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Abstract:
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This study analyzes the challenges faced by large corporations while adopting open innovation (OI), a strategical approach that has been used for the last two decades to connect external solutions with corporate innovation needs and accelerate innovation cycles to a fast-changing market. The primary objective of this research is to analyze the impact of emerging technologies, such as artificial intelligence (AI) and big data, on improving open innovation processes effectivity for the stakeholders involved (large corporations, startups and innovation consultancies) and develop a framework to implement the technologies for each level of maturity in OI. The methodology involved a combination of qualitative and quantitative research methods including semi-structured interviews with corporate leaders, innovation consultants, and experts from the innovation ecosystem, which aimed to capture insights into the challenges associated with open innovation and the strategic value it provides and develop thematic analysis, word cloud visualizations, and comparative coding. From that, key themes were identified, enabling an exploration of the factors impacting open innovation effectiveness. Quantitative data was collected to analyze patterns, trends, and correlations among different sectors. The combined analysis focused on organizational challenges, technology integration, and the effects of collaboration models on innovation outcomes, that revealed recurring themes where open innovation faces the greatest inefficiencies, such as cultural resistance, limited absorptive capacity, and strategic organizational inertia. It has been found that by incorporating AI and big data into the open innovation framework, companies can make data-driven decisions, optimize partner selection, and improve the efficiency of collaborative projects, therefore, integrating these technologies into open innovation can streamline processes and create more impactful outcomes. The primary academic contribution of this study lies in expanding the existing body of knowledge on open innovation by integrating digital tools and data analytics into the framework and provides a theoretical foundation for future research, emphasizing the need for continuous adaptation of open innovation models to incorporate technological advancements. The findings enrich our understanding of how large corporations can better align open innovation practices with emerging digital strategies, thereby bridging the gap between theory and application in organizational innovation research. On the business side, this research offers actionable insights for industry leaders seeking to enhance their companies’ competitive edge through open innovation, it also provides a roadmap for corporations to adopt more agile and adaptive approaches to innovation. These practices include leveraging AI to automate routine innovation tasks, applying big data analytics to support strategic decision-making, and refining partner selection processes, that not only promise to improve operational efficiency and reduce time-to-market but also foster a culture of continuous learning and collaboration, which is essential for long-term success in a dynamic market. In conclusion, this study establishes that integrating emerging technologies into open innovation is more than necessary for large corporations aiming to maintain a competitive advantage, it serves as a foundation for them to reevaluate their innovation strategies, adopting a more holistic approach that combines technological capabilities with effective open innovation practices. This research contributes both theoretically and practically by outlining a structured, technology-enhanced model for open innovation, which is essential for companies navigating the complexities of today’s global market. |
