Title: Segmenting consumers on social networks based on individual motivations for engagement in eWOM communication with self-organising maps

Authors: Manijeh Bahrainizad; Hamid Izadi

Addresses: Faculty of Business and Economics School, Persian Gulf University, 7516913817, Bushehr, Iran ' Business and Economics School, Persian Gulf University, 7516913817, Bushehr, Iran

Abstract: Social networks allow consumers to share marketing content and advertise products for free through electronic word-of-mouth (eWOM) communication. Consumers regularly share eWOM regarding their motivations. Some of these motivations have been identified in previous studies. This study aimed to understand how consumers can be segmented on social networks according to the individual motivations for engagement in eWOM. A self-organising map was used for segmentation. Data were collected by sending questionnaire links to 385 Iranian users of Telegram, Facebook, and Instagram using a convenience sampling method. A total of three segments were identified: active users with low individual motivation, who spent a relatively long time on social networks, but had low individual motivations to participate in eWOM activities. Active users with high individual motivations were young users who devoted the most time to social networks and were highly motivated. Finally, inactive users with individual motivations who despite having sufficient individual motivations, spent very little time on social networks. This study, by applying a self-organising map for segmenting consumers on social networks, provides insights into how companies should make strategies to engage consumers in eWOM communication, taking into account individual motivations.

Keywords: electronic word-of-mouth communication; eWOM; individual motivations; segmentation; self-organising maps; SOMs; social networks.

DOI: 10.1504/IJIMA.2025.144182

International Journal of Internet Marketing and Advertising, 2025 Vol.22 No.1, pp.1 - 32

Received: 12 Feb 2022
Accepted: 03 Jul 2022
Published online: 31 Jan 2025 *

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