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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.

Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.

As one of the most influential theories in strategic management, Hambrick and Mason’s Upper Echelons Theory has yielded significant conceptual and empirical advancements linking executive characteristics and perceptions to decision-making. Specifically, work on this theory consistently shows that CEOs’ decisions are biased by personal characteristics to the benefit and detriment of firms. While this stream of research links executive decision processes to outcomes such as executive dismissals, analyst evaluations, and press coverage, surprisingly little is understood about if and whether the information CEOs convey is subject to the same filtering process by a firm’s key evaluators. Thus, in this chapter, we aim to extend Upper Echelons Theory by positing that a double filtering process occurs whereby the cognitive aids CEOs use can be informed by not only their cognitive base and values but also the characteristics and priorities of those who evaluate the nonverbal and verbal signals they send. To do so, we build on recent conceptual and empirical advancements to make a case for the decision-making biases and tendencies that influence signal interpretation by three key evaluator groups internal and external to the firm: boards of directors, financial analysts, and the media. We conclude by considering the implications of evaluators’ information filtering and how this more holistic view of Upper Echelons decision-making can enable executive teams to be strategic with the cognitive aids they use to influence evaluations.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

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.

In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of artificial intelligence (AI). Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multimodal, and virtually real time. Such explosion in feedback content has also been accompanied by a rapid development of AI and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field.

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.

Our ongoing program of research works with teachers to help them become more autonomy supportive during instruction and hence more able to promote students’ classroom motivation and engagement.

We have published five experimentally based, longitudinally designed, teacher-focused intervention studies that have tested the effectiveness and educational benefits of an autonomy-supportive intervention program (ASIP).

Findings show that (1) teachers can learn how to become more autonomy supportive and less controlling toward students, (2) students of the teachers who participate in ASIP report greater psychological need satisfaction and lesser need frustration, (3) these same students report and behaviorally display a wide range of important educational benefits, such as greater classroom engagement, (4) teachers benefit as much from giving autonomy support as their students do from receiving it as teachers show large postintervention gains in outcomes such as teaching efficacy and job satisfaction, and (5) these ASIP-induced benefits are long lasting as teachers use the ASIP experience as a professional developmental opportunity to upgrade the quality of their motivating style.

Our ASIP helps teachers learn how to better support their students’ autonomy during instruction. The value of this teaching skill can be seen in teachers’ and students’ enhanced classroom experience and functioning.