Revenue Management Strategies Research Articles

Unveiling customer choice with salience theory: The link between room price and breakfast demand

Salience theory posits that decision-makers pay more attention to the most outstanding—salient—attributes of available options, ultimately impacting decision-makers’ choices. This study proposes extending this theory to the decision of adding an extra component to a product, with special significance for the hospitality industry. Hotels tend to charge a fixed amount to add breakfast to a reservation. Drawing on the salience theory, we show that this constant surcharge makes the demand for breakfast-included rooms dependent on the room’s price. The empirical application conducted on a sample of over 22,000 reservations supports the predictions that the probability of selling breakfast-included rooms rises (1) after a room price increase and (2) less so if consumers do not anticipate the price hike. Beyond the critical theoretical extension, this paper brings relevant managerial implications for dynamic pricing strategies for breakfast, which, in turn, may become a game changer for hotel revenue management strategies.

Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management

Small and medium-sized enterprises (SMEs) are vital contributors to global economies, but they often face unique financial challenges that threaten their long-term stability. This review presents a comprehensive analysis of forecasting financial stability in SMEs, focusing on strategic budgeting and revenue management as key pillars. Strategic budgeting plays a crucial role in aligning financial plans with business goals, managing risks, and adapting to market changes. Techniques such as zero-based budgeting, activity-based budgeting, and rolling budgets offer SMEs flexibility in allocating resources and ensuring optimal cash flow management. Meanwhile, effective revenue management involves the use of pricing strategies, sales channel diversification, and customer segmentation to optimize revenue streams and improve profitability. The interplay between strategic budgeting and revenue management is critical for creating dynamic forecasting models that help SMEs anticipate financial challenges and make informed decisions. By integrating both strategies, SMEs can better manage risks, enhance financial predictability, and achieve long-term sustainability. This review also explores the role of technological tools, such as financial software and predictive analytics, in supporting financial decision-making processes. Tools like customer relationship management (CRM) systems and AI-driven forecasting platforms enable SMEs to optimize revenue potential and refine budgetary practices. The review provides recommendations for SMEs to implement best practices in financial planning, suggesting regular budget reviews and proactive revenue management strategies to respond to market fluctuations. By leveraging strategic budgeting and revenue management, SMEs can enhance their financial resilience and improve their capacity to navigate economic uncertainties. The review concludes with insights into emerging trends in financial forecasting, emphasizing the importance of continuous adaptation to maintain financial stability.

Determining factors affecting the upselling acceptance of business class seats among Filipino passengers: An extended theory of planned behavior approach

Upselling of business class seats has been implemented by many commercial airlines as part of their revenue management strategies. The purpose of this was to determine factors affecting the upselling acceptance of business class seats among Filipino passengers by utilizing an extended Theory of Planned Behavior (TPB) approach. A total 323 Filipinos voluntarily answered an online questionnaire that was distributed through a purposive sampling approach. The questionnaire consisted of 41 items that covered various factors such as Price (PR), Perceived Value (PV), Social Status (SS), Facilitating Conditions (FC), Hedonic Motivation (HM), Subjective Norm (SN), Attitude (A), Perceived Behavioral Control (PBC), and Intention (I). Partial Least Squares Structural Equation Modeling (PLS-SEM) showed SN was the most significant factor affecting passengers' intention to accept business class upselling offer, followed by PBC, PR, A, and FC. In addition, PV, FC, and A had significant effects on PBC which subsequently led to I. Furthermore, HM, P, and SS had significant effects on A which subsequently led to SN and I. Meanwhile, only two hypotheses were not supported by the study. This study is one of the first studies that explored the upselling in the business class seats using behavioral factors and additional variables to determine their intentions. The results of this study could help the airline industry to evaluate their strategy in revenue management. Finally, the findings of this study add to the current literature and help airlines’ marketing strategy and promote seat bidding.

The impact of intermediate option availability on consumers' choice: The moderating role of price roundness

Given the surge in consumer demand and rapid inflation, businesses have turned their attention to premiumization as a means of market segmentation. Building upon previous studies that highlight the significance of the compromise effect in gaining market share for intermediate options, this research examines the feasibility of revenue management strategies using 'psychological pricing' in hospitality and tourism products. By investigating choice behaviors related to intermediate options, we explore how subtle pricing manipulations can enhance the compromise effect. Through five experiments, we validate our hypotheses. Our findings not only extend the literature on pricing behavior and hospitality concerning rounded pricing but also provide insights on how to strategically adjust psychological pricing to align with real-time market conditions.

