We'll Be Honest, This Won't Be the Best Article You'll Ever Read: The Use of Dispreferred Markers in Word-of-Mouth Communication

Abstract

Consumers value word-of-mouth communications in large part because customer reviews are more likely to include negative information about a product or service than are communications originating from the marketer. Despite the fact that negative information is frequently valued by those receiving it, baldly declaring negative information may come with social costs to both communicator and receiver. For this reason, communicators sometimes soften pronouncements of bad news by couching them in dispreferred markers, including phrases such as, "I'll be honest," "God bless it," or "I don't want to be mean, but . . ." The present work identified and tested in five experiments a phenomenon termed the dispreferred marker effect, in which consumers evaluate communicators who use dispreferred markers as more credible and likable than communicators who assert the same information without dispreferred markers. We further found that the dispreferred marker effect can spill over to evaluations of the product being reviewed, increasing willingness to pay and influencing evaluations of the credibility and likability of the evaluated product's personality.

Consequences of customer engagement behavior: when negative Facebook posts have positive effects

“…Naturally, one could argue that positive brand-related Facebook posts trigger positive impressions and negative posts result in negative impressions. Contrary to this intuitive expectation and building on previous research (e.g., Berger et al 2010;Ein-Gar et al 2012;Hamilton et al 2014), we suggest that, under certain conditions, a small piece of negative information, such as a negative Facebook comment, might affect the product evaluation in a positive way.…”

“…However, negatively valenced information has been found to be more diagnostic and influential than positively valenced information in the context of product judgments (Chevalier and Mayzlin 2006;Hamilton et al 2014;Herr et al 1991;Park and Lee 2009). These findings suggest a negativity bias in processing information, whereby negative information has a stronger impact on judgment and decision making than objectively equivalent positive information (Sen and Lerman 2007;Skowronski and Carlston 1989).…”

How online word‐of‐mouth impacts receivers

“…For example, words that signal temporal contiguity (e.g., today; just got back) increase the helpfulness and impact of positive reviews by shifting attributions of cause toward the product and away from the sender (Chen & Lurie, ). Similarly, when senders use dispreferred markers (e.g., I don't want to be mean, but...) in front of negative information, this produces a player signal that the sender is credible and likeable, which increases receivers’ product evaluations and willingness to pay (Hamilton, Vohs, & McGill, ).…”

Development of the Persuasion Knowledge Scales of Sponsored Content (PKS-SC)

“…The model describes people's personal knowledge about the goals and tactics of persuasion agents, and about how people use this knowledge to cope with persuasive attempts. With 2508 cites on Google Scholar to date, the model has proven relevant in numerous contexts such as interpersonal sales (Campbell and Kirmani 2000), word-of-mouth (Hamilton, Vohs, and McGill 2014;Packard, Gershoff, and Wooten 2016), brand placement (Matthes, Schemer, and Wirth 2007;Matthes and Naderer 2016), advergames (Van Reijmersdal et al 2015), corporate social responsibility marketing (Pomering and Dolnicar 2009), religious and pharmaceutical marketing (McGraw, Schwartz, and Tetlock 2012), and various types of online advertising (Boerman, Willemsen, and Van Der Aa 2017;Ham 2017;Tutaj and van Reijmersdal 2012;Wojdynski and Evans 2016).…”