Perceived green-shipping knowledge and competency demands among Chinese enterprise respondents: an exploratory network analysis

Workforce-needs surveys can conflate broad endorsement, uniform answering and specific relations among knowledge domains. We applied a measurement-aware diagnostic analysis to perceived green-shipping knowledge and competency demands. In a non-probability online survey, 93 Chinese enterprise respondents rated seven study-specific knowledge domains and selected six generic competencies; no invitation denominator was retained. Ratings were ceiling-concentrated (80.6–91.4% rated 4 or 5), and 41 respondents used one rating across all seven domains. A regularised network retained 15 of 21 positive edges, but centrality was unstable (CS = 0.10 for strength; CS = 0.20 for expected influence). We compared the observed edges with two post hoc simulation benchmarks, one reproducing a general importance factor and the other fixing the composition and margins of the 41 uniform-response records, using 20,000 datasets per benchmark under fixed-penalty and penalty-reselected estimation. Energy efficiency–clean fuels alone met the four-condition sensitivity criterion (q = 0.00105–0.00367); digitalisation–ESG was model-selection sensitive. No knowledge–competency correlation survived multiplicity correction. Thus, the survey does not establish a stable population network or observed task coupling. It identifies one association that persists under the specified sensitivity analyses and a diagnostic approach for separating survey-wide endorsement from candidate pair-specific workforce needs.