IMPLEMENTATION OF HOTEL REVENUE MANAGEMENT STRATEGIES DURING A MARTIAL LAW

The Hotel Revenue Management strategy is used to measure the future occupancy, ADR, REVPAR, TREVPAR. Prices impact on the further development of business models implemented by the hotels is analysed. The purpose of this study is to determine the influence of Revenue Management Strategy on KPI and Revenue indicators at hotels in Ukraine. The research method used is a quantitative descriptive research method. The study used a sample of 12 respondents with 8 statements. Data were collected by observing participants, reviewing company documents, and seeking input from key informants. In crisis conditions, in particular caused by martial law in Ukraine, hotels reorient themselves to new segments, study needs and adapt services to new security requirements. One of the ways to increase efficiency and occupancy can be revenue management strategies adapted to crisis or post-crisis situations. We have proposed a typology of income management strategies adapted to new conditions.

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.

The impact of dynamic pricing on restaurant customers’ perceptions and price sentiment

Purpose This study aims to examine how dynamic pricing impacts customer perceptions of restaurants and sentiment toward prices via online reputation metrics. In addition, to deepen the debate on dynamic pricing, a novel definition is drawn by exploring the specific forms of discrimination that can manifest in different industries. Design/methodology/approach Leveraging a comprehensive data set of restaurant reviews sourced from TripAdvisor, the study focuses on restaurants affiliated with one of the largest groups of restaurants in Spain. We used a quasi-experimental method (difference-in-differences), to study how dynamic pricing strategies influence customers’ perceptions of value based on numerical ratings. Meanwhile, we used a Bidirectional Encoder Representations from Transformers model on the textual component of reviews to dissect the emotional nuances of dynamic pricing. Findings Results did not reveal a causal impact of dynamic pricing strategies on customers’ perceptions. Moreover, the sentiment analysis shows no heightened negative view after introducing dynamic pricing in restaurants compared to the control group. Contrary to what previous literature suggests, our findings indicate that implementing dynamic pricing does not adversely affect customers’ perceptions or sentiments regarding prices in restaurants. Research limitations/implications The quasi-experimental setting of the study presents inherent challenges in establishing causality that require further investigation using controlled experimental settings. Nevertheless, our study reveals that restaurant customers do not perceive dynamic pricing as unfair. This finding is critical for restaurant managers when considering the implementation of dynamic pricing and revenue management strategies. In addition, our study highlights the importance of considering not only numerical ratings but customer sentiment analysis as well. This more holistic approach to assessing the impact of pricing strategies can give restaurant managers a deeper understanding of customer reactions. In addition, a more rigorous definition of dynamic pricing is provided, clarifying its nature and its distinction in using different price discrimination. Originality/value This study contributes to the evolving understanding of dynamic pricing strategies’ impact on customers’ perceptions and sentiments in the restaurant industry. It aims to fill the gap in understanding customer reactions to algorithmically determined prices (via revenue management systems such as DynamEat) in this industry. The combination of causal inference and sentiment analysis offers a novel perspective, shedding light on the nuanced connections between dynamic pricing implementation and customers’ emotions.

Host Scaling and Revenue Management: Is there a link?

This study explores how a revenue management capability can be created by Airbnb hosts and the role played by their degree of professionalization, defined as the number of listings (apartments) the hosts manage. Considering the explorative nature of this research, a qualitative approach based on interviews has been adopted. The sample includes 55 interviews realized in the Milan destination (Italy). A protocol was created using the four theoretical blocks. These latter identify the main processes of revenue management: i) identifying the required information to establish the revenue management strategy, ii) making strategic and then tactical pricing and revenue management decisions, iii) managing communication with the guests, and iv) making available software dedicated to revenue management. Milan was selected for two reasons: first, is the second leading Italian destination after Rome in terms of overnights. Second, Milan attracts different targets, such as business, leisure, and trade fairs. Therefore, there are continuous changes in off- and on-pick revenue constituting an ideal setting for exploring the creation of a revenue management capability.Three groups of hosts were identified: i) single hosts, ii) hosts managing three to five listings, iii) hosts managing more than 10 listings. Results show strong differences between single hosts and those managing 10 or more listings, explaining why professional hosts are able to extract more value from their listings compared to single hosts. Two theoretical contributions are proposed. First, the research adds to the knowledge about how a revenue management capability (RMC) is created in the context of short-term rentals (STR). The crucial role of professionalization is confirmed, and a complex set of relationships emerge between tangible (such as the presence/absence of a RM software and dedicated staff) and cultural (such as the orientation towards price discrimination and to revenue or price maximization, as well as the approach to guest selection) barriers to RMC. Second, findings can explain why quantitative studies have sometimes shown controversial results on factors affecting price and revenue performance. Interviews with multiple hosts highlighted how they often manage third-party listings based on agreements that include a limit price, reducing the rooms for dynamic and discrimination price, and more generally revenue maximization.

