Length of stay control: Is it a fair inventory management strategy in hotel market?

Abstract

According to past revenue management (RM) research, length of stay control (LOSC) plays a significant role in building effective RM systems and increasing revenues in the hotel industry. While LOSC has been widely implemented in the hotel’s online booking systems, little attention has been paid to this important non-pricing RM practices. Thus, the current study aims to provide a holistic view of customers’ perceptions toward hotel’s LOSC practice. In particular, this study (1) investigates the impact of LOSC practice on customers’ perceived fairness and subsequent behaviors and (2) tests the moderating role of customers’ membership status on perceived fairness. A between-subjects factorial design and partial least squares structural equation modeling (SEM) analysis were employed to test the structured relationships. Research findings demonstrate that customers perceive LOSC as unfair, such poor fairness perceptions leading to negative word-of-mouth and a decrease in willingness to book. Hotels’ loyalty program members are likely to perceive LOSC as much more unfair than those who are not. The current study is the first study to reveal the critical roles of hotels’ LOSC practice and loyalty membership on customers’ perceived fairness and subsequent behavior intentions.

Keywords

length of stay control loyalty membership negative word-of-mouth perceived fairness revenue management willingness to book

Introduction

Revenue management (RM) is the application of information systems and pricing strategies to allocate the right capacity to the right customer at the right price at the right time through the right distribution channel in order to maximize revenue or yield (Denizci Guillet and Mohammed, 2015; Kimes, 1989). In the hospitality industry, RM is one of key operational strategies to maximize revenues with given resources by deploying two strategic levers such as pricing tools (e.g. price discrimination, rate fences, dynamic pricing) and non-pricing tools (e.g. capacity management, overbookings, length of stay control (LOSC)) (Ivanov and Zhechev, 2012; Kimes, 2002; Kimes and Wirtz, 2015). Due to the rapid development of information and communication technologies, both RM practices have been tremendously changed and employed in various ways (Erdem and Jiang, 2016; Ivanov, 2014; Wilson et al., 2015). For example, the top 5 US hotel companies actively implement various RM tools in their online reservation systems as shown in Table 1.

Table 1.

Examples of the US hotels’ pricing and non-pricing online RM practices for one-night stay during the peak season.

City	Hilton	Marriott	Intercontinental	Sheraton	Ramada
San Francisco	LOSC	LOSC	Pricing	×	×
Orlando	LOSC	LOSC	LOSC	LOSC	Pricing
New York City	LOSC	LOSC	LOSC	LOSC	Pricing
San Antonio	LOSC	×	×	×	LOSC
Las Vegas	LOSC	LOSC	Pricing	LOSC	×
Boston	Pricing	×	Pricing	×	×

Note: Adapted and modified from Wilson et al. (2015). LOSC: length of stay control implemented (i.e. no available room for only one night but available for multiple nights); Pricing: higher room rate for one night than multiple nights; ×: the same rate for those who stay for multiple nights.

Hotels’ LOSC refers to setting limits on the minimum and, rarely, maximum number of nights required when the reservation is made based on the demand, which is one of the common industry practices for RM (Ivanov, 2014). LOSC is the unique feature of hotel RM systems and has been widely implemented as one of the key non-pricing RM practices in the hotel industry (Chiang et al., 2006; Guillet, 2020; Nair, 2019; Xu et al., 2019). The unique characteristics of hospitality and tourism services and products are demand seasonality and variability (Kandampully, 2000; Mauri, 2013). Hence, hotel managers have implemented RM practices to maximize profits or minimize economic loss in such fluctuating demand cycle (Park et al., 2016; Pullman and Rodgers, 2010; Upchurch et al., 2002). For instance, LOSC allows hotels to set up the minimum (or maximum) required stay for their maximum revenues during the peak season and to protect revenues from the shoulder days during the low demand (Kimes and Chase, 1998; Vinod, 2004; Weatherford, 1995; Wofford, 2013). Although LOSC plays an important role in building effective RM systems (Denizci Guillet and Mohammed, 2015) and increasing revenues (Guillet, 2020; Nair, 2019; Xu et al., 2019), LOSC has not been frequently discussed in the hospitality literature (Ivanov, 2014; Ivanov and Zhechev, 2012). To the best of our knowledge, there is no study attempting to explicitly consider the impact of LOSC on perceived fairness and/or behavioral intentions. Therefore, the current study aims to examine whether LOSC is perceived as fair and acceptable by customers and affects their subsequent behaviors.

Furthermore, the present study investigates a moderating role of customers’ previous relationships (e.g. customers’ previous hotel stay/purchases or loyalty membership) on the relationship between LOSC and perceived fairness since past RM research has identified the negative effects of RM in terms of customer relationship marketing perspectives (Erdem and Jiang, 2016; Shoemaker, 2003; Wang, 2012; Wirtz et al., 2003). Hotel loyalty programs are considered as a distinct avenue to enhance customer loyalty and long-term relationships and also a proxy of customers’ previous investments in the relationship with the hotel (Kimes and Wirtz, 2015; Xie and Chen, 2013). In particular, LOSC may result in intermittent availability to loyal customers and cause them to view it as unfair because fairness perceptions in exchange situations are judged by the outcomes they receive and/or the relationships that both parties have made (Homans, 1961; Ivanov, 2014; Ivanov and Zhechev, 2012). However, there is still a lack of research investigating the role of hotel loyalty membership programs in the context of RM.

