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论文编号：	14655
作者编号：	1120201098
上传时间：	2024/6/5 17:28:21
中文题目：	制造业工业机器人应用对企业经营绩效的影响研究
英文题目：	Research on the Influence of Industrial Robot Application on Enterprise Operating Performance in Manufacturing Industry
指导老师：	黄福广
中文关键字：	工业机器人应用；经营绩效；要素配置；价值增值；运营分工调整
英文关键字：	Industrial Robot Application; Enterprise Operating Performance; Resource Allocation; Value Added; Operating Division of labor Adjustment
中文摘要：	“工业4.0”与“工业互联网”等概念于2011年后的相继提出对制造业企业生产经营产生了巨大冲击，依托物联网、大数据、云计算等功能的工业机器人应用迅猛发展，已充分融入社会生产经营生活等各领域，从而成为推动第四轮科技革命发展的重要内容。对于中国人口红利消减、经营成本高企、供求矛盾凸显等问题，发展工业机器人应用是实现中国工业智能升级和经济高质量发展的关键所在。为加速制造大国到制造强国的转变、实现制造业高质量发展，《关于推进工业机器人产业发展的指导意见》及《中国制造2025》相继于2013年和2015年颁布，均明确“在2020年形成较为完善的工业机器人产业体系”。伴随着新冠疫情后工业机器人应用潜能的全面激发，2023年1月，十七部门联合印发《“机器人+”应用行动实施方案》，旨在通过分类施策拓展工业机器人应用深度和广度，培育机器人发展和应用生态，为加快建设制造强国、数字中国，推进中国式现代化提供有力支撑。《2024年政府工作报告》更是重点强调了“深化大数据、人工智能等研发应用，开展‘人工智能+’行动......实施制造业数字化转型行动，加快工业互联网规模化应用......”。近年来，得益于宏观层面的国家战略支持，国际机器人联合会数据显示，中国工业机器人应用存量已于2016年超越美国、德国、日本等发达国家，位居全球第一；而微观层面诸如小米机器人赋能产业链、美的收购机器人巨头库卡、富士康机器人推广错配及特斯拉机器人安全事故等正反面案例引发热议。值得说明的是，工业机器人应用反映新技术、创造新价值、塑造新动能、适配新业态等特征内涵，正契合于当前发展新质生产力的论述。在推动制造业工业智能升级、加快形成新质生产力的背景下，探究工业机器人应用对于企业经营绩效的实际效果、要素配置及其分工表现具有重要的现实意义，本文试图对此进行有益的拓展。 在理论基础与实践经验层面对于工业机器人应用的实际效果尚在探讨阶段，“促进论”认为工业机器人应用不仅通过资本技能结合推动生产技术进步，其要素配置优化产生的运营管理改善以及企业分工调整实现的价值增值还将有效提升企业经营绩效；“无效论”认为工业机器人应用的错误使用预期、收益分配失衡、数据统计偏误、自主创造局限、过度推广错配及配套应用滞后等问题不会提升企业经营绩效，甚至存在负面影响。此外，部分理论也基于完全竞争和超额收益的角度认为，工业机器人应用在短期提升企业经营绩效，而在长期无效。作为一种能够自动控制、重复编程、多功能应用的机器设备，工业机器人应用不仅是工业智能升级的关键环节，还是实现高质量发展的重要力量。 综上，本文在中国情境下拓展财务研究框架探讨工业机器人应用对于企业经营绩效的影响，首先，本文对企业投资理论、企业效率理论、劳动分工理论、交易成本理论及资源基础理论进行了系统梳理，进而对工业机器人应用、经营绩效及企业分工的相关文献进行归纳总结。其次，根据理论分析提出研究假设，考虑到中国工业机器人应用在2010年后快速兴起且2020年以后新冠疫情对实体制造业冲击较大、存在跨期不可比的问题，借鉴已有文献的通用做法，选取2011—2019年中国沪深A股制造业上市公司作为初始样本，构建“巴蒂克工具变量”计算企业工业机器人应用程度，实证检验工业机器人应用对于企业经营绩效的影响。再次，通过将企业价值分为资产现值与增量价值考察企业经营绩效，引入全要素生产率度量企业经营绩效并反映从要素投入价值到要素产出价值（“资产现值→增量价值”）的过程，结合要素配置与价值增值的双重视角考察工业机器人应用提升企业经营绩效过程中的要素配置机制和运营分工表现。最后，根据资源基础理论认为企业异质性核心资源能力是经营绩效提升的关键，对工业机器人应用提升企业经营绩效的具体情境进行拓展分析。本研究旨在推动相关领域的理论创新和文献拓展丰富，并为实现企业高质量发展提供经验证据。 本文主要研究结论如下： 第一，中国制造业工业机器人应用不存在索洛绩效悖论，通过引入全要素生产率测度企业经营绩效，研究发现工业机器人应用显著提升企业经营绩效。进一步地，选取2015年实施的《中国制造2025》作为工业机器人应用的“准自然实验”，同时综合工具变量法、Heckman两阶段检验、匹配法检验、安慰剂检验等多种方法及各类稳健性检验对“工业机器人应用提升企业经营绩效”的核心结论进行验证，结果表明上述结论具有显著的统计与经济学含义。 第二，从要素配置的视角来看，在全要素生产率框架下生产技术进步、运营管理改善及投资决策优化是工业机器人应用提升企业经营绩效的作用路径。具体地，对于生产技术进步，工业机器人作为技术创新对于传统生产工具及工艺流程的更新替代会优化企业现有生产经营模式，一方面，机器人投资应用产生的劳动创造及劳动替代将加速人力资本升级与资产结构优化，进而突出企业市场竞争优势、拓展创新收入并提升企业经营绩效；另一方面，机器人应用创新压缩的运营成本有助于重塑企业资本结构选择并激励创新投入，资本与技能的充分结合将持续推动企业创新并提升研发收益。对于运营管理改善，工业机器人应用不仅能在复杂、危险工作的环境中持续精密运行，还能缩短“股东→管理层→员工”委托代理链条，避免机会主义动机引发的经营绩效损失。此外，工业机器人的生产性固定资产属性使其投资增长会形成规模经济。对于投资决策优化，工业机器人的“智能互联”模式不仅会精准处理历史产供销信息记录、优化投资经营决策，其承载的多功能模块交互平台还会对内外部各类要素资源进行跨领域、跨系统、跨边界实时优化配置，充分实现企业内部生产经营全流程与外部市场的跨企业对接、提升资本配置效率。 第三，从价值增值的视角来看，通过价值增值法测度企业分工程度，“工业机器人应用→经营分工深化→实现价值增值”的作用链条是工业机器人提升企业经营绩效的路径。具体地，对于企业分工，相比于压缩内部管控成本，工业机器人应用在提升企业经营绩效的过程中，外部交易成本降低占据主导地位，进而表现出企业分工专业程度提升。对于集团分工，工业机器人应用重塑了企业生产运营模式，有助于企业集团以新设子公司的形式分离生产流程业务，具体表现为子公司及新增子公司数量增加，企业从子公司购入商品关联交易金额上升。对于市场分工，一方面，通过大数据的精准筛选与深度学习，各生产部门及生产流程中“信息孤岛”产生的市场壁垒得以破除，企业上下游供应链选择范围得以拓展，具体表现为供应链集中度下降；另一方面，以“物”、“网络”和“信息”为导向的机器人应用突破了传统信息系统与物理要素配置的界限，致使企业以向外部市场直接交易中间产品的形式分离生产流程业务，具体表现为企业核心资产剥离与外购金额上升。 第四，鉴于资源基础理论认为企业异质性核心组织资源能力是经营绩效提升的关键，根据中国工业机器人应用提升企业经营绩效的核心结论。进行如下拓展：其一，工业机器人应用的企业经营绩效提升作用在资本密度、产权性质、融资约束及政府补贴等约束条件下存在差异，这表明应当重点把握工业机器人在生产中的推广和应用，根据企业不同发展状况和经营特征，制定因势利导、全面协调的工业机器人发展战略。其二，分位数回归发现相比于经营绩效较为“平均”的企业，工业机器人应用赋能企业将对经营绩效极低和经营绩效极高的企业产生更为显著的提升作用，这表明工业机器人应用可以摆脱传统要素资源配置的束缚、重塑企业组织分工并突破价值增值瓶颈。其三，随着企业数字化转型程度提升，工业机器人应用对企业经营绩效的提升作用增强，这表明推动物联网、大数据、云计算等新一代数字信息技术能够与工业机器人应用形成协同效应、实现“数智融合”。其四，工业机器人应用兼具“向上溢出”和“向下溢出”的产业链传导效应提升企业经营绩效，这表明应当合理引导工业机器人应用的产业链溢出效应，通过工业机器人应用对于经营绩效提升的技术以及非技术因素传递，充分向上下游产业链辐射扩散，以此带动不同行业、区域间的生产运营立体协调发展。 本文的创新之处主要包括： 第一，拓展了公司财务经营绩效分析关注于投资现金流的理论研究思路。在研究内容上，经典公司财务研究将企业价值定义为一系列投资收入流折现，而企业经营绩效则反映为企业投资的资本形成效率，上述分析模糊了投资外其他要素配置信息及经营绩效变化对应的运营分工调整。