Adapt or die! Embrace digital transformation for better firm performance: a causal recipe

Purpose – A central challenge firms face in digital transformation is fostering sustainable growth in firm performance. This study explores complex causal pathways by examining digital transformation management capacity, strategic digital vision, organizational mindfulness, organizational change capacity, market turbulence and firm type as potential predictors of firm performance. Design/methodology/approach – The study integrates fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural networks (ANN). fsQCA identifies equifinal configurations from a sample of employees directly involved in digital transformation initiatives. The ANN analysis assesses the predictive power of these configurations to reveal the relative importance of each predictor. Findings – The results identify three equifinal configurations that lead to high firm performance, highlighting how different combinations of organizational (i.e. digital transformation management capacity, strategic digital vision, organizational mindfulness and organizational change capacity) and environmental (i.e. market turbulence and firm type) conditions can generate similar outcomes. These findings underscore the interplay of multiple factors in preventing the paradox of digitalization. Meanwhile, the ANN analysis indicates that organizational change capacity is the most critical predictor of firm performance. Practical implications – The findings provide strategies for leveraging digital transformation to enhance firm performance. Firms can achieve this by fostering adaptability, implementing ongoing digital initiatives or developing strategic digital programs using tools like real-time analytics, cross-functional teams and AI-driven platforms. Originality/value – The study advances a non-linear perspective on digital transformation and firm performance by using fsQCA to capture equifinality (multiple distinct pathways yielding similar outcomes) and ANN to identify the relative importance of each causal condition.