The present research would be an initial attempt to fill these gaps and enrich the body of knowledge in the RM literature by revealing the role of LOSC and customers’ loyalty membership. We focus on (1) examining the effect of hotels’ LOSC practices on customers’ perceived fairness and subsequent behaviors including willingness to book (WTB) and negative word-of-mouth (NWOM), (2) investigating the moderating effect of the hotel loyalty program membership as a proxy of the customers’ previous relationships on perceived fairness toward LOSC practices, and (3) thus providing a holistic view of customers’ reactions toward hotels’ LOSC practices.

Literature review and hypothesis development

Hotel RM and customer fairness perceptions

Recent RM studies focus on the effects of RM on issues related to customers and operational management, besides the RM system development and modeling (Denizci Guillet and Mohammed, 2015; Erdem and Jiang, 2016). Ivanov and Zhechev (2012) broadly divide RM tools into the two aspects: pricing and non-pricing tools. The first one is more related to manipulating room rates directly influencing room pricing, while the second one focuses on controlling hotels’ perishable assets, rooms. With these fixed assets, the hotel industry has been using various RM pricing policies and practices including dynamic pricing (Mattila and Choi, 2005), fencing and framing conditions (Kimes and Wirtz, 2004; Wirtz and Kimes, 2007), and price presentation on the hotel website (Noone and Mattila, 2009). Despite hotels’ active involvement in their RM, however, customers would perceive their pricing RM practices to be unfair when they have not received any information on transactions or rationalized pricing decisions (Choi and Mattila, 2005; Kimes, 2002; Noone and Mattila, 2009). In turn, such perceived unfairness would lead to customers’ negative emotional attachments to the hotels or some other negative consequences (e.g. lower shopping intentions, complaints, or legal actions) (Campbell, 1999; Xia et al., 2004).

The notion of fairness is almost synonymous with equity or justice (Oliver, 1997) and has been widely employed to explain an individual’s reactions to various conflict situations (Blodgett et al., 1997). A seller’s fair behavior plays a critical role in building a long-term relationship between a seller and a customer (Thaler, 1985). Customers’ fairness perceptions originate from “the principle of dual entitlement” proposed by Kahneman et al. (1986). They argue that an increase in price is perceived to be fair, if it results from an increase in cost. However, if an increase in price is not related to any increase in cost, customers perceive it to be unfair and are not likely to transact with a seller. Xia et al. (2004) suggest that perceived price differences can lead to perceptions of advantaged inequality (i.e. the customer pays less than other customers or expectations based on his or her previous transactions) or disadvantaged inequality (i.e. the customer pays more). An understanding of the customer’s fairness perceptions toward the hotel’s RM practices is very important to hotel operators because perceived fairness significantly influences customers’ satisfaction and behavioral intentions (Denizci Guillet and Mohammed, 2015; Oliver, 1997).

In this study, perceived fairness refers to the customer’s perceived judgment of whether outcome differences between the hotel’s LOSC practice and his/her online booking activity are reasonable, acceptable, or justifiable (Bolton et al., 2003). Customers evaluate fairness based on three factors: outcomes (distributive fairness), the policies and procedures by which the outcome is produced (procedural fairness), and interpersonal treatment they experience during the process (interactional fairness) (Blodgett et al., 1997). Among three different types of fairness, this study focuses on distributive fairness and investigates how customers’ fairness perceptions can be affected by different outcomes of room availability caused by LOSC. Furthermore, there are several moderating factors such as RM information (Choi and Mattila, 2005) and familiarity (Wirtz and Kimes, 2007) that influence customers’ fairness perceptions. Recent studies focus on the roles of social media and user-generated information on RM (Noone and McGuire, 2013, 2014; Noone et al., 2011). Noone and McGuire (2013), for example, demonstrate that price and non-price information (e.g. user-generated content, review valence, and brand name) play a dominant role in assessing customers’ pre-purchase value of a hotel and making their booking decision. Table 2 summarizes previous RM studies in the hotel context.

Table 2.

Overview of past RM studies.

