The role of market knowledge in recognizing and exploiting entrepreneurial opportunities in technology intensive firms

Entrepreneurial opportunity recognition is an increasingly prevalent phenomenon. Of particular interest is the ability of promising technology based ventures to recognize and exploit opportunities. Recent research drawing on the Austrian economic theory emphasizes the importance of knowledge, particularly market knowledge, behind opportunity recognition. While insightful, this research has tended to overlook those interrelationships that exist between different types of knowledge (technology and market knowledge) as well as between a firm’s knowledge base and its entrepreneurial orientation. Additional shortfalls of prior research include the ambiguous definitions provided for entrepreneurial opportunities, oversight of opportunity exploitation with an extensive focus on opportunity recognition only, and the lack of quantitative, empirical evidence on entrepreneurial opportunity recognition. ^ In this dissertation, these research gaps are addressed by integrating Schumpeterian opportunity development view with a Kirznerian opportunity discovery theory as well as insights from literature on entrepreneurial orientation. A sample of 85 new biotechnology ventures from the United States, Finland, and Sweden was analyzed. While leaders in all 85 companies were interviewed for the research in 2003-2004, 42 firms provided data in 2007. Data was analyzed using regression analysis. ^ The results show the value and importance of early market knowledge and technology knowledge as well as an entrepreneurial company posture for subsequent opportunity recognition. The highest numbers of new opportunities are recognized in firms where high levels of market knowledge are combined with high levels of technology knowledge (measured with a number of patents). A firm’s entrepreneurial orientation also enhances its opportunity recognition. Furthermore, the results show that new ventures with more market knowledge are able to gather more equity investments, license out more technologies, and achieve higher sales than new ventures with lower levels of market knowledge. Overall, the findings of this dissertation help further our understanding of the sources of entrepreneurial opportunities, and should encourage further research in this area. ^

A generic model of execution for synthesizing domain-specific models

Software engineering researchers are challenged to provide increasingly more powerful levels of abstractions to address the rising complexity inherent in software solutions. One new development paradigm that places models as abstraction at the forefront of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code.^ Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process.^ The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources.^ At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM's synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise.^ This dissertation investigates how to decouple the DSK from the MoE and subsequently producing a generic model of execution (GMoE) from the remaining application logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis component of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions.^ This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.^

The role of market knowledge in recognizing and exploiting entrepreneurial opportunities in technology intensive firms

Entrepreneurial opportunity recognition is an increasingly prevalent phenomenon. Of particular interest is the ability of promising technology based ventures to recognize and exploit opportunities. Recent research drawing on the Austrian economic theory emphasizes the importance of knowledge, particularly market knowledge, behind opportunity recognition. While insightful, this research has tended to overlook those interrelationships that exist between different types of knowledge (technology and market knowledge) as well as between a firm’s knowledge base and its entrepreneurial orientation. Additional shortfalls of prior research include the ambiguous definitions provided for entrepreneurial opportunities, oversight of opportunity exploitation with an extensive focus on opportunity recognition only, and the lack of quantitative, empirical evidence on entrepreneurial opportunity recognition. In this dissertation, these research gaps are addressed by integrating Schumpeterian opportunity development view with a Kirznerian opportunity discovery theory as well as insights from literature on entrepreneurial orientation. A sample of 85 new biotechnology ventures from the United States, Finland, and Sweden was analyzed. While leaders in all 85 companies were interviewed for the research in 2003-2004, 42 firms provided data in 2007. Data was analyzed using regression analysis. The results show the value and importance of early market knowledge and technology knowledge as well as an entrepreneurial company posture for subsequent opportunity recognition. The highest numbers of new opportunities are recognized in firms where high levels of market knowledge are combined with high levels of technology knowledge (measured with a number of patents). A firm’s entrepreneurial orientation also enhances its opportunity recognition. Furthermore, the results show that new ventures with more market knowledge are able to gather more equity investments, license out more technologies, and achieve higher sales than new ventures with lower levels of market knowledge. Overall, the findings of this dissertation help further our understanding of the sources of entrepreneurial opportunities, and should encourage further research in this area.

A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.