在研究方法上，资本形成效率、“投资—投资机会敏感性”、“Richardson（2006）投资效率”等财务绩效测度方法均无法深入企业投资外的要素配置信息。本研究通过将企业价值分为资产现值与增量价值考察企业经营绩效，从要素配置与价值增值的双重视角考察工业机器人应用提升企业经营绩效的作用“黑箱”，一方面，考虑到财务研究中企业投资经营等活动等价于新古典经济学的企业生产行为，且企业投资机会价值作为增量价值无法反映投资外的要素配置信息，引入全要素生产率度量企业经营绩效并反映“资产现值→增量价值”从要素投入价值到要素产出价值的作用，在全要素生产率框架下综合企业生产经营流程的生产技术进步、运营管理改善及投资决策优化等信息考察工业机器人应用的要素配置机制。另一方面，与Myers将增量价值视为企业未来投资的期权价值不同，本研究以价值增值法测度企业分工程度并验证了“工业机器人应用→经营分工调整→实现价值增值”的经营绩效提升链条，结合企业内外部交易成本权衡、集团子公司设立及交易状况、市场上下游供应链变化等方面分析，以价值最大化目标激励下的企业运营分工调整探明企业经营绩效变动对应的企业组织结构演化。 第二，提供了中国工业机器人应用不存在“索洛绩效悖论”的理论逻辑支撑。无论是文献层面关于工业机器人应用“促进论”与“无效论”的研究，还是实践层面关于小米机器人产业链赋能、美的收购机器人巨头库卡等正面案例以及富士康机器人错配、特斯拉机器人安全事故等负面案例的讨论，对于工业机器人应用产生的经济后果始终是理论界与实践界重点关注的话题。然而，现有关于中国工业机器人应用的研究囿于数据资料和实证方法限制，多聚焦于单一理论分析或实践案例总结，实证分析多聚焦于地区、行业等宏观层面的经济后果探讨。不同于既有文献，借鉴“巴蒂克工具变量”以研究主体初始份额构成及时变增长率反映行业不同暴露情况下的同等外生冲击影响这一思路，本研究计算了企业工业机器人应用程度并结合要素配置与价值增值的视角讨论企业工业机器人应用的经济后果，不仅验证了“工业机器人应用提升企业经营绩效”的核心结论，更系统梳理了企业投资理论、企业效率理论、劳动分工理论、交易成本理论及资源基础理论与上述实证结果的逻辑一致性，重点分析了工业机器人应用提升企业经营绩效提升过程中对应的要素配置机制、企业分工表现以及具体情境影响。以上研究是揭示中国工业机器人应用与制造业实体经济发展融合、回应中国新发展阶段不存在工业机器人应用索洛绩效悖论的直接证据。 第三，丰富了资源基础理论对于企业异质性核心组织资源能力的研究思路。资源基础理论将企业视为有形资源、无形资源以及组织资源能力的集合，认为异质性核心组织资源能力是企业提升经营绩效的关键。然而，企业异质性核心组织资源能力在实证设计和变量测度上较为困难，实证研究多是在一般均值回归的基础上结合异质分组分析展开，更多文献研究则通过案例分析的形式对企业具体的异质性核心组织资源能力进行讨论。在上述情况下，一方面，异质性分组实证分析存在颗粒度较大、无法细致反映相关变量条件分布影响的特征；另一方面，案例分析的个体研究结论并不适用于大样本实践经验推广。本研究结合具体研究情境，不仅根据资本密度、产权性质、融资约束以及政府补贴等传统异质性约束条件对工业机器人应用差异进行分组分析，还运用分位数分析的方法对企业经营绩效和企业数字化转型“数智融合”程度的差异化特征对企业资源基础进行讨论。此外，本文还对工业机器人应用的上下游产业链传导影响进行了分析。上述实证分析是对资源基础理论关于企业异质性核心组织资源能力的探索性讨论，不仅有助于补充丰富现有文献的相关成果，也为“十四五”时期发展工业机器人应用、实现企业高质量发展提供了经验启示。
英文摘要：	After 2011, the concepts of "Industry 4.0" and "Industrial Internet" were put forward one after another, which had a great impact on the production and operation of manufacturing enterprises. The application of industrial robots relying on the functions of Internet of Things, big data and cloud computing has developed rapidly and has been fully integrated into various fields such as social production, operation and life, thus becoming an important content to promote the development of the fourth round of scientific and technological revolution. For China's declining demographic dividend, high operating cost and prominent contradiction between supply and demand, the development of industrial robot application is the key to realize the upgrading of industrial intelligence and high-quality economic development in China. In order to accelerate the transformation from a big manufacturing country to a powerful manufacturing country and realize the high-quality development of manufacturing industry, the Guiding Opinions on Promoting the Development of Industrial Robot Industry and Made in China 2025 were promulgated in 2013 and 2015, both of which clearly stated that "a relatively complete industrial robot industry system will be formed in 2020". With the full stimulation of the application potential of industrial robots after the COVID-19 epidemic, in January, 2023, 17 departments jointly issued the "Robot Plus" application action implementation plan, aiming at expanding the application depth and breadth of industrial robots through classified policies, cultivating the development and application ecology of robots, and providing strong support for accelerating the construction of a manufacturing power, a digital China, and promoting Chinese modernization. The Government Work Report 2024 emphasizes "deepening the research and development of big data, artificial intelligence and other applications, carrying out the action of" artificial intelligence+"... implementing the digital transformation of manufacturing industry, and accelerating the large-scale application of industrial Internet ...". In recent years, thanks to the macro-level national strategic support, the International Federation of Robotics data show that the application stock of industrial robots in China has surpassed the developed