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• W4385063315 abstract "In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of cities’ efforts. The success of both top-down implemented innovation district and community-level innovation ecosystems is complex and has not been well examined. Yet, limited data shed light on the association between indicators and innovation performance on a smaller scale than regional. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovation with various innovation scoreboards. The present paper initiates the discussion by focusing on the following research query: In what manner can we harness real-time big datasets from unconventional sources to detect novel indicators of innovation and augment the prevailing toolkits for assessing innovation? For this purpose, the city of Boston has been selected as a case study to reveal the importance of its neighborhood’s different characteristics in achieving high innovation performance. The study uses a large geographically distributed dataset across Boston’s 35 zip code areas, which contains various business, entrepreneurial-specific, socio-economic, and other types of data that can reveal contextual urban dimensions, with the ultimate goal to incorporate the new innovation indicators derived from the non-traditional datasets into an integrated assessment framework. Additionally, to depict the level of innovation in specific zip code areas, novel metrics associated with innovation locations are put forth. The findings of this analysis seek to establish a new innovation index that can generate innovative analysis models applicable in various scenarios. Leveraging the knowledge extracted from non-traditional datasets, the objective is to provide valuable insights for strategic decision-making, resource allocation, and the management of innovation endeavors. This study contributes to the ongoing discourse on innovation by introducing a new theoretical framework that elucidates the connections between urban context, socio-economic characteristics of cities, and innovation performance." @default.
• W4385063315 created "2023-07-23" @default.
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• W4385063315 date "2023-01-01" @default.
• W4385063315 modified "2023-09-26" @default.
• W4385063315 title "A New Perspective on the Prediction of the Innovation Performance: A Data-Driven Methodology to Identify Innovation Indicators Through a Comparative Study of Boston’s Neighborhoods" @default.
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• W4385063315 doi "https://doi.org/10.1007/978-3-031-36049-7_28" @default.
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