Relationship between Perceived Value and Purchase Intention in Manufacturing Enterprises’ Mobile Interaction Platforms

Abstract

Mobile interaction platforms of manufacturing enterprises have become core channels for digital marketing and customer value co-creation. Understanding how these platforms influence customers’ purchase intention (PI) is of critical importance for advancing digital transformation. Traditional research has primarily relied on questionnaire-based static val-idation of the relationship between perceived value and PI, which has limited capacity to reveal the dynamic formation of value perception and often neglects authentic, fine-grained behavioral data of users. In this study, sequential pattern mining was innovatively introduced into the domain of consumer behavior research to dynamically identify and quantify the pathways of value perception on mobile platforms. A value perception measurement method, ValuePath-K, integrating sequence length, was developed, together with an efficient mining algorithm, ValuePathMiner-Final, to extract key behavioral patterns driving purchase deci-sions from user interaction sequences. Through these methods, the dynamic mechanisms linking the dimensions of perceived value and PI were uncovered. The findings provide not only a new paradigm for advancing the perceived value–PI theory in the context of industrial digitalization but also quantifiable decision support for the optimization of platform design, precision marketing, and customer relationship management in manufacturing enterprises.