| Under the guidance of "Made in China 2025" and subsequent related policies,China has comprehensively promoted the intelligent transformation of manufacturing enterprises.At present,most manufacturing enterprises are in the initial stage of intelligent transformation.IT is the key resource for the intelligent transformation of enterprises.However,the large investment and long recovery period of IT make manufacturing enterprises bear huge financial pressure and investment risks in the early stage of transformation,and the enterprise changes caused by the use of IT have an impact on the TFP of enterprises.Except for the typical intelligent manufacturing demonstration enterprises with obvious resource advantages and transformation dividends,most manufacturing enterprises in China have uneven effects on the use of IT.How to effectively improve the TFP of enterprises through the investment of IT has become a very confusing problem for manufacturing enterprise managers.Therefore,IT is of great significance to study the specific influence and influence mechanism of IT investment on the TFP of smart manufacturing enterprises in China,and to explore the enterprise controllable variables that can effectively adjust the relationship between IT investment and TFP of smart manufacturing enterprises,so as to release the IT use potential of manufacturing enterprises and enhance the TFP of enterprises.Firstly,this paper combs the relevant research literature and the theoretical basis of resource-based view,dynamic capacity theory and absorptive capacity theory;Then,based on the perspective of cost efficiency,the influence mechanism system of IT investment on the TFP of enterprises is constructed,and the resource use efficiency of enterprises is divided into material,human,technical and marketing resource use efficiency,and the R&D investment intensity is selected as the regulating variable to establish an empirical research model.Then,Python software is used to establish the framework of intelligent related keyword bars,and 245 listed enterprises of intelligent manufacturing are screened out.Taking their panel data from 2016 to 2021 as research samples,the empirical models are regressed by Stata statistical software,and the research conclusions are drawn.The results show that:(1)IT investment in intelligent manufacturing enterprises has a significant negative impact on the TFP of enterprises,and the degree of negative impact is weakened after a period of lag.(2)The IT investment of intelligent manufacturing enterprises indirectly affects the TFP of enterprises by affecting the efficiency of enterprise resource use.Among them,the indirect influence of enterprise material and human resource utilization efficiency is negative in the short term and positive in the long term;The indirect effect of marketing resource use efficiency is not significant in the short term,but positive in the long term;The indirect influence of technical resource utilization efficiency is negative in both short and long term.(3)The intensity of enterprise R&D investment can positively adjust the influence of IT investment of intelligent manufacturing enterprises on the total factor productivity of enterprises.The research value of this paper is as follows: In practice,the conclusion of this paper will help to promote the intelligent transformation process of manufacturing enterprises.Intelligent manufacturing enterprises need to continue to increase IT investment,fully tap the improvement potential of their own resource use efficiency,and indirectly promote the improvement of total factor productivity through the influence mechanism of resource use efficiency;IT is necessary to continue to increase the investment in R&D to weaken the adverse impact of IT investment on the TFP of enterprises in the early stage of intelligent transformation.Academically,this paper constructs the indirect influence mechanism system of the new generation IT investment on the TFP of enterprises,and tests the moderating effect of R&D investment intensity on the relationship between them,which provides a new perspective for studying the related issues of intelligent transformation of manufacturing enterprises. |