Author(s) and year	Type of RM practices	Predictors	Outcomes	Key findings
Choi and Mattila (2004)	Pricing RM	Hotel variable pricing practices, information about room pricing practice offered and comparison standard	Fairness perception	The study examines the effect of variable pricing practices on customers’ perceptions of fairness during the hotel reservations process. Only when customers had higher room rates and compared their rates with others, they perceived the RM practice less fair than other cases. In addition, information moderates the negative effect of demand-based variable pricing on the fairness perception.
Wirtz and Kimes (2007)	Pricing RM	Pricing practices (framing and fencing conditions), familiarity	Perceived fairness	Familiarity with RM pricing practices plays a moderating role on perceived fairness. Framing of prices and fencing condition had strong effects on perceived fairness when customers are less familiar with pricing RM practices.
Beldona and Kwansa (2008)	Pricing RM	Cultural orientation	Perceived fairness toward demand-based pricing	Cultural orientation (i.e. vertical individualism) has a positive impact on fairness perceptions toward demand-based pricing practices.
Noone and Mattila (2009)	Pricing RM	Price presentation (blended- vs. nonblended-rates), familiarity with BAR pricing	WTB	The study examines the effect of variable pricing practices on customers’ perceptions of fairness during the hotel reservations process. Only when customers had higher room rates and compared their rates with others, they perceived the RM practice less fair than other cases. In addition, information moderates the negative effect of demand-based variable pricing on the fairness perception.
Hwang and Wen (2009)	Non-pricing RM	Hotels overbooking and compensation practices, gender, booking channel, membership status, reputation time before arrival, reserved length of stay, payment source	Perceived fairness, customer loyalty	Customers who perceive a hotel’s overbooking and compensation practices as unfair are less likely to be loyal to the hotel in the future. Women tend to perceive hotel’s overbooking as more unfair than men.
Heo and Lee (2011)	Pricing RM	Frequency of use (familiarity, price knowledge), price consciousness, consumer characteristics	Fairness perceptions	More frequent users of hotels, younger and more educated guests consider RM practices as fair. In addition, household income and price consciousness of hotel guests and their genders play a significant role in changing their perceptions toward RM practices.
Taylor and Kimes (2011)	Pricing RM	Information, familiarity and brand class	Return intention, perceived fairness	Brand class significantly affects perceived fairness only for respondents who are familiar with RM practices. Also, perceived fairness is positively associated with return intention.
Noone and McGuire (2013)	Pricing RM	Price, non-price information	Purchase decision	Non-price information has a significant impact on hotel choice as price does. In addition, both price and non-price information can be used when consumers make hotel choice.
Noone and McGuire (2014)	Pricing RM	Variable pricing practices, consumer reviews and aggregate consumer ratings	Consumers’ pre-purchase evaluations	Price is not associated with perceived quality when user-generated content is present. Both price and user-generated content play a significant role in enhancing perceived value.
El Haddad (2015)	Pricing RM	Implementation of RM pricing practices	Upscale budget hotel managers’ perceptions	The implementation of RM systems affects profitability in luxury, mid-scale, and budget hotel sectors when this practice is clearly communicated to hotel employees.
Vaeztehrani et al. (2015)	Pricing/non-pricing RM	Price discounts, room availability guarantee, customer groups (occasional vs. loyal)	Revenue	While the loyalty programs may decrease short-term net revenue, hotel expected net revenue can be increased once discounted room availability guarantee was applied.
Riasi et al. (2017)	Pricing RM	Length of stay	Average room rate	As customer’s length of stay increases, hotels charge more per night.
Rodríguez-Algeciras and Talón-Ballestero (2017)	Pricing RM	Price optimization, marketing expense	Revenue	Booking data from a cruise company show that dynamic pricing based on prices and marketing expense lead to an increase in revenue by 8% to 20%.
Gibbs et al. (2018)	Pricing RM	Dynamic pricing strategies	Average daily room rate, occupancy rate	Airbnb hosts use different dynamic pricing strategies as compared to hotels. Empirical data show that Airbnb hosts implement dynamic pricing strategies less than hotels.
Kimes and Ho (2018)	General RM practices	—	—	RM practices should be implemented differently for luxury consumers because of a different role of price and experience.
Current study	Non-pricing RM	LOSC, loyalty membership status (moderator)	Perceived fairness, NWOM, WTB	The hotel’s minimum LOSC practice should be cautiously implemented because it can negatively affect customers’ fairness perception toward RM practices, word-of-mouth intention, and WTB. The current study is the first study to identify the critical roles of LOSC in the hotel setting.

Note: RM: revenue management; LOSC: length of stay control; NWOM: negative word-of-mouth; WTB: willingness to book.

The role of LOSC

LOSC is an advocating practice for hotels to protect their inventories during peak and slow times by requiring customers to stay in hotels for the minimum number of nights. RM researchers (e.g. Choi and Kimes, 2002; Wofford, 2013) consider LOSC one of effective and least complicated RM tools to maximize room revenues. Weatherford (1995) suggests that incorporating a LOSC policy into the hotel RM system allows hotels to obtain an additional 2.94% in revenues. In the online booking environment, when a customer tries to make an online room reservation for only one night via a hotel website, he or she may not find any available rooms at all due to the hotel’s LOSC practice. Thus, LOSC results in different search outcomes about room availability by the number of room nights requested by customers, even for the same type of room at the same hotel. A recent study of Wilson et al. (2015) finds that LOSC is a widely used RM practice in the hotel industry when the demand is high due to various events (i.e. a college graduation, major sporting events, or festivals) or when the demand is low (i.e. Thanksgiving holidays). Despite LOSC’s frequent implementation for the current hotel operations, not many RM researchers have been fully dedicated to investigate how customers perceive hotels’ LOSC practices (Ivanov and Zhechev, 2012; Kimes and Chase, 1998; Vinod, 2004). Thus, it would be timely for RM researchers to identify how customers perceive hotels’ LOSC practices and how they react to unexpected outcomes (i.e. no rooms available), because nothing is fully disclosed about the practices in their online reservation systems.

