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<p>At the interface of strategic technology management, innovation policy, and regional development, the Quintuple-Innovation-Helix-plus-Artificial-Intelligence-framework (QIH + AI) provides a promising foundation for establishing future-oriented sustainable smarter specialization strategies (S4). This article outlines the theoretical foundations of the QIH + AI framework, including concepts which analyses the role of knowledge and learning: strategic management of technological learning (SMOTL). higher order technological learning (HOTL), bandwidth of optimal technological learning (BOTL) and the distinction of data, information, knowledge, experiences, wisdom and intuition (DIKEWI). Additionally, we consider the concept of Ambidextrous, Robust and Resilient Impact Assessment of Sustainable Smarter Specialization Strategies (AR2IA/S4). In this context, the question arises of how sustainable smarter specialization strategies can be developed, successfully implemented, and measured. The QIH + AI framework identifies crucial guidelines and statements regarding the development of these strategies, including the stakeholders involved, the knowledge flows between the economy, politics, science, society, and the natural environment, the role of artificial intelligence, and the temporal evolution of the entire entrepreneurship and innovation ecosystem. This sets the stage of the Mapping, Metrics, Measurement, Meaning, Assessment, Monitoring, and Management (M4AM2) theory, policy, practice and politics (TP3) toolkit.</p>

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The Quintuple-Innovation-Helix-plus-Artificial-Intelligence (QIH + AI): A Prospective Retrospective and the Use Case of the Saxony Entrepreneurship and Innovation Ecosystem (EIE)

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Elias G. Carayannis,
  • Steffen Preissler

摘要

At the interface of strategic technology management, innovation policy, and regional development, the Quintuple-Innovation-Helix-plus-Artificial-Intelligence-framework (QIH + AI) provides a promising foundation for establishing future-oriented sustainable smarter specialization strategies (S4). This article outlines the theoretical foundations of the QIH + AI framework, including concepts which analyses the role of knowledge and learning: strategic management of technological learning (SMOTL). higher order technological learning (HOTL), bandwidth of optimal technological learning (BOTL) and the distinction of data, information, knowledge, experiences, wisdom and intuition (DIKEWI). Additionally, we consider the concept of Ambidextrous, Robust and Resilient Impact Assessment of Sustainable Smarter Specialization Strategies (AR2IA/S4). In this context, the question arises of how sustainable smarter specialization strategies can be developed, successfully implemented, and measured. The QIH + AI framework identifies crucial guidelines and statements regarding the development of these strategies, including the stakeholders involved, the knowledge flows between the economy, politics, science, society, and the natural environment, the role of artificial intelligence, and the temporal evolution of the entire entrepreneurship and innovation ecosystem. This sets the stage of the Mapping, Metrics, Measurement, Meaning, Assessment, Monitoring, and Management (M4AM2) theory, policy, practice and politics (TP3) toolkit.