B – Same price range.

Results and Discussion

First, the realism perception was higher for this study, M = 5.32, SD = 1.28 vs. 4 [neutral point]; t(304) = 18.02, p <.001.

Second, in the main analysis for the hotel preference, we conducted 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) × 2 (level of price dispersion: narrow vs. wide) analysis of variance (ANOVA). No main effect was significant, but the interaction effect was significant, F(1, 301) = 10.12, p = .002, η² = .033, as shown in Figure 4. Planned contrast analysis indicated that for the narrow price dispersion conditions, the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (M = 4.61, SD = 1.85) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 3.86, SD = 1.93), F(1, 301) = 6.27, p =.013, η² = .020. On the other hand, for the wide price dispersion conditions, the results featured an opposite pattern. Specifically, the preference for Hotel B was lower when the price of Hotel B was the same [& Hotel A’s price was different] (M = 3.70, SD = 1.96) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 4.30, SD = 1.68), F(1, 301) = 3.97, p =.047, η² = 0.013.

Figure 4.

Results of Study 2: Preference for Hotel B [Cambria Hotel Magnificent Mile]. Choice share of Hotel B [Cambria Hotel Magnificent Mile].

Additionally, in the main analysis for the choice, we conducted 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) × 2 (level of price dispersion: narrow vs. wide) bi-logistic regression analysis with Hayes’s (2017) macro model no. 1 with 5,000 bootstrap samples. The interaction effect was significant (effect = −2.41, t = −4.97, p < .001, 95% CI −3.356, −1.459). Further analysis indicated that for the narrow price dispersion conditions, the choice share of Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (M = 74.7%) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 39.5%, p <.001, 95% CI: 0.814, 2.203). On the other hand, for the wide price dispersion conditions, the results featured an opposite pattern. Specifically, the choice share of Hotel B was lower when the price of Hotel B was the same [& Hotel A’s price was different] (M = 41.6%) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 63.6%, p = .007, 95% CI: −1.549, −0.252]), as shown in Figure 4.

Study 3

This study aims to test the moderating role of the travelers’ childhood SES on hotel preference between same and different price dispersion (i.e., hypothesis 2) options. We expect that the effect of price dispersion will be stronger for low (vs. high) childhood SES.

Method: Subjects, Design, and Procedure

Participants were 202 US adults (average age = 38.85, 55.9% female) from an online panel (i.e., Amazon Mechanical Turk) for nominal compensation. Participants were assigned to one of 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) conditions in a between-subjects experimental design.

First, participants were asked to imagine that they were planning a vacation to Auckland, New Zealand, and that they were searching for a hotel online (comparing prices from different hotel reservation agents) and found two hotels. Then, after exposure to two hotel options (e.g., [Hotel A] Sofitel Auckland vs. [Hotel B] Hilton Auckland), they were asked to select one hotel along a 7-point scale (1 = I definitely choose Hotel A; 7 = I definitely choose Hotel B). The price for each hotel was presented with the local currency (i.e., NZ $) and systematically manipulated in that all price from 5 different OTAs (i.e., Expedia, Booking.com, Wotif, Agoda, & Hotels.com) was the same NZ $180 for the same price range condition, whereas the price was different from five different OTAs (e.g., $180, $185, $191, $196, and $199) for the different price range conditions, as shown in Figure 5. We also specified the lowest price for all options. After stating their preference for a hotel, participants were asked to rate their childhood on three items (e.g., “My family usually had enough money for things when I was growing up,” based on Griskevicius et al. 2013) along a 7-point scale (1 = strongly disagree, to 7 = strongly agree, Cronbach’s α = .824) and their current SES along the same scale (e.g., “I don’t need to worry too much about paying my bills”; Cronbach’s α = .855). Finally, participants completed the question regarding their previous experience of booking or staying at hotels in the last two years, demographic questions about their gender and age, and were thanked.

Figure 5.

