Sustainable Model for Analyzing the Impact of Social Media on Customer Buying Behavior: A Case Study

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Bshair Alharthi
Mohammed A. Farsi

Abstract

In today's digital landscape, chatbots have become widely used across various applications, particularly in systems that provide intelligent assistance to users. To enhance user experience, these systems are often equipped with chatbots capable of processing user inquiries and delivering accurate, timely responses. This study proposes a structured framework for evaluating the impact of social media on customer buying behavior through a case study focused on selecting the most suitable Online Chatbot Platform (OCP). The selection of an optimal OCP is a complex multi-criteria decision-making (MCDM) problem, involving multiple interdependent and often conflicting factors. One of the key challenges in this process is the potential loss of critical decision information and underestimation of engagement complexity in a neutrosophic environment. To address this challenge, this paper introduces a hybrid MCDM framework that integrates Interval-Valued Neutrosophic Sets Analytical Hierarchy Process (IVNSs-AHP) with Weighted Aggregated Sum Product Assessment (WASPAS). The IVNSs-AHP method is employed to determine the relative importance of the evaluation criteria, while WASPAS is used to rank different OCP options. A comprehensive set of assessment criteria, grounded in the principles of sustainable development, was established through an extensive literature review and expert consultations to ensure the practical applicability of the proposed MCDM model. 

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How to Cite
Alharthi, B. and Farsi, M.A. (2025) “Sustainable Model for Analyzing the Impact of Social Media on Customer Buying Behavior: A Case Study”, Sustainable Machine Intelligence Journal, 10, pp. 67–85. doi:10.61356/SMIJ.2025.10491.
Section
Original Article

How to Cite

Alharthi, B. and Farsi, M.A. (2025) “Sustainable Model for Analyzing the Impact of Social Media on Customer Buying Behavior: A Case Study”, Sustainable Machine Intelligence Journal, 10, pp. 67–85. doi:10.61356/SMIJ.2025.10491.