An increasing body of research has addressed what a tourism experience is and how it should best be measured and managed. One conclusion has been to recommend observational methods such as facial expression analysis. The chapter uses facial expression analysis to determine whether the emotions of employees in the tourism industry affect the emotions of their customers, following a pattern of emotional contagion. The findings show that emotional valence and arousal are both contagious. Furthermore, the findings show that arousal is less contagious at a higher likelihood to recommend, likely due to higher employee arousal during approximately the middle third of their conversation. Furthermore, findings demonstrate that emotion measurement is now possible at reasonable convenience for the tourism industry and gives a unique insight into tourists' actual experiences that is more precise and valid than self-report alone, though with certain costs and stringent methodological limitations.
TopIntroduction
An increasing body of research has addressed tourism experiences. Scholars have asserted definitions of what a tourism experience is, how it can be measured, and how it should best be managed. Two developments have recently occurred in this stream of research. First, several authors have presented the argument that emotions are the core component of experiences and offer the best entry point for measuring and managing experiences (Bastiaansen et al., 2019; Li, Scott, & Walters, 2015; Moyle, Moyle, Bec, & Scott, 2017; Skavronskaya et al., 2017). Second, several studies showed that emotions in tourism experiences could be effectively measured using unobtrusive multi-method approaches that capture the ebb and flow of emotion continuously over time from behaviour, the body, and the brain, creating additional insights over self-report alone (Bastiaansen et al., 2019; Bastiaansen et al., 2018; Kim & Fesenmaier, 2015; Shoval, Schvimer, & Tamir, 2018).
The present chapter extrapolates these developments to a novel technological development: facial expression analysis software, which automatically derives metrics of an individual’s emotions based on digital video recordings of their facial expressions. The authors of the present chapter apply this software to address an important practical and theoretical issue in tourism: whether the emotions of employees in the tourism industry affect the emotions of their customers. The effect of emotions in one individual triggering a similar emotion in another individual is known as emotion contagion (Hatfield, Cacioppo, & Rapson, 1993). In this study, the researchers concretely use facial expression analysis at an urban tourist information centre to explain emotion contagion between visitors and employees. This study fulfils two goals: to determine if emotion contagion indeed occurs during interactions at a tourist information centre, and to determine if facial expression analysis is a viable methodology for measuring the continuous ebb and flow of emotion during such tourist experiences. These are crucial issues for the design and management of tourist experiences by destination management organisations, and for these organisations to optimally allocate employee selection and training resources.
Before addressing the empirical data, this chapter synthesises literature about tourism experiences in general, then zooms in on the crucial role of emotions, and how continuous emotion measurement such as facial expression detection has become increasingly accessible. The chapter then covers present knowledge about emotion contagion in employee-customer interactions. The context of the tourist information centre in Gent is explained, followed by the methods and findings of the empirical portion of the chapter. The findings highlight that emotion is contagious between information centre employees and visitors, further reinforcing the importance of emotion detection and management uncovered in the literature review. The chapter concludes with discussions of theoretical and practical implications and recommendations for future research.