Stimuli from Study 3: Hotel A – Same price range & Hotel B – Narrow price range dispersion. Hotel A – Narrow price range & Hotel B – Same price range dispersion.

Results and Discussion

First, when we conducted a two-way (different hotel sets) ANOVA, the main effect was not significant (F (1, 200) = 2.12, p =.147, η² = .010). The pattern of results indicated that the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (M = 4.51, SD = 1.87) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 4.12, SD = 1.96). Therefore, the different price range (i.e., $180 ~ $199) could be inferred as a narrow price dispersion based on the results of Study 1.

Importantly, this effect above was qualified with significant interaction between the experimental factor and the travelers’ childhood SES. We used Hayes’s (2017) macro model #1 with 5,000 bootstrap samples (i.e., IV: different hotel sets, 1 = hotel B – the same price range [& Hotel A – different price range], 2 = Hotel B – different price range [& Hotel A – same price range], Moderator: childhood SES, DV: relative preference for Hotel B) in order to test the moderation model. In overall, the interaction effect was significant (effect = .34, t = 1.98, p = .005, 95% CI: [.001, .686]). For travelers whose childhood SES was relatively low (i.e., -1 SD), the main effect of the experimental factor (i.e., different hotel sets) was significant (effect = -.90, t = -2.37, p = .019, 95% CI: [-1.653, -.153]). Specifically, the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (estimated M = 4.67) compared to when the price of Hotel B was different [& Hotel A’s price was same] (estimated M = 3.77). In contrast, for those whose childhood SES was relatively high (i.e., +1 SD), the main effect was not significant (effect = .16, t = .43, p = .671, 95% CI: [-.590, .914]), as shown in Figure 6. In sum, the results of this study supported H2.

Figure 6.

Results of Study 3:

We also conducted further analysis to test for alternative explanations. In order to test the general wealth effect, we also conducted a similar analysis for the current SES of the traveler. The results indicated that the interaction effect was not significant (effect = .16, t = .86, p = .393, 95% CI: [-.210, .532]). Second, in order to test the previous experience of booking and staying at a hotel, we also conducted the same analysis regarding the childhood SES for those who have either previous booked or stayed at a hotel within the previous two years (new n = 180). The interaction effect was still significant (effect = .34, t = 1.85, p = .066, 90% CI: [.036, .646) and the detailed pattern was similar. Finally, in order to control for the effect of education level and age factors, we further conducted a similar analysis (i.e., education level and age as moderators). The interaction between IV and education was not significant (effect = .31, t = .86, p = .394, 95% CI: [-.405, 1.025]) nor the interaction between IV and age (effect = -.03, t = -1.22, p = .225, 95% CI: [-.073, .017]). In sum, the results of further analysis support our core argument of the moderating role of the childhood SES.

Study 4

The study aims to replicate the moderating role of the travelers’ childhood SES in an additional context in order to aid generalizability of the findings. One weakness of study 3 is that the lowest price option was suggested as representative to a specific OTA. In this study, we did not specify the OTA that features the lowest price. In addition, we used US currency rather than the local currency even though the destination was outside of the United States.

Method: Subjects, Design, and Procedure

Participants were 168 US adults (average age = 41.70, 55.4% female) from an online panel (i.e., Amazon Mechanical Turk) for nominal compensation. Participants were assigned to one of 2 (different hotel sets: Hotel A with different price range & Hotel B with the same price range vs. Hotel A with the same price range & Hotel B with different price range) conditions in a between-subjects experimental design.

