Innovation is the engine of development for enterprises, and there is an increasing trend to adopt an open innovation strategy. However, how to manage external resources in an open, collaborative and complementary man...Innovation is the engine of development for enterprises, and there is an increasing trend to adopt an open innovation strategy. However, how to manage external resources in an open, collaborative and complementary manner, and in a shared environment that will yield the greatest networking effects, it is a challenging task. Because there is no such a satisfactory model for an open innovation strategy that combine operational mechanisms with the management of external resources. This article tries to fill the gap by adopting a resource-based perspective to construct an overall open innovation （OOI） business model. In this model, external resources are classified as industrial and non-industrial entities, to enable the identification of the interaction methods between manufacturing enterprises and external resources. The management of external resources involved in a Technology Open Innovation （TOI） cycle is given particular attention that includes： 1） the classification of the external resources of a TOI, 2） the general mechanisms extracted to promote qualified resources in and unqualified resources out, and 3） a business model to conceptualize the collaboration between enterprises and external resources. A case study of TOI is also provided to empirically verify its feasibility. This paper contributes to the literature by providing an original operational model and mechanism design for an open innovation strategy that is capable of managing external resources effectively.展开更多
Purpose-Topic segmentation is one of the active research fields in natural language processing.Also,many topic segmenters have been proposed.However,the current challenge of researchers is the improvement of these seg...Purpose-Topic segmentation is one of the active research fields in natural language processing.Also,many topic segmenters have been proposed.However,the current challenge of researchers is the improvement of these segmenters by using external resources.Therefore,the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.Design/methodology/approach-New topic segmenters(TSS-Onto and TSB-Onto)are proposed based on the two well-known segmenters C99 and TextTiling.The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource.Subsequently,an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.Findings-Based on this study,the authors showed that adding semantic knowledge,which is extracted from a domain ontology,improves the quality of topic segmentation.Moreover,TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.Research limitations/implications-The main limitation of this study is the used test corpus for the evaluation which is not a benchmark.However,we used a collection of scientific papers from well-known digital libraries(ArXiv and ACM).Practical implications-The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.Originality/value-The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge.This knowledge is extracted from an ontological external resource.展开更多
基金This study is supported by the China Social Science Foundation （15BGL007）, and the authors herewith express their appreciation for its support. The authors thank the valuable comments and suggestions from the annonimous reviewers, and acknowledge the editorial assistance in revising this paper.
文摘Innovation is the engine of development for enterprises, and there is an increasing trend to adopt an open innovation strategy. However, how to manage external resources in an open, collaborative and complementary manner, and in a shared environment that will yield the greatest networking effects, it is a challenging task. Because there is no such a satisfactory model for an open innovation strategy that combine operational mechanisms with the management of external resources. This article tries to fill the gap by adopting a resource-based perspective to construct an overall open innovation （OOI） business model. In this model, external resources are classified as industrial and non-industrial entities, to enable the identification of the interaction methods between manufacturing enterprises and external resources. The management of external resources involved in a Technology Open Innovation （TOI） cycle is given particular attention that includes： 1） the classification of the external resources of a TOI, 2） the general mechanisms extracted to promote qualified resources in and unqualified resources out, and 3） a business model to conceptualize the collaboration between enterprises and external resources. A case study of TOI is also provided to empirically verify its feasibility. This paper contributes to the literature by providing an original operational model and mechanism design for an open innovation strategy that is capable of managing external resources effectively.
文摘Purpose-Topic segmentation is one of the active research fields in natural language processing.Also,many topic segmenters have been proposed.However,the current challenge of researchers is the improvement of these segmenters by using external resources.Therefore,the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.Design/methodology/approach-New topic segmenters(TSS-Onto and TSB-Onto)are proposed based on the two well-known segmenters C99 and TextTiling.The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource.Subsequently,an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.Findings-Based on this study,the authors showed that adding semantic knowledge,which is extracted from a domain ontology,improves the quality of topic segmentation.Moreover,TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.Research limitations/implications-The main limitation of this study is the used test corpus for the evaluation which is not a benchmark.However,we used a collection of scientific papers from well-known digital libraries(ArXiv and ACM).Practical implications-The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.Originality/value-The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge.This knowledge is extracted from an ontological external resource.
