Marketers increasingly use behavioral targeting in location-based mobile marketing (LBMM). However, highly personalized marketing messages like this may backfire by eliciting consumer reactance. We ...suggest that LBMM efficacy depends on its potential to minimize consumer reactance, which can be achieved by effectively combining location targeting (in-store vs. out-store), behavioral targeting (based on consumers’ product category involvement PCI), and the type of promotion offered (price vs. non-price promotion). Results of a field study, a virtual reality experiment, and two online experiments show that although in-store mobile ads are generally more effective in increasing sales than out-store mobile ads, this is only the case if consumers have low PCI with the advertised product category, because this decreases their reactance. To attract consumers to stores by out-store LBMM, we show that firms should offer price promotions to consumers with low PCI and non-price promotions to consumers with high PCI, because these combinations of location targeting, behavioral targeting, and type of promotion elicit the least reactance and therefore result in a higher probability to buy.
Brands often seek endorsements by consumers on social media (e.g., likes on Facebook). But is this marketing strategy feasible for all brands? To answer this question, this research investigates in ...seven studies the processes that underlie consumers' intention to endorse brands on social media. We suggest that consumers aim to signal their identity by endorsing brands online. Based on the Brands as Intentional Agents Framework and related research in (social) cognition and consumer behavior, we argue that consumers on social media primarily want to emphasize their warmth rather than their competence. Experimental studies 1, 2, and 3 distinguish between nonprofit and for-profit brands and show that brand warmth (and not competence) mediates the effect of brand type (nonprofit vs. for-profit) on consumers' intentions to endorse brands and branded content on social media. Experiment 4 demonstrates that this process is moderated by brand symbolism (moderated mediation). A high level of brand symbolism increases the positive effect of warmth on consumers' intention to endorse brands online, but only for for-profit brands. The fifth experiment shows that these effects are conditional upon the public vs. private distinction in consumer behavior: consumers prefer to publicly affiliate with nonprofit (vs. for-profit) brands but with regard to private affiliations, there is no difference between both types of brands. In experiment 6, the causal role of warmth (vs. competence) is further examined. Finally, we demonstrate that perceptions of brands' warmth and not competence reduce the efforts that brands need to make to achieve consumers' endorsements on their real brand pages on Facebook.
•We examine brands' ease to acquire online endorsements by their consumers.•Brand warmth is a key driver of consumers' online brand endorsements; competence is not.•The effect of warmth can be amplified by a highly symbolic value of a brand.•Warmth of a brand predicts the ease of brands to obtain real likes on Facebook.•This makes it easier for nonprofit brands to acquire consumers' online endorsements.
The Covid-19 pandemic increases consumers' worries and makes them experience a loss of control over their lives. We investigate how these factors affect the roles that brands play in consumers' ...lives. Results of a longitudinal survey (N = 5,393) and an online experiment (N = 387) show that brands gain relevance and are more firmly included in consumers' self-concepts if consumers experience more worries about Covid-19 and a lack of control. Brands can benefit from this by addressing worries associated with the crisis in their advertisements. This is particularly effective for consumers who express greater worry about the Covid-19 pandemic.
To what extent do consumers incorporate the identity of brands they endorse on social media into their self-concept? We argue that, contrary to popular belief, online brand endorsements may not ...necessarily lead to inclusion of the brand into the self and may, consequently, lead to contrast effects that negatively affect consumers’ self-evaluations. We test our hypotheses across five studies and find that consumers who endorse a brand on social media subsequently negatively adjust their self-evaluations on traits that reflect the brand's key personality traits. This effect occurs only if they endorse the brand (i.e., by “liking” or “following” it), but not when they just visit the brand's social media page. Moreover, the effect is moderated by brand symbolism, with stronger effects if the brand is perceived to have low brand symbolism. The downwards shift in consumers’ self-evaluations negatively affects brand outcomes. We also explore the role of incentives as counter mechanism. In conclusion, our findings reveal a dark side to promoting consumer endorsement of brands on social media, one with implications not only for consumers but also for brands.
Purpose
This paper aims to demonstrate that self-persuasion can be used as a marketing technique to increase consumers’ generosity and that the efficacy of this approach is dependent on consumers’ ...involvement with target behavior.