countries such as the United States, Germany and Japan in 2016, ranking first in the world; At the micro level, positive and negative cases such as Xiaomi robot industry chain, Midea's acquisition of robot giant KUKA, Foxconn's robot promotion mismatch and Tesla robot safety accidents have sparked heated discussions. It is worth noting that the application of industrial robots reflects new technologies, creates new values, shapes new kinetic energy and adapts to new formats, which is in line with the current discussion on developing new quality productive forces. Under the background of promoting the upgrading of industrial intelligence in manufacturing industry and accelerating the formation of new quality productive forces, it is of great practical significance to explore the actual effect, factor configuration and operation performance of industrial robot application for enterprise management performance. This paper attempts to expand this. The practical effect of industrial robot application is still in the stage of discussion on the theoretical basis and practical experience. The "promotion theory" holds that industrial robot application not only produces technological innovation growth through the combination of capital and skills, but also improves the operation process generated by the optimization of its element configuration and the value-added realized by the adjustment of operation division will effectively improve the business performance of enterprises. The theory of "ineffectiveness" holds that the application of industrial robots will not improve the business performance of enterprises, or even have negative effects, such as incorrect use expectations, unbalanced income distribution, biased data statistics, limitations of independent creation, excessive promotion and mismatch, and lagging supporting applications. In addition, some theories also believe that the application of industrial robots can improve the business performance of enterprises in the short term, but it is ineffective in the long term. As a kind of mechanical equipment with automatic control, repeated programming and multi-functional application, the application of industrial robots is not only a key link in upgrading industrial intelligence, but also an important force to accelerate the formation of new quality productive forces and achieve high-quality development. To sum up, this paper expands the financial research framework in the context of China to explore the impact of industrial robot application on enterprise performance. Firstly, this paper systematically sorts out the enterprise investment theory, enterprise efficiency theory, division of labor theory, transaction cost theory and resource-based theory, and then summarizes the relevant literature on industrial robot application, business performance and enterprise division of labor. Secondly, according to the theoretical analysis, the research hypothesis is put forward. Considering that the application of industrial robots in China has risen rapidly after 2010, and the COVID-19 epidemic has had a great impact on the physical manufacturing industry after 2020, and there are incomparable problems in different periods, and drawing lessons from the common practices of the existing literature, we select the listed companies in the A-share manufacturing industry in Shanghai and Shenzhen in China from 2011 to 2019 as the initial samples, and construct the "Batick tool variable" to calculate the application degree of industrial robots in enterprises, and empirically test the impact of industrial robot application on the business performance of enterprises. Thirdly, by dividing enterprise value into asset present value and incremental value, the enterprise's operating performance is investigated, and the total factor productivity is introduced to measure enterprise's