The curious case of Hotel King’s Cross

Learning outcomes The learning outcomes of this study are to recall the fundamental concept of revenue management in the hotel industry (remembering); explain the various performance measures used to evaluate room revenue in hotels (understanding); use revenue management strategies to improve room revenue in hotel operations (applying); and examine and evaluate the optimal solution for revenue enhancement, considering factors such as capacity management, duration control and differential pricing (analyzing). Case overview/synopsis This case study delves into the challenges faced by Hotel King’s Cross, a business hotel located in Pune, Maharashtra, in the year 2022. A week before Christmas Eve, Soham Dande, the hotel’s revenue manager, sought a meeting with Rohan Chopra, the director of sales and marketing, to discuss “revenue optimization for the hotel.” During their meeting, Dande mentioned that the hotel had fallen behind its budgeted room sales targets for 2022 across various metrics, such as room booking nights, occupancy percentage, average room rate and revenue per available room. Furthermore, the hotel was trailing behind its competitors. The situation was compounded by the management’s decision to raise the targets for 2023 by 5%–7%, factoring in upcoming events, competitive performance and pandemic-related losses over the past two years. Chopra faced the dilemma of formulating an action plan to achieve the ambitious 2023 targets and establish Hotel King’s Cross as a market leader. Complexity academic level Students undertaking executive development programs and graduate-level courses in non-profit hospitality and tourism management, as well as revenue management courses in the executive MBA, management development and graduate MBA programs, may all benefit from this case study. Supplementary materials Teaching notes are available for educators only. Subject code CSS12: Tourism and hospitality.

Revenue Management Strategies in Airline Industry: A Literature Review

The airline industry has rapidly evolved, fostering intense competition among companies. This competition drives airlines to formulate strategies for revenue maximization, giving rise to Revenue Management. This literature review spans the past 13 years, examining the development of Airline Revenue Management methods. By analyzing 22 journals with at least Q2 and SINTA 2 indexing, three primary scopes emerge: Quantity Decision, Pricing Decision, and Structural Decision. Airlines predominantly employ dynamic pricing and programming to optimize revenue by adapting to ongoing changes. The development trend in Airline Revenue Management indicates a shift towards faster and more accurate processing through increased integration with simulation and algorithm programming. This paper identifies the three main scopes involved in revenue management strategies and explores the diverse approaches airlines take to optimize income. Notably, dynamic pricing and programming remain prevalent methods, adapting to changing decision variables. The evolving landscape emphasizes integration with advanced technology for efficient processing. The study utilizes numerical and case studies to exemplify the ongoing development of Airline Revenue Management methods within this dynamic industry.

The Effect of Electronic Fiscal Device Management Systems on Revenue Collection: A Case of Ilala Municipal Council

The study examined the effect of the electronic fiscal device management systems on Revenue Collection: A Case of Ilala Municipal Council. The study adopted a convergent research design. The sample of 387 respondents was selected through simple random and purposive sampling. Data collection involved self-administered questionnaires and interviews to gather primary data. The Quantitative data were analyzed using descriptive and inferential statistics by the help of Software Package for Social Science version 26, while qualitative data was analyzed by using content analysis. The results from the findings indicated a positive correlation coefficient of 0.650 between Fiscal Device Management Systems and Revenue Collection. The regression model for Fiscal Device Management Systems as the predictor yielded a coefficient of determination (R squared) value of 0. 523 meaning that the fiscal device management system could explain the increase in revenue collection by 52.3% and thus to optimize the effect of information systems on revenue collection, the council should prioritize user engagement and education. Empowering users with knowledge and training on these systems is likely to increase adoption rates and improve revenue collection. Moreover, conducting comparative studies across different LGAs in Tanzania could offer valuable insights into how similar factors influence revenue collection in diverse administrative settings, leading to a more comprehensive understanding of effective revenue management strategies.

