Dec 2019 (Vol. 59, Issue 4): SOCIAL-MEDIA MARKETING
How Do Brands’ Facebook Posts Induce Consumers’ e-WOM Behavior?
Informational vs. Emotional Message Strategy: A Computational Analysis
Research analyzing how a brand’s Facebook posts could generate e-word of mouth (eWOM) has often relied on content analysis and users’ motivations. New work by Taemin Kim (Incheon National University, South Korea), Hyejin Kim (DePaul University) and Yunhwan Kim (Hankuk University Foreign Studies, Seoul) takes a big-data approach in predicting the impact of a post’s unique features on eWOM activity, with some unexpected outcomes.
Using software-based regression analysis—a form of statistical modeling—and data from 46 companies from the Fortune 100 Index, the authors dissected the impact of a post’s message strategies. Results showed that eWOM activity increases when brands use a combination of informational- and emotional-based messaging, including:
- the use of multimedia content, such as photos and videos;
- brand names;
- subjective words evoking emotional, evaluative and judgmental expression (vs. objective and factual information);
- social-processing words evoking cognitive and emotional processes in the text.
Contradicting earlier research, the study also shows that using a link in a brand post as an informational message strategy would decrease the number of “likes” from consumers. And in terms of emotional messaging: “The more positively intense a brand post was, the less likely it would receive a positive comment from consumers.”
The researchers emphasize that the main takeaways were “not to show that one message strategy in crafting brands’ posts is superior to or more effective than the other. Rather, the results could serve as a guideline.” Companies, they suggest, “should craft brand posts on Facebook carefully by incorporating informational and emotional aspects of a brand in parallel to induce consumers’ eWOM behaviors effectively.”
Big-data driven, computational analysis informed these outcomes, aided by a sentiment-analysis technique in Python and Linguistic Inquiry and Word Count text analysis software. The researchers collected brand posts on Facebook from 46 of the top 50 companies from the Fortune 100 Index. A total of 71,112 posts—harvested from the moment when each company joined Facebook until August 8, 2015— were used for analysis. Post collection included unique identities of each post, time stamps, post types, identities and names of creators and the number of likes and comments for each post. Comment collection included the identity of the post on which each comment was made, time stamps, messages and the identity and name of each commenter.
The study’s findings “give advertisers and marketers useful insights to maximize visibility of their brand posts on social media and enhance the relationship with their consumers in social media,” the authors wrote.
“Especially when including a link or positively aroused contents, practitioners should implement such factors carefully, depending on the purposes of promotion.” If the goal is to generate a high number of consumer comments, for example, brand managers should use caution when creating highly positive posts because people may not respond positively to them.