operating performance and reflect the function of "asset present value → incremental value" from factor input value to factor output value. Combined with the dual perspectives of factor allocation and value-added, the specific factor allocation mechanism and operation division performance in the process of improving enterprise's operating performance by using industrial robots are investigated. Finally, according to the resource-based theory, it is considered that the heterogeneous core resource ability of enterprises is the key to improve business performance, and the specific situation of improving business performance by using industrial robots is expanded and analyzed. The purpose of this study is to promote theoretical innovation and literature development in related fields, and provide empirical evidence for accelerating the formation of new quality productive forces and realizing high-quality development of enterprises. The main conclusions of this paper are as follows: Firstly, there is no Solow performance paradox in the application of industrial robots in China manufacturing industry. By introducing total factor productivity to measure the business performance of enterprises, it is found that the application of industrial robots significantly improves the business performance of enterprises. Furthermore, Made in China 2025, which was implemented in 2015, was selected as the "quasi-natural experiment" of industrial robot application. At the same time, the core conclusion of "Industrial robot application improves enterprise performance" was verified by integrating various methods such as instrumental variable method, Heckman two-stage test, matching method test and placebo test. The results show that the above conclusion has significant statistical and economic significance. Secondly, from the perspective of factor allocation, under the framework of total factor productivity, technological innovation growth, improvement of operation management and optimization of investment decision-making are the action paths for industrial robots to improve enterprise performance. Specifically, for the growth of technological innovation, industrial robots, as the replacement of traditional production tools and technological processes by technological innovation, will optimize the existing production and operation mode of enterprises. On the one hand, the labor creation and labor substitution generated by robot investment and application will accelerate the upgrading of human capital and the optimization of asset structure, thus highlighting the competitive advantage of enterprises in the market, expanding innovative income and improving the business performance of enterprises; On the other hand, reducing the operating cost of robot application innovation is helpful to reshape the choice of enterprise capital structure and stimulate innovation investment. The full combination of capital and skills will continue to promote enterprise innovation and improve R&D income. For the improvement of operation management, the application of industrial robots can not only run continuously and accurately in a complex and dangerous working environment, but also shorten the principal-agent chain of "shareholders→management→employees" and avoid the loss of operating performance caused by opportunistic motives. In addition, the productive fixed assets of industrial robots will promote the scale economy under the investment effect. For investment decision optimization, the "intelligent interconnection" mode of industrial robots will not only accurately handle historical production, supply and marketing information records and optimize investment and operation decisions, but also carry out cross-domain, cross-system and cross-border real-time optimal allocation of various internal and external factor resources on its multi-functional