• Andre Gomes De Souza

Student thesis: Phd


Outbound OI is informed by the idea that organisations should use alternative pathways to externalise their knowledge and commercialise their technologies. Ambidexterity suggests a high level of balance between opposite, competing and contrasting objectives such as exploration and exploitation, as well as radical and incremental innovation. The study focuses on outbound OI processes of technology creators and consumers in their current markets and the strong mutual interactions between the literature of outbound OI and ambidexterity. Innovation streams discuss patterns of innovation and are one way to mobilise exploration and exploitation in service and product development. The theory on which the thesis is developed views innovation as an evolutionary system. This vision is applied to the Apache Hadoop, the industry-standard ecosystem for the analysis of big data. Through an interpretive case study, the thesis investigates outbound OI processes in four case studies in six industry sectors in 25 organisations in Brazil. The work contributes to the research on ambidexterity in outbound OI processes in technology creators and consumers in their current markets. It suggests an interrelationship between the code base of the Apache Hadoop distributions (community and enterprise) and the way firms may innovate (discontinuous, architectural and incremental). Additionally, the thesis adds additional case studies on outbound OI processes in digital service platforms.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorKhaleel Malik (Supervisor) & Dimitri Gagliardi (Supervisor)


  • Open Source
  • Digital Ecosystems
  • Outbound Open Innovation
  • Ambidexterity
  • Apache Hadoop

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