According to commodity theory, people may differently assess the value of material goods or intangible services dependent upon scarcity (Brock, 1968). The term “scarcity” is commonly interchangeable with “unavailability,” referring to scarcity and other limits on availability (Heo et al., 2013). Several studies assess the knowledge of a product’s scarcity is positively associated with customers’ preference (Verhallen, 1982), desirability of a product (Lynn, 1989), and anticipated price appreciation (Lynn and Bogert, 1996). Specifically, Lynn and Bogert (1996) demonstrate that scarcity increases the anticipated price appreciation of products because when people realize a scarcity of the products, they believe price appreciation results from product scarcity. At the same time, perceived scarcity enhances customers’ product desirability because people desire scarce products more than available ones (Lynn and Bogert, 1996). In line with these views, if a customer wants to stay at a hotel during the peak season (e.g. New Year’s Eve in New York City), he or she may know that hotel rooms in downtown New York City will be scarce, and thus perceived scarcity of a hotel room will enhance anticipated price appreciation and desirability. In turn, customers may admit or accept the room rate differences between a one-night stay and multiple nights or a previous stay due to pricing RM practices, and want to book that room, thus reducing unfairness perceptions toward such a pricing RM practice. Instead, customers who do not find any available rooms for a one-night stay due to the LOSC practice may perceive this outcome more negatively than others because the hotel has taken his or her chance away to book a room regardless of their willingness to pay higher rates, price appreciation, or desirability of a one-night stay during the peak time. Moreover, the no room available outcome (e.g. denied service resulting from non-pricing RM practices) can significantly decrease customers’ future spending with service providers and satisfaction because they perceive this outcome more negatively and severely (Lindenmeier and Tscheulin, 2008; Wangenheim and Bayón, 2007). Therefore, this study proposes the following hypothesis:

H1: The LOSC practice is negatively associated with customers’ perceived fairness.

Membership status of hotel loyalty program as a moderator

Past RM studies have not actively discussed the relationships that customers and the exchange partner (e.g. a hotel) have made, even though customers’ fairness perceptions in this exchange situation resulted from both the received outcomes and the previous relationships with the exchange partner (Homans, 1961). In this study, the researchers examine how customers’ previous investments influence their fairness perceptions toward the LOSC practice. Considering customers’ membership status of the hotel loyalty program as their business relationship to the hotel, they expect to have various benefits based on previous transactions. The hotel’s loyalty reward program membership has been recognized as a critical marketing tool to enhance long-term customer relationships with and commitment to the hospitality industry (Xie and Chen, 2013). The hotel loyalty reward program offers various points and benefits to the members based on their past transactions. In particular, hotels encourage customers to stay longer or reward frequent guests by offering different program tiers and benefits (Berman, 2006). For example, the Hilton’s Honors program has four tiers (i.e. Diamond-, Gold-, Silver-, and Blue-level) and all levels except for a Blue-level can be only obtained by the number of transactions or stays customers have made.

Studies (i.e. Hwang and Wen, 2009; Wangenheim and Bayón, 2007) suggest that customers who achieve a certain status (e.g. “gold” or “diamond” membership) in hotels’ loyalty reward program are likely to consider their investments in the customer relationship as greater than those who do not have such status. Therefore, they argue that hotel loyalty reward program members might perceive the same negative outcome as more critical and unfair than nonmembers because they deserve better outcomes and benefits. In turn, high-tier loyalty reward program members perceive the negative consequence of denying service (i.e. nonavailability outcome) as more critical and unfair than other counterparts (Wangenheim and Bayón, 2007)

As hypothesized in H1, we posit that customers would likely view a non-pricing LOSC practice as more unfair than a pricing RM practice. Accordingly, a customer with a hotel loyalty reward program membership status would likely perceive a non-pricing LOSC practice (i.e. no room availability) as less fair and more negatively than a pricing RM practice (i.e. a higher daily rate). Taken all together, customers’ perceived fairness of hotels’ LOSC practice vary depending upon their previous relationships. In particular, the negative effects of non-pricing LOSC on perceived fairness should be stronger for customers with a hotel loyalty reward program than those with no loyalty reward program. Therefore, this study proposes the following hypothesis:

H2: Customers who have a hotel loyalty reward program membership perceive the LOSC practice as less fair than those with no loyalty reward program membership.

Perceived fairness of LOSC and subsequent behavioral intentions

Existing literature asserts that customers’ fairness perceptions significantly influence consumer satisfaction, loyalty, and other subsequent behaviors (Oliver, 1997). Blodgett et al. (1993) suggest that perceived justice plays a critical role in determining whether a customer will subsequently exit or engage in NWOM. NWOM is defined as the interpersonal or informal negative communication with consumers about the characteristics of a business or a product that denigrates the object of the communication (Weinberger et al., 1981; Westbrook, 1987). Similarly, Blodgett et al. (1997) examine the effects of distributive, interactional, and procedural justice on customers’ re-patronage and NWOM intentions. They find that all justice perceptions have a strong positive impact on re-patronage intentions, and poor justice perceptions lead to NWOM intentions. Noone (2012) shows that poor distributive justice perceptions are positively associated with NWOM intentions.

Another important behavioral intention on RM research is WTB (Noone and Mattila, 2009). In this study, WTB is defined as the likelihood that the customer intends to make a hotel reservation in the online booking environment (Noone and Mattila, 2009). Booking intentions are considered to be important in RM practices and fairness perceptions in the reservations process (Mattila and Choi, 2005; Noone and Mattila, 2009) because perceived fairness toward RM practices is a significant antecedent to repurchase intention and should, thus, be an important predictor of WTB (Mattila and Choi, 2005). Perceived fairness has a strong positive impact on WTB during the reservation process (Mattila and Choi, 2005; Noone and Mattila, 2009). Similarly, Campbell (1999) demonstrates that perceived unfairness leads to lower shopping intentions. Therefore, this study proposes the following two hypotheses:

H3: Customers’ perceived fairness is negatively associated with their likelihood to engage in NWOM.

H4: Customers’ perceived fairness is positively associated with their WTB.

