Research On The Impact Of Ambidextrous Learning On The Performance Of Digital Transformation Of High-tech Smes

Digitalization has become the only way for the development of SMES.On the one hand,digitalization promotes the free flow of capital,information and other resource elements,reduces the information asymmetry between enterprise transactions and makes decision-making efficient,making enough space for innovation activities of SMES.On the other hand,the development and innovative use of digital technology has subverted the current industry environment.Facing this environmental trend,if SMES do not introduce digital technology to transform or reshape the value creation mode and business process,they will not be able to maintain evolutionary adaptability in the current competition,so as to not only succeed but also maintain success in the uncertain environment.However,whether from the current enterprise practice or theoretical research,it is not easy to succeed in digital transformation.Therefore,it has become the focus of current academic research to find suitable theories and make theoretical contributions to the sound and rapid transformation of SMES.The ambidextrous learning theory is logically related to the digital transformation of enterprises.On the one hand,ambidextrous learning theory divides organizational learning into exploratory learning and exploitative learning,in which exploratory learning represents activities such as change,adventure,experiment,discovery and innovation,while exploitative learning represents activities such as refining,production,efficiency,implementation and execution,which together form the basis of competitive advantage of enterprises,especially in the period of digital transformation of enterprises.On the other hand,the existing documents of enterprise digital transformation show that the key to the success of transformation is the exchange and cooperation between enterprises and different subjects and the efficient and creative use of production factors such as knowledge,technology and data.The two are logically related.Therefore,by reviewing the dual learning theory,strategic consistency theory and dynamic capability theory and combing the relevant research literature,this paper constructs a theoretical model with dual learning as the independent variable,dynamic capability as the intermediary variable,digital transformation performance as the dependent variable and strategic consistency as the regulating variable.Therefore,this paper aims at SMES undergoing digital transformation,mainly for middle and senior managers and professional and technical personnel of enterprises,and collects 214 valid questionnaires and makes empirical tests..The results suggest that:(1)Exploratory learning,exploitative learning and ambidextrous balance have a significant positive impact on the digital transformation performance of SMES,while ambidextrous interaction has no significant impact,which suggests that exploration and exploitation have a positive effect on the digital transformation of SMES,and a balanced ambidextrous learning is more effective than simultaneous ambidextrous learning.(2)Dynamic ability plays a partial intermediary role in exploratory learning,exploitative learning and digital transformation performance,and a complete intermediary role in ambidextrous balance and digital transformation performance,which may be caused by the difference of learning methods.(3)Strategic consistency plays a positive role in the relationship between ambidextrous learning and digital transformation performance,and dynamic ability and digital transformation performance.In the state of low strategic consistency,exploratory learning and intermittent ambidextrous learning are not conducive to digital transformation performance.In the state of high strategic consistency,exploratory learning,utilization learning and intermittent ambidextrous learning all have a positive impact on the digital transformation performance.Therefore,high tech SMES can focus on utilization learning at the initial stage of transformation,and dynamically adjust the learning mode with the deepening of the transformation stage to better promote the improvement of digital transformation performance.