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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.

With the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation (IER) and whether and how IER impacts their own affective well-being.

For the underlying study, an AI-based emotion recognition software was developed in order to assist service employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.

Augmenting service employees with AI significantly improved their IER activities. Employees in the AI (vs control) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.

Service firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replacing employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.

The present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.

Inspired by the dynamic changes in our daily lives enabled via quantified-self technologies and the urgent need for more studies on the human-computer interaction design mechanisms adopted by these applications, this study explores the value of user affective experience mirroring and examines the empowerment effect of meaningful gamification in a psychological self-help system (PSS) that aids people in work stress relief.

Based on an analysis of the existing systems and theories in relevant fields, we conducted mixed-method research, involving semi-structured interviews, experience sampling experiments and user bio data triangulations, to identify the benefits of user affective experience mirroring and examine the impact of visual impact metaphor–based (VIM) meaningful gamification on PSS users.

For a gamified PSS, users generally perceive VIM as arousing more feelings of enjoyment, empathy, trust and usefulness, empowering them to gain more mastery and control over their emotional well-beings, especially with relieving their occupational stress and upbringing their level of perceived happiness. Overtime, VIM-based meaningful gamification further boosts such value of a PSS.

Weaving together meaningful gamification and psychological empowerment theories, the results emphasized that successful empowerment of user through gamification in PSSs relies heavily on whether a deeper and meaningful affective connection can be established with the users, in short, “meaningful gamification for psychological empowerment”. Such an understanding, as demonstrated in our research framework, also sheds light on the design theories for persuasive technology and human influence tactics during human computer interactions.

The results of the study demonstrate to practitioners how to make the best use of gamification strategies to deeply relate to and resonate with users. Even without complicated game-play design, meaningful gamification mechanisms, such as VIM facilitate the empowerment of users while gaining their appreciation, establishing a deeper connection with them and eventually generating persuasive effects on intended future behavioural outcomes.

The effective management of work-related stress with handy tools such as a VIM-based PSS can be beneficial for many organizations and, to a large extent, the society.

This study proposed and empirically demonstrated the empowerment effect of meaningful gamification for PSS users. In this cross-disciplinary study, theories from different research domains were synthesized to develop a more thorough and multi-faceted understanding of the optimal design strategies for emerging information systems like this VIM-based PSS.

This review scopes evidence on the use of social robots for older adults with depressive symptoms, in the scenario of smart cities, analyzing the age-related depression specificities, investigated contexts and intervention protocols' features.

Studies retrieved from two major databases were selected against inclusion and exclusion criteria. Studies were included if used social robots, included older adults over 60, and reported depressive symptoms measurements, with any type of research design. Papers not published in English, published as an abstract or study protocol, or not peer-reviewed were excluded.

28 relevant studies were included, in which PARO was the most used robot. Most studies included very older adults with neurocognitive disorders living in long-term care facilities. The intervention protocols were heterogeneous regarding the duration, session duration and frequency. Only 35.6% of the studies had a control group. Finally, only 32.1% of the studies showed a significant improvement in depression symptoms.

Despite the potential for using social robots in mental health interventions, in the scenario of smart cities, this review showed that their usefulness and effects in improving depressive symptoms in older adults have low internal and external validity. Future studies should consider factors as planning the intervention based on well-established supported therapies, characteristics and needs of the subjects, and the context in which the subjects are inserted.

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

Organizational behavior scholars have long recognized the importance of a variety of emotion-related phenomena in everyday work life. Indeed, after three decades, the span of research on emotions in the workplace encompasses a wide variety of affective variables such as emotional climate, emotional labor, emotion regulation, positive and negative affect, empathy, and more recently, specific emotions. Emotions operate in complex ways across multiple levels of analysis (i.e., within-person, between-person, interpersonal, group, and organizational) to exert influence on work behavior and outcomes, but their linkages to human resource management (HRM) policies and practices have not always been explicit or well understood. This chapter offers a review and integration of the bourgeoning research on discrete positive and negative emotions, offering insights about why these emotions are relevant to HRM policies and practices. We review some of the dominant theories that have emerged out of functionalist perspectives on emotions, connecting these to a strategic HRM framework. We then define and describe four discrete positive and negative emotions (fear, pride, guilt, and interest) highlighting how they relate to five HRM practices: (1) selection, (2) training/learning, (3) performance management, (4) incentives/rewards, and (5) employee voice. Following this, we discuss the emotion perception and regulation implications of these and other discrete emotions for leaders and HRM managers. We conclude with some challenges associated with understanding discrete emotions in organizations as well as some opportunities and future directions for improving our appreciation and understanding of the role of discrete emotional experiences in HRM.

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.

Previous research has demonstrated the importance of emotion recognition ability in negotiations and leadership, but scant research has investigated the role of emotion recognition ability in service contexts. The purpose of this paper is to propose and test a compensatory model in which service employees’ emotion recognition ability helps enhance their job performance, particularly when employees score low on the agreeableness personality dimension or have low cognitive ability.

With a two-wave multisource dataset collected from a service center of a large retail bank, multiple regression analysis was used to test the moderating roles of agreeableness and cognitive ability on the relationship between service employees’ emotion recognition ability and their performance.

Service employees’ emotion recognition ability helped enhance their job performance. However, the positive effect of emotion recognition ability on job performance was only statistically significant when employees’ agreeableness or cognitive ability was low.

The findings have important implications for how service organizations select and recruit employees. In particular, service employees with low agreeableness or cognitive ability may still be able to perform well when possessing high emotion recognition ability. Therefore, emotion recognition ability should be considered in the selection and recruitment process.

Going beyond self-report measures of emotion recognition and using a performance measure from organizational records, this study is one of the first to examine how emotion recognition ability interacts with personality and cognitive ability in predicting service employees’ effectiveness in a service organization.

Advances in technology over recent years made it possible to use machines and artificial intelligence to develop commercially viable solutions for companies to listen to consumers, decode the meaning, and respond accordingly. In parallel, solutions have been developed that are able to automatically track facial expressions of consumers when reacting to a given marketing stimulus.

The authors look at how marketing executives can apply these technologies to generate enhanced customer insights, providing a realistic context for future applications. The focus is on bringing researchers and managers closer to those moments of truth and our ability to understand customer emotions, emotional reaction, everyday language, and ultimately brand engagement.

The chapter covers the application of commercially viable use cases for (1) the automated measurement of emotions through facial coding to optimize advertizing and content, and (2) the use of voice coding technology to design interactive chatbots as an alternative to traditional surveys. In the outlook, the authors describe the potential that these technologies provide for future research and further use cases.