Search results

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.

In this chapter, we investigate whether the option contract can coordinate a supply chain when supply chain members have fairness concerns. Specifically, we consider a supply chain consisting of a manufacturer and a retailer where the two members can be either rational or fair-minded, and explore the condition under which the supply chain can be coordinated using an option contract. We follow the traditional newsvendor model by assuming that the market demand is stochastic with a cumulative distribution function and the retail price is exogenous. Under the option contract, the manufacturer’s decision variables include its option price and its exercise price, and the retailer is to decide its order quantity. We derive the equilibrium results for four different scenarios, that is, (i) both the two members are rational, (ii) the supplier is rational but the retailer is fair-minded, (iii) the supplier is fair-minded but the retailer is rational, and (iv) both are fair-minded. While the option contract can coordinate the supply chain when either of the two members is rational, we also find that when both the two members are rational, the option contract can coordinate the supply chain only under some specific conditions. Furthermore, we investigate whether the two members will suffer the disadvantageous or the advantageous inequality in the equilibrium and find some interesting findings.

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 the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.

Food insecurity in the Latin American region has become a complex problem that significantly impacts people's physical and mental well-being. The factors causing food insecurity are varied, ranging from social, political, and economic causes. Ensuring access to food is not a task with limited responsibilities; on the contrary, both public and private institutions must contribute to creating sustainable and innovative solutions. In general, it is necessary to ensure that the food system flows correctly, ensuring the availability of balanced and nutritious food for the diet of the inhabitants of a given nation. Alternative solutions apart from the government's help include sustainable cultivation, finger millet, and close cooperation with the farmers from the agriculture sector. The present research aims to consolidate theoretical information on the Latin American situation and seek the leading solutions of the parties involved.