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Online consumer reviews (OCRs) have emerged as a particularly important type of user-generated information about a brand because of their widespread adoption and influence on consumer decision-making. Much of the existing OCR research focuses on quantifiable OCR features such as star ratings and volume. More research that examines the influence of review elements, aside from numeric ratings, such as the verbatim text, particularly in services contexts is needed. The purpose of this research is to investigate the impact of service failures on consumer arousal and emotions.

The authors present three behavioral experiments that manipulate service failure and linguistic elements of OCRs by using galvanic skin response, survey measures and automated facial expression analysis.

Negative OCRs lead to the greatest levels of arousal when consumers read OCRs. Service failure severity impacts anger, and referential cohesion, an observable property of text that helps a reader better understand ideas in the text, negatively moderates the relationship between service failure severity and anger.

The authors are among the first to empirically test the effect of emotional contagion in a user-generated content context, demonstrating that it can occur when consumers read such content, even if they did not experience the events being described. The research uses a self-report and physiological measures to assess consumer perceptions, arousal and emotions related to service failures, increasing the robustness of the literature. These findings contribute to the marketing literature on OCRs in service failures, physiological measures of consumers’ emotions, the negativity bias and emotional contagion in a user-generated content context.

This study aims to investigate how message sidedness affects the impact of fake news posted on social media on consumers' emotional responses.

The study involves a face-tracking experiment in which 198 participants were exposed to different fake news messages concerning the COVID-19 vaccine. Specifically, participants were exposed to fake news using (1) a one-sided negative fake news message in which the message was entirely unfavorable and (2) a two-sided fake news message in which the negative message was mixed with favorable information. Noldus FaceReader 7, an automatic facial expression recognition system, was used to recognize participants' emotions as they read fake news. The authors sampled 17,450 observations of participants' emotional responses.

The results provide evidence of the significant influence of message sidedness on consumers' emotional valence and arousal. Specifically, two-sided fake news positively influences emotional valence, while one-sided fake news positively influences emotional arousal.

The current study demonstrates that research on fake news posted on social media may particularly benefit from insights regarding the potential but often overlooked importance of strategic design choices in fake news messages and their impact on consumers' emotional responses.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.

This research aims to examine whether the facial appearances and expressions of Airbnb host photos influence guest star ratings.

This research analyzed the profile photos of over 20,000 Airbnb hosts and the guest star ratings of over 30,000 Airbnb listings in New York City, using machine learning techniques.

First, hosts who provided profile photos received higher guest ratings than those who did not provide photos. When facial features of profile photos were recognizable, guest ratings were higher than when they were not recognizable (e.g. faces too small, faces looking backward or faces blocked by some objects). Second, a happy facial expression, blond hair and brown hair positively affected guest ratings, whereas heads tilted back negatively affected guest ratings.

This research is the first, to the best of the authors’ knowledge, to analyze the facial appearances and expressions of profile photos using machine learning techniques and examine the influence of Airbnb host photos on guest star ratings.

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.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

This study aims to introduce an affective computing‐based method of identifying student understanding throughout a distance learning course.

The study proposed a learning emotion recognition model that included three phases: feature extraction and generation, feature subset selection and emotion recognition. Features are extracted from facial images and transform a given measument of facial expressions to a new set of features defining and computing by eigenvectors. Feature subset selection uses the immune memory clone algorithms to optimize the feature selection. Emotion recognition uses a classifier to build the connection between facial expression and learning emotion.

Experimental results using the basic expression of facial expression recognition research database, JAFFE, show that the proposed facial expression recognition method has high classification performance. The experiment results also show that the recognition of spontaneous facial expressions is effective in the synchronous distance learning courses.

The study shows that identifying student comprehension based on facial expression recognition in synchronous distance learning courses is feasible. This can help instrutors understand the student comprehension real time. So instructors can adapt their teaching materials and strategy to fit with the learning status of students.

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.