module interactive platform, so as to fully realize the cross-enterprise docking of the whole internal production and operation process with the external market and improve the efficiency of capital allocation. Thirdly, from the perspective of value-added, the degree of enterprise division of labor is measured by value-added method, and the action chain of "industrial robot application→deepening division of labor→realizing value-added" is the path for industrial robots to improve enterprise performance. Specifically, for enterprise division of labor, compared with reducing internal management and control costs, in the process of improving enterprise performance, the reduction of external transaction costs dominates the application of industrial robots, thus showing the improvement of enterprise division of labor professionalism. For the group division of labor, the operation mode of industrial robot "intelligent factory→intelligent manufacturing→intelligent service→intelligent products→intelligent data" reshapes the enterprise production operation mode, which helps the enterprise group to separate the production process business in the form of newly established subsidiaries, which is manifested in the increase in the number of subsidiaries and new subsidiaries, and the increase in the amount of related transactions of goods purchased by enterprises from subsidiaries. For the market division of labor, on the one hand, through the accurate screening and deep learning of big data, the market barriers generated by "information islands" in various production departments and production processes has been broken, and the selection range of upstream and downstream supply chains of enterprises has been expanded, which is manifested in the decline in supply chain concentration; On the other hand, the robot application oriented by "things", "network" and "information" has broken through the boundary between the traditional information system and the configuration of physical elements, resulting in the separation of production process business in the form of directly trading intermediate products to the external market, which is embodied in the stripping of core assets and the increase of outsourcing amount. Fourthly, in view of the resource-based theory that the resource capability of heterogeneous core organizations is the key to improve business performance, according to the core conclusion of improving business performance by using industrial robots in China. First, the role of industrial robots in improving the business performance of enterprises is different under the constraints of capital density, property rights, financing constraints and government subsidies, which shows that we should focus on the promotion and application of industrial robots in production, and formulate a comprehensive and coordinated development strategy for industrial robots according to different development conditions and business characteristics of enterprises. Second, quantile regression found that compared with enterprises with average operating performance, enterprises with industrial robot application empowerment will have a more significant promotion effect on enterprises with extremely low operating performance and extremely high operating performance, which shows that industrial robot application can get rid of the shackles of traditional factor resource allocation, reshape the organizational division of labor and break through the bottleneck of value-added. Third, with the improvement of digital transformation of enterprises, the application of industrial robots has enhanced the performance of enterprises, which shows that promoting the new generation of digital information technologies such as the Internet of Things, big data and cloud computing can form a synergistic effect with the application of industrial robots