在心理学和其他社科研究领域, 大量实证文章建立中介效应模型, 以分析自变量对因变量的影响过程和作用机制。检验中介效应最流行的方法是Baron和Kenny的逐步法, 但近年来不断受到批评和质疑, 有人甚至呼吁停止使用其中的依次检验, 改用目前普遍认为比较好的Bootstrap法直接检验系数乘积。本文对相关的议题做了辨析, 并讨论了中介分析中建立因果关系的方法。综合新近的研究成果, 总结出一个中介效应分析流程, 并分别给出显变量和潜变量Mplus程序。最后介绍了中介效应模型的发展。

<p>Mediation models are frequently used in the research of psychology and other social science disciplines. Mediation indicates that the effect of an independent variable on a dependent variable is transmitted through a third variable, which is called mediator. In most applied research, Baron and Kenny&rsquo;s (1986) causal steps approach has been used to test mediating effect. In recent years, however, many methodological researchers questioned the rationality of the causal steps approach, and some of them even attempted to stop its use. Firstly, we clarify the queries on the causal steps approach one by one. Secondly, we propose a new procedure to analyze mediating effects. The new procedure is better than any single method that constitutes the procedure in terms of Type I error rate and power. The proposed procedure can be conducted by using observed variables and/or latent variables. Mplus programs are supplied for the procedure with observed variables and/or latent variables. Finally, this article introduces the development of mediation models, such as mediation model of ordinal variables, multilevel mediation, multiple mediation, moderated mediation, and mediated moderation.</p>

新质生产力是推动企业高质量发展的强劲推动力和支撑力，研究其驱动因素对于企业发展至关重要。本研究基于动态资源基础观，利用2015-2022年中国A股上市公司数据，实证分析了数智化转型对企业新质生产力的影响及异质性特征，并对吸收能力的中介作用和市场竞争强度的调节作用进行了检验。研究发现：（1）数智化转型对企业新质生产力水平的提升有显著影响；（2）吸收能力在数智化转型与企业新质生产力关系间发挥中介作用；（3）市场竞争强度在数智化转型与企业新质生产力关系间发挥正向调节作用；（4）相较于国有、中西部地区、衰退期的企业，非国有、东部地区、成长期和成熟期的企业实施数智化转型更能促进新质生产力水平提升。研究结论为企业数智化转型和提升新质生产力提供了理论支持，并丰富了相关实证研究。

New quality productivity is a powerful driving force and supporting force to promote the high-quality development of enterprises. This view has been widely recognized by the community. Accelerating the development of new quality productivity will help enterprises enhance their competitive advantage and realize sustainable development. However, the existing literature focuses on exploring the connotation characteristics and value significance of new quality productivity, and there are few empirical research models to explore its driving forces. Some studies believe that the digital intelligence transformation is the core driving force to lead the innovation and development of enterprises, and whether the digital intelligence transformation can improve the new quality productivity level of enterprises is an urgent topic to be discussed.Based on the dynamic resource-based view, using the data of China A-share listed companies from 2015 to 2022, this paper empirically analyzes the influence and heterogeneity of digital intelligence transformation on the new quality productivity of enterprises, and tests the mediating role of absorptive capacity and the moderating role of competitive intensity. It is found that: (1) the digital intelligence transformation has a significant impact on the improvement of the new quality productivity level of enterprises, and this conclusion still holds after various robustness tests. (2) the digital intelligence transformation can improve the absorptive capacity and new quality productivity of enterprises, and the absorptive capacity plays a mediating role in the relationship between the digital intelligence transformation and new quality productivity of enterprises. (3) the competitive intensity plays a positive role in moderating the relationship between the digital intelligence transformation and the new quality productivity of enterprises. (4) heterogeneity analysis shows that, compared with state-owned enterprises, enterprises in the central and western regions and in recession, non-state-owned, eastern regions, growing and mature regions can promote the improvement of new quality productivity by implementing digital intelligence transformation. The theoretical contributions of this study are as follows: first, the existing literature focuses on the connotation characteristics and value significance of new quality productivity, while there are few empirical research models to explore its driving factors. This study examines the influence of digital intelligence transformation on the new quality productivity level of enterprises through empirical research, which not only provides theoretical reference for promoting the transformation of digital intelligence and improving the new quality productivity level of enterprises, but also enriches the empirical research on the relationship between digital intelligence transformation and new quality productivity of enterprises. Second, based on the dynamic resource-based view, the theoretical model of &quot;digital intelligence transformation-absorptive capacity-new quality productivity of enterprises&quot; is constructed, and the intermediate transmission mechanism of absorptive capacity is deeply analyzed, which opens the &quot;black box&quot; in the process of digital intelligence transformation and empowerment of new quality productivity of enterprises. Thirdly, by including the industry competitive intensity at the macro level as a moderating variable, the boundary conditions of the impact of digital intelligence transformation on the new quality productivity of enterprises are further clarified, which is helpful to clarify the complex and diverse relationship between them and enrich the relevant research on the dynamic resource-based view.

数字技术应用不断深化推动多源异构数据的收集、 流通、 交互与价值实现, 使得经济运行机制发生变革. 本文从技术创新视角衡量企业的数据驱动能力, 将数据驱动能力和数据要素引入生产函数, 剖析数据要素赋能制造业企业降本增效的内在机制, 研究表明: 1) 数据的产生、 分享和应用可显著提升制造业企业的生产效率; 2) 进一步对比不同技术密集度企业数据价值释放的进程与差异发现, 数字技术应用和数据要素的投入有效降低了低、 中技术制造业企业的制造成本以及高技术企业的销售费用; 3) 当高技术企业具备更强大的数据驱动能力时, 其生产成本反而增加了, 表明我国高技术制造业企业尚未实现数据驱动下的产业转型升级. 此外, 本文基于产业关联理论和产业集聚理论, 将空间因素纳入分析框架, 研究发现: 产业链视角下, 制造业生产全流程、 全产业链、 全生命周期数据的融通与开发利用, 通过提高区域产业链的协同效能, 从而进一步提升企业的生产效率. 本研究拓展了数字经济理论与机制分析的研究内容, 为充分释放数据要素价值, 优化资源要素配置效率提供证据支持.