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This chapter is concerned with the social pillar of sustainability and how management education can assist in ensuring the equity among people that is necessary to achieve sustainability. The chapter considers how a sense of responsibility towards ensuring equity and fairness is derived and the sources of this. It argues that early education teaches aspects of fairness, but at a higher education level, further education is a working context continues to be necessary but is very often absent. It is at this stage that the educators in management have a role and responsibility. Unfortunately in a work context, people tend to be considered merely as operands within a production process and not as real people, and thus considerations of fairness and concern tend to be eliminated, with a tendency towards exploitation. This of course is not sustainable, and the chapter argues that at the higher education level this can be addressed with noticeable effect.

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 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.

In business and management, cross-national and cross-cultural comparisons between countries have been a topic of interest for many decades. Not only do firms engage in business in different countries around the world but also within countries. The population has become more diversified over time, making cross-cultural comparisons within country boundaries increasingly relevant. In comparisons across cultural groups, measurement invariance (MI) is a prerequisite; however, in practice, MI is not always attained or even tested. Our study consists of three parts. First, we provide a bibliometric analysis of articles on cross-cultural and cross-national topics in marketing to provide insight into the connections between the articles and the main themes. Second, we code articles to assess whether researchers follow the recommended steps as outlined in the multigroup confirmatory factor analysis (MGCFA) approach. The results indicate that MI testing is incorporated in the toolbox of many empirical researchers in marketing and that articles often report the level of invariance. Yet, most studies find partial invariance, meaning that some items are not comparable across the cultural groups studied. Researchers understand that MI is required, but they often ignore noninvariant items, which may decrease the validity of cross-cultural comparisons made. Third, we analyze the dissemination of MI in the broader literature based on co-citations with Steenkamp and Baumgartner (1998), a widely cited article on MI in the field of marketing. We conclude by noting methodological developments in cross-cultural research to enable addressing noninvariance and providing suggestions to further advance our insight into cross-cultural differences and similarities.

Carbon emission is one of the most important problems of today. In this framework, it is important for countries to take the necessary actions to solve this problem. Energy use is one of the most important causes of carbon emissions. Choosing fossil fuels in this process increases the carbon emission problem. Therefore, it is understood that countries should be more sensitive about energy types. In this context, renewable energy (RE) sources are recommended by experts. However, due to some problems of these energy types, it does not seem possible to meet all energy needs from these sources. It is thought that nuclear energy will produce a permanent solution to the carbon emission problem. In this context, it is recommended that the use of nuclear energy be put on the agenda by countries.

The objective of this chapter is to interpret a supply chain as an ontological entity with being-in-the-world of spacetimemattering. A case study approach is adopted to reveal the strategies undertaken by one of China’s fastest growing Internet companies – Xiaomi Inc. – to create competitive advantage through its management of product design and supply chain integration. Utilizing publicly available data, we analyze the company with quantum storytelling and network analysis techniques. Our analysis concludes that Xiaomi’s success originates from two aspects. First, Xiaomi is a good storyteller, who makes stories appealing to customers by involving them into product design and branding. Second, Xiaomi’s parsimonious supply chain substantially improves its market responsiveness and reduces disruption risks; more importantly, it helps to offer products of great value to customers.