Research on Human-Machine Collaboration Efficiency in the Digital Transformation of the Labor Market

Facing the accelerated digital transformation of the global labor market, this study focuses on the efficiency gap and driving mechanisms of human-machine collaboration, establishing a “three-dimensional dynamic evaluation framework” encompassing technological adaptability, organizational agility, and social acceptance. Based on Eurostat panel data and cross-industry questionnaires, analyses using SEM-Bootstrapping and fsQCA configuration methods reveal:(1) Threshold effects in technological adaptability: A manufacturing robot density exceeding 3.2 units/100 workers enhances efficiency coefficients by 82%. A 43% coverage ratio of surgical robots in healthcare enables efficiency leaps. Resource mismatch rates in manufacturing decrease by 28%,with service response times compressed to 1.2 hours(via elasticity analysis).(2) Optimal interval relationships: Agile teams achieving cross-functional reorganization within≤3 days at 28%-34%capacity in manufacturing boost efficiency by 23%.Multidisciplinary collaboration in healthcare reduces misdiagnosis rates by 28%.(3) Social adaptation drives heterogeneous behaviors: Policy packages(tax incentives + skill certification + data openness) accelerate transformation cycles by 14 months in manufacturing and service sectors. Service industries require UTAT model thresholds(β≥0.3) with anxiety indices decreasing by 37%through human takeover hotkey optimization. Key contributions include:(1) Innovatively proposing the time-varying efficiency formula η(t)=α(T)+β(O)+γ(E)to reorient change rates of technology, organization, and environment;(2) Developing DERI guidelines to quantify dynamic applicability measurements;(3) Clarifying industry-specific pathways: Clarifying industry-specific pathways: Manufacturing efficiency equation: E=0.38T+0.41O+0.21E(R2=0.79),Service quality constrained by policy lag: E=0.27T+0.33O+0.40E.