Improving Balanced Scorecard Implementation Through Machine Learning: The Conditional Impact of Environmental Uncertainty in SMEs

Environmental uncertainty requires firms to respond rapidly to unforeseen changes in order to survive, especially small and medium enterprises (SMEs) which are more adaptive to market changes and are unable to directly control the external environment. A successful strategy implementation starts in the formulation stage and a failure to find the linkage between strategy formulation and strategy implementation is a step toward strategy failure. Hence, this study investigates the roles of environmental uncertainty as moderation between strategy and Balanced Scorecard (BSC) as a way of strategy implementation system among Malaysian SMEs. Using Partial Least Square, this study demonstrated that environmental uncertainty acts as moderation between BSC and all Miles and Snow’s strategy typology except for Defender strategy. Furthermore, this paper introduces a Machine Learning (ML)—supported framework that leverages SMEs’ performance and market data to predict BSC outcomes under uncertain environments. The integration of ML provides data-driven insights, improves adaptability, and complements traditional statistical approaches.