Design/methodology/approach
An experimental field-study was conducted to investigate the effects of self-persuasion versus direct persuasion attempts versus no persuasion attempts on consumers’ tipping behavior in a lunchroom. Additionally, in a lab experiment, the moderating role of involvement on self-persuasion versus direct persuasion was tested.
Findings
The results reveal that self-persuasion is more effective than direct persuasion attempts or no persuasive messages in increasing consumers’ generosity. This is moderated by consumers’ involvement with the target behavior. For consumers with high involvement, self-persuasion is more effective than direct persuasion, while no differences were found for consumers with moderate or low involvement.
Practical implications
The scope of self-persuasion is not limited to the inhibition of undesired behavior, but it also extends to the facilitation of desired behavior, which considerably broadens the scope of this technique. Self-persuasion might be used as a marketing technique to influence consumers’ purchase behavior. This might be particularly viable in situations in which consumers feel high involvement with products or behavior.
Originality/value
Recently, research in health psychology demonstrated that self-persuasion is a very effective way of inhibiting undesired, addictive behavior and being more successful than direct persuasion. Yet, insufficient knowledge is available about the efficacy of self-persuasion with regard to promoting other target behaviors. In particular, its potential as a marketing technique to influence consumers’ behavior and its boundary conditions are still understudied.
Recent research increasingly highlights that consumers engage in online brand endorsements (e.g. Facebook likes) to signal their identity, but has failed to explain why different consumers use this ...type of signaling to differing degrees. This paper addresses this gap by looking at a culturally constructed individual difference variable, namely self-construal. Self-construal, which can be independent or interdependent, refers to the extent that people define themselves in terms of the relations they have with others. In four studies, this research shows that consumers' self-construal is related to their intention to endorse brands online. In particular, high levels of interdependent self-construal positively affect consumers' intention to endorse brands online (Studies 1A & 1B). This effect is mediated by an increased perception of brands' symbolic value (Study 2). Moreover, this positivity bias toward symbolic brand cues is conditional upon consumers' brand attitude (Study 3). These findings demonstrate that consumers' identity plays a central role in their brand perception and brand-related social media use.
The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products ...or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.
•We argue that sentiment analysis is inexpedient to identify relevant eWOM.•Instead, we use machine learning (ML) algorithms focussing on relevance for brands.•We compared these to other text analysis techniques for 16 brands across 8 industries.•ML achieves higher accuracy compared to sentiment- and dictionary-based approaches.•Our algorithms are applicable across different industries and social media platforms.
As it has been demonstrated that subliminal advertising can affect consumers' decisions – if the ad is goal relevant – the question rises whether consumers are able to shield themselves from ...subliminal influences. In the present research it was examined whether warning people of the presence of subliminal ads could decrease subliminal advertising effects. In Study 1, it was demonstrated that warning people of subliminal ads indeed diminished priming effects on consumer choice, whereas subliminal advertising effects were replicated for people who were not warned (i.e., people for whom the primed brand was goal relevant were more likely to select it when primed). Study 2 extended these findings, revealing that both participants warned before and after the priming manipulation were less influenced by subliminal brand primes than controls. This suggests that the warning does not decrease participants' sensitivity to the prime, but instead affects the influence of the prime at the behavioral level. Several explanations and implications are discussed.
•Can people protect themselves against subliminal advertisements?•As in previous research, goal-relevant brand primes affected choice•Brand primes no longer affected choice when people were warned against subliminal ads•Both people warned before and after priming failed to be influenced•Thus, vigilance seems to diminish the link between prime and behavior
Decision-making in marketing has changed dramatically in the past decade. Companies increasingly use algorithms to generate predictions for marketing decisions, such as which consumers to target with ...which offers. Such algorithmic decision-making promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Not surprisingly, machine learning is a trending topic for marketing researchers and practitioners. However, machine learning also introduces important challenges to the marketing landscape. We discuss this development by outlining recent progress and future research directions of machine learning in marketing. Specifically, we provide an overview of typical machine learning applications in marketing and present a guiding framework. We position the articles in the Journal of Business Research’s Special Issue on “Machine Learning in Marketing” within this framework and conclude by putting forward a research agenda to further guide future research in this area.
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