The Impact and Significance of Implementing the Income Cycle on MSMEs: A Case Study of Bakso Mbah Min Tangsel City

This research explores the impact and significance of implementing the income cycle on micro, small and medium enterprises (MSMEs), especially those represented by the Mbah Min Meatball Trader in Pamulang, South Tangerang. By using relevant theories and frameworks, this research aims to present a better understanding of how the implementation of the revenue cycle affects the financial, sustainability and development aspects of these MSMEs. An in-depth analysis of revenue management strategies, their impact on liquidity, profitability, and the business's ability to face market challenges is the main focus of this research. The expected result of this research is to provide a richer and deeper view of the benefits, challenges and implications of implementing the income cycle for MSMEs in the food industry, especially for the Mbah Min Meatball Trader. It is hoped that the practical implications of this research can contribute to improving financial management strategies and business growth for similar MSMEs and serve as a guide for relevant stakeholders in supporting the development of MSMEs in the region.

Revenue management: changes in the analytical landscape of the enterprise through improving the organization of accounting and analysis

Income management is an important concept in the system of managing the financial and economic activities of an enterprise. A solid revenue management strategy requires the use of analytics and available data to forecast product demand, make strategic pricing decisions, and maximize revenue.
 The article examines the dynamics of the income structure of enterprises and presents a sectoral analysis of companies according to the sectoral distribution of the market environment. The main trends are established and the main problems of the company's income management arising in conditions of uncertainty and risks are outlined. The paper presents the role of income in the management system of the enterprise, which directly affects the efficiency of the process of managing the economic activity of the enterprise.
 In the course of the study, ways of improving the formation and use of accounting and analytical information about the company's income for management purposes were proposed. The scheme of the organization of income analysis for the formation of analytical information is presented. Reasonable importance of management reporting, which is intended to provide understanding, clarity and direction of management activities. When considering the content of the management report, we recommend taking into account visualization forms and design layouts. The KPI dashboard is a visual tool that provides a centralized view of various data sources for effective tracking and optimization of goals. With the help of indicators and KPIs, companies can get an overview of their performance at an operational or strategic level.

Comparative Analysis of Dynamic Pricing Strategies for Online Hotel Bookings: A Case Study of Luxury Hotels in Major Tourist Destinations

This study conducted a comparative analysis of pricing models and strategies employed by luxury hotels in major tourist destinations. The analysis focused on revenue generation, customer satisfaction, customer segmentation, demand patterns, flexibility, and adaptability to market conditions. The findings highlighted the effectiveness of various pricing models, including time-based pricing, demand-based pricing, competitor-based pricing, and segmentation-based pricing, in maximizing revenue and enhancing customer satisfaction. The implications of the findings on revenue management and pricing strategies in luxury hotels were discussed, emphasizing the importance of customer segmentation, advanced technology, and continuous monitoring. The study also identified limitations and suggested potential areas for further research.

Restaurant revenue management and analytical assessment of its effectiveness

The effectiveness of a revenue management system is important for every business, as revenue is a factor that directly affects profit. This issue is especially important for the restaurant business, where the level of costs is much higher than the average in the economy. The subject of the study is modern restaurant revenue management systems used in foreign practice. The goal of the article is to study the theoretical aspects of revenue management and evaluate its effectiveness in the restaurant business. Objectives: (1) to clarify the conceptual apparatus of restaurant revenue management, (2) to study the indicators for assessing the effectiveness of revenue management and their use in the restaurant business, (3) to determine the possibilities of using factor analysis of contribution margin in restaurant revenue management. The study was conducted using the method of a systematic review of scientific literature on this topic. The results of the study showed an ambiguous interpretation of the conceptual apparatus of revenue management and the need to adapt the indicators for assessing the effectiveness of revenue management to the conditions of the restaurant business. Conclusions: yield management, unlike revenue management, is applied to specific conditions and the restaurant business has the corresponding characteristics. Taking into account the specifics of the restaurant business, it is important to analyze the menu in terms of the contribution of each assortment item to profit. For an overall assessment of the restaurant menu, it is advisable to use a multi-criteria ABC-analysis. To quantify the effectiveness of the applied revenue management strategy in the operational order, the indicators CMPASH (Contribution Margin per Available Seat Hour) and CMPASM (Contribution Margin per Available Square Meter) should be used. To study the change in contribution margin as a result of management decisions made based on the results of ABC-analysis, it is advisable to conduct a factor analysis.

