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This study examines how firms’ use of competitor-focused accounting information, specifically competitor monitoring information, impacts their pricing, demand, and overall revenue performance. The monitoring activities examined are the scope of monitoring, monitoring above and below one’s own hotel class (i.e., market segment), and the extent of reciprocity of monitoring. Competitor analysis is a central element in strategic management accounting (SMA), yet little empirical research has been done since companies do not disclose competitor monitoring activities. Proving the value of competitive monitoring provides strong support for SMA. Archival, proprietary monitoring information regarding pricing, demand, and revenue were obtained from one of the largest hotel markets in the United States. Using regression, we modeled the relationships between performance measures (pricing, demand, and revenue) and monitoring behaviors, while controlling for quality (hotel characteristics and management skill), competitive intensity, hotel class, geographic location, and ownership type. Our results indicate that two aspects of competitor monitoring impact hotel pricing that, in turn, impacts hotel demand and revenue performance. Specifically, a hotel monitoring more competitors (what we refer to as Scope) achieves higher prices with unchanged demand, resulting in higher revenue performance. Most hotels monitor within their class. However, deviating from one’s class has profound outcomes: looking at lower (higher) quality hotels results in a hotel setting lower (higher) prices, resulting in higher (unchanged) demand and lower (higher) revenue performance. Surprisingly, we did not find support for the reciprocity of monitoring. That is, whether the competitors monitored by a hotel, in turn follow the target, has no impact on hotel revenue performance outcomes. While the SMA literature notes the importance of competitor monitoring, this study fills a gap in an important, under-researched area by documenting the link between competitor monitoring behaviors and organizational revenue performance. This may help promote greater diffusion of SMA practices.

Unlike manufacturing firms where the production of goods can be adjusted according to the demand of customers, hospitality firms do not have the ability to alter the capacity of the changing demand of guests in a short period of time. Given the relatively fixed capacity or supply, maximizing revenue through inventory control is essential for hospitality operations. This chapter covers operations inventory control extracted from the field of revenue management. First, the concept of capacity management and planning is enclosed and various capacity management tactics and inventory control strategies are explored. Next, the management and principles of space inventory through inventory-based restrictions, strategic pricing, displacement analysis, and distribution channel management are addressed. Finally, the respective applications of these principles, strategies, and tactics in several hospitality sectors are discussed.

Successful revenue management programs are found in industries where managers can accurately forecast customer demand. Airlines, rental car agencies, cruise lines, and hotels are all examples of industries that have been associated with revenue management. All of these industries have applied revenue management, whether it be complex overbooking models in the airline industry or simple price discrimination (i.e., having a tiered price system for those making reservations ahead of time versus walk-ups) for hotels.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

This study explores the short- and medium-term effects generated by the Milan Expo 2015, adopting a microeconomic approach. The focus is on the hospitality sector. The study embraces nine years, identifying three intervals: pre- (2011–2014), during- (2015) and post-Expo (2016–2019). The time span does not include the Covid-19 pandemic period, which started in 2020. The dataset is composed of daily data. Three research questions are explored. First, an overall evaluation of the short- and medium-term effects is performed. Second, the seasonal effects are measured. Finally, the impacts for different classes of hotels are considered. The findings are supportive for the legacy generated by the Milan Expo. The results confirm the ability of the Milan Expo to strengthen the leisure segment. Positive results have been observed for all classes of hotels, relevantly augmenting the real revenue per available room (RevPAR). Luxury hotels achieved the highest increase of RevPAR, while economy class hotels registered the highest percentage of increase of RevPAR.

Purpose: This paper presents a review of the current state and potential capabilities for application of robots, artificial intelligence and automated services (RAISA) in hotel companies.

Design/methodology/approach: A two-step approach was applied in this study. First, the authors make a theoretical overview of the robots, artificial intelligence and service automation (RAISA) in hotels. Second, the authors make a detailed overview of various case studies from global hotel practice.

Findings: The application of RAISA in hotel companies is examined in connection with the impact that technology has on guest experience during each of the five stages of the guest cycle: pre-arrival, arrival, stay, departure, assessment.