The overall procedure of this study was quite similar to that of study 3 except for a few modifications. First, participants were asked to imagine that they were planning a vacation to Budapest, Hungary and that they were searching for a hotel online (comparing prices from different hotel reservation agents) and found two hotels. Then, after being exposed to two hotels options (e.g., [Hotel A] Sofitel Budapest vs. [Hotel B] Hilton Budapest), they were asked to select one hotel along a 7-point scale (1 = I definitely choose Hotel A; 7 = I definitely choose Hotel B). The price for each hotel was presented with US currency (i.e., US $) and systematically manipulated in that all price from 5 different OTAs (i.e., Expedia, Booking.com, Wotif, Agoda, & Hotels.com) was the same US $150 for the same price range condition, whereas the price was different from 5 different OTAs (e.g., $150, $157, $162, $170, & $175) for the different price range condition, as shown in Figure 7. Rather than specifying the lowest OTAs as in Study 3, we simply presented the lowest price. After answering their preference for a hotel, participants were asked to rate their childhood (Cronbach’s α = .874) and their current SES (Cronbach’s α = .924). Finally, participants completed demographic questions about their gender and age and were thanked.

Figure 7.

Stimuli from Study 4: Hotel A – Same price range & Hotel B – Narrow price range dispersion. Hotel A – Narrow price range & Hotel B – Same price range dispersion.

Results and Discussion

First, when we conducted a 2-way (different hotel sets) ANOVA, the main effect was significant, F(1, 166) = 4.08, p = .045, η² = .024, in that the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (M = 5.31, SD = 1.77) compared to when the price of Hotel B was different [& Hotel A’s price was same] (M = 4.70, SD = 2.19). Therefore, we can infer that the different price ranges (i.e., $150–$175) could be a narrow price dispersion based on the results of Study 1.

Importantly, this effect above featured a significant interaction between the experimental factor and childhood SES. We used Hayes’s (2017) macro model 1 with 5,000 bootstrap samples as similar to study 2. Overall, the interaction effect was significant (effect = .35, t = 1.79, p = .075, 90% CI: 0.026, 0.669). For travelers whose childhood SES was relatively low (i.e., −1 SD), the main effect of the experimental factor (i.e., different hotel sets) was significant (effect = −1.37, t = −2.72, p = .007, 95% CI: −2.358, −0.377). Specifically, the preference for Hotel B was higher when the price of Hotel B was the same [& Hotel A’s price was different] (estimated M = 5.40) as that when the price of Hotel B was different [& Hotel A’s price was same] (estimated M = 4.03). In contrast, for those whose childhood SES was relatively high (i.e., +1 SD), the main effect was not significant (effect = −0.10, t = −0.22, p = .824, 95% CI: −0.960, 0.765), as shown in Figure 8. In sum, the results of this study supported hypothesis 2.

Figure 8.

Results of Study 4: Preference for Hotel B [Hilton Budapest].

As in the previous study, the interaction between the current SES and the experimental factor was not significant (effect = 0.23, t = 1.19, p = .234, 95% CI: −0.150, 0.608).

General Discussion and Implications

Summary of Our Studies

With the increasing presence of dynamic pricing in the tourism and travel industry, travel agencies, hotels, and marketers seek to balance the best strategies to increase revenue and profits without experiencing backlash from consumers. The current research conducts four studies to provide insights on how the magnitude of price dispersion in online pricing can influence consumers’ evaluations and intentions. Across the results of the four experiments, we contribute to better knowledge about pricing strategies that can be adopted by online travel agencies. First, we demonstrate a relationship between the magnitude of the price dispersion and individuals’ preferences for travel options. Indeed, no price dispersion will be preferred to a narrow price dispersion, while the contrary will be true for wide price dispersion (i.e., wide price dispersion is preferred to no price dispersion). Our subsequent experiments demonstrate that this effect is consistent across different contexts and categories. This research also provides evidence of a boundary condition to this relationship, childhood SES, as influencing the impact of price dispersion (Studies 3 and 4) on travelers’ preferences. Indeed, the main effect of price dispersion was only significant for individuals with lower childhood SES, while it did not influence consumers with high childhood SES.

