Abstract
User- and marketer-generated content items on social media platforms are supposed to have an impact on economic target variables, such as variables measuring consumers' purchase behavior. The position of each content item – and thus the impact on economic variables – changes with newly appearing items. We propose a hierarchy score to capture the dynamics of the content items on social media platforms. In order to mimic the reduced visibility of earlier content items, our hierarchy score computes the position of content items based on the number of text line equivalents of content items above a particular item. Employing the proposed hierarchy score in a dynamic regression framework for data of a large online store yields improved estimates and predictions compared to a variety of other models.
Original language | English |
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Pages (from-to) | 43 - 55 |
Journal | Decision Support Systems (DSS) |
Volume | 113 |
DOIs | |
Publication status | Published - 2018 |
Austrian Classification of Fields of Science and Technology (ÖFOS)
- 502050 Business informatics