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This chapter examines the financial performance efficiency of the restaurant sector in Croatian counties over the period 2013–2017. Today's tourists are ambitious explorers who travel in order to find and explore new experiences and motives for travelling as long as there are interesting things, activities and offers which correspond to their preferences. Among the many motives that today's tourist decides to travel, gastronomic tourism certainty plays an important role. The observation period began in 2013, since that was the year when Croatia acceded to the European Union and joined all the other prominent European food destinations. In order to evaluate the financial performance efficiency, the methodology of the data envelopment analysis (DEA) was applied separately to the data processing of each year. The results of the Charnes–Cooper–Rhodes model showed that only four counties (Lika-Senj, Zadar, Istria and Dubrovnik-Neretva) achieved continuous efficiency over the whole observed period. In 2013 the results of scale efficiency showed that 10 counties (Krapina-Zagorje, Karlovac, Bjelovar-Bilogora, Lika-Senj, Požega-Slavonia, Zadar, Šibenik-Knin, Split-Dalmatia, Istria and Dubrovnik-Neretva) achieved a score of 1, and in later years there was a decrease. One of the main obstacles of the existing inefficiencies in the entire restaurant sector in Croatian counties is certainly changeable tax legislation and lack of employees in the restaurant sector.

Purpose: The purpose of this chapter is to review and critically evaluate robots, artificial intelligence and service automation (RAISA) applications in the restaurant industry to educate professors, graduate students, and industry professionals.

Design/methodology/approach: This chapter is a survey of applications of RAISA in restaurants. The chapter is based on the review of professional and peer-reviewed academic literature, and the industry insight section was prepared based on a 50-minute interview with Mr. Juan Higueros, Chief Operations Officer of Bear Robotics.

Findings: Various case studies presented in this chapter illustrate numerous possibilities for automation: from automating a specific function to complete automation of the front of the house (e.g., Eatsa) or back of the house (e.g., Spyce robotic kitchen). The restaurant industry has already adopted chatbots; voice-activated and biometric technologies; robots as hosts, food runners, chefs, and bartenders; tableside ordering; conveyors; and robotic food delivery.

Practical implications: The chapter presents professors and students with a detailed overview of RAISA in the restaurant industry that will be useful for educational and research purposes. Restaurant owners and managers may also benefit from reading this chapter as they will learn about the current state of technology and opportunities for RAISA implementation.

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.

The April 20, 2010, Gulf Coast Oil Spill was a tragic crisis. This study examined the effect of the 2010 Gulf Coast Oil Spill on the restaurant industry in Louisiana. The study quantified key issues affecting state restaurants in the wake of this particular crisis. The study found that oysters were the seafood with the least availability following the crisis. Most of the restaurants, or 54.8%, did not take any menu items off of their menu; however, 24.2% eliminated one or two items and 21% removed three or more items from their menu due to the oil spill. Casual Restaurants at 38.3% and Family Dining Restaurants at 31.2% had the highest percentages of out-of-state purchases of seafood due to the oil spill. Casual Theme Restaurants laid off the most employees due to the oil spill with an average of eight employees. The highest reported costs from the spill were product costs and marketing/advertising.

The competitiveness of tourist destinations has been the subject of great research interest in recent decades. Nevertheless, and despite the diversity in the literature, studies focusing on the empirical validation of tourism destination models of competitiveness have still to be completed. Hence, this research project seeks to contribute to filling this shortcoming through the identification and evaluation of the factors underlying tourism destination competitiveness in Portugal. The study methodology adopted requires primary data that were sourced from a questionnaire deployed as a structured research instrument based upon the variables put forward by the Dwyer and Kim model (2003). Through recourse to structural equation models, the results report the existence of significant relationships between resources, supply and tourism destination management as the core and essential factors to the competitiveness of a particular tourist destination.

This chapter addresses the emerging trends in Australia’s food destinations and analyzes different demands for this experience from a Korean tourism market perspective. Tourism Australia’s report on the international market research was analyzed, and the findings indicate that four main food experiences were sought by Koreans. A sense of landscapes plays an important role in enhancing their local food experiences. Multicultural food, health conscious markets, and food shopping are also crucial for developing Australia’s competitive advantage in this area. This chapter suggests marketing implications and directions for future research to explore cross-cultural gaps in food culture and behaviors from the perspectives of Asian tourism markets.

Given Bali's prominence in Indonesia's hospitality industry, tourist hypermobility regarding food preferences following the COVID-19 pandemic continues to be a source of contention. As such, this chapter will investigate extreme mobility by examining how ‘the flashpackers’ construct their nomadic lifestyles. This chapter focuses on the ‘freedom to eat’ in a variety of different types of restaurants throughout the destination – creating a pattern of hypermobility. Flashpacker's hypermobility data come from a network analysis of TripAdvisor reviews – with Bali (Indonesia) serving as the case study location. According to the analysis's findings, there are eight distinct preference groups for food and beverage products in Bali. It was discovered that at least five ‘star factors’ contributed to preferences across the eight groups, including (1) ratings, (2) popularity, (3) ownership, (4) social media and (5) price. In light of these factors, the chapter concludes that research into hypermobility in small island destinations is still in its infancy. It is critical to promote thorough research into the influence and impact of hypermobility on all facets of the tourism industry, including local communities and to demonstrate the industry's economic contribution.

This paper investigated the effectiveness of using a Restaurant Week promotion to market a group of restaurants during a traditionally slow period in the calendar year for the city of New Orleans. A questionnaire was developed by the communications committee of the local restaurant association. The questionnaire included questions to ascertain why restaurants chose to participate in Restaurant Week. The results of the study showed that the Restaurant Week campaign was successful for the participating restaurants in several ways. Success was demonstrated in increase in sales, a better understanding of menu item sales, an awareness of the strongest days of promotion, and methods of making reservations. The majority of restaurants experienced an increase of sales up to 20%. For both lunch and dinner promotions, Friday ranked as the strongest day of the promotion. The use of Open Table increased as a means of making reservations during Restaurant Week.

The study here examines how business actors adapt to changes in networks by analyzing their perceptions or their network pictures. The study is exploratory or iterative in the sense that revisions occur to the research question, method, theory, and context as an integral part of the research process.

Changes within networks receive less research attention, although considerable research exists on explaining business network structures in different research traditions. This study analyzes changes in networks in terms of the industrial network approach. This approach sees networks as connected relationships between actors, where interdependent companies interact based on their sensemaking of their relevant network environment. The study develops a concept of network change as well as an operationalization for comparing perceptions of change, where the study introduces a template model of dottograms to systematically analyze differences in perceptions. The study then applies the model to analyze findings from a case study of Norwegian/Japanese seafood distribution, and the chapter provides a rich description of a complex system facing considerable pressure to change. In-depth personal interviews and cognitive mapping techniques are the main research tools applied, in addition to tracer studies and personal observation.

The dottogram method represents a valuable contribution to case study research as it enables systematic within-case and across-case analyses. A further theoretical contribution of the study is the suggestion that network change is about actors seeking to change their network position to gain access to resources. Thereby, the study also implies a close relationship between the concepts network position and the network change that has not been discussed within the network approach in great detail.

Another major contribution of the study is the analysis of the role that network pictures play in actors' efforts to change their network position. The study develops seven propositions in an attempt to describe the role of network pictures in network change. So far, the relevant literature discusses network pictures mainly as a theoretical concept. Finally, the chapter concludes with important implications for management practice.