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Brand warmth elicits feedback, not complaints

  • Original Empirical Research
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Abstract

Consumers perceive brands on their intended goals that can benefit or harm consumers. These warmth perceptions become consequential when a consumer experiences a product-harm incident. Conventional wisdom suggests that brand warmth may inhibit consumers from reporting such incidents to the brand and/or regulators. However, the authors’ analyses of field data show that brand warmth increases the number of reports of harm incidents. Yet consumers’ underlying motive is to provide feedback rather than complain. Indeed, using machine learning and regressions, and laboratory experiments, the authors demonstrate that brand warmth boosts the proportion of feedback (vs. complaint) reports. Next, they theorize and show that brand warmth induces consumer benevolence, which drives the consumer toward feedback (vs. complaint). Lastly, the authors demonstrate that if managers of a warm brand acknowledge the consumer’s feedback motive in their recovery messages, such acknowledgement enhances consumer satisfaction. The research extends the discipline’s knowledge on how a brand’s warmth perceptions impact consumers’ responses in the aftermath of a product-harm incident and what intervention managers can use in such a context.

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Data availability

All data (experimental and observational) and code files are available with each of the three authors.

Notes

  1. We prefer the term “product-harm incident” over its closely related term “product failure” (e.g., Folkes, 1984) for two reasons. First, product-harm incident clarifies that the product harmed the consumer and thus emphasizes lack of safety in the product (Dunn & Dahl, 2012). Second, it emphasizes the discrete incident rather than the broader term “failure,” which suggests stable attribution and negative valence (Darke et al., 2010).

  2. We clarify that, in our research, brand warmth refers to consumers’ perceptions before the harm incident.

  3. Adapted from Xie and Peng’s [2009] definition of organizational benevolence.

  4. The control condition is the brand’s status quo response that thanks the consumer for their feedback but does not include any acknowledgment of the customer’s motive behind reporting the harm incident.

  5. We thank an anonymous reviewer for asking us to control for these covariates.

  6. We chose a negative binomial regression specification because our dependent variable is a count variable with incomparable mean and standard deviation. A likelihood ratio test confirmed the choice of a negative binomial model over Poisson regression (Logliknb =  − 2741.5, X2(21) = 303.3, p < .001, LoglikPoisson =  − 23,593.1, X2(21) = 24,151.0, p < .001).

  7. The reported results in Table 3 are robust by including a Gaussian copula term for warmth to correct for the potential endogeneity of brand warmth.

  8. The fixed effects negative binomial model excludes observations for firms that did not experience any variation in the number of reports over their tenure and are thus estimated with 539 (and not 1,448) observations.

  9. As in Study 1, a likelihood ratio test confirmed the superiority of a negative binomial model over Poisson regression (Logliknb =  − 1094.3, X2(11) = 44.2, p < .001, LoglikPoisson =  − 7730.8, X2(11) = 1176.3, p < .001).

  10. Because we are interested in comparing the two types of consumer reports, we follow extant research (e.g., Han et al., 2014; Yoon 2013) to estimate a repeated measures ANOVA.

  11. We chose RA-based coding as the preferred method of coding in this study, because the corpus of text in Study 3 is much smaller compared to Studies 1 and 2 (185 reports versus ~ 1400 in our observational studies), and thus perhaps too small for training a machine learning model. However, the results of this study are robust to alternate measures of feedback and complaining obtained from the seeded semi-supervised LDA model (F(1, 183) = 6.36, p = .0125)).

  12. Unless otherwise stated, all items were measured on a seven-point Likert scale where 1 = “strongly disagree” and 7 = “strongly agree”.

  13. Mathematically, conducting a one-way ANOVA with the difference between the two motives (i.e., Δ motive = feedback minus complaint) as the DV is the same as conducting a repeated-measures ANOVA with the two motives. This sameness can also be seen in our one-way ANOVA results, where feedback was significantly higher than complaint (Mhigh-warmth = 3.35 vs. Mlow-warmth = 1.46, F (1, 188) = 47.52, p < .001) for the warmer brand. These results mirror the Study 4’s finding, which used repeated-measures ANOVA. Therefore, we used a difference score as the DV.

  14. We thank an anonymous reviewer for reminding us of this limitation of our observational data studies.

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Authors and Affiliations

  1. Quantitative Marketing and Analytics, Desautels Faculty of Management, McGill University, 1001 Sherbrooke St. W, Montreal, QC, H2L 1L3, Canada

    Vivek Astvansh

  2. Data Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Vivek Astvansh

  3. UCD Michael Smurfit School of Business, University College Dublin, Carysfort Ave, Carrysfort, Blackrock, Co., Dublin, Ireland

    Anshu Suri

  4. Haslam College of Business, University of Tennessee, Knoxville, 453 Haslam Business Building, Knoxville, TN, 37996, USA

    Hoorsana Damavandi

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Astvansh, V., Suri, A. & Damavandi, H. Brand warmth elicits feedback, not complaints. J. of the Acad. Mark. Sci. (2024). https://doi.org/10.1007/s11747-024-01009-w

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