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My Research Interest ​

Evaluation of Critical Success Factors for Competitive Advantage in New Product Development of Medical Imaging Devices

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Motivation

Medical imaging device (MID) development demands an intricate balance between engineering innovation, regulatory compliance, and market responsiveness. Despite technological advances, many companies struggle to translate R&D efforts into sustainable market advantage. My research investigates the underlying Critical Success Factors (CSFs) that determine whether new medical imaging products succeed or fail in global markets.

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Research Approach

A quantitative research design was applied to identify and validate key CSFs influencing competitive advantage.

  • Sample: 333 survey responses from professionals across Europe, North America, and Asia

  • Independent variables: Organizational capability, market orientation, execution excellence, cost efficiency, and product strategy

  • Methodology: Statistical analysis and structural equation modeling were used to assess the strength and mediation of relationships among variables.

Key Findings

  • Product Strategy and Regulatory Effectiveness emerged as the strongest predictors of competitive advantage.

  • Other CSFs—such as market orientation, cost efficiency, and execution excellence—showed indirect effects, mediated through regulation and strategy.

  • The analysis led to the creation of a conceptual framework called the “Diamond of MID Success”, capturing the interdependencies among these factors.

Key Findings & Contributions

  • Product Strategy and Regulation emerged as the most significant levers influencing competitive advantage in MID development, both directly and via interaction effects.

  • Other variables (market orientation, cost efficiency, execution excellence) exerted influence more strongly when mediated through regulation and strategy, rather than direct paths.

  • I distilled these into a conceptual “Diamond of MID Success” model, highlighting interdependencies among these factors.

  • Based on empirical evidence, I propose actionable best practices for both academia and industry to structure R&D and strategic planning.

Broader future research topics- I am carrying out now 

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  • Integrate AI and ML to accelerate design decisions and innovation speed.

  • Strengthen supply chain resilience through digital visibility and predictive analytics.

  • Build data-connected ecosystems enabling real-time collaboration and faster execution.

  • Digital twin on enhancing quality and speed

  • Apply generative AI to optimize innovation workflows and investment prioritization.

  • Embed sustainability and ESG metrics within adaptive innovation management frameworks.

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