Theoretical Implications

These findings make several important theoretical contributions to the tourism literature. First, this work offers new insights into the role of price dispersion on travel decision making. Dynamic pricing and price dispersion are omnipresent in online settings, and this article extends our knowledge in the area of travel behaviors. Our research suggests that the magnitude of price dispersion between the travel options can significantly influence preferences and decisions, which results in a deeper understanding of how dynamic pricing can generate more revenue and can effectively be integrated into a revenue management system for online businesses. This article strengthens our knowledge about how price dispersion impacts the evaluation of alternatives in the travel and tourism domain.

Second, this study further extends theory related to the importance of the attribute distance in the judgment. Indeed, this research demonstrates that the magnitude of price dispersion influences the relative preference for travel options. This suggests that consumers are using different option sets as a reference point and that the comparison between different price dispersions leads to distinct preferences. Our study investigates price dispersion to build on previous work by demonstrating that no price dispersion can increase evaluations compared to a narrow price dispersion, while wide price dispersion is preferred to no price dispersion.

Third, participants in this study were exposed to the multiple options simultaneously. The price information was easy to compare each other from different OTAs within the same hotel option as well as from different hotels. Basically, the decision mode in this study can be categorized to the joint evaluation mode rather than the separate evaluation mode (see Hsee 1996; Hsee, Loewenstein, and Blount 1999). We expect the similar pattern for the separate evaluation mode; however, it is possible that the evaluation of the different price dispersion (i.e., narrow vs. wide price dispersion) could be similar in that it is very difficult to evaluate the narrowness of the price difference without the explicit comparison case (i.e., the same price dispersion). Future study needs to investigate the moderating role of the evaluation mode in our main predictions.

Finally, investigations between consumer’s socioeconomic background and travel preferences and choices are limited (except Park, Kim, and Kim 2020). Indeed, this research extends the understanding of travel and tourism behaviors from the evolutionary psychology perspective (e.g., Crouch 2013; J. Kim and Seo 2019; Kock 2020; Kock et al. 2020). Building on previous work (Carey and Markus 2016; Kraus, Piff, and Keltner 2009; Snibbe and Markus 2005), we provide further evidence that early socioeconomic status influences individuals’ information processing and decision making in their later stage of life. Examining this concept in the travel domain, our findings demonstrate that consumers with low SES tend to consider price information much more seriously. Specifically, we add to this research by showing that consumers with low childhood SES can be more sensitive to price dispersion in online travel domains than consumers with high childhood SES.

Practical Implications

In addition to the theoretical contributions, this work offers several managerial implications. Zhuang, Leszczyc, and Lin (2018) show that price variations are very common in the travel industry due to the pervasive adoption of online hotel booking sites. Given the increased price variability in the online channel, our study helps travel agents better understand how their pricing strategies could affect consumers’ choices. Our studies provide converging evidence that the presentation of different price options offered by hotels or online travel agencies significantly influences consumer’s choices. Thus, practitioners should closely monitor the prices of their offerings as well as those of its competitors and adjust prices depending on the disparity and dynamics of prices at that moment. In addition, companies could develop different pricing strategies according to their offerings. Specifically, same pricing options would be better for economy rooms, which tend to have narrow price dispersion to increase consumer choice. However, for upscale rooms, which are likely to show a wide price range, practitioners should set a competitive price to increase consumer choice.

Besides, this research suggests that travel agents and marketers should incorporate childhood SES as a new segmentation and targeting variable. Although online travel agencies have adopted various demographic and psychographic variables to segment the market, they have currently overlooked childhood SES. According to our findings, consumers who grew up in adverse environments are sensitive to price dispersion and are inclined to choose options without price dispersion. Therefore, when targeting those with relatively low childhood SES, travel agents and marketers should pay closer attention to price dispersion and adjust the prices in a way to maximize their revenue-gaining opportunities. Alternatively, travel agents can also differentiate their offerings depending on the price dispersion and consumers’ childhood SES.

