关键词: 创新“言行不一”, 创新“言行一致”, 动态QCA, 制造业单项冠军企业, MOA理论

Abstract:

In the context of China’s push toward high-quality development in the manufacturing sector, the phenomenon of “innovation talk-action discrepancy” has attracted growing attention. While some firms emphasize innovation narratives in their disclosures, their actual innovation outputs often fail to align. This inconsistency can distort resource allocation and undermine the effectiveness of innovation policies. To explore the underlying mechanisms of such discrepancies, this study selects China’s manufacturing “single champion” firms—recognized as exemplary representatives of the “little giant” or specialized and sophisticated enterprises—as its research sample. These firms play a vital role in driving breakthroughs in key technologies, and their innovation behaviors have strong signaling effects in both policy and market environments.

Drawing upon the Motivation-Opportunity-Ability (MOA) framework, we identify six antecedent conditions and employ dynamic Qualitative Comparative Analysis (QCA) based on panel data to examine the complex configurations that lead to either consistency or inconsistency between innovation disclosures (“talk”) and innovation outcomes (“action”). Innovation discrepancy is measured by the residual between textual innovation disclosures in annual reports and actual patent applications. Firms are classified into “talk-action consistent” and “talk-action inconsistent” categories based on the standardized residual range.

Our findings reveal that no single factor alone is necessary to explain the emergence of innovation talk-action discrepancy. Instead, multiple configurations of conditions—representing different behavioral logics—jointly account for the observed patterns. Specifically, three distinct pathways are identified as drivers of “talk-action inconsistency”: (1) policy-driven signaling, wherein firms exaggerate innovation language to align with government incentives; (2) packaging innovation, where firms focus on impression management for investors; and (3) survival-oriented strategies in resource-constrained environments. In contrast, two main configurations lead to “talk-action consistency”: (1) prudent innovation, associated with firms having strong internal capabilities and long-term orientation, and (2) pragmatic innovation, driven by technological strength and strategic clarity.

Although the configurations themselves remain relatively stable over time, our dynamic analysis reveals that the consistency of the “talk-action inconsistent” configurations exhibited sharp fluctuations in 2020, likely linked to the COVID-19 outbreak and subsequent policy shocks. Conversely, the consistency of “talk-action consistent” configurations increased notably in 2022, coinciding with the nationwide reopening and renewed emphasis on industrial upgrading.

The study contributes to the literature in several ways. First, it introduces a unified analytical framework that integrates innovation disclosures and outputs, moving beyond prior studies that often treated them as separate domains or conducted only comparative analyses. Second, it adopts a configurational perspective to examine the interplay among multiple antecedents rather than relying on linear or single-variable approaches. Third, by incorporating temporal dynamics through panel-based QCA, the study addresses the limitations of static cross-sectional designs and provides richer insights into the evolution of innovation behavior under changing external environments.

Practically, the findings offer valuable implications for both policymakers and enterprise managers. For government agencies, the results underscore the importance of refining innovation incentive mechanisms and developing more reliable performance evaluation systems that account for both input and output dimensions. For regulatory bodies and investors, the study highlights the need for more sophisticated disclosure verification tools—such as big data analysis and natural language processing—to detect inflated innovation narratives. For enterprises, especially those in traditional manufacturing industries, the results suggest that building robust internal innovation capabilities and fostering consistent innovation execution can help achieve authentic and sustainable growth.

Overall, this study enriches the theoretical understanding of innovation behavior among “little giant” firms and provides actionable recommendations to promote the high-quality development of China’s specialized and sophisticated enterprises in the manufacturing sector.

Key words: innovation “inconsistency”, innovation “consistency”, dynamic QCA, manufacturing champion firms, motivation-opportunity-ability theory