Designing a Viral Marketing Effectiveness Model in Social Networks
Keywords:
viral marketing, social networks, adaptive fuzzy inference system, word-of-mouth marketingAbstract
The purpose of this study was to design a model to evaluate the effectiveness of viral marketing in social networks using the adaptive neuro-fuzzy inference system (ANFIS). The study employed a mixed-method sequential exploratory design (qualitative–quantitative). The qualitative phase utilized thematic analysis involving 15 experts in viral marketing selected through purposive sampling. The quantitative phase involved 377 valid questionnaires analyzed via partial least squares structural equation modeling and ANFIS. Data collection tools included semi-structured interviews and Likert-scale questionnaires. Instrument validity and reliability were confirmed using CVI, CVR, and Cronbach’s alpha metrics. The results indicated six key components influencing viral marketing in social networks: message type and content, benefits, influencing techniques, key features, consequences, and individual characteristics. Quantitative modeling with ANFIS revealed that customer-based brand equity, brand reputation and credibility, individual characteristics, and message features significantly influence viral marketing effectiveness. The model accurately predicted the viral marketing variable, confirming its applicability. This study confirmed that viral marketing on social networks is an effective, low-cost, and rapid means of disseminating marketing messages. Tailoring messages based on audience characteristics and applying strategic communication techniques can significantly enhance the success of viral marketing campaigns.
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