Methodology

Research design data collection

A 2 × 2 between-subjects factorial design was employed with LOSC practices (No: pricing RM practice vs. Yes: LOSC (non-pricing RM practice)) and hotel loyalty reward program membership status (gold membership vs. no membership) as treatments and perceived fairness, NWOM, and WTB as the dependent variables. This study decided to use a term ‘Gold’ membership for those who are a member of hotel loyalty reward program based on the results of pretest, which show no differences in fairness perceptions among three different types of membership status, silver, gold, and diamond.¹ A scenario-based survey was conducted in this study, which has been frequently used in RM practices and perceived fairness research (Noone and Mattila, 2009; Wirtz and Kimes, 2007). All scenarios are shown in the Online Appendix.

An online self-administered survey was performed, hosted on Qualtrics, by using a convenience sampling method. A convenience sample of 500 US adult consumers who have made online room reservations for their hotel stay during the past 12 months was obtained from an online survey company. Each participant was randomly assigned to one of four scenarios. After reading the same background information, each participant read the assigned scenario and was asked to answer perceived fairness, NWOM, WTB, manipulation check questions, realism check questions, and demographic questions.

Measures

All measurement items were measured with a seven-point scale, adopted from previous studies and modified to fit the nature of this study. Perceived fairness was measured by three items, anchored with 1: “very unfair” to 7: “very fair” for the first item (Campbell, 1999) and 1: “strongly disagree” to 7: “strongly agree” for the other two items (Kimes, 1994). WTB was measured with two items adopted from the study of Mattila and Choi (2005), with anchorage of from 1: “highly unlikely” to 7: “highly likely.” Three items were adopted to measure NWOM with anchorage of 1: “strongly disagree” to 7: “strongly agree” (Blodgett et al., 1997; Noone, 2012). The pilot test was conducted with a total of 98 undergraduate students majoring in hospitality and tourism management at a northeastern university in the United States to ensure clarity of each question. Results of the pilot test were used for refining and modifying the survey questions for the actual survey later. A summary of measurement items of each construct is shown in Table 4.

Table 3.

Sample characteristics.

Characteristics	Frequency (n = 431)	Percentage (%)
Gender
Male	183	43.5
Female	248	57.5
Age
25–34 years old	132	30.6
35–44 years old	168	39.0
45–54 years old	82	19.0
Over 54 years old	49	11.4
Annual household income
Less than US$40,000	181	42.0
US$40,000–US$80,000	125	29.0
More than US$80,000	125	29.0
Education
High school or below	55	12.8
Associate degree	38	8.8
Bachelor degree	190	44.1
Graduate degree	148	34.3
Experience of online hotel reservations within 12 months
Never	0	0
1–3 times	276	64.0
4–6 times	83	19.3
More than 6 times	72	16.7
Frequency of hotel stays within 12 months
Never	0	0
1–3 days	181	42.0
4–6 days	98	22.7
More than 6 days	152	35.3

Table 4.

Results of measurement model (n = 431).

Construct and measurement item	Mean	SD	Factor loading
Perceived fairness (composite reliability = 0.923; Cronbach’s α = 0.896; AVE = 0.706)
How would you rate the hotel’s revenue management policy and practice, in general, based on the number of nights a guest stays at the hotel?	4.11	1.38	0.814
The hotel is behaving in a fair fashion.	4.13	1.46	0.789
I consider the outcome of this scenario to be acceptable.	3.73	1.58	0.842
I consider the hotel’s length of stay control policy and practice as ethical.	4.06	1.51	0.881
How would you rate the hotel’s online reservation service?	4.04	1.53	0.873
WTB (composite reliability = 0.961; Cronbach’s α = 0.919; AVE = 0.925)
The likelihood that I would consider staying at this hotel for celebrating “New Year’s Eve” is:	3.64	1.72	0.960
The likelihood that I would book this room is:	3.55	1.73	0.964
NWOM (composite reliability = 0.922; Cronbach’s α = 0.873; AVE = 0.798)
The likelihood that I would warn my friends and relatives not to stay at this hotel is:	3.99	1.68	0.831
If this had happened to me, I would complain to my friends and relatives about this hotel.	4.41	1.62	0.918
If this had happened to me, I would make sure to tell my friends and relatives not to stay at this hotel.	3.96	1.68	0.927

Note: AVE: average variance extracted; WTB: willingness to book; NWOM: negative word-of-mouth.

In addition, three items were developed to ensure whether the condition of each treatment was manipulated effectively. For the LOSC condition, we used three items anchored by 1: “strongly disagree” and 7: “strongly agree”: (1) “Based on the scenario given, the daily room rate quoted for the two-night stay was different from the daily room rate for the one-night stay,” (2) “Based on the scenario given, the daily room rate quoted for the one-night stay was much higher than the daily room rate for the two-night stay,” and (3) “Based on the scenario given, I was offered any available rooms for the one-night stay.” For the membership condition, we also used three items anchored by 1: “strongly disagree” and 7: “strongly agree”: (1) “Based on the scenario given, I consider myself to be a loyal customer of this hotel,” (2) “Based on the scenario given, I feel emotionally attached to this hotel,” and (3) “Based on the scenario given, I am a member of the hotel loyalty reward program.” To ensure whether LOSC and membership conditions were successfully manipulated, we ran one-way analyses of variance (ANOVAs). The difference in means for the LOSC condition was significantly different from zero (F = 626.5, p < 0.001) and the difference in means for the membership condition was also significantly different from zero (F = 772.5, p < 0.001), suggesting that our manipulations were effective. In addition, two realism check questions—(1) I find this scenario to be personally relevant and (2) I can imagine myself in the same situation—were asked by using a seven-point Likert-type scale, 1: “strongly disagree” to 7: “strongly agree,” to determine if respondents perceived the situations were realistic. Respondents perceived the scenario personally relevant (M = 5.07, SD = 1.45) and imagined themselves in the same situation (M = 5.74, SD = 1.41).

