Facebook likes and corporate revenue: testing the consistency between attitude and behavior

Abstract

Whether a person’s attitude is predictive or consistent with their behavior is a topic that has generated much research in the literature. The current study attempts to address this research question using a big data approach in a social media advertising context. Both attitude (i.e. clicking the “like” button for a company’s Facebook posts) and behavior (i.e. purchasing products from the company) are measured in a naturalistic setting. The goal is to examine whether Facebook “likes” on companies’ posts are significantly related to those companies’ revenue. The authors estimate panel models by using nearly eight years of data containing the S&P 500 companies’ Facebook activities in conjunction with their financial performance information. The results suggest that the number of Facebook “likes” is positively associated with revenue across the models. The study concludes with specific theoretical and practical implications and limitations.

Keywords:

• Social media marketing
• social media metrics
• attitude-behavior consistency
• facebook
• big data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors may provide data sets upon request.

Notes

1 Though the big data approach can provide researchers with opportunities to capture subtle yet reliable signals in a real-world business environment, the risk of employing the big data model is that even small effects may become statistically significant with sufficiently large samples. Therefore, we should note that balancing the need for a large dataset with the risk of overfitting is crucial for the model’s effectiveness in future research (see Provost and Fawcett 2013; Varian 2014 for details).