Does Willingness to Pay for Rate Conditions Depend on the Booking Window? A Novel Time-Dependent Conjoint Analysis Approach

Abstract

This research note draws attention to a novel choice-based conjoint (CBC) model by which guests’ preferences toward hotel room rate conditions can be predicted as a function of time. Through the model, the notion is explored whether willingness to pay for rate conditions (without penalty) depends on the size of the booking window. The modified choice model includes time as an additional “attribute.” This attribute does not present a feature of the choice propositions, but instead is associated with the choice context. An empirical study was carried out to demonstrate the proposed model using three common booking conditions (i.e., free cancelation, free date change, pay on departure). The results show significant and positive time-dependent mean components for cancelation and date change conditions. Despite a limited sample size, all significant effects have expected directions (no parameter reversals) providing evidence of the robustness of the model. The results indicate that the ability to cancel or change a booking is preferred more when the booking window is bigger. Accordingly, as the time period between the advanced purchase and intended date of arrival increases, the willingness to pay for rate conditions also increases. This finding has major practical implications for price and revenue optimization. Limitations of the proposed time-dependent model are discussed.

Strategic consumer behavior in online hotel booking

“…Third, our findings provide additional empirical support for recent studies in hotel-booking behavior. By introducing the role of risk attitude, our study adds further evidence to previous works that outline the time dependency of hotel booking decisions (Arenoe & van der Rest, 2019).…”

“…Specifically, consumers will attach more value to this rate when the lead time is long, as it helps overcoming uncertainty toward future outcomes. Indeed, the ability to cancel or change a booking is preferred more when the booking window is longer (Arenoe & van der Rest, 2019). Therefore, we posit that the utility of the free cancellation rate increases as the booking window increases.…”

Willingness-to-pay for robot-delivered tourism and hospitality services – an exploratory study

“…Hence, disagreement with the statement means the respondents are not willing to pay more, although it is unclear whether they are willing to pay the same or less and the agreement does not indicate how much more respondents are willing to pay. Discrete choice analysis/conjoint analysis in which respondents are given product options with different prices and attributes, one of which is automation (Arenoe and van der Rest, 2020;Daziano et al, 2017). It allows the calculation of the implied WTP for individual product attributes.…”

You don’t have to tip the human waiters anymore, but … Unveiling factors that influence consumers’ willingness to pay a price premium for robotic restaurants

“…Various approaches have been adopted by previous researchers to measure consumer WTPp, including Vickrey fifth-price full bidding auction (Vecchio, 2013), discrete choice analysis (Masiero et al , 2015), conjoint analysis (Arenoe and van der Rest, 2020), contingent valuation (Veisten, 2007), agreement/disagreement scale (Hwang and Kim, 2021) and predetermined price anchors (Namkung and Jang, 2017). In the field of automated technologies, Cunningham et al (2019) used a four-point Likert scale to assess the willingness of consumers to pay for fully autonomous cars compared to manually driven cars (1 = less, 2 = the same, 3 = more and 4 = much more).…”