Yan Jiang

Purpose: In recent years, a global trend toward nationalism has emerged. This study adopts an attention-based view (ABV) of the firm to investigate the influence of firms’ nationalist tendencies on their green innovation engagement and its boundary conditions. Design/methodology/approach: Using longitudinal data from a cross-industry panel of Chinese listed firms spanning the years 2007–2021, we employed a multidimensional fixed effects linear regression approach to test the hypotheses. Findings: This study empirically demonstrates that firms with stronger nationalist tendencies are significantly more inclined to engage in green innovation, with robust results. Furthermore, the findings reveal that government environmental orientation and firm environmental subsidies positively moderate the relationship between firms’ nationalist tendencies and green innovation, while industry environmental uncertainty serves as a negative moderator. Originality/value: By examining the influence of firms’ nationalist tendencies on their engagement in green innovation, this study enriches the research on the effects of nationalist tendencies and the antecedents of green innovation. Furthermore, this study integrates nationalist tendency into the ABV, offering novel empirical evidence on the situational attention factors that shape the effectiveness of firms’ nationalist tendencies in promoting green innovation, thereby advancing the study of the ABV framework.

Digital transformation has become a prominent trend in both the private and public sectors. Different from studies focusing on the transformation of the private sector, this study aims to investigate the impact of public sector digitalization, i.e., government digital transformation (GDT), on land use efficiency (LUE). Utilizing the introduction of the Big Data Bureau (BDB) as a quasi-natural experiment to GDT, this study employs a staggered difference-in-differences method (DID) and finds that GDT is significantly and positively related to LUE, denoting that cities undergoing GDT exhibit increased LUE relative to non-participants. These findings are robust after performing parallel trend tests, conducting the instrumental variable method and propensity score matching methods or entropy balance, considering heterogeneous treatment effects, and using other robustness checks. Furthermore, the heterogeneity analysis shows that GDT has a significantly stronger impact among eastern cities, cities with a greater degree of marketization, or cities with advanced Internet infrastructure. Finally, the rationale behind this effect is that GDT upgrades industrial structures and promotes innovation activities, thereby increasing LUE. This study adds to the literature by providing novel insights into the effects of public sector digitalization and its role in improving LUE.