Analyzing adoption factors of data-driven nudging for e-commerce platforms using an integrated decision model

The exchange of goods on e-commerce platforms has become an essential channel in contemporary digital landscapes. Utilizing data-driven nudging as a strategic approach to shaping consumer behavior should consider numerous influential factors. Nevertheless, there remains a deficiency in analyzing the factors that facilitate the implementation of data-driven nudging on e-commerce platforms. This work explores the influencing factors of data-driven nudging adoption in e-commerce platforms using a probabilistic linguistic decision framework that incorporates the DEMATEL (decision-making trail and evaluation laboratory)-ISM (interpretive structural modeling) method. To uncover the challenges and opportunities of using data-driven nudging for e-commerce platforms, we identify sixteen influencing factors based on SWOT (strengths, weaknesses, opportunities, and threats) and a literature review. Then, the probabilistic linguistic DEMATEL method is introduced to depict the interaction relationships among these factors. Subsequently, the ISM method is used to build these factors’ contextual linkages and hierarchical structures. Finally, we conducted a questionnaire survey to obtain the data for the analysis framework. The results show that the main strength is “the desirability of handling and utilizing a product ( θ 4 )”, the main weakness is “misdirection ( θ 5 )”, the primary opportunity is “Making appropriate policies ( θ 12 )”, and the main threat is “affecting freedom and autonomy ( θ 13 )”. Our research provides a new analytical tool for identifying the factors that utilize data-driven nudging in e-commerce platforms, offering practical implications for both e-commerce platforms and merchants.