Business Intelligence for Big Data-Driven Product Innovation: An Analysis Based on the Subject-Strategy-Process (SSP) Framework

Abstract

Artificial intelligence (AI) has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes. This study proposes a subject-strategy-process (SSP) framework for business intelligence (BI) for big data-driven product innovation through logical deduction, drawing on the theory of big data cooperative assets and an adaptive innovation perspective on enterprise-user interaction. The aim is to explore new mechanisms through which AI influences product innovation in manufacturing. This study indicates three aspects. Firstly, the two-way involvement of humans and AI forms a dual feedback-enhancement mechanism of factor combination and knowledge accumulation. This mechanism drives structural changes in innovation subjects and forms a new foundation for strategic and process transformations in product innovation. Secondly, the alignment between an enterprise’s cognitive strategy about AI, competitive strategy, organizational culture, business model, and ecosystem jointly shapes the integrated application of AI in innovation processes. Thirdly, the new features of the big data-driven product innovation process include full-process diffusion from the fuzzy front end, nonlinear iteration of demand-solution pairs, and generative self-testing in intelligent manufacturing. Taken together, the study demonstrates that the SSP framework is well- suited to analyzing the new mechanisms of BI for big data-driven product innovation, which offers a fresh lens for examining the relationship between AI and product innovation.

Keywords

artificial intelligence (AI) / product innovation / business intelligence (BI) / big data cooperative assets / subject-strategy-process (SSP) framework

RIGHTS & PERMISSIONS

Higher Education Press