An integrated MCDM approach for B2C e-marketplace selection in the healthy food sector

Stochastic modeling and data analysis, which address uncertainty and complexity in business and industry, are important tools for optimizing decision-making processes. When it comes to providing access to healthy food, the importance of decision-making becomes even more prominent. In this work, the aim was to determine the criteria that healthy food businesses consider when choosing business-to-consumer (B2C) e-marketplaces and to evaluate the performance of alternative platforms. Nine main criteria were determined through a literature review and interviews with ten decision-makers (DMs), as sellers. To overcome the limitations of traditional fuzzy sets in handling uncertainty and inconsistent information, a hybrid decision-making method integrating interval-valued neutrosophic sets (IVNSs) with the stepwise weight assessment ratio analysis (SWARA) and evaluation based on distance from average solution (EDAS) methods was applied. Unlike existing approaches, this methodology better addresses expert uncertainty and inconsistency by simultaneously handling membership in truthiness, uncertainty, and falsity within an interval. Interval-valued neutrosophic (IVN)-SWARA was used to weight the selection criteria for e-marketplace platforms, and these weights were used to rank the platforms using IVN-EDAS. The findings show that integration capacity, site traffic density, and cost-effectiveness are the most important criteria in e-marketplace selection, while ease of membership and platform design usability were the least important. Theoretically, this research fills a gap in the literature by addressing B2C e-marketplace selection from the seller's perspective and better handles inconsistencies compared to standard fuzzy sets using IVN fuzzy sets. In addition, the proposed approach offers special solutions for healthy food businesses and sheds light on advanced research for other sectors and regions.