A Soft Computing Approach for Investigating the Dominance of Femluencing and Brand Evangelism on Customers’ Purchase Intentions

Imagine the advertisements: Dove's "Real Beauty Sketches,”; "Shot on iPhone," Neutrogena’s “See What's Possible,” and “End your wait for effortless shaving with Gillette Mach3.” All these are examples of promotional campaigns that inundated the entire world. But the question is, what was the driving force that overwhelmed the customers? Is it the extreme love and trust for the brand, or is it the influence of the feminine message? This paper seeks answers to these questions using a novel soft computing framework utilizing Picture Fuzzy Set (PIFS). The underlying intention is to compare the effects of femluencing (FEMC) (influencer marketing with feminine content) and brand evangelism (BEG) (extreme loyalty to a brand) on customers’ purchase intention (CPI). The theoretical framework of Stimulus-organism-response (SOR) is used to finalize the factors (related to FEMC and BEG) in tune with supportive past studies and experts’ views. Then, it develops an expert decision-making framework using the COBRAC (Comparisons between Ranked Criteria) method with Picture Fuzzy Frank aggregation. We conduct a force field analysis (FFA) to compare the effects of FEMC and BEG. We observe that BEG marginally wins over FEMC based on its influence on CPI. The deviation from consistency (Δ =0.0000) is found to be insignificant while calculating the weights of FEMC and BEG factors, suggesting the robustness of the model. The insignificant variations in the ranking while conducting sensitivity analysis reflect the stability of the model. The approach and findings of this study shall provide valuable insight to the researchers and strategic decision-makers.