The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

Purpose The length of stay (LoS) is of major importance from the perspective of the management of tourist destinations. As tourists heavily rely on the online reviews of other travelers as a primary information source, this study aims to empirically examine how the LoS can influence the online reviews for hotels, with special emphasis on the textual review content. Design/methodology/approach This study analyzes online review data collected from Booking.com by using the Linguistic Inquiry and Word Count program to operationalize review depth, analytical thinking and the authenticity reflected in customer reviews. Based on the analyzed data, this study used a series of regression analyses to understand the impacts of the LoS on online reviews. Findings The author’s analysis found that a longer stay at a hotel causes consumers to be more likely to post online reviews that not only include a numerical rating as well as written content but also lengthier and more detailed descriptions of their hotel experiences. Further analysis found that the LoS at hotels causes systematic differences in the linguistic attributes of the review content. Specifically, consumers who stay longer tend to write reviews with more analytical information, resulting in consumers perceiving the online reviews as more authentic. Research limitations/implications Although the LoS has been considered a significant issue in tourism, studies examining the impact of different lengths of stay on consumers’ post-purchase behaviors are limited. In this light, the author’s findings demonstrate how the LoS can change the linguistic attributes of online reviews. It expands the body of knowledge of the LoS in tourism. Originality/value This study represents the first attempt to empirically examine and reveal how the different length of stay at a hotel systemically influences consumer review-posting behaviors.

Section: Discussionmentioning

confidence: 99%

Impact of the length of stay at hotels on online reviews

Kim

Han²

2022

“…The most recent research has used more sophisticated theoretical and methodological approaches, beyond the typical reliance on survey data and linear choice models, to understand the complex, nonlinear dynamics of hotel choice. For example, using machine learning and a decision tree model, Lee and Kim (2021) examined online travelers’ booking behavior on Expedia.com considering geographical distance between origin and destination. Wang et al (2020) proposed a picture fuzzy the interactive multi-criteria decision making (TODIM) method based on bounded rationality behavioral decision-making to identify differences in preferences among five traveler types.…”

Section: Literature Reviewmentioning

confidence: 99%

How do consumers select between hotels and Airbnb? A hierarchy of importance in accommodation choice

Mody

Jung

Doğru

et al. 2022

Purpose The purpose of this study is to examine the impact of key decision-making attributes on consumers’ choice of accommodation among and between hotels and Airbnb. Design/methodology/approach The study used a choice-based conjoint approach using 21 key decision-making factors that impact consumers’ choice of accommodation across five segments ranging from economy to luxury. Latent class estimation was used to identify segments of respondents who tend to have similar preferences for accommodation. Findings The results showed the presence of a consistent pattern of decision-making across the five accommodation segments, culminating in a hierarchy of importance in accommodation choice. The 21 key decision-making attributes comprised three tiers in order of decreasing importance: quality and service, amenities, and accessibility and safety. Further, latent class analysis indicated the presence of a hotel group and an Airbnb group of customers, which allowed us to identify how both types of providers might maximize the value of their offers to encourage customer switch. Research limitations/implications The accommodation landscape is extremely dynamic (particularly as the COVID-19 pandemic unfolds) and complex. The present study cannot capture all of its intricacies but provides an invaluable foundation for future research on the topic of consumer choice in an evolving and competitive accommodation market. Originality/value Extant research on accommodation choice has focused on hotels or Airbnb only. Moreover, research that has considered both types of accommodation simultaneously is limited in its conceptual and methodological scope. The present study synthesizes the fragmented literature on consumers’ accommodation choices and offers a holistic and coherent schematic – the hierarchy of importance in accommodation choice – that can be used by future researchers and practitioners alike.

“…The LoS has been suggested to potentially affect how a consumer makes a decision (Mariani et al , 2019). However, few prior studies have tried to examine this issue (Kim and Han, 2022; Lee and Kim, 2021). The LoS has primarily been studied as an outcome variable in the extant literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The LoS has primarily been studied as an outcome variable in the extant literature. In line with this, the literature has attempted to identify determinants and situational constraints in tourists’ decision-making regarding LoS at a hotel (Lee and Kim, 2021). Only a few studies have examined how LoS influences post-purchase behaviors (Kim and Han, 2022; Wang et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, scholars in the hospitality and tourism fields have paid close attention to exploring Online customer reviews the determinants of LoS at tourist destinations (Alegre and Pou, 2006), as this understanding would assist hotel managers in effectively enticing prospective customers to opt for longer stays (Peypoch et al, 2012). Thus, LoS has mainly been studied as a dependent variable (Lee and Kim, 2021), particularly in terms of its relationship with profitability and revenue management (Nair, 2019). However, studies investigating LoS as a predictor of consumers' post-purchase behaviors, particularly online review-posting behaviors regarding consumption experiences, are rare (Kim and Han, 2022;Wang et al, 2018), although there have been studies suggesting the potential influence of LoS on customer decision-making processes (Mariani et al, 2019;Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Examining the negative relationship between length of stay at a hotel and customer satisfaction: evidence from online customer reviews

Kim

Han

2023

Purpose Studies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online customer reviews, this study aims to fill this gap in the extant literature on the relationship between length of stay and customer satisfaction level. Design/methodology/approach The authors collected and used online review data on hotels in London for this study. A series of linear regression analyses were conducted to examine the effect of length of stay on customer satisfaction as measured by review ratings. The authors used the Mahalanobis matching approach to confirm the empirical findings. Findings This analysis shows that length of stay is negatively associated with customer satisfaction. Additionally, the authors find that this negative relationship is stronger in high-end hotels than in low-end hotels. Research limitations/implications The research findings contribute to the literature by shedding light on a new stream of research, namely, length of stay. Additionally, the research findings offer novel insights that could help hotel managers understand the trade-off between longer stays and customer satisfaction. Originality/value To the best of the authors’ knowledge, this is one of the first few studies to show the systematic impact of length of stay on the valence of online review ratings, as well as the moderating effect of hotel levels by analyzing customer online reviews on hotel experiences.