A MIXED METHOD GROUNDED THEORY(MM-GT) WITH INTELLIGENT GIS AND MACHINE LEARNING APPROACH TO UNIVERSITY BASED BIOTECH BUSINESS INCUBATORS(UBBI) CLUSTERS: A STRONG STRUCTURATION AND SPATIAL AGGLOMERATION VIEW

Abstract

This study investigates and integrates University Based Biotech Business Incubators(UBBI) and tech transfer clusters based on several cases from different Regional Innovation System(RIS).The study further develops a conceptual framework based on the ensuing themes, patterns and categories with grounded theory for UBBI Clusters and tech transfer spinoffs, their embedded Regional Innovation System (RIS) and UBBI roles. The study also includes an assessment on how UBBIs clusters are formed, Universities roles and perspectives, knowledge flow and innovation ecosystem development, UBBI regional transformation or changes and their substantive and Dynamic Capabilities. This study also explores how these UBBIs absorb the attributes and elements within their ecosystem to facilitate research and product development and the specific capabilities these UBBIs have developed overtime in their value chain based on the Biotech Cluster’s life cycle.
Based on a Mixed Method Grounded Theory Approach(MM-GT) with Intelligent GIS and an application of Strong structuration theory(SST), this study examines the impact of external conditions on UBBI clusters’ structures and how the actors respond and build resilience to disruptions and bottlenecks such as influence of regulation, policies development on the UBBI’s Cluster and impact of the social structure and active agents or actors on the ecosystem. Using the cases from different RIS, this study further uses ensuing grounded theory to develop a model based for Intelligent Geography Information System(GIS) with Machine and Deep Learning (ML, DL with Generative AI) that could be used for the simulation of UBBI’s spatial agglomeration and clusters formation with their dynamic capabilities.