A meta-analysis of hotel revenue management

Binesh, Fatemeh; Belarmino, Amanda; Raab, Carola

doi:10.1057/s41272-020-00268-w

Cited by 11 publications

(13 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…scale in the context of hospitality generally defined as a management process regarding pricing strategy, demand modeling and forecasting, inventory and price optimization, distribution channel management, and performance evaluation (Baker et al, 2020;Binesh et al, 2021;Denizci Guillet, 2020;Sainaghi, 2020). RMP is therefore the pricing aspect of RM (Ng et al, 2017;Xu et al, 2019).…”

Section: Measurementmentioning

confidence: 99%

Perceived unfairness of revenue management pricing: developing a measurement scale in the context of hospitality

Meatchi

Camus

Lecointre-Erickson

2021

IJCHM

View full text Add to dashboard Cite

Purpose This paper aims to offer a multi-dimensional scale for measuring the concept of perceived unfairness of revenue management pricing (RMP) in the context of hospitality. Design/methodology/approach To develop a measurement scale for the perceived unfairness of RMP, the authors conducted a qualitative study using the critical incident technique to identify the key components of our measurement tool. They then collected two samples of quantitative data enabling them to have compelling evidence of the scale’s reliability and validity. Findings This research identified three dimensions of perceived unfairness of RMP in the context of hospitality: perceived normative deviation, perceived opacity and negative effects. The new scale proposed here is an alternative measurement instrument that could be useful for detecting and correcting some negative aspects of RMP. Practical implications This measurement scale will help hotel managers to detect potential feelings of unfairness in relation to the RMP policies. It might also be used within the framework of market analyses and pricing strategy plans. Finally, the results of this research show that transparency, fairness and ethics based pricing could help hotel managers increase their revenue-per-available-room during and post COVID-19 pandemic. Originality/value This research develops a complete measurement scale for perceived unfairness of RMP, including cognitive and affective dimensions. The richness of this scale will help hospitality companies effectively identify the indicators that denote perceived unfairness of RMP, making them better equipped to handle customer dissatisfaction.

show abstract

Section: Measurementmentioning

confidence: 99%

Perceived unfairness of revenue management pricing: developing a measurement scale in the context of hospitality

Meatchi

Camus

Lecointre-Erickson

2021

IJCHM

View full text Add to dashboard Cite

show abstract

“…In the third segment of the review (Table 3), denoted as part C, the focus shifted to systematically examining the intersection of tourism with artificial intelligence (AI), economic growth and/or CO 2 emissions. Binesh et al (2021) conducted a comprehensive meta-analysis of hotel revenue management literature, identifying key themes, methodological approaches, and geographical trends while highlighting research gaps and avenues for future investigation. Bhuiyan et al (2021) delved into the multifaceted consequences of disasters and pandemics, notably COVID-19, on tourism, economies, and mitigation strategies, synthesizing a wealth of research findings and providing directions for future exploration.…”

Section: Screening Resultsmentioning

confidence: 99%

Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model

Gricar

2023

JRFM

View full text Add to dashboard Cite

This study delves into the intricate process of predicting tourism demand, explicitly focusing on econometric and quantitative time series analysis. A meticulous review of the existing literature is carried out to comprehensively understand the various methods for forecasting “unpredictable” shocks of tourism demand on an ex-ante basis. The PRISMA method has been implemented. Drawing on scholarly research, this study pinpoints the critical challenges in accurately predicting tourism demand, making it a valuable resource for tourism professionals and researchers seeking to stay on top of the latest forecasting techniques. Moreover, the study includes an overview of published manuscripts from the current decade, with mixed results from the 32 manuscripts reviewed. The study concludes that virtual tourism, augmented reality, virtual reality, big data, and artificial intelligence all have the potential to enhance demand forecasting in time series econometrics.

show abstract

“…It is crucial to mention that current research mainly focuses on dynamic pricing (Abrate et al, 2019;Binesh et al, 2021;Vives et al, 2018) while not considering differences in capacity allocation in the market segments. This is mainly caused by the fact thatthe current studies mainly focus on predefined market sub-segment (commonly groups and individual) and do not consider the diversification of the market strategies and their combination.…”

Section: Discussionmentioning

confidence: 99%

Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach

2022

QAS

View full text Add to dashboard Cite

This paper discusses the problem of hotel room sales, emphasizing the allocation of a specific amount of rooms for predefined market segments using the extension of the Expected Marginal Seat Revenue model, which can be perceived as one of the crucial activities in modern revenue management. The proposed extension of this model can improve data-based decision-making quality and the service provider's overall performance. This model uses stochastic non-linear programming to solve the problem of room allocation for the market segment and deals with the booking limits to help decide whether to accept or decline the booking request based on the behaviour of selected market segments or the expected current state of available capacities and other elements. To extend the model to fit better the managerial decision-making, the factors of no-shows and cancellations are implemented. The proposed model should be used to strengthen the quality of revenue management-focused decisions mainly focused on dynamic pricing, and the room allocation is omitted. The model respects the need for a more comprehensive perception of the problem and contains data-based factors like quantifying customer (or customer segment) behaviour and no-shows and cancellations that affect the selling strategy.

show abstract

A meta-analysis of hotel revenue management

Cited by 11 publications

References 61 publications

Perceived unfairness of revenue management pricing: developing a measurement scale in the context of hospitality

Perceived unfairness of revenue management pricing: developing a measurement scale in the context of hospitality

Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model

Re-solving Extended Expected Marginal Seat Revenue Model Using Stochastic Approach

Contact Info

Product

Resources

About