and realize "digital intelligence integration". Fourth, the industrial robot application has both "upward spillover" and "downward spillover" industrial chain conduction effects to improve the business performance of enterprises, which indicates that the industrial chain spillover effect of industrial robot application should be reasonably guided, and the technical and non-technical factors of industrial robot application to improve business performance should be fully transmitted to the upstream and downstream industrial chains, so as to promote the three-dimensional coordinated development of production and operation between different industries and regions. The innovations of this paper mainly include: Firstly, it expands the theoretical research method of financial performance analysis focusing on investment cash flow. In terms of research content, the classic corporate finance research defines corporate value as a series of investment income streams, while corporate performance reflects the capital formation efficiency of corporate investment. The above analysis blurs the allocation information of other factors outside investment and the adjustment of operational division of labor corresponding to the change of business performance. In terms of research methods, the financial performance measurement methods such as capital formation efficiency, "investment-investment opportunity sensitivity" and "Richardson(2006) investment efficiency" can't go deep into the factor allocation information outside the enterprise investment. By dividing enterprise value into asset present value and incremental value, this study examines the "black box" of industrial robot application to improve enterprise performance from the dual perspectives of factor allocation and value-added. On the one hand, considering that enterprise investment and operation in financial research are equivalent to enterprise production behavior in neoclassical economics, and enterprise investment opportunity value as incremental value can not reflect the factor allocation information outside investment, Total factor productivity is introduced to measure the business performance of enterprises and reflect the function of "present value of assets→incremental value" from factor input value to factor output value. Under the framework of total factor productivity, the factor allocation mechanism of industrial robot application is investigated by integrating the information of technological innovation growth, operation management improvement and investment decision optimization of enterprise production and operation process. On the other hand, unlike Myers, who regards incremental value as the option value of future investment, this study measures the degree of enterprise division of labor by value-added method and verifies the value-added chain of "industrial robot application→adjustment of business division of labor→improvement of business performance". Combined with the analysis of internal and external transaction cost balance, establishment and transaction status of group subsidiaries, changes of upstream and downstream supply chains in the market, this paper tries to find out the organizational "black box" of changes in business performance by adjusting business division of labor under the incentive of value maximization. Secondly, it provides micro evidence that there is no "Solow performance paradox" in the application of industrial robots in China. Whether it is the concern of literature research on the "promotion theory" and "ineffectiveness theory" of industrial robot application, or the discussion of positive cases such as Xiaomi robot industrial chain empowerment, Midea's acquisition of robot giant KUKA, and negative cases such as Foxconn robot mismatch and Tesla robot safety accident, the economic consequences of industrial robot