Effect of Innovation Management and Data Driven Decision Making On Startup Performance

This paper is a conceptual study that takes a closer look at how innovation management and data-driven decision-making (DDD) can influence startup performance. Based on the Analysis of the Resource-Based View (RBV), Dynamic Capabilities Theory, Innovation Diffusion Theory and Decision Theory, the study will construct a multidimensional model to relate the independent variables of innovation management and DDD to startup performance as the dependent variable. Its conceptual framework highlights the important dimensions of innovation management such as strategic innovation, product and process innovation, organizational culture, and knowledge management and the dimensions of DDD which are particularly crucial big data analytics, business intelligence, predictive analytics and managerial decision accuracy. The research determines the positive interactive effect between innovation management and DDD on the financial, market, and innovation performance of startups, with the moderating variables being leadership, organizational culture, and market dynamics. Its findings have both theoretical implications and practical suggestions to the managers of the startups, the policymakers and scholars and formulate a theoretical model which can be demonstrated in the future. It has proposed strategies to enhance the practices on the basis of innovation, implementation of efficient data-driven decision-making systems, and the creation of responsive organizational culture with a goal of maximizing the development and sustainability of the startups.