طراحی مدل چت‌بات‌های هوش مصنوعی مبتنی بر شناسایی کلمات کلیدی در وب‌سایت‌های تبلیغات سلف‌سرویس

شیلا  معینی رستگار; محمدعلی شریعت; ابوالفضل  دانایی

Authors

Shila Moeini Rastegar Department of Business Management, Se.C., Islamic Azad University, Semnan, Iran Author https://orcid.org/0009-0008-6406-4579
Mohammad Ali Shariat Department of Industrial Engineering, Se.C., Islamic Azad University, Semnan, Iran https://orcid.org/0000-0002-6855-3520
Abolfazl Danaei Department of Media Management, Se.C., Islamic Azad University, Semnan, Iran Author https://orcid.org/0000-0001-9973-3877

Keywords:

AI chatbot, keywords, self-service advertising website

Abstract

This study aimed to design and validate an artificial intelligence chatbot model based on keyword identification for self-service advertising websites, with an emphasis on improving intelligent interaction, advertising process efficiency, and user experience. This applied study employed a sequential exploratory mixed-methods design. In the qualitative phase, a systematic grounded theory approach was used, and 19 theoretical and practical experts in artificial intelligence, information technology, marketing, e-commerce, and related fields were purposively selected until theoretical saturation was achieved. Data were collected through semi-structured interviews and analyzed using open, axial, and selective coding. In the quantitative phase, a researcher-developed questionnaire derived from the qualitative findings was administered to users of self-service advertising platforms. Following data screening, 380 questionnaires were analyzed using confirmatory factor analysis and structural equation modeling with SPSS and SmartPLS. Structural equation modeling indicated that economic and advertising performance pressures (β=0.801, t=62.358), institutional, legal, and risk-related requirements (β=0.807, t=64.391), and advertising problem complexity and semantic ambiguity of demand (β=0.710, t=19.018) significantly predicted the central phenomenon. The central phenomenon significantly affected strategies (β=0.707, t=18.603), while intervening factors (β=0.783, t=51.826) and contextual conditions (β=0.765, t=39.109) significantly affected strategies. Strategies, in turn, significantly predicted outcomes (β=0.747, t=32.144). All t-values exceeded the critical value of 1.96. The R² values for the central phenomenon, strategies, and outcomes were 0.689, 0.661, and 0.558, respectively. The overall goodness-of-fit index was 0.575, indicating satisfactory model fit. One-sample t-tests also confirmed significant internal and external validity of the proposed model (p<0.001). The proposed model demonstrates that effective AI chatbot design in self-service advertising requires integration of keyword-processing and user-intent capabilities with contextual conditions, intervening factors, and strategic mechanisms. Such integration can enhance operational efficiency, user experience, and the strategic value of digital advertising platforms.

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