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Emotion recognition and emotion expression/regulation are important aspects of emotional intelligence (EI). Although the construct of EI is widely used and its components are part of many investigations, there is still no sufficient picture set that can be used for systematic research of facial emotion recognition and practical applications of individual assessments. In this research we present a new Facial Action Coding System validated picture set consisting of six emotions (anger, disgust, fear, happiness, sadness, and surprise). Basic principles of stimulus development and evaluation process are described. The PFA-U can be used for future studies in organization for the assessment of emotion recognition, emotion stimulation, and emotion management.

Facial expression recognition by human observers is affected by subjective components. Indeed there is no ground truth. We have developed Discrete Choice Models (DCM) to capture the human perception of facial expressions. In a first step, the static case is treated, that is modelling perception of facial images. Image information is extracted using a computer vision tool called Active Appearance Model (AAM). DCMs attributes are based on the Facial Action Coding System (FACS), Expression Descriptive Units (EDUs) and outputs of AAM. Some behavioural data have been collected using an Internet survey, where respondents are asked to label facial images from the Cohn–Kanade database with expressions. Different models were estimated by likelihood maximization using the obtained data. In a second step, the proposed static discrete choice framework is extended to the dynamic case, which considers facial video instead of images. The model theory is described and another Internet survey is currently conducted in order to obtain expressions labels on videos. In this second Internet survey, videos come from the Cohn–Kanade database and the Facial Expressions and Emotions Database (FEED).

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.

Purpose – The advancement of multimedia technology has spurred the use of multimedia in business practice. The adoption of audio and visual data will accelerate as marketing scholars become more aware of the value of audio and visual data and the technologies required to reveal insights into marketing problems. This chapter aims to introduce marketing scholars into this field of research.Design/methodology/approach – This chapter reviews the current technology in audio and visual data analysis and discusses rewarding research opportunities in marketing using these data.Findings – Compared with traditional data like survey and scanner data, audio and visual data provides richer information and is easier to collect. Given these superiority, data availability, feasibility of storage, and increasing computational power, we believe that these data will contribute to better marketing practices with the help of marketing scholars in the near future.Practical implications: The adoption of audio and visual data in marketing practices will help practitioners to get better insights into marketing problems and thus make better decisions.Value/originality – This chapter makes first attempt in the marketing literature to review the current technology in audio and visual data analysis and proposes promising applications of such technology. We hope it will inspire scholars to utilize audio and visual data in marketing research.

Theorists, such as Darwin and Aristotle, have long argued that facial expressions communicate information about a person's emotional state. Recently, validated coding strategies for facial expressions have been developed, which enable researchers to reliably assess a person's affect. Although social, health, and clinical psychologists have regularly employed these objective measures of facial expressions (OMFE), occupational stress and well-being researchers are yet to benefit from this method. The subsequent chapter integrates the facial expression and occupational well-being literature. Specifically, we discuss the advantages of OMFE over self-reports and implications of OMFE for future research on occupational well-being.

This chapter examines EI, presents a history of EI including the various models, and a discussion of the three streams approach to classifying EI literature. The author advocates for the efficacy of the Stream One Ability Model (SOAM) of EI citing previous authors and literature. The commonly used SOAM instruments are discussed in light of recent studies. The discussion turns to alternate tests of the SOAM of EI including Situational Judgment Tests (SJTs). Recommendations include an analysis of SOAM instruments, a new approach to measurement, and increased use of SJTs to capture the four-branch ability model of EI.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Creating memorable tourism experiences (MTE) is vital to obtain sustained tourism visits. In the digital era, infusions of various digital technologies in tourism services without admitting tourist emotions could jeopardize the experience. Drawing from a Service-Dominant Logic (S-DL) perspective, this study explains the complexity of digital tourism experience in the service system view, highlighting the importance of emotions as resources. It is composed of actors' orchestrations, connected by shared emotions, and enabled by sensory stimuli facilitated by the digital tourism ecosystem throughout the tourism journey. This study proposes a Memorable Digital Tourism Experience (MDTE) framework by identifying the focal actors, recognizing the emotions, and determining the moderating role of sensory stimuli enabled by various novel technologies. At last, several agenda and practical guidelines are proposed on how to operationalize the framework and different methodologies to explore Memorable Digital Tourism Experience.