Optimal room charge and expected sales under discrete choice models with limited capacity

Saito, Taiga; Takahashi, Akihiko; Tsuda, Hiroshi

doi:10.1016/j.ijhm.2016.06.006

Cited by 11 publications

(19 citation statements)

References 22 publications

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“…Big data analytics is also one of the most notable themes for real‐world revenue management practice for hospitality and tourism research (Wang et al, 2015). Saito et al (2016), in their study about revenue management, introduced an optimal room charge and expected sales model based on features of the fully transparent hotel booking system. Jackson (2016) discussed the dichotomy between explanation and prediction.…”

Section: Discussionmentioning

confidence: 99%

Automated content analysis: The review of the big data systemic discourse in tourism and hospitality

et al. 2021

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Tourism is one of the economic activities that has changed most since large‐scale multidimensional data set, so‐called big data, was provided. The scientific literature on big data and tourism with the hospitality industry has been growing exponentially, which is, for this reason, hard to comprehend and synthesise manually. Our research question is how to familiarise oneself with the current knowledge of the relationship between the big data and the complex changes in the tourism and hospitality industry. An automated content analysis procedure was used to identify key topics and concepts of interest to the researchers. A careful review of the abstracts and full texts was carried out, resulting in a final list of 308 papers published in 154 journals. We used the Leximancer software 5.0 to create an automated content analysis. Seven topics (tourism, data, search, model, industry, online and travel) from papers published between 2011 and 5/2020 were found. We propose future development trends of big data in sustainable tourism.

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Section: Discussionmentioning

confidence: 99%

Automated content analysis: The review of the big data systemic discourse in tourism and hospitality

et al. 2021

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“…Despite the general critical elements to determine hotels’ optimal information-sharing behaviors (Nash equilibrium) in previous research, this study identified market demand as the most significant indicator in determining hotels’ information-sharing behaviors. As previous research (Krishna, 2007; O’Connor, 2003; Saito et al, 2016) indicated, hotels use several different tools (e.g., web scrapping services, STR reports) to learn about competitors’ private information (e.g., key performance indicator or KPI). However, it is hotels that determine whether they should submit their private demand information to STR, which makes it available to other hotels in the marketplace, or they should publish information to the third parties.…”

Section: Discussionmentioning

confidence: 99%

“…Each hotel has a constant marginal cost (operating costs) for each unit sold (Gu, 1997; Saito et al, 2016), but different hotels can have different costs. An upscale hotel would have higher marginal costs than a midscale one, which in turn would have higher costs than an economy hotel.…”

Section: Model Buildingmentioning

confidence: 99%

“…For example, due to the perishable nature of the product (i.e., room nights), hotels offer discount rates when demand is weak but not during strong demand. This way, hotels need to sell more rooms daily even though the prices may be discounted as empty rooms represent a lost opportunity to generate revenues (El Haddad, 2015; Saito et al, 2016). Given the competitive nature of the industry, it is critical for decision makers (e.g., hotel managers) to understand the dynamics of average daily rates (ADR) and occupancy.…”

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confidence: 99%

“…While some information about room prices is easily determined from such websites, many factors of significance cannot be learned from the respective websites. Although discounted pricing information is available, direct indicators, such as occupancy rate, average room rate, gross operating profit, marginal cost, and room sales are not (Chattopadhyay & Mitra, 2019; O’Neill & Mattila, 2006; Saito et al, 2016). This information is only fostered through information systems whereby managers have access to historical data used for budgeting and financial decisions.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimal Information-Sharing Behaviors among Hotels: Game-Theoretical Approach

Kim

Slutsky²,

Thapa

2020

Journal of Hospitality & Tourism Research

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The lodging industry uses diverse pricing strategies to maximize revenues. Given the uncertain nature of decision parameters (demand conditions), sharing information among competitors can dramatically affect hotels’ profits. This study examines the decisions of hotels to share or not to share private information with competitors to maximize profits via a game-theoretic model. In a two-stage model, hotels decide whether to share information in the first stage, and then, in the second stage, they compete in setting prices along the lines of a standard price competition model. Results include that hotels share demand information with their competitors if there is a low demand signal but conceal information if the demand signal is high. This study provides a standard price competition model that can assist hotel managers in decision making on revenue management, including room pricing strategy.

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Modeling Tourism Using Spatial Analysis Based on Social Media Big Data: A Review

Chen

Alfred

Eboy

2021

Lecture Notes in Electrical Engineering

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Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data has been analyzed in many different disciplines. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on analyzing the appropriate techniques used to handle different types of data for the purpose of social media analytics. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that three types of data that were least used for social media analytics that includes Bluetooth, WIFI and mobile roaming data. In contrast, other types of data have received more attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analysis social media data.

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Optimal room charge and expected sales under discrete choice models with limited capacity

Cited by 11 publications

References 22 publications

Automated content analysis: The review of the big data systemic discourse in tourism and hospitality

Automated content analysis: The review of the big data systemic discourse in tourism and hospitality

Optimal Information-Sharing Behaviors among Hotels: Game-Theoretical Approach

Modeling Tourism Using Spatial Analysis Based on Social Media Big Data: A Review

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