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Understanding and leveraging the social network services in innovation ecosystems
In today’s knowledge-based economies, it is generally accepted that innovations are integral to the foundation of both regional and national economic development, as well as one of the main causes for social and technical transitions. In an effort to boost and benchmark innovation, metrics and indicators have been designed to measure its various stages of development in order to gain insight into what is driving results. In an effort to make such measurements, a systems approach had been adopted in order to capture the dynamic and complex nature of innovation. However, an ecosystem approach has recently begun to attract attention as a framework for studying innovation. The term “innovation ecosystem” is often employed to explain a large and diverse set of participants and resources essential to the success of any innovation. Literature on innovation ecosystems emphasizes both the importance of a network of linkages between multiple actors and taking a holistic approach to include all players in the ecosystem. This is done to provide synergy, which has an effect on the overall outcome. This dissertation advances the existing research on innovation ecosystems by incorporating the soft aspects of innovation and studying social network services (SNSs) as a complementarity within said ecosystem. SNS platforms (e.g. Twitter, Facebook) provide opportunities for mass communication and interaction, both of which mediate societal discussion. These platforms create a unique opportunity to inform a holistic approach to innovation. The purpose of this thesis is to discuss the importance of SNSs in innovation ecosystems and attempt to operationalize the valuable data within SNSs for a deeper understanding of innovation. First, this thesis introduces the measurement and evaluation practices used, with particular effort made to highlight how the term “ecosystem” first emerged and then became associated with studies on innovation. To that end, an in-depth analysis of the innovation ecosystem research and citation network was conducted to assess the growing body of literature on this topic. Secondly, this study utilizes SNS data at both the microand the meso-level, meaning the company-, community-, and national-level, and provides novel insights. To do so, advanced textual analyses were performed and machine learning models were employed to explore the content of SNSs. These analysis resulted in several interesting findings regarding the role of content producer and content quality in the overall interaction within SNSs. This attempt to leverage SNSs for data was then furthered to include the design of a metric used to evaluate and establish benchmarks for counties based on entrepreneurial-oriented activity. For a more exploratory approach, SNSs data was analyzed to ascertain whether patterns existed within discussion topics and in proximity over time. Finally, the theoretical impact and methodological contributions to the literature on innovation ecosystems is included to show a novel approach to the use of SNS data. The findings should help scientists and practitioners to engage with SNSs in a more confident manner when an ecosystem-oriented approach is taken to evaluate innovation.
EOSC: Twitter Data
innovation measurement, intangible innovation, innovation ecosystem, social network services, text analysis, natural language processing, fi=School of Business and Management, Tuotantotalous|en=School of Business and Management, Industrial Engineering and Management|
innovation measurement, intangible innovation, innovation ecosystem, social network services, text analysis, natural language processing, fi=School of Business and Management, Tuotantotalous|en=School of Business and Management, Industrial Engineering and Management|
EOSC: Twitter Data
In today’s knowledge-based economies, it is generally accepted that innovations are integral to the foundation of both regional and national economic development, as well as one of the main causes for social and technical transitions. In an effort to boost and benchmark innovation, metrics and indicators have been designed to measure its various stages of development in order to gain insight into what is driving results. In an effort to make such measurements, a systems approach had been adopted in order to capture the dynamic and complex nature of innovation. However, an ecosystem approach has recently begun to attract attention as a framework for studying innovation. The term “innovation ecosystem” is often employed to explain a large and diverse set of participants and resources essential to the success of any innovation. Literature on innovation ecosystems emphasizes both the importance of a network of linkages between multiple actors and taking a holistic approach to include all players in the ecosystem. This is done to provide synergy, which has an effect on the overall outcome. This dissertation advances the existing research on innovation ecosystems by incorporating the soft aspects of innovation and studying social network services (SNSs) as a complementarity within said ecosystem. SNS platforms (e.g. Twitter, Facebook) provide opportunities for mass communication and interaction, both of which mediate societal discussion. These platforms create a unique opportunity to inform a holistic approach to innovation. The purpose of this thesis is to discuss the importance of SNSs in innovation ecosystems and attempt to operationalize the valuable data within SNSs for a deeper understanding of innovation. First, this thesis introduces the measurement and evaluation practices used, with particular effort made to highlight how the term “ecosystem” first emerged and then became associated with studies on innovation. To that end, an in-depth analysis of the innovation ecosystem research and citation network was conducted to assess the growing body of literature on this topic. Secondly, this study utilizes SNS data at both the microand the meso-level, meaning the company-, community-, and national-level, and provides novel insights. To do so, advanced textual analyses were performed and machine learning models were employed to explore the content of SNSs. These analysis resulted in several interesting findings regarding the role of content producer and content quality in the overall interaction within SNSs. This attempt to leverage SNSs for data was then furthered to include the design of a metric used to evaluate and establish benchmarks for counties based on entrepreneurial-oriented activity. For a more exploratory approach, SNSs data was analyzed to ascertain whether patterns existed within discussion topics and in proximity over time. Finally, the theoretical impact and methodological contributions to the literature on innovation ecosystems is included to show a novel approach to the use of SNS data. The findings should help scientists and practitioners to engage with SNSs in a more confident manner when an ecosystem-oriented approach is taken to evaluate innovation.