Het AI adoptie proces: de bepalende elementen voor succesvolle adoptie van AI en de waarde ervan voor organisaties

Translated title of the thesis: The AI adoption process: determinants of successful AI adoption and its value for organizations
  • J. Wigman

Student thesis: Master's Thesis


Despite the widespread renewed belief in the potential of AI, tangible results on macro level and organizational level are not yet visible, showing that companies are struggling to create business value from AI initiatives. These lagging results have driven researchers to study the process of technology adoption in the light of AI, but there is has not resulted yet in a comprehensive and integrated view of the different dynamics in the AI adoption process and the type of value that can be created with AI. This study aims to deliver just that: “What are the determinants of successful AI adoption and what type of value can it create for organizations?”.
Based on a systematic literature review, existing research is captured in a theoretical framework. This framework is then validated and improved in a single case study design focussing on which AI-readiness factors in the framework play a role in practice and why, and what types of value can be created.
This study presents a framework of the process of adoption and value creation of AI based on a combination of the Technology-Organizational-Environmental- framework (TOE) and the task-technology fit (TTF) model that is emperically validated to understand why and how adoption works in practice and what type of value can be created with it. The proposed framework provides more insight as it also describes the why and how of each factor, the relations between the elements in the framework, and lists 18 AI readiness factors. Additionally, the framework also creates a better understanding of the 3 types of value that can be created with AI and how they relate to each other. Implications
The results highlight the importance of the user in the AI adoption process in terms of value delivered and ease of use to drive AI adoption. AI designers and developers can use these findings to focus on how they can maximize user acceptance and thus AI adoption. Management can learn from the framework what factors play a role in AI adoption, with leadership and culture as the most discussed items as drivers. Although the type of leadership and culture is specific to each organisation, and is therefore not generalizable based on a single case study, the findings from the literature study providing evidence that leadership and culture influence AI adoption is.
Date of Award29 Jun 2023
Original languageDutch
SupervisorSamaneh Bagheri (Examiner) & Khoi Nguyen (Co-assessor)


  • AI readiness
  • AI adoption
  • Artificial intelligence
  • Innovation theory
  • Value creation

Master's Degree

  • Master Business Process management & IT (BPMIT)

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