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Purpose – With the rapid proliferation of digital technology for creating and disseminating content in different forms – textual, music, video – both firms and consumers have a number of alternative technology and formats available for creating and consuming content. While this has led to more value for consumers, the firms have had mixed results. Some firms have seen their value erode through the adoption of newer formats as compared to the older ones (e.g., streamed music format vs. CD format), and other firms have been generally reluctant to embrace newer technology and formats for the similar reasons.Design/methodology/approach – In this chapter, we review the research issues in designing and pricing such digital content and formats and the various strategies that firms can adopt in ensuring that both firms and consumers benefit through the use of newer formats.Findings – We review and discuss the extant research in this domain and identify research issues for future research.Value/originality – As more traditional products morph into digital services, it is critical that these issues are addressed so that the content creation industry can survive in the short term and prosper in the long term.

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.

Organizations are increasingly required to take up extended responsibilities for social and environmental outcomes, including in global value chains. To address these challenges, the organization must call upon stakeholders to engage, contribute, and innovate, and in turn, this requires the organization to have a stronger social basis for its relationships. An integrative model of global value chain management based on social cooperation shifts the focus from corporate reputation to value chain reputation, from a firm-centric view of corporate reputation to a multistakeholder conception of value chain reputation. This approach conceptualizes reputation as a dynamic and potentially vulnerable organizational feature which cannot always be managed by public relations but requires a more stable notion grounded in something more permanent in the organization’s character, history, and the quality of its relationships with stakeholders. We consider the prospects for attending to organizational integrity as a stabilizing force for its public reputation. Integrity may be adopted as a hypernorm for motivating stakeholders who share a concern for the organization’s reputation. Co-creating reputation depends upon a social bond of cooperation developed by stakeholders caring about the organization and in turn, the organization caring about its stakeholders. This socialized understanding of reputation-building is grounded in an ethic of care and manifested through joint purposes, boundary-crossing processes, collaboration practices, and a division of labor into which value chain members are integrated and brought into relation with one another. We propose a model of global value chain management that discusses organizational capabilities required for such an approach.

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.

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.

Corporate social responsibility (CSR) is intimately related to culture and ethics of the country in which the company is located. It is difficult to define “culture.” It is a combination of values, belief, and morality law in a society. Society is a group of people who follow common set of values and norms. Usually individuals in a society are bounded with specific religion. This bondage depends on nature of the religion (e.g., Christianity, Hinduism, and Islam). The norms are inherent within the values which determine such items as individual freedom, democracy, women’s freedom, social justice, and collective responsibility. Sometimes culture and religion also determine the formation and break down of nation states. India-Pakistan and Rwanda-Burundi are examples. Social structure depends on religious values and occupational system.

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.