Finally, this research offers timely, important implications during the global pandemic (i.e., COVID-19). The health crisis has brought ramifications on the tourism industry worldwide. The dramatically changing, threatening circumstances have served to increase perceived uncertainty and threat (J. Kim and Lee 2020; S. Kim et al. 2021), leading travelers to more explicitly seek accurate information as well as deliberately comparing alternatives in order to reduce the perceived risk associated with traveling. This research shows that the magnitude of price dispersion between various online options can alter travelers’ choices. During disruptive events and global crises, consumers may try to regain control of a situation by adopting some defensive strategies. Thus, we assume that the current COVID-19 crisis would lead travelers to seek security and make choices that are deemed more safe, resulting in greater preference for option sets with no price dispersion.

Limitations and Future Research

This work has several limitations, which offers opportunity for future investigation. First, this research exclusively collected data through online panels using a scenario-based method. Although this methodology has been commonly used in the tourism literature (e.g., Cui et al. 2020; Seo, Ko, and Kim 2021; Lee et al. 2020), further research could investigate this phenomenon using field experiments, survey data, and actual travel decisions. Second, there may be other factors that influence how price dispersion is being processed. For example, quantity of information available for consumers, product attributes, travel categories (e.g., high-priced vs. low-priced resorts), time pressure, and assessment of perceived risk might make a difference in the results of this study. For example, does the magnitude of price dispersion feature more prominently in consumer decision making when the risk associated with travel is more pronounced (e.g., first time traveling to a destination). Future research is needed to determine how price dispersion plays a role in these different situations. Addressing these questions would enhance our understanding of price dispersion on consumers’ preferences. Third, in this study, we measured the childhood SES with subjective scales; future studies can use more objective scales (e.g., parents’ education level). Fourth, we mainly focused on the comparison between narrow price dispersion and the same price across different childhood SES in studies 3 and 4. Future studies can consider investigating the moderating role of childhood SES on the different price conditions (e.g., narrow vs. wide dispersion) in order to provide higher external validity.

In addition, travelers frequently make multiple reservations (e.g., airline ticket, hotel, and car rentals) for their trip. Future research could investigate the impact of the price dispersion across the repeated, but different, decisions. For example, the initial decision of hotel booking may significantly influence the subsequent decision in terms of price dispersion. In this situation, the way of decision framing (e.g., J. Kim, Kim, et al. 2020) or the preciseness of price (Cui, Kim, and Kim 2020; J. Kim, Cui, et al. 2020) could influence the impact of the price dispersion on the price judgment. Finally, this research incorporated childhood SES as a function of childhood experiences in examining its impact on traveling preferences and decisions, but other variables such as generation, childhood and current poverty levels, and childhood stress factors could all be investigated in future work.

Footnotes

Author Note

All authors contributed equally.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Jungkeun Kim

Jooyoung Park

Kyuseop Kwak

Author Biographies

Marilyn Giroux is a senior lecturer of Marketing at the Auckland University of Technology, New Zealand. Her main research interests are in consumer decision-making, branding and communications.

Drew Franklin is a lecturer in marketing at the Auckland University of Technology, New Zealand. His core research interests have been established from within the domains he has been most involved in as a practitioner, namely, business to business marketing, services marketing, customer relationship management and relationship marketing (and trust, specifically), as well as both organizational and consumer decision making within different industry contexts.

Jungkeun Kim is an associate professor of Marketing at the Auckland University of Technology, New Zealand. His main research interests are in consumer and travel decision making and behaviors. His research has been published in Journal of Marketing Research, Journal of Consumer Psychology, Journal of Travel Research, and among others. He loves golf.

Jooyoung Park is an assistant professor of Peking University HSBC Business School. Her research interests include consumer information processing, motivation and self-regulation, gender, and childhood socioeconomic status. Her work appeared in academic journals including Psychological Science, Journal of Consumer Psychology, Cornell Hospitality Quarterly, and among others.

Kyuseop Kwak is a lecturer in Marketing Discipline Group, University of Technology Sydney. His research interests lie in consumer choice decision making and multiple item purchases in retail environment.

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