Abstract:

Intelligent manufacturing provides a direction for manufacturing enterprises to gain competitive advantage in the era of digital economy. Meanwhile, innovation becomes an important way for manufacturing enterprises to promote digital and intelligent transformation in order to achieve high-quality development. However, in the extant literature on intelligent manufacturing and digital transformation, much attention has been largely paid to topics at macro-level topics, such as technology composition and business pattern. Scholars seldom paid attention to the stimulation of innovation from micro-level perspectives, such as team processes, social relationship network, and mentor-protege interactions. Accordingly, this study aims to develop a new mentoring theory based on the context of intelligent manufacturing, which provides a new direction for enterprises to stimulate team dual innovation from micro perspectives.

First, this study theoretically explored the conceptual connotation of mentoring in intelligent manufacturing. We discussed how it was distinct from traditional mentoring from three aspects, including participants, interaction patterns, and relational characteristics. Then, we theoretically discussed how to develop and validate a measure for this construct based on the three-dimension structure of mentoring in the traditional work context, including career guidance, social support and role model. In essence, mentoring in intelligent manufacturing, which usually develops based on an intelligent platform, belongs to a type of social networks that include dyadic, trilateral, one-to-many and many-to-many interpersonal interactions. In this sense, social network analysis is an appropriate approach to measure mentoring in intelligent manufacturing. This study cannot only help to enrich our understanding of mentoring in the context of intelligent manufacturing, but also provide a theoretical framework and relevant tools for future research on the development and effectiveness of mentoring in the context of intelligent manufacturing.

Second, in order to help enterprises successfully cross the ‘last mile’ of digital and intelligent transformation, this study reveals the role of mentoring in stimulating team dual innovation in the context of intelligent manufacturing through integrating the extant literature on team-level outcomes of mentoring and prior research on intelligent manufacturing. We deem that mentoring in intelligent manufacturing can help to enhance team incremental and radical innovation through generating two processes, team transition and team action respectively. Specifically, team technical reflection and team procedural improvement are sequential phases in the team transition process, whereas team transactive memory systems and team knowledge integration are sequential phases in the team action processes. Meanwhile, interpersonal process, which runs through all episodes in the whole team process, provides an important guarantee for team transition process and team action processes. Specifically, team cognitive trust, a variable in interpersonal process, acts as a boundary condition of the team transition process. Moreover, on the basis of three processes in the TAR model, we add human-AI process as a new type of team process that is specific to the context to intelligent manufacturing. We believe that team technical trust, a variable in human-AI process serves as a boundary condition about how mentoring in intelligent manufacturing affects team dual innovation through activating transition and action processes in the team.

Finally, this study also builds a systematic framework for the establishment and optimization of the mentoring theory in intelligent manufacturing, which further provides new directions for facilitating the evolution of work force from ‘line workers’ to ‘innovative talents’ in the digital intelligent transformation process of enterprises. In sum, this study contributes to both the theory and the practice not only by updating the concept of mentoring in intelligent manufacturing, which enriches the understanding of the micro mechanism of enterprise innovation in intelligent manufacturing, but also by building a systematic model as a guidance to the practices of talent nurturing and developing in this context.

Key words: mentoring in intelligent manufacturing, team dual innovation, team process model