A Decision Support System for Assessment of Digital Marketing Platform Selection Using Novel Circular Intuitionistic Fuzzy Dombi Aggregation Operators

Abstract

Social media services such as Facebook, YouTube, Instagram, and Telegram are an essential part of digital marketing, as they increase target reach, engagement, and brand exposure. Nevertheless, it is hard to measure their effectiveness because of different user behaviors, the format of the content, and ever-changing algorithms. The idea of circular intuitionistic fuzzy set (Cir-IFS) and Dombi operations are strong tools to handle doubt and ambiguity in multi-attribute decision-making (MADM) issues. This study established two novel aggregation operators (AOs), namely circular intuitionistic fuzzy Dombi prioritized weighted averaging (CIFDPWA) and circular intuitionistic fuzzy Dombi prioritized weighted geometric (CIFDPWG) operators. The mathematical properties of established AOs, such as idempotency, boundedness, and monotonicity, are properly established. To justify the proposed model, a real-life case study will be discussed on how to choose the appropriate social media platform for digital marketing purposes. The proposed theory is a suitable tool for a precise investigation of MADM problems, such as assessing platforms like Facebook, YouTube, Instagram, and Telegram against numerous attributes, including user engagement, algorithmic fairness, data privacy, and usability. The established AOs are applied to rank the alternatives, revealing their efficiency in solving the complexities of the modern digital decision-making framework. Furthermore, the framework of Cir-IFS surpasses our diagnosed theory by incorporating circular degree, whereas simple IFS fails to aggregate information that includes circular degree. To confirm the validity of the established model, we compare it with existing models. Then, we deliberate on a solid conclusion.