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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.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

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 research used a Phenomenography approach of Eye Tracking to study the Biometric changes when participants were subjected to eight static subliminal images hidden in seven differently designed packages. Embeds or static subliminal stimulus in the form of pictures and words were hidden in seven different perfume packages and were used to study the changes experienced between the two groups, one of which was subjected to subliminal stimulus. This study was conducted in the Neuro Lab located in the SP Jain Sydney campus. A total of 46 healthy participants were separated into two groups, with one group shown image packages with static subliminal stimulus while the other group was shown image packages without any subliminal stimulus. Eye Tracking data was collected using iMotions. A detailed analysis of the Area of Interest (AOI), Fixation and Heat Map revealed that only a percentage of the participants visited the AOI with the hidden subliminal stimulus, but the participants who noticed the AOIs with the subliminal stimulus especially the male participants spent more time in the AOI indicating that they could be consciously processing the subliminal static stimulus. A statistical analysis of the time to first fixations (TIFF) revealed that the subliminal stimulus was not the first point of attraction.

The chapter examined the consumer meanings of product placements embedded in social games in different cultural contexts.

The theoretical perspective guiding the study is phenomenology, and the essay assignment and in-depth interviews were used to collect data.

The chapter was based on two qualitative research projects. Findings revealed that consumers in both countries appreciated certain characteristics of product placement in the context of social game, such as subtleness (naturalness) and unobtrusiveness (users’ freedom of choice and proactive choice); consumers’ real-world consumption in both countries seems to be more or less influenced by the product placement in social games; and while the young American consumers didn’t construct specific meanings for Facebook, the Chinese white-collar consumers actively created meanings for the Chinese social-network site.

The chapter offered some thick descriptions and in-depth analyses of product placements in social games in different cultural contexts from consumers’ experiential perspectives to enrich our theoretical understanding of product placement in the new media environment as well as to add valuable insights to the research literature on new advertising formats in general.

No study to date has been conducted to explore the product placement in social games in different cultural contexts. The study fills the research gap by exploring US college-aged consumers’ and Chinese white-collar consumers’ interpretations of product placements in the context of social games.

Extant research presents firms’ purpose as a consensual and positive attribute. This paper introduces an alternative perspective, which sees firms’ purposefulness as defined in relation to specific audiences. A firm’s purposefulness to a focal audience can be either positive or negative. Audiences find firms with which they share a common prioritization of issues more purposeful in absolute terms. Audiences find firms with which they share a common understanding of issues positively purposeful. Conversely, audiences find firms with an opposite understanding of issues negatively purposeful. Audiences harness specific resources to support firms they find positively purposeful and to oppose firms they find negatively purposeful. This paper introduces topic modeling and word embeddings as two techniques to operationalize this audience-based approach to purposefulness.

This chapter extends research on peer review by utilizing and assessing an emerging methodology: automated textual analysis. In a corpus of 38 papers successfully revised for publication in Administrative Science Quarterly, the authors found that measures based on exact wording (measured by plagiarism detection) and sentence similarity (measured by Word Mover’s Distance) performed well in capturing differences between original submissions and published papers. They identified the same overall pattern of revision that authors reported (intensive revision of Theory and Discussion sections, limited modification of Methods), and were strongly correlated with the turnover in references and hypotheses that occurred in the course of peer review. Automated textual analysis can usefully contribute to the study of manuscript change in peer review and other social scientific contexts, particularly as available textual corpora grow in size.