| Innovation is the first driving force to lead the development of enterprises,enterprises that grasp innovation can grasp the future.With the advent of the era of mobile Internet,the traditional closed innovation of enterprises has long been unable to meet the rapidly changing market demands,while the open innovation that can integrate a variety of internal and external resources has been favored by the majority of enterprises.Open innovation is a new concept proposed by Professor Henry Chesbrough of Harvard University in 2003,aiming at the closed innovation paradigm in the era of industrial economy.Its essence is a process in which enterprises fully integrate internal and external resources to improve organizational innovation performance,and look for various channels to commercialize products and ideas.Open innovation community is the main form of enterprise open innovation,which is a platform based on Internet for online users to participate in enterprise innovation and knowledge sharing.The practice of open innovation community has activated the vitality of user idea.For example,Wikipedia,founded in 2001,except for a small number of staff,almost all of the work is done voluntarily by network volunteers from all over the world.Statistics show that more than 1.2 million people participated in the compilation of Wikipedia and contributed more than 30 million entries in various languages.Xiaomi technology,a Chinese enterprise,started its business in 2010 with MIUI system as the starting point,the number of users in MIUI community has reached more than 500000 in only one year.Xiaomi’s enterprise strategy of user participation in innovation makes Xiaomi mobile stand out in the fierce competition,which also promotes it to become the youngest company among the top 500 companies in the world in 2019.Although there have been many successful cases of open innovation,as a new innovation mode,it still faces three important challenges :(1)Due to the different backgrounds of users in the open innovation community,there is great uncertainty in the quality of users’ creativity,which leads to the heavy work of creativity evaluation and screening.(2)How to effectively identify leading users from a large number of users,continue to pay attention to their content generation or invite them to deeply participate in enterprise innovation.(3)There is "empty bar syndrome" in the open innovation community created by some enterprises.How to motivate users to actively participate in and contribute continuously is another big challenge for the continuous success of open innovation communities.Therefore,based on a large amount of data generated by users in the open innovation community,it is of great significance to use text mining technology to study the characteristics of users’ innovation content and behavior to solve the above challenges.The paper reviews the literature on the open innovation community user adoption factors,lead user identification indicators and methods,users’ continuous contribution behavior and incentives in open innovation community,and summarizes the gaps of previous studies in these aspects and points out that:(1)There are two main deficiencies in the literature on user idea adoption in the past: first,although the previous studies have examined some factors that influence the creative adoption possibility,these factors have not been well sorted out,and some important factors have not been well tested,such as the network structure characteristics of users in the community,the influence of titles,etc.Second,the above studies are mainly based on the variables extracted from the creative characteristics,user characteristics and user interaction data,but lack of research on the influence of text emotional factors.(2)Previous research on the identification of leading users mainly focused on the identification of traditional offline innovative users,while the research on the identification of innovative users in open innovation platform is limited;In terms of research methods,questionnaire survey and manual selection are mainly used to identify innovative leading users,which is not conducive to effectively identify innovative leading users in large-scale users.(3)Previous studies on user continuous participation in open innovation communities mainly focus on the participation behavior and user contribution behavior of leading users,but there is a lack of research on the actual continuous participation return of different types of users,which is not conducive to the continuous contribution of users and the sustainable development of open innovation community.For user idea adoption,lead user identification and shortcomings in the course of user participation in return problem in OIC,the dissertation is based on text mining perspective,from the perspective of the user behavior in the OIC,using the MIUI community user generated data to the user idea adoption has carried on the empirical research,to contrast experiment of lead user identification method,the problem of user participation in investment returns the empirical research.The main work and innovation completed in the paper can be summarized as the following four aspects:(1)According to user innovation adoption problem,using the data from the user data and behavior innovation theoretical framework based on Aristotle’s persuade(logical appeal,appeal to personality,emotional appeal),build the affective factors contain the user’s creative adoption possibility factor model,the model is mainly focused on the factors affecting user creative adoption of precursor in the OIC,namely enterprise audit under the condition of limited capacity,what are the signs that creative potential to adopt value by the user.The model allows the OIC with insufficient review ability to select more potentially valuable ideas through screening mechanism design first.(2)The possibility factor model of creative adoption focuses on examining the influence of emotional factors on adoption.Two important insights are gained.First,emotional factors influence the innovation adoption process.Creative emotion is introduced into the possibility model of creative adoption for the first time.It is found that creative emotion significantly affects creative adoption,and attention has a negative moderating effect on creative emotion.This insight is important for OICs to guide users’ emotions in a way that makes OICs a place for innovation rather than a platform for complaining or formality.Secondly,creative emotion is the real emotional appeal of users’ product experience,which includes users’ expectation of product demand or emotional expression of pain points of users’ demand.If the enterprise considers this factor in the product innovation iteration,the real needs of users will be seen and satisfied.(3)Build the leading user identification index system and identification model in OIC.The research uses the published content data and objective behavior data to develop the user’s content information index,user emotion index and behavior data index,and constructs the leading user identification index system.This paper proposes a lead user identification model based on the comparison of three unsupervised clustering algorithms,explores the possibility of leading users being identified in OIC,and expands the application of clustering algorithm in lead user identification.At the same time,it also lays the groundwork for the next topic--the research on the return on investment of users’ participation in open innovation communities.(4)Study on the return on investment of users’ participation in open innovation community,and discuss how users’ participation in OIC is rewarded.This study enriches the understanding of the return on user participation in OIC.Based on the social exchange theory,the social exchange resources of user participation in OIC are identified through the lens of attention capital and cognitive capital,that is,the social return and economic return of users in OIC are explained from the perspective of user attention investment and social capital.In the process of research on user participation and investment returns,users are divided into two groups,the leading user group and the ordinary user group,for empirical analysis respectively,which is conducive to a deeper understanding of the heterogeneity of different user groups on the demand for returns.The results show that the leading user group and the ordinary user group have significant differences in social returns.The study enriches the literature on user value mining and incentive in open innovation communities,provides a perspective of user innovation adoption decision in open innovation,expands the identification method of leading users,and provides an important reference for the incentive mechanism design of open innovation communities in enterprises. |
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