Seeded word-of-mouth marketing strategy: mapping and analysis of a network of political supporters

Abstract

Objective: To analyze how a structure-based seeding strategy can improve word-of-mouth marketing efficiency in proportional elections.Methodology: This study is quantitative and qualitative. In the quantitative phase, an egocentric social network analysis (SNA) of a candidate was conducted. Using Bonacich's degree, intermediation and power centralities, the study identified, then interviewed nine seed actors, which through the snowball technique, led to the identification of 31 other interviewees and the formation of a total network of 232 actors. In the qualitative phase, the reasons for supporting ego and alters as well as their preceding characteristics were analyzed using report content analysis.Originality / Relevance: This is a study of the characteristics of the actors along with their centrality in the strategy of seeding, spreading and maintaining the reputation of the political actor, especially candidates for proportional elections.Results: The characteristics of the most central supporters within the network reflect the bonds of trust built and deposited in the candidate, with ideology and friendship being the most prone to a more effective word-of-mouth marketing. The precedents for commitment and perceived value of word-of-mouth propensity were also verified.Theoretical / Methodological Contributions: This study includes the discussion of proportional elections in electoral marketing, highlighting the power of supporters through analysis of social network marketing. It also contributes to the analysis of the characteristics of actors in the effectiveness of seeding strategy in word-of-mouth marketing.Contributions to Management: The candidate may adopt a seeding strategy in his campaign, thereby promoting effectiveness in resource allocation, while allowing the supporter to understand the difference of his role as a supporter.

Engaging Stakeholders in Extraction Problems of the Chilean Mining Industry through a Combined Social Network Analysis‐Analytic Network Process Approach

“…As expected, for betweenness, A11ULS has the highest value, indicating that it has the highest potential to connect Besides, it is not recommended that the network depends so much on a single actor. Actors with the highest betweenness (high and differentiated) have the power to break the link among various actors in the network, weakening the group, and thus must be treated with caution [52]. Centrality analysis also uncovers the betweenness centrality of several actors such as A7, A8, A9, and A10 and all the associations located on the edges of the network, indicating the presence of structural holes.…”