Table A3 Reporting architecture: typical... | Emerald Publishing

Table A3

Reporting architecture: typical evidence and common gaps (with references)

Area	What credible disclosure looks like	Common gap noted in practice
Cyber–KM artifacts	Named playbooks, threat models, and lessons-learned repositories referenced with dates or versions	Generic statements about “awareness” without identifiable artifacts (Alsehrawy et al., 2022; Uden and He, 2017)
Learning loops	Descriptions of after-action reviews, cross-functional exercises, and how outputs changed procedures	Absence of a traceable link between incident learning and operational changes (Bebbington et al., 2008; Higgins et al., 2018)
Reuse/coverage measures	Simple indicators such as adoption rates, training coverage, near-miss capture or audit findings	No metrics to judge whether knowledge was actually used beyond the originating team
DT–environment linkage	Concrete DT use cases (for example, arrival scheduling or berth optimization) tied to emissions-relevant decisions	High-level claims about “efficiency” without an operational pathway to GHG outcomes (de Vicente-Lama et al., 2023; Di Vaio et al., 2025a, 2025b, 2025c; Homayouni et al., 2025; Notteboom et al., 2022)

Area	What credible disclosure looks like	Common gap noted in practice
Cyber–KM artifacts	Named playbooks, threat models, and lessons-learned repositories referenced with dates or versions	Generic statements about “awareness” without identifiable artifacts (Alsehrawy et al., 2022; Uden and He, 2017)
Learning loops	Descriptions of after-action reviews, cross-functional exercises, and how outputs changed procedures	Absence of a traceable link between incident learning and operational changes (Bebbington et al., 2008; Higgins et al., 2018)
Reuse/coverage measures	Simple indicators such as adoption rates, training coverage, near-miss capture or audit findings	No metrics to judge whether knowledge was actually used beyond the originating team
DT–environment linkage	Concrete DT use cases (for example, arrival scheduling or berth optimization) tied to emissions-relevant decisions	High-level claims about “efficiency” without an operational pathway to GHG outcomes (de Vicente-Lama et al., 2023; Di Vaio et al., 2025a, 2025b, 2025c; Homayouni et al., 2025; Notteboom et al., 2022)