Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages

Consumer-to-consumer brand message sharing is pivotal for effective social media marketing. Even as companies join social media conversations and generate millions of brand messages, it remains unclear what, how, and when brand messages stand out and prompt sharing by consumers. With a conceptual extension of speech act theory, this study offers a granular assessment of brands’ message intentions (i.e., assertive, expressive, or directive) and the effects on consumer sharing. A text mining study of more than two years of Facebook posts and Twitter tweets by well-known consumer brands empirically demonstrates the impacts of distinct message intentions on consumers’ message sharing. Specifically, the use of rhetorical styles (alliteration and repetitions) and cross-message compositions enhance consumer message sharing. As a further extension, an image-based study demonstrates that the presence of visuals, or so-called image acts, increases the ability to account for message sharing. The findings explicate brand message sharing by consumers and thus offer guidance to content managers for developing more effective conversational strategies in social media marketing

When nothing is what it seems. A Digital Marketing Research Agenda

Digital breakthroughs continue to challenge prevailing understandings of markets and marketing practices, bringing exciting opportunities to reimagine our offerings. Looking through the lens of digital surrealism, we identify key trends emerging in the field: (1) Is AR (Augmented Reality) for real?; (2) There is no better PR than GDPR (General Data Protection Regulation); (3) A persona is not a persona; (4) Min(e)d your language, and; (4 ¾) Raise your voice. Maybe. Based on these trends we develop an agenda for future research that enables the realization of the opportunities that the digital space offers

Seeing eye to eye: social augmented reality and shared decision making in the marketplace

Firms increasingly seek to improve the online shopping experience by enabling customers to exchange product recommendations through social augmented reality (AR). We utilize socially situated cognition theory and conduct a series of five studies to explore how social AR supports shared decision making in recommender–decision maker dyads. We demonstrate that optimal configurations of social AR, that is, a static (vs. dynamic) point-of-view sharing format matched with an image-enhanced (vs. text-only) communicative act, increase recommenders’ comfort with providing advice and decision makers’ likelihood of using the advice in their choice. For both, these effects are due to a sense of social empowerment, which also stimulates recommenders’ desire for a product and positive behavioral intentions. However, recommenders’ communication motives impose boundary conditions. When recommenders have strong impression management concerns, this weakens the effect of social empowerment on recommendation comfort. Furthermore, the stronger a recommender’s persuasion goal, the less likely the decision maker is to use the recommendation in their choice

From words to pixels: text and image mining methods for service research

Purpose: The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research. Design/methodology/approach: On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided. Findings: The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services. Research limitations/implications: This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods. Practical implications: The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience. Originality/value: The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning)

Mindful consumption: Three consumer segment views

As consumers are moving away from mindless consumerism, a mindful consumption literature has emerged that is based on Buddhist and psychological perspectives of mindfulness. While the idea of mindful consumption has great potential, there is little empirical research to date that comprehensively examines the consumer perspective on the role of mindfulness on consumption. To provide a grounded consumer perspective, the authors segment mindful consumption views from open-end text using a mixed method of clustering and text mining. By analyzing the segmentation structure, the authors discover various consumer views of mindful consumption, such as careful economic based consumption, monitoring activities of firms, and being informed about the impact of consumption choices. The authors compare the empirical results with the academic literature to provide directions for future research

Unveiling What is Written in The Stars: Analyzing Explicit, Implicit and Discourse Patterns of Sentiment in Social Media

Deciphering consumer’s sentiment expressions from Big Data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, Sentiment Analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on Speech Act Theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text mining study using more than 45,000 consumer reviews, demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers’ behavior and are generalizable to other social media contexts such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implication