Beyond feasibility filters: How expertise heterogeneity enables innovation recognition

Deskripsi

Organizations often struggle to identify promising innovations that balance novelty and feasibility in multidisciplinary domains, yet how does evaluator expertise heterogeneity shape these assessments? This study examines how evaluator expertise influences innovation evaluation through a field experiment with National Aeronautics and Space Administration's (NASA) Astrobee Robotic Arm Challenge, involving 354 evaluators assessing 101 solutions. Domain-spanning evaluators assign higher novelty ratings while maintaining similar feasibility ratings compared to domain-specific evaluators. Domain-adjacent evaluators show higher ratings on both dimensions. Human-LLM analysis of 3007 evaluator comments reveals a two-stage process: feasibility filtering (evaluating minimum viability) followed by integrative assessment (evaluating enhancement potential). Different expertise types serve complementary functions: domain-spanning evaluators recognize enhancement potential while maintaining rigorous standards; domain-adjacent evaluators show openness to novel approaches; domain-specific evaluators ensure technical rigor. These findings suggest effective innovation evaluation depends on strategically combining complementary expertise types rather than identifying optimal individual evaluators.