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This study aims to adopt a holistic approach to understand cruise revenue management (RM) practices that cover ticket and onboard revenues, through a cross-disciplinary literature review and practitioner interviews. An integrated cruise RM framework was developed and served as a blueprint for future cruise studies and practices.

A multi-stage approach was adopted, including a systematic literature review, two-waves of interviews with 26 cruise industry practitioners and the development of a holistic RM framework.

This study clarifies cruise RM functions across product planning, delivery stages and identifies ticket and onboard RM components. These are incorporated into the integrated framework, with weather and itinerary/ route attractiveness as additional considerations. Interviews revealed that there is no difference in the RM cycle before and during the COVID-19 pandemic, although strategies and tactics may vary in response to the market situation.

Suggestions are made regarding product and service bundling and ways for ticket and onboard revenue teams to work together to optimize total revenue. Future research directions are also provided under the categories of RM applications and concepts, ticket core activities, onboard core activities and overall issues.

To the best of the authors’ knowledge, this is the first paper to conduct a cross-disciplinary systematic literature review of cruise RM without imposing publication dates or specific databases and the first to develop an integrated cruise “total” RM framework that includes ticket and onboard revenues.

This 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.

This 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.

Further 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.

This 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.

The 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.

This 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.

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).

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.

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.

Teaching notes are available for educators only.

CSS12: Tourism and hospitality.

Pricing studies have largely focused on sellers' pricing strategies and price determinants. To expand earlier work on sellers' pricing decisions, this study considers time as a major factor driving sellers' ticket prices in the secondary market. Specifically, because most secondary market transactions occur in the last moments before a game, this study considers how resellers adjust ticket prices within a few days prior to a game day including an actual game day.

To examine the impact of time on secondary market ticket prices for Major League Baseball (MLB), ticket prices were collected from StubHub (one of the largest secondary ticket markets) four times per game: from 3 days to 1 day prior to a game day and on the actual game day. Additionally, 10 control variables were obtained from previous research on price determinants (N = 19,155). A multiple regression model was created based on the extant literature regarding secondary market ticket prices.

Results indicate the number of days before a game negatively influenced ticket prices: resellers decreased ticket prices consistently during the last few days prior to a game's first inning. Specifically, secondary market ticket prices decreased relatively dramatically on an actual game day. Time had no significant effects on ticket prices 2 days prior to a game day. In addition to the role of time, league affiliation and the number of all-star players were identified as key price determinants in the secondary market. Moreover, changes in weather forecasts and the home team starting pitcher's ERA played significant roles in price changes.

Despite containing a relatively high number of data observations compared with prior pricing studies, this study's findings were limited to certain teams. Additionally, as only MLB secondary market ticket pricing was considered, different outcomes and implications may apply in other major sport ticket markets (e.g. NBA, NFL, NHL and MLS) featuring distinct league structures, policies and demand.

This study offers practical guidance for sellers' pricing decisions. Most secondary ticket market sellers lowered their ticket prices relatively dramatically on an actual game day. Reducing ticket prices prior to a game day can lead to greater chances to avoid unsold tickets that compromise revenue management. This study's results also afford professional sport organizations and secondary ticket market consumers a clearer understanding of the factors resellers consider when setting ticket prices.

Although previous studies have uncovered essential elements influencing ticket prices and consumer demand in the secondary ticket market, little work has examined how time affects sellers' pricing decisions within a few days prior to a game day. Little is known about the elements that significantly influence sellers’ decisions to adjust (i.e. increase or decrease) ticket prices in the secondary market as well. This topic deserves ongoing attention, as new outcomes can supplement previous studies' findings due to changing market environments.

We revisit the problem of redesigning the Master in Business Administration (MBA) program, curriculum, and pedagogy, focusing on understanding and seeking to tame its “wicked problems,” as an intrinsic part and challenge of the MBA program venture, and to render it more realistic and relevant to address major problems and their consequences. We briefly review the theory of wicked problems and methods of dealing with their consequences from multiple perspectives. Most characterization of problems classifies them as simple (problems that have known formulations and solutions), complex (where formulations are known but not their resolutions), unstructured problems (where formulations are unknown, but solutions are estimated), and “wicked” (where both problem formulations and their resolutions are unknown but eventually partially tamable). Uncertainty, unpredictability, randomness, and ambiguity increase from simple to complex to unstructured to wicked problems. A redesigned MBA program should therefore address them effectively through the four semesters in two years. Most of these problems are real and affect life and economies, and hence, business schools cannot but incorporate them into their critical, ethical, and moral thinking.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

This paper aims to investigate the links between daily review sentiment and the hotel performance measures of occupancy rate (OR), average daily rate (ADR) and revenue per available room (RevPAR).

The authors conducted review sentiment analyses in three moments (−1, −7 and −14 days) before arrival time using a data set of budget hotel performance and online reviews. The aim was to identify the effect of review sentiment in the budget hotel market on the three performance metrics.

Daily sentiment positively affects ADR and negatively affects OR and RevPAR, but only up to a certain threshold, after which the trend reverses. Prices increase with the level of sentiment, and high prices lead to low OR and RevPAR only when the sentiment scores are low. When they are high, they are associated with low rates, which lead to high OR and RevPAR.

Daily review sentiment can be viewed as a valuable “barometer” indicating a hotel’s daily operational effectiveness. Daily sentiment can thus allow hotel managers to adjust their dynamic pricing strategies more accurately.

This study identifies daily sentiment as an alternative predictor of hotel performance. In addition to the roles of valence and volume in the decision-making process, the authors found that daily review sentiment can be an “in-the-moment” factor with a high impact, encouraging consumers to complete their transactions. This study suggests that aggregated measures such as the total number of reviews and overall ratings of the hotel should not be the sole consideration in reputation management.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The 170-year-old Master in Business Administration (MBA) program is becoming obsolete and inefficient to address today's real-world problems, and is facing mounting criticism from business scholars, management deans, and academic scholars alike. Reviewing major criticisms, this chapter suggests a new design for the MBA program that will not only address the criticisms but also accept a paradigm shift that will spearhead it in coming decades. The redesigned MBA “structure” proposes a four-semester full-time program, during which each semester delves into deeper marketplace problems of increasing complexity (i.e., from simple to complex to unstructured to wicked problems) and deals with these problems with new levels of critical thinking skills and ethical reasoning processes tempered by corresponding entrepreneurial knowledge, skills, and values. The “content” of the redesigned program is anchored around five major themes of business learning: namely, intrinsic motivation management, creativity and innovation management, productivity management, revenue management, and eco-sustainability management, each geared to generate professional entrepreneurial knowledge, and skills and values urgently needed today. Numerous beneficial features of this newly redesigned integrated business management program (MBA) are also discussed.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.