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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.

Purpose – This paper proposes a new procedure for measuring affective responses during social interaction using facial thermographic imaging.

Methodology – We first describe the results of several small pilot experiments designed to develop and refine this new measure that reveal some of the methodological advantages and challenges offered by this measurement approach. We then demonstrate the potential utility of this measure using data from a laboratory experiment (N=114) in which we used performance feedback to manipulate identity deflection and measured several types of affective responses – including self-impressions and emotions.

Findings – We find warming of the brow (near the corrugator muscle) and cheek (near the zygomatic major muscle) related most strongly to emotion valence and self-potency, with those whose brows and cheeks warmed the most feeling less positive emotion and less potent self-impressions. Warming in the eye area (near the orbicularis oculi) related most closely to undirected identity deflection and to positive self-sentiments. Positive self-views and strong identity disruptions both contributed to warming of the eyes.

Implications – The rigor of contemporary sociological theories of emotion exceeds our current ability to empirically test these theories. Facial thermographic imaging may offer sociologists new assessments of affect and emotion that are ecologically valid, socially unreactive, temporally sensitive, and accurate. This could dramatically improve our ability to test and develop affect based theories of social interaction.

This chapter draws on developmental intergroup theory, parental ethnic-racial socialization literature, anti-bias curricula, and prejudice intervention studies to address the appropriateness of discussing race and racism in early childhood settings. Existing literature about teacher discussions surrounding race and racism is reviewed, best practices are shared, and the need for more research in this area is highlighted. The construct of parental ethnic-racial socialization is mapped onto early childhood anti-bias classroom practices. The chapter also outlines racial ideologies of teachers, specifically anti-bias and colorblind attitudes, and discusses how these ideologies may manifest in classroom practices surrounding race and racism. Colorblind ideology is problematized and dissected to show that colorblind practices may harm children. Young children’s interpretations of race and racism, in light of children’s cognitive developmental level, are discussed. Additionally, findings from racial prejudice intervention studies are applied to teaching. Early literacy practices surrounding race and racism are outlined with practical suggestions for teachers and teacher educators. Moreover, implications of teacher practices surrounding race and racism for children’s development, professional development, and teacher education are discussed.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

Political science is often derided for being a “soft” science, one unable to generate hard predictions about political behavior, or without the ability to test its hypotheses, unlike physics, biology, or, among the social sciences, economics. Standards of hypothesis testing, data collection, and testing were unfairly seen to be lacking in comparison with the hard sciences. Accordingly, political scientists often had to struggle to have the knowledge produced about political behavior taken seriously. It would not be too remiss to identify an inferiority complex among political scientists, when they discussed the pantheon of scientific disciplines and their low position in it.

Racism occurs in many ways and varies across countries, evolving and adapting to sociocultural history, as well as contemporary economic, political and technological changes. This chapter discusses the multilevel dimensions of racism and its diverse manifestations across multiracial societies. It examines how different aspects of racism are mediated interpersonally, and embedded in institutions, social structures and processes, that produce and sustain racial inequities in power, resources and lived experiences. Furthermore, this chapter explores the direct and indirect ways racism is expressed in online and offline platforms and details its impacts on various groups based on their intersecting social and cultural identities. Targets of racism are those who primarily bear the adverse effects. However, racism also affects its perpetrators in many ways, including by limiting their social relations and attachments, and by imposing social and economic costs. This chapter thus analyses the many aspects of racism both from targets and perpetrators' perspectives.