Revenue Management Capability and Host Professionalization Degree

This study explores how a revenue management capability can be created by Airbnb hosts and the role played by the professionalization degree, defined as the number of listings managed by a host. The theoretical lenses used by this study is the resource-based. Based on previous studies on revenue management in the field of hospitality, a wide list of routines, skills, know-how and coordination mechanisms are considered to explain how the revenue management capability can be created by a host. In particular, the revenue management process can be segmented into four major components: i) identifying the required information to set the revenue management strategy, ii) taking strategic and then tactical pricing and revenue management decisions, iii) managing the communication and interaction with the guests, and iv) the availability of a software dedicated to revenue management.
 The study is based on qualitative data collected through interviews in the Milan destination. In line with previous studies, three different groups of hosts where identified: i) single hosts (also called group one), ii) hosts managing 3-5 listings (group two), iii) hosts managing more than 10 listings (group 3). The interviews were recorded, transcribed and coded using the software NVIVO. Results reports strong differences between the three groups of hosts and especially between single and big hosts (those managing ten or more listings). The goals itself are different: while single hosts prefer to select guest and maximize price, big hosts, supporting higher fixed costs, are strongly focused on revenue. All the four blocks are therefore managed in a different way. Small hosts are widely involved in the operating processes (check-in, cleaning, guest interaction) and in many cases the short-term rental is only an additional activity (the main work in another). Therefore, the time dedicated to data collection and revenue management strategy is limited. The opposite is for big hosts that are usually organized as a company with employees specialized per function and with a strong focus on revenue maximization. The paper is supportive for the emerging idea of professionalization degree. The study traces theoretical as well as practical conclusions. The main study limitations are listed.

관광호텔 객실매출액 예측에 관한 연구

The hotel industry is essential to the success of the tourism industry. It provides accommodation options, generates significant revenue, creates employment opportunities, drives destination development, and plays a crucial role in branding and marketing a destination. Therefore, forecasting room sales is critical for the success of a tourist hotel. Based on the predictions, hotels can optimize their revenue management strategies, allocate resources more efficiently, plan marketing and promotional activities, budget and plan for future periods, and gain a competitive advantage. For this, this study proposed Seasonal ARIMA Model to forecast weekly room sales 1 year in advance. A comprehensive account of the model selection process and the level of accuracy achieved in forecasting are provided in this paper. The findings demonstrate that the proposed research method has achieved a high level of accuracy in forecasting. Accordingly, policymakers and hotel operations can leverage this model to make informed decisions and proactively manage potential risks in the future.

A longitudinal analysis of revenue management strategies and measures implemented in the hospitality industry during the COVID-19 crisis

PurposeThis research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.Design/methodology/approachThis qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.FindingsFurther professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Research limitations/implicationsThis study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.Practical implicationsThe revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Originality/valueThis work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

Crisis Management Strategy for Recovery of Small and Medium Hotels after the COVID-19 Pandemic in Thailand

The COVID-19 crisis has created serious and complex challenges for the hospitality industry. A body of literature has identified crisis management practices in the hotel industry at different phases of the crisis. However, the existing literature mainly includes research on large and leading hotels, and knowledge of crisis management practices for small and medium-sized (SME) hotels, particularly at the recovery stage of the crisis, is limited. This paper explores the post-COVID recovery strategies of 386 SME hotels in the upper northern part of Thailand. Structural equation modeling (SEM) was used to test the proposed hypothesis. The results indicate that customer relations (CR)-related and service provision (SV)-related strategies significantly affect SME hotel recovery. Meanwhile, cost-saving (CS) and revenue management (RM) strategies do not have a direct effect but are associated with CR and SV in facilitating SME hotel recovery. This paper provides useful information to assist SME hotel owners and managers in managing how to recover from the pandemic.

Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects

With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of “congestible capacity”; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.