Data analysis

To test the proposed research hypotheses, this study conducted partial least squares structural equation modeling (PLS-SEM) analysis. PLS-SEM is a variance-based SEM and follows a regression approach minimizing the residual variances of endogenous constructs (Hair et al., 2019). The PLS-SEM approach can be applied to develop theories further or sophisticated models by focusing on the variance in the dependent variable (Hair et al., 2017, 2019). In addition, PLS-SEM is preferred when measures were developed with a Likert-type scale and collected survey data followed a non-normal distribution (Ali et al., 2018; Hair et al., 2017). Finally, PLS-SEM works well with a multipath model based on the experimental research design by controlling for measurement error and providing a holistic view with multiple dependent variables (MacKenzie, 2001; Vinzi et al., 2010). Thus, PLS-SEM can improve experimental research in social sciences (MacKenzie, 2001) and thus be a good alternative analysis instead of ANOVA (Vinzi et al., 2010). SmartPLS 3.0 software (Ringle et al., 2015) was employed to examine the proposed research hypotheses.

To estimate both the measurement model and the structured model, we followed Anderson and Gerbing’s (1988) two-step approach—confirmatory factor analysis (CFA) and SEM. First, CFA was used to test the measurement model (Hair et al., 2006). Based on the CFA results, internal reliability, convergent validity, and discriminant validity were investigated. Then, the structural relationships were examined based on PLS-SEM.

Results

Characteristics of sample

A total of 431 usable responses were obtained to test structural relationships. The majority of respondents were between 25 and 44 years old (39.0%) and had a Bachelor degree (44.1%). More than half of the respondents (57.5%) were female and more than a half of them (58.0%) reported to earn more than US$40,000 annual household income. In terms of the previous online reservation experience, 64% of them made online room reservations once to three times and about 58% of them have stayed for more than 4 days during the past 12 months. Detailed information about respondents’ background and characteristics is shown in Table 3.

Testing the measurement model

The reliability and validity of the measurement model were assessed based on internal consistency and CFA. Since model fit measures should be very cautiously and tentatively considered for PLS-SEM estimations (Hair et al., 2017, 2019), we used nonparametric evaluation criteria based on bootstrapping and blindfolding (Hair et al., 2017). As shown in Table 4, this study tested the adequacy of the measurements by evaluating the reliability of the individual measures, convergent validity, and the discriminant validity of the constructs (Hair et al., 2017). First, the smallest value of Cronbach’s α was 0.873, indicating satisfactory levels of internal reliability and inter-item reliability (Hair et al., 2006). Second, for convergent validity, item loadings ranged from 0.789 to 0.964, representing acceptable ranges (Hair et al., 2006). The composite reliability of all latent variables exceeded the recommended threshold of 0.7, and the average variance extracted (AVE) for each construct exceeded 0.5, resulting in good convergent validity for each construct (Hair et al., 2019). Fourth, discriminant validity was examined by the square roots of the AVE of the four constructs that were measured reflectively with multiple items. As presented in Table 5, the square roots of the AVE exceeded the inter-construct correlations and thus the discriminant validity was satisfied (Fornell and Larcker, 1981). We also assessed cross-loadings to establish more rigorous discriminant validity (Hair et al., 2019). As shown in Table 6, each indicator’s loading on the construct was considerably higher than all of its cross-loadings with the other constructs. Overall, the Fornell–Larcker criterion, as well as the cross-loadings, provided strong evidence for the constructs’ discriminant validity.

Table 5.

Correlation matrix and discriminant assessment.

	1	2	3	4	5
1. LOSC	1.000
2. Membership	−0.030	1.000
3. Perceived Fairness	−0.362	−0.127	0.841
4. WTB	−0.251	−0.013	0.562	0.962
5. NWOM	0.034	0.088	−0.363	−0.291	0.893

Note: LOSC: length of stay control; WTB: willingness to book; NWOM: negative word-of-mouth.

Table 6.

Results of the cross-loadings (alternative discriminant validity check).

Construct and measurement items		1	2	3	4	5
1. LOSC	LOSC1	1.000	−0.030	−0.362	−0.251	0.034
2. Membership	Mem1	−0.030	1.000	−0.127	−0.013	0.088
3. Perceived fairness	PF1	−0.308	−0.167	0.814	0.466	−0.239
	PF2	−0.163	−0.067	0.789	0.402	−0.339
	PF3	−0.384	−0.079	0.842	0.467	−0.258
	PF4	−0.321	−0.082	0.881	0.492	−0.339
	PF5	−0.324	−0.135	0.873	0.522	−0.351
4. WTB	WTB1	−0.242	−0.024	0.525	0.960	−0.264
4. WTB	WTB2	−0.24	−0.002	0.554	0.964	−0.295
5. NWOM	NWOM1	0.026	0.109	−0.271	−0.239	0.831
	NWOM2	0.082	0.071	−0.364	−0.291	0.918
	NWOM3	−0.025	0.064	−0.327	−0.247	0.927

Note: LOSC: length of stay control; WTB: willingness to book; NWOM: negative word-of-mouth.

