Unveiling the Temporal Dynamics: The Impact of Knowledge Source Structure on Innovation through Time-Series Analysis

Disruptive innovation plays a critical role in driving technological progress and reshaping industries by challenging established paradigms and fostering new opportunities for growth. While previous research has largely focused on the static relationship between knowledge characteristics and disruptive innovation, the temporal dynamics of how knowledge structures influence disruptive innovation remain unclear. This study addresses this gap by examining the absorptive capacity process, distinguishing between Potential (PAC) and Realized (RAC) absorptive capacities. Using global patents spanning 1980 to 2010, we employed Autoregressive Distributed Lag (ARDL) and Granger causality tests to analyze the long-run, short-run, and causal relationships. The ARDL findings reveal a temporal conflict. In the long-run, PAC, as measured by knowledge source diversity, promotes disruptive innovation. In contrast, an exploratory path of RAC, measured by knowledge breadth, is detrimental. These effects are reversed in the short-run. Here, PAC negatively impacts disruptive innovation, whereas RAC, when measured by knowledge depth, provides a positive impact. The Granger causality tests further uncover a bidirectional feedback loop between PAC and disruptive innovation, and reveal that disruptions are an endogenous driver of the exploratory RAC path. These findings underscore the importance of aligning knowledge management strategies with temporal dynamics to foster sustained innovation.