Abstract
Twitter provides an important channel for brands to seed electronic word of mouth (eWOM) by followers retweeting brand messages, but prior research has not established a theoretical framework for how brands can maximise eWOM. This study presents and tests a theoretical model incorporating interactive, textual and visual tweet features to predict eWOM, using tweets by leading brands from three industries. Industry was found to be an important moderator of the effect of tweet features; after controlling for the reach and frequency of tweets, hashtags, retweet requests and photos were consistently associated with a higher retweet rate across industries, but the effect of URL links, non-initial mentions and video varied across industries, in some cases decreasing the retweet rate. Implications for research and practice are discussed.
Original language | English |
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Pages (from-to) | 1120-1148 |
Number of pages | 29 |
Journal | Journal of Marketing Management |
Volume | 33 |
Issue number | 13-14 |
DOIs | |
Publication status | Published - 2 Sept 2017 |
Bibliographical note
Publisher Copyright:© 2017 Westburn Publishers Ltd.
Keywords
- marketing
- online social networks
- word-of-mouth advertising