How COVID-19 changed things and what we did about it

COVID-19 changed the travel industry and brought the airline industry to its knees. COVID-19 was a demand shock—an unobservable, sudden change that changed customer behaviour. There are also smaller micro-level shocks such as special events or changes in competition. Shock detection methods employed by airlines today are often quite rudimentary in practice. Gatti Pinheiro et al. (2022) develop a science-based shock detection framework based on statistical hypothesis testing which enables fast detection of demand shocks. Past reviews of studies concerning competitive pricing strategies lack a unifying approach to inter-disciplinarily structure research across economics, marketing management, and operations. Whereas Manishimwe et al. (2022b)¹ COVID-19 study of entrepreneurial marketing (EM) practices as a resilience strategy examines the customer-centric influence of EM on business performance of hotels during the COVID-19 crisis. Using a cross-sectional survey design, Manishimwe et al. (2022b) collected primary data from 578 owner-managers and top managers of 66 hotels using structured questionnaires. Three regression estimations were reported. The estimation in model 1 indicates that calculated risk-taking has a significant positive influence on general performance. The estimation in model 2 suggests that calculated risk-taking and customer intensity have a significant positive influence on revenue per room, while the other five EM dimensions do not. The estimation in model 3 indicates that calculated risk-taking has a significant positive influence on market share.

Gerpott and Berends (2022) review the literature to develop a framework structuring scholarly work on competitive pricing models within the context of online retail markets. The consumer seller’s expected price for their old product in the resale online market is most of the time significantly higher than the potential buyer transaction price, which results in fewer chances of transaction closure. Bhagirath et al. (2022) paper aims to develop accurate pricing models for used cars using machine learning techniques and establish a relationship among seller's expected price, actual transaction price, and probability of transaction closure.

There are many reasons why people in Revenue Management (RM) departments are interested in measuring efficiency of RM practices. Such measure helps, for instance, identify weaknesses in RM systems, quantify impacts of certain decisions, evaluate new revenue management methods, and get some score for the skills of employees involved in revenue management routines. Nikitin and Tolvanen (2022) developed a for revenue management efficiency based on the information ratio of the analyst decisions. Revenue management practices require accurate forecasts for optimal rate decisions, and therefore researchers and industry are keen on identifying the most accurate methods. Webb (2022) discusses the challenges of forecasting when predictions exceed capacity. The empirical investigation confirms the importance of considering how to manage these predictions in the evaluation phase and demonstrates how the choice may sway overall accuracy measures and bias the results of model performance.

To attract a transient market, hotels primarily use several distribution channels, such as the following: the hotel directly, central reservation offices, travel agents, and online booking systems. Yet, little attention has been paid to the revenue management implications with regard to the focus on distribution channels. Lee et al. (2022) study examines the effects of channels and prices on brand dimensions and to study the role of hotel loyalty membership in the relationships. Noting that Online Travel Agents have the advantage of higher visibility, hotels can add value to the guest experience at their properties. In this way, the brand experience can be customized to align with a customer’s individual wants and needs to encourage brand loyalty. As a suggestion, hoteliers could work to influence guests’ brand identification to bring about positive customer evaluations of the hotel brand and thus, enhance their brand loyalty.

Research by Bonham et al. (1992), Bonham and Gangnes (1996), and Arguea and Hawkins (2015) found that while long-run effects on the tax base from an increase in the bed tax are negligible, short-run effects can be sizable. Research by Arguea and Hawkins (2022) on the tax base change separates quantity and pre-tax price effects. Thus, results indicate that for Florida properties, hotel-nights changes are somewhat more likely and pre-tax price effects are very rare. As hotels are very focused on profits and a continuous flow of revenue, the study adds to our knowledge of this concern for industry managers. It can be argued that Smith Travel Research² data exist to help property managers and parent-company managers measure and compare location revenue. The results reveal a risk to short-run quantity of rooms sold at these properties. The unmeasured benefit is new funds for generating additional demand.

Demirciftci and Belarmino (2022) study uses a cross-cultural approach that compared the use of competitive intelligence by US and Turkish revenue managers by conducting a qualitative study of revenue managers in these locations. Both sets of revenue managers share a similar definition of competitive intelligence and use technology to gather competitive intelligence. The findings also suggest integrity issues for data in Istanbul and that ethical challenges are a central part of competitive intelligence usage.