制造企业智能化转型绩效影响因素的组态效应 ——基于“动机-能力-机会”（AMO）理论分析框架

The Configuration Effect of Factors Influencing the Performance of Intelligent Transformation in Manufacturing Enterprises: Based on AMO Theoretical Analysis Framework

摘要:

制造企业智能化转型是多种要素协同作用的结果，而如何提升转型绩效是当前中国制造企业面临的主要难题。但学界针对此议题的深入研究尚较为缺乏。对此，首先对制造企业智能化转型概念作出定义并分析其内涵要素；其次，鉴于组织、团队或个体的绩效水平会受到其能力（ability）、动机（motivation）和机会（opportunity）等多方面因素的影响，基于“动机-能力-机会”（AMO）理论框架构建制造企业智能化转型绩效影响因素的组态模型；最后，采用面向全国制造企业的103份有效调查问卷数据，并结合典型案例，运用模糊集定性比较分析方法（fsQCA）分析企业转型能力（企业动态能力、企业知识禀赋）、转型动机（内在转型需求、外部竞争压力）以及转型机会（政府政策支持、数字基础设施）对于样本企业智能化转型绩效的组态效应。结果表明：能力、动机和机会均无法单独构成制造企业高智能化转型绩效的必要条件；存在8种会导致企业高智能化转型绩效的条件组态并可归纳为能力主导机会支持型、动机主导能力支持型、动机主导机会支持型、机会主导能力支持型和能力、动机、机会兼备型五大组态类型；有1种条件组态会导致企业非高智能化转型绩效，可归纳为能力、动机、机会兼不具备型。由此得到启示：制造企业在向智能制造转型升级的过程中须重视对自身能力、动机和机会三者情况进行综合性考量，结合实际选择合理的转型升级路径。

Abstract:

The intelligent transformation of manufacturing enterprises is the result of the synergy of multiple factors, and how to improve transformation performance is currently the main challenge faced by Chinese manufacturing enterprises. However, there is still a lack of in-depth research on this issue. In this regard, this study first defines the concept of intelligent transformation of manufacturing enterprises and analyzes its connotation. Secondly, considering that the performance level of organizations, teams, or individuals is influenced by various factors such as their ability, motivation, and opportunity, a configuration model of the influencing factors of intelligent transformation performance in manufacturing enterprises is constructed based on the "Ability-Motivation-Opportunity" (AMO) theoretical framework. Finally, using 103 valid questionnaires for manufacturing enterprises across the country, combined with typical cases, the fuzzy set Qualitative Comparative Analysis method (fsQCA) was used to analyze the configuration effects of enterprise transformation abilities (dynamic capabilities, knowledge endowments), transformation motivations (internal transformation needs, external competitive pressures), and transformation opportunities (government policy support, digital infrastructure) on the intelligent transformation performance of sample enterprises. The results indicate that ability, motivation, and opportunity alone cannot constitute the necessary conditions for the high intelligence transformation performance of manufacturing enterprises. There are eight conditional configurations that can lead to high intelligence transformation performance in enterprises, which can be summarized as five configuration types, including “ability-led opportunity-supportive”, “motivation-led ability-supportive”, “motivation-led opportunity-supportive”, “opportunity-led ability-supportive” and “ability-, motivation-, and opportunity-combined”. There is one type of conditional configuration that can lead to non-high intelligence transformation performance in enterprises, which can be summarized as “ability, motivation and opportunity non-availability”. From this, it can be inferred that manufacturing enterprises must pay attention to comprehensive consideration of their own abilities, motivations, and opportunities in the process of transforming and upgrading to intelligent manufacturing, and choose a reasonable transformation and upgrading path based on actual conditions.