DATA-DRIVEN DECISION MAKING IN ENTREPRENEURSHIP: A SYSTEMATIC REVIEW OF BUSINESS GROWTH AND INNOVATION MODELS

Abstract

This study investigates the strategic evolution of data-driven entrepreneurship within the contemporary digital landscape. Although data utilization has become widespread, the specific mechanisms facilitating venture growth and innovative resilience remain inconsistently defined in scholarly literature. A systematic literature review was conducted following the PRISMA 2020 protocol, synthesizing twenty-three peer-reviewed articles indexed in the Scopus database. The synthesis revealed that data-driven entrepreneurship functioned as a socio-technical synergy where artificial intelligence acted as a cognitive extension for founders. Findings indicate that analytics-driven strategies increase gross value added by thirty-two percent and enhance user engagement by ten percent. However, ventures faced significant hurdles, including the information technology productivity paradox and algorithmic biases. The research concluded that successful digital scaling required the strategic orchestration of data resources alongside visionary leadership. These results offered a robust framework for navigating the evidentiary logic of modern entrepreneurial ecosystems.