Structural equation model and hypothesis testing

The four hypotheses were examined to validate the theoretical framework proposed in this study (see Figure 1). Based on the criterion suggested by Hair et al. (2019), we assessed the hypothesized relationships on the basis of the explained variance (R ²) of the dependent variables, path coefficients (β), and their levels of significance obtained from a bootstrapping resampling method (4310 resamples) (Chin, 1998; Vinzi et al., 2010). Based on the PLS-SEM analysis, all hypotheses were supported at p < 0.001 (see Table 7 for more details). First of all, LOSC was negatively associated with perceived fairness (H1: β = −0.367, t = 8.595, p < 0.001) and respondents’ membership status of a hotel loyalty reward program significantly moderated the relationship between LOSC and perceived fairness (H2: β = −0.181, t = 4.248, p < 0.001); therefore, this study supported H1 and H2. Secondly, perceived fairness was negatively associated with NWOM (H3: β = −0.365, t = 7.511, p < 0.001) and was positively associated with WTB (H4: β = 0.562, t = 14.252, p < 0.001), supporting H3 and H4.

Figure 1.

Structural equation model and hypotheses testing results.

Table 7.

Results of hypothesis test.

Structural paths	β	SE	t Ratio	f ²	Hypothesis testing
LOSC → Perceived fairness (H1)	−0.367	0.043	8.595*	0.164	Supported
LOSC × Membership → Perceived fairness (H2)	−0.181	0.043	4.248*	0.040	Supported
Perceived fairness → NWOM (H3)	−0.365	0.048	7.511*	0.151	Supported
Perceived fairness → WTB (H4)	0.562	0.039	14.252*	0.461	Supported

Note: LOSC: length of stay control; WTB: willingness to book; NWOM: negative word-of-mouth. R ² for perceived fairness = 0.177; R ² for NWOM = 0.129; R ² for WTB = 0.314.

*p < 0.001.

Next, the effect size, f ², was examined to evaluate the change in R ² value (a substantive impact on the endogenous latent variables) when a specified exogenous construct is omitted from the proposed research model (Hair et al., 2017). According to guidelines suggested by Cohen (1988), values of 0.02, 0.15, and 0.35, respectively, indicate small, medium, and large effects. As presented in Table 7, f ² values for all exogenous constructs are significant and effective. All the R ² of the endogenous constructs in the model exceeded the 10% benchmark recommended by Falk and Miller (1992).

Discussions

Summary of findings

This study attempts to fill a gap in the RM literature by examining customers’ reactions toward hotels’ RM practices by comparing non-pricing and pricing approaches during their online reservation activities. Findings of this study indicate that respondents perceive higher room rates shown on the hotel website to be fairer than no room availability of the hotel due to the minimum LOSC set by the hotel. Customers fairly accept a hotel’s RM practice that offers them a higher room rate for a one-night stay during a peak season in a sense that, due to the resource scarcity, room rates are expected to be higher or the highest of all periods. On the other hand, customers tend to feel they are treated unfairly when they find hotels do not disclose room rates at all for the one-night stay because of their implementation of the minimum LOSC during a peak season. As indicated in H1, customers think hotels just take away their rights to book a room and do not give them any chances to consider paying more for their stay under the RM implementation of the minimum LOSC.

As indicated in the study of Hwang and Wen (2009), this study confirms that respondents’ affiliation with a hotel’s loyalty reward program results in different fairness perceptions of the hotel’s RM practices. Respondents’ status in a hotel’s loyalty reward program seems to have strong moderating effects on their perceived fairness and its RM practices. Findings of this study demonstrate that respondents’ membership status in a hotel loyalty program plays a significant role in their judging the LOSC practice. If respondents have a membership of the hotel’s loyalty reward program, they tend to perceive the hotel’s minimum LOSC to be less fair than those who do not. The hotel’s strict policy on the minimum stay is perceived to be unfair and to mistreat the respondents when considering their business commitment to and investment in the hotel up to now, which they think would be a potential loss of benefits given by the hotel. Thus, hotels must cautiously develop their RM practices, in particular, in implementing the LOSC approaches for customers who are a member of their loyalty reward program.

Since customers’ perceived fairness of hotels’ RM practices is a key indicator for measuring their purchase intentions, this study assesses these relationships by using two endogenous constructs: NWOM and WTB. As proved in the previous studies (e.g. Choi and Mattila, 2005; Noone, 2012), this study asserts that respondents’ perceived fairness toward hotels’ RM practices, especially the LOSC technique, affects their NWOM and WTB. Generally, hotels’ tight restrictions to the minimum LOSC during a prime season are not favorably and fairly accepted by respondents and they feel much worse if respondents are a member of a hotel’s loyalty reward program.

Due to the experimental nature of the current study, two treatments were developed and tested to measure respondents’ fairness perceptions of the LOSC practice, considering the hotel’s loyalty reward program membership as a moderator. In order to measure the effects of the minimum LOSC on the fairness perception, the LOSC practice was manipulated by two levels of conditions, Yes (presence of the minimum LOSC) and No (no presence of the minimum LOSC or the commonly used RM practice, pricing method as a counterpart of LOSC). In this way, this study can measure the genuine effects of the minimum LOSC, compared to the commonly used RM method in the industry, on customers’ fairness perceptions of both RM practices.

