Platform economics has promoted open innovation through sufficient channels to reveal and exchange knowledge with experts or valued customers.However,innovation also suffers from information stickiness and product div...Platform economics has promoted open innovation through sufficient channels to reveal and exchange knowledge with experts or valued customers.However,innovation also suffers from information stickiness and product diversification risks.In order to design an innovation strategy on a platform,this study incorporated these risks into game models of open innovation and proposed strategies to promote open innovation and welfare through equilibrium analysis.On the basis of the literature analysis of these risks,stochastic pay-off functions were constructed to regulate and stabilize the knowledge exchange flows.From equilibrium analysis of the game models,we conclude that:1)stickiness and diversification are critical factors for open innovation on a platform;2)at the beginning,a broad search is necessary to acquire diverse knowledge;at the middle stage,regulation of knowledge exchange is critical to achieving equilibrium and higher profits;and 3)global welfare could be elevated through adjustment of knowledge size and friction of communication.展开更多
In the context of information overload,companies often struggle to effectively identify valuable ideas on their open innovation platforms.In this article,we propose an idea adoption strategy based on machine learning....In the context of information overload,companies often struggle to effectively identify valuable ideas on their open innovation platforms.In this article,we propose an idea adoption strategy based on machine learning.We used data from a well-known open innovation platform,Salesforce,and extracted characteristic variables using the Information Adoption Model.Four classification models were then constructed based on AdaBoost,Random Forest,SVM and Logistic Regression models.Due to significant differences in the number of positive and negative samples in the OIP,we used the SMOTE method to address the problem of data imbalance.The results of the study showed that the ensemble learning models were more accurate in identifying valuable ideas than the individual machine learning models.When comparing the two ensemble learning models,AdaBoost outperformed Random Forest in predicting both positive and negative class samples.The SMOTE-AdaBoost model achieved a recall of 0.93,a precision of 0.92 and an impressive AUC of 0.98 in identifying adopted ideas,which could well identify valuable ideas and has implications for improving the efficiency and quality of idea adoption in OIP.The shortcoming of this work is that it only investigated a single platform.In the future,we will consider extending this method to different platforms and multiple classification problems.展开更多
基金This paper was supported by the scientific research project of the Jiangsu Provincial Department of Culture and Tourism(21YB04).
文摘Platform economics has promoted open innovation through sufficient channels to reveal and exchange knowledge with experts or valued customers.However,innovation also suffers from information stickiness and product diversification risks.In order to design an innovation strategy on a platform,this study incorporated these risks into game models of open innovation and proposed strategies to promote open innovation and welfare through equilibrium analysis.On the basis of the literature analysis of these risks,stochastic pay-off functions were constructed to regulate and stabilize the knowledge exchange flows.From equilibrium analysis of the game models,we conclude that:1)stickiness and diversification are critical factors for open innovation on a platform;2)at the beginning,a broad search is necessary to acquire diverse knowledge;at the middle stage,regulation of knowledge exchange is critical to achieving equilibrium and higher profits;and 3)global welfare could be elevated through adjustment of knowledge size and friction of communication.
基金Supported by the National Natural Science Foundation of China(72171090)the Guangdong Basic and Applied Basic Research Fund(2023A1515011551)。
文摘In the context of information overload,companies often struggle to effectively identify valuable ideas on their open innovation platforms.In this article,we propose an idea adoption strategy based on machine learning.We used data from a well-known open innovation platform,Salesforce,and extracted characteristic variables using the Information Adoption Model.Four classification models were then constructed based on AdaBoost,Random Forest,SVM and Logistic Regression models.Due to significant differences in the number of positive and negative samples in the OIP,we used the SMOTE method to address the problem of data imbalance.The results of the study showed that the ensemble learning models were more accurate in identifying valuable ideas than the individual machine learning models.When comparing the two ensemble learning models,AdaBoost outperformed Random Forest in predicting both positive and negative class samples.The SMOTE-AdaBoost model achieved a recall of 0.93,a precision of 0.92 and an impressive AUC of 0.98 in identifying adopted ideas,which could well identify valuable ideas and has implications for improving the efficiency and quality of idea adoption in OIP.The shortcoming of this work is that it only investigated a single platform.In the future,we will consider extending this method to different platforms and multiple classification problems.
