A Novel Decision-Making Algorithm for the Assessment of Creative Product Value Transformation Based on Customer Preferences Using Bipolar Intuitionistic Hesitant Fuzzy Information

Assessing factors affecting creative product value transformation is crucial for businesses aiming to remain competitive and customer-centric. It enables companies to identify and prioritize key influences, such as customer preferences and market trends. By understanding these factors, businesses can align their offerings with consumer expectations, enhance perceived value, and build stronger brand loyalty. This process also supports efficient resource allocation, adaptation to changing market dynamics, and the development of sustainable practices. This paper introduces Bipolar Intuitionistic Hesitant Fuzzy Sets (BIHFSs) as a novel and comprehensive framework to enhance decision-making processes, particularly in evaluating creative products where ambiguity and uncertainty arise from diverse customer preferences and attributes. By integrating Bipolar fuzzy sets with intuitionistic hesitant fuzzy sets, the proposed model overcomes limitations in existing methods, offering a more generalized approach capable of handling complex, uncertain data. Additionally, a series of power aggregation operators, including the Bipolar Intuitionistic Hesitant Fuzzy Weighted Averaging (BIHFWA) and Bipolar Intuitionistic Hesitant Fuzzy Weighted Geometric (BIHFWG) operators, were developed with their properties. These aggregation operators improve the model’s ability to assess multiple product attributes, such as aesthetic appeal, durability, functionality, and price, providing valuable insights for optimizing product design. An algorithm was also developed to aggregate BIHFS information for Multi-Criteria Decision-Making (MCDM), guiding decision-makers in selecting the best alternative. The paper also presents a comparative analysis of the proposed and existing methods, discussing their benefits and limitations. The results demonstrate that the BIHFS model aligns well with existing techniques, validating its effectiveness and applicability in real-world scenarios.