Implications

This study offers both theoretical and practical implications for the fields in hospitality RM. Even though hotels’ minimum LOSC has been one of the most frequently implemented RM practices, they are not commonly recognized by the general public because room availability and rates are hidden if the information submitted by people is not met with hotels’ LOSC requirements. Is it a fair practice for hotels to implement for the purpose of maximizing their revenues? The present study answers this question. Findings of this study provide a lesson for hotel management showing that the LOSC practice is perceived to be less fair than the pricing practice (i.e. higher or highest room rates charged during the peak season) and the LOSC practice must be cautiously implemented for those who are a member of a hotel’s loyalty reward program. The loyalty reward members seem to be not happy with the LOSC practice and seriously view such a practice as unfair and, in turn, their perceived unfairness leads to NWOM and a decrease in their WTB a room in a near future.

Findings of this study assert that there exists a negative relationship between the minimum LOSC and fairness perception, leading to NWOM and WTB. For the authors’ best knowledge, it might be a first attempt to measure the effects and magnitude of both RM practices on customers’ fairness perceptions and offer a new research horizon in assessing both RM practices from the perspectives of customers’ fairness and hotels’ effective implementation. Adopting the distributive fairness as part of the justice theory in the study’s framework, this study examines respondents’ perceived fairness toward hotels’ minimum LOSC, compared to the room rate-focused RM practice. Between the two RM practices, the minimum LOSC appears to be much unfairer than the other RM practice.

Findings of this study also proves that the hotel’s loyalty reward program plays a moderating role in the relationship between RM practices and fairness perceptions. Respondents’ business commitment to or additional investment to the hotel turns out to have much stronger interaction effects on their perceived fairness toward the minimum LOSC, compared to the other RM practice. These results introduce a new moderating factor, a hotel’s loyalty reward program, into the RM study, besides RM information (Choi and Mattila, 2005) and RM familiarity (Wirtz and Kimes, 2007).

In order to minimize potential negative effects of LOSC practices on customers’ behavior intentions, hotels must cautiously implement LOSC practices for those who are a member of the loyalty program by providing sufficient information of their LOSC practices or alternative options when they try to book a room for a one-night stay during the peak season. As indicated in the study by Wirtz et al. (2003), this study suggests that hotels need to consider preferred availability policies for loyal customers and build clear communication channels about their LOSC practice with them. These strategies will ultimately reduce customer dissatisfaction while enhancing a long-term relationship and leveraging the appropriate level of revenues. Hotels may want to educate their customers about the LOSC practice in order to reduce negative reactions toward this practice (Kimes and Wirtz, 2004).

Limitations and suggestions for future research

Despite the advantages of using a scenario-based experimental design, this study has some limitations. First of all, respondents are asked to imagine that they want to book a luxury hotel room for celebrating “New Year’s Eve” in New York City. While a daily room rate is manipulated based on the demand, respondents may have a different price appreciation depending on their income level. Therefore, the responses are vulnerable to personal biases. Even though participants in this study were recruited from the panel of online marketing research company and our sample had a relatively good distribution by its socio-demographic categories, this sample may not fully represent the general population, resulting in the lack of generalizability of findings of our study. In addition, we developed and presented the scenarios, manipulating LOSC conditions so that participants were able to recognize that hotels employed LOSC practices in their online reservation systems. While consumers become more aware of price differences and different room availability when they book a hotel room with the help of various online search tools and information sources (Chen and Schwartz, 2006), in practice, there is still a possibility that some potential customers might not know the real meaning of no available room at the time they make a reservation.

While this study examines the moderation effect of customers’ membership status of the hotel’s loyalty reward program, several studies have shown that providing information about the hotel’s pricing RM practices have a positive impact on customers’ perceived fairness (Choi and Mattila, 2004, 2005; Kimes, 1994). More specifically, when given information on the hotel’s RM practices during the reservation process, customers perceive the practices to be fair. Therefore, future research should investigate the moderating effect of information about LOSC practices on customers’ fairness perceptions.

In order to measure the effects of the minimum LOSC on customers’ perceived fairness, this study manipulated different types of RM practices and the hotel loyalty reward program as a moderator. Due to the unique nature of experimental design, we only considered LOSC practices and hotel loyalty reward program, leading to relatively low R ² values for perceived fairness and NWOM constructs. While it is not surprising to see such small explanatory power by controlling other influential factors, it is noteworthy that future research will examine the availability of LOSC information disclosure (e.g. Choi and Mattila, 2004), the level of consumer’s familiarity with LOSC (e.g. Noone and Mattila, 2009), socio-demographic information (Hwang and Wen, 2009), brand class (Taylor and Kimes), and cultural orientation (Beldona and Kwansa, 2008) to better predict customers’ perceived fairness toward LOSC. While customer’s perceived fairness is the main determinant of NWOM (Blodgett et al., 1993), other antecedents such as product importance, attitude toward complaining, and controllability need to be considered to strengthen the model’s overall explanatory power of NWOM. Furthermore, future research can measure specific fairness perceptions including distributive, procedural, and interactional fairness (Blodgett et al., 1993) to predict NWOM and WTB (Blodgett et al., 1993, 1997).

Supplemental material

supplement_material - Length of stay control: Is it a fair inventory management strategy in hotel market?

supplement_material for Length of stay control: Is it a fair inventory management strategy in hotel market? by Minwoo Lee, Miyoung Jeong and Linda J Shea in Tourism Economics

Footnotes

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 iD

Minwoo Lee

Supplemental material

Supplemental material for this article is available online.

Note

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