Date of Award
Dissertation - Rollins Access Only
Doctor of Business Administration (DBA)
Dr. Tim Ozcan
Dr. Tracy Kizer
This study sought to identify approaches for organic growth through consumer engagement and features within the Facebook platform, such as Facebook reactions. This study determined whether Facebook reactions can predict the likelihood of consumer engagement actions, such as commenting and sharing. Post content and Facebook reaction data were extracted through Facebook API and Python programming from thirty-five firm fan pages. Data extraction began on the date the Facebook reactions feature was activated, February 24, 2016, and ended on February 24, 2018. A hierarchical multiple regression was conducted for analysis of Facebook reaction predictability. For sharing behavior, results showed that consumers were more likely to share firm-generated content to their own personal “in-group” when their peers have responded with higher volumes of “Love” and “Wow” Facebook reactions. Consumers also shared less when their peers responded with higher volumes of “Haha” and “Sad” Facebook reactions. Commenting behavior increased with higher volumes of “Love,” “Wow,” and “Angry” Facebook reactions while higher volumes of “Haha” and “Sad” Facebook reactions reduced the likelihood of commenting. This study provides an additional contribution to the consumer engagement and e-word-of-mouth literature regarding Facebook reaction predictability. Managers can utilize these results, as a benchmark to compare with their own consumer engagement data and determine the best strategy for increasing organic growth and consumer engagement behaviors.
Clayton, Monica V., "Facebook Reactions: An Investigation of E-Word of Mouth Indicators and Their Predictability of Consumer Engagement" (2019). Dissertations from the Executive Doctorate in Business Administration Program. 15.