Antecedents of Retweeting in a (Political) Marketing Context. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Walker, Lorna; Baines, Paul R.; Dimitriu, Radu; Macdonald, Emma K.
• Is Part Of: Psychology & marketing. Volume 34:Issue 3(2017)
• Page Start: 275
• Page End: 293
• Publication Date: 2017-03
• Subjects: Marketing -- Psychological aspects -- Periodicals; Motivation research (Marketing) -- Periodicals; Marketing -- Aspect psychologique -- Périodiques; Motivation, Études de (Marketing) -- Périodiques; 658.80019
• DOI: 10.1002/mar.20988
• Languages: English
• ISSNs: 0742-6046

Abstract

ABSTRACT: Word of mouth disseminates across Twitter by means of retweeting; however, the antecedents of retweeting have not received much attention. We used the chi‐square automatic interaction detection (CHAID) decision tree predictive method (Kass, 1980 ) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (1) using message structure variables only, (2) source variables only, (3) message content and sentiment variables only, and (4) a combined model using source, message structure, message content, and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007 ; Wu, 2013 ) but concurring with others (Hennig‐Thurau, Wiertz, & Feldhaus, 2014 ). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, while others are associated with very low levels of retweeting, such as those mentioning the sender's personal life. Managerial implications and research directions are presented. We make a methodological contribution by illustrating how CHAID predictive modeling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context.