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In this commentary of "How Support for Black Lives Matter Impacts Consumer Responses on Social Media," we discuss issues related to the research approach, findings, and offer suggestions for future research. In this commentary, we focus on the racial justice movement popularly known as BlackLivesMatter (BLM). We begin our comments by briefly noting the history that led to the formation of the BLM organization and movement. This sets the stage for our comments on "How Support for Black Lives Matter Impacts Consumer Responses on Social Media"—an issue important for marketers who must evolve with an increasingly socially inspired economy. As BLM is a polarizing issue that is often misunderstood, we first note a potential internal validity issue with research on this topic. We then discuss issues specific to Blackout Tuesday, the intervention of interest in the paper. These include the comparability of the treatment and control platforms; the need for a clear baseline during this turbulent period; the possibility that the followers in the control condition were also treated; the parallel trends assumption; and the analysis and interpretation of the authors' headline finding that firms supporting Blackout Tuesday faced a backlash in terms of a reduction in follower growth. Throughout this comment, we offer suggestions for future research that tackles the response to social-political issues like BLM. History: K. Sudir served as the senior editor for this article. [ABSTRACT FROM AUTHOR]

We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth (OWOM) from its valence, which has three dimensions--attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model (DHLM) for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model. Our estimation accounts for potential endogeneity in the key measures. Among the different OWOM measures, only the valence of recommendation OWOM is found to have a direct impact on sales; i.e., not all OWOM is the same. This impact increases over time. In contrast, the impact of attribute advertising and emotion advertising decreases over time. Also, consistent with prior research, we observe that rational messages (i.e., attribute-oriented advertising) wears out a bit faster than emotion-oriented advertising. Moreover, the volume of OWOM does not have a significant impact on sales. This suggests that, in our data, "what people say" is more important than "how much people say." Next, we find that recommendation OWOM valence is driven primarily by the valence of attribute OWOM when the product is new and driven by the valence of emotion OWOM when the product is more mature. Our brand-level results help us classify brands as consumer driven or firm driven, depending on the relative importance of the OWOM and advertising measures, respectively. [ABSTRACT FROM AUTHOR]