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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.

This study explored the impact of integrating digital tools on professional preparation in literacy, specifically an online digital video portal for teachers’ self-observation of instructional practice.

As a design experiment (Bradley & Reinking, 2011), a graduate-level Reading Education course was revisioned for blended learning to accommodate the professional development of practicing teachers in a rural, remote context. This chapter focuses on understanding how teachers experience video as a platform for reflection on and improvement of practice, with implications for those who seek to incorporate digital video into literacy professional development.

Through video analysis mediated by the use of a self-evaluation guide and a collaborative, online community, teacher-learners reflected on their own and their peers’ pedagogy and language interactions with students. After overcoming initial struggle with watching themselves on the video, the close analysis of clips became a powerful catalyst for professional growth. Teachers’ reflections shifted from outward-directed to inner-directed.

To successfully integrate video analysis in Reading Education practicums and professional development for in-service teachers, consideration should be given to technical as well as pedagogical components. Purposefully building in various scaffolds, for example, technical tutorials, prompts to focus video analysis, and safe platforms for sharing and collaboration, proved to be beneficial for teacher-learners in our courses.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

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.

Online learning can present challenges and barriers for students, especially when it comes to self-motivation and discipline. Non-traditional learners and those who may be underprepared are often the students most likely to seek virtual learning options. As a result, methods of supporting online learners must be intentional and robust to stay attentive to students’ needs. The American Women’s College (TAWC) at Bay Path University designed its Social Online Universal Learning (SOUL) model to promote degree completion through a constellation of evidence-based practices that cultivate student engagement in a personalized online learning environment. SOUL employs an innovative adaptive technology approach with Universal Design for Learning (UDL) principles to promote accessibility and affordability. Foundational to these frameworks is a commitment to leveraging technology to gather data that drives action-oriented analytics, triggering interventions by faculty and staff and generating predictive models to inform wrap-around support. SOUL’s high-tech, high-touch attributes give students agency over their unique learning paths and provide instructors and administrators the meaningful insights needed to target efforts in a personalized yet scalable way, to promote and positively impact student success. Lessons learned in the process of developing data-driven “high-tech, high-touch” practices are presented.

Rather than organize as traditional firms, many of today’s companies organize as platforms that sit at the nexus of multiple exchange and production relationships. This chapter considers a most basic question of organization in platform contexts: the choice of boundaries. Herein, I investigate how classical economic theories of firm boundaries apply to platform-based organization and empirically study how executives made boundary choices in response to changing market and technical challenges in the early mobile computing industry (the predecessor to today’s smartphones). Rather than a strict or unavoidable tradeoff between “openness-versus-control,” most successful platform owners chose their boundaries in a way to simultaneously open-up to outside developers while maintaining coordination across the entire system.

This chapter describes the theoretical contributions of Fractal Change Management (FCM) in relation to Quantum Storytelling theory and practice. Building on the application of complexity theory in the hard sciences as well as social contexts, this chapter considers the areas of overlap and difference between FCM and its theoretical fellows, summarizing selected concepts from FCM, considering the strengths and weaknesses of the method in various contexts, as well as its development over time. Prior studies in the yoga and nonprofit communities are briefly discussed along with ongoing work with software developers. Areas for further study are examined in detail, as a way to establish an antenarrative for this line of inquiry that honors its lineage as well as its contributions to the body of knowledge.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.