Exploring the mechanism of path-creating strategy for latecomers: a combined approach of econometrics and causal machine learning

This paper combines econometrics and causal machine learning to investigate how latecomers' path-creating strategies affect their catch-up performance. Using data from 283 high-tech manufacturing firms listed on the Shanghai and Shenzhen stock exchanges (2007-2019), OLS regression shows a positive impact of path-creating on technological catch-up, mediated by technological capability. Technological innovation appropriability positively moderates the path-creating-capability relationship, while cumulativeness negatively moderates the path-creating-catch-up relationship but positively moderates the capability-catch-up relationship. Machine learning confirms these findings but reveals heterogeneity. Individual treatment effect analysis using Shapley values and decision trees illustrates the complex interplay of factors influencing the strategy's effectiveness, offering insights for strategic decision-making.

Yuanyang Teng, Yicun Li, Xiaobo Wu