application have always been the focus of theoretical and practical circles. However, the existing research on the application of industrial robots in China is limited by data and empirical methods, and most of them focus on theoretical analysis and practical case summary, while some empirical analysis focuses on macro-economic consequences such as regions and industries. The research difficulty of industrial robot application lies in its variable measurement, especially the limitation of relevant statistical data at the micro-enterprise level. Different from the existing literature, drawing lessons from the idea of "Batick's instrumental variables" to study the composition of the initial share of the subject and the time-varying growth rate to reflect the same exogenous impact under different exposure conditions, this study calculates the application degree of industrial robots at the enterprise level and discusses the economic consequences of the application of industrial robots in enterprises from the dual perspectives of factor allocation and value-added, which not only verifies the core conclusion of "industrial robot application improves enterprise performance", but also fully discusses the corresponding factor allocation mechanism and enterprise division of labor performance in the process of improving enterprise performance based on this conclusion. The above conclusions are direct evidence to reveal the integration of industrial robot application and manufacturing real economy development in China, and to respond that there is no Solow performance paradox of industrial robot application in the new development stage of China. Thirdly, it enriches the research thinking of resource-based theory on the resource capacity of heterogeneous core organizations of enterprises. Resource-based theory regards enterprises as a collection of tangible resources, intangible resources and organizational resource capabilities, and holds that heterogeneous core organizational resource capabilities are the key to improving business performance. However, it is difficult to design the heterogeneous core organizational resource capacity of enterprises in an empirical way and measure the variables. Empirical studies are mostly based on the general mean regression combined with heterogeneous grouping analysis, and more literature studies discuss the specific heterogeneous core organizational resource capacity of enterprises through case analysis. Under the above circumstances, on the one hand, the empirical analysis of heterogeneous grouping has the characteristics of large granularity and can not reflect the influence of conditional distribution of related variables in detail; On the other hand, the individual research conclusion of case analysis does not apply to the promotion of large sample practical experience. Based on the specific research situation, this study not only analyzes the application differences of industrial robots according to the traditional heterogeneous constraints such as capital density, property rights, financing constraints and government subsidies, but also discusses the enterprise resource base by using quantile analysis method. In addition, this paper also analyzes the spillover effects of upstream and downstream industrial chains of industrial robots. The above empirical analysis is an exploratory discussion on the resource capabilities of heterogeneous core organizations of enterprises based on resource-based theory, which not only helps to enrich the relevant achievements of existing literature, but also provides empirical enlightenment for developing industrial robot applications, accelerating the formation of new quality productive forces and realizing high-quality development of enterprises during the 14 th Five-Year Plan period.
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