Presenting the Knowledge Extraction Model of Users' Trust in Marketing Based on Information and Social Network Data

This research aims to present a model for extracting knowledge on users' trust in marketing based on information and social network data. The study was applied in grounded theory. Cognitive framing was employed using various data collection methods such as library study, examination of specialized sources and texts, and semi-structured interviews. Using purposive sampling, 14 experts were interviewed in 2023. The interviews were coded using ATLAS.TI software. To validate the results, data were evaluated and analyzed based on triangulation. The findings indicate a model of 18 concepts and 59 initial codes with 6 main axes. The conditions for this model include 6 concepts, background conditions with 3 concepts (information & communication technology infrastructure in the country, emulation of solutions from top companies in social network marketing, & digital economy), intervening conditions with 3 concepts (society's general knowledge level about information & communication technology tools, society's inclination toward engagement in social networks, & environmental changes), strategies with 3 concepts (facilitating user interaction for exchange of opinions under advertisement, electronic word-of-mouth advertising, & education), and consequences with 3 concepts (trust in & acceptance of advertisements by users, customer satisfaction, and economic productivity). The results indicate that the user trust model in social network-based marketing fundamentally utilizes network communications and interactions to structure trust and confidence.

Presenting the Knowledge Extraction Model of Users' Trust in Marketing Based on Information and Social Network Data Keywords:

Knowledge extraction , Information , and Social Network Data , electronic word-of-mouth advertising , Digital marketing