Research implications: Its implications can be searched with respect to future research. It deals with topics such as how different generations (guests and employees) perceive RAISA in the hotel industry and what is the attitude of guests in different categories of hotels (luxury and economy) towards the use of RAISA. It also shows what is the attitude of different types of tourists (holiday, business, health, cultural, etc.) and what kinds of robots (androids or machines) are more appropriate for different types of hotel operations.

Practical implications: The implications are related to the improvement of operations and operational management, marketing and sales, enhancement of customer experience and service innovation, training and management.

Originality/value: This book chapter complements and expands research on the role of RAISA in the hotel industry and makes some projections about the use of technologies in the future.

Purpose: The purpose is to introduce the fundamental economic concepts that must be wrestled with the incorporation of robots, artificial intelligence and service automation (RAISA) into the travel, tourism and hospitality industries.

Design/methodology/approach: This chapter uses cost-benefit analytical framework of the incorporation of RAISA technologies into travel, tourism and hospitality industries.

Findings: The chapter elaborates on the economic fundamentals of RAISA adoption into the travel, tourism and hospitality industries. The analysis reveals that many financial and non-financial costs and benefits need to be considered when taking a decision to use RAISA technologies. Automation of tasks leads to simultaneous substitution and enhancement of human employees. Introduction of RAISA technologies results on inevitable deskilling of some and upskilling of other tourism and hospitality jobs.

Research limitations/implications: The chapter is conceptual and conclusions are limited by the views and interpretations of the authors.

Practical implications: RAISA technologies will become increasingly omnipresent in the travel, tourism and hospitality industries. That is why an understanding of the costs and benefits and many of the practical impediments to the incursion of RAISA into the workplace should be understood to make a transition from human-performed tasks to technology-performed tasks.

Social implications: Replacement of human labour will have significant social implications for the workforce and employers.

Originality/value: This is one of the few publications that discuss the economic aspects of the incorporation of RAISA technologies into travel, tourism and hospitality industries.

Online reputation management (ORM) plays a significant role in the tourism industry. Tourists are more and more interested to express their opinions about their experiences/satisfaction not only with their friends but also on social media. ORM is largely used not only by tourist destinations but also by other companies operating in the tourism industry. This chapter aims to draw attention to the importance of intermediaries in tourism, their reputation in general, and especially their ORM and its specifics. This contribution also characterizes different types of intermediaries and their different roles in the distribution process of tourism services. These roles are important and can be even more significant in the “new normal” post-COVID-19 time. In the scientific literature and research, there is not much attention given to intermediaries as a whole and even less to their ORM and its specific solutions. But practical contributions can be found. Due to the specific activities and roles of different types of intermediaries, also their reputation is influenced not only by tourists but also by their suppliers. Their ORM has also some specifics and needs specific solutions. Their reputation is depending not only on customers' satisfaction with their own services but also on the reputation of tourism service providers, whose services they offer and mediate either individually or in the form of their own product, e.g., package tours. Specific attention in this chapter is given to intermediaries such as OTAs (Online Travel Agencies/Agents) and tour operators. At some time, these two types of intermediaries help to increase the reputation of tourism services providers, e.g., hotels. The chapter describes the situation in the field of intermediaries with a conceptual model, their ORM, and summarizes its specifics.

The aim of this chapter is to present in a pedagogical way the main digital tools used by tourism-related businesses, especially by hospitality businesses. The main purpose of this chapter is to illustrate our discussion with concrete examples and to give a set of advices for efficient use of those tools.

Literature review was conducted on conceptual issues, as well as managerial and marketing aspects of digital tools, their value and use in the hospitality industry.

This chapter highlights the fact that needs in terms of digitalisation depend on the size of the hotel. The main differences can be explained by differences in terms of hotel capacity, and digital technologies should be customised to different types of structures.

This chapter is exploratory in nature, based on a literature review.

It provides clear and practical guidance about the way independent hospitality businesses could use digital tools for marketing purposes. It also suggests the most efficient digital technologies to improve their performance in the field of marketing and customer relationship management.

The chapter demonstrates the huge gap between best practices in the hospitality industry and the way independent enterprises really use, in practice, the digital tools for marketing purposes. It shows how digital technologies could be used in a more efficient way, to take advantage of their full potential.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.