Growing concern over food safety and adulteration has thrust milk traceability technologies to the forefront of agrifood supply chains. This qualitative study explores the technological, organisational, and environmental (TOE) determinants of traceability technology adoption in Saudi Arabia’s dairy sector. In-depth semi-structured interviews with nine senior managers from small-, medium-, and large-scale dairy farms were analysed thematically in NVivo. Thematic analysis revealed that technological cost and compatibility played crucial role, while contrary to the prior literature, respondents downplayed technological complexity, arguing that training could offset it. Organisational culture and employee resistance were the primary inhibitors within dairy firms. Saudi Vision 2030, post COVID-19 consumer pressure and competitor pressure emerged as the dominant environmental factors. The findings offer insights for managers and policymakers on how to improve supply chain transparency, operational efficiency, product quality, and consumer trust while advancing several UN SDGs.

Growing concern over food safety and adulteration has thrust milk traceability technologies to the forefront of agrifood supply chains. This qualitative study explores the technological, organisational and environmental (TOE) determinants of traceability‑technology adoption in Saudi Arabia’s dairy sector. In‑depth interviews with nine senior managers from small‑, medium‑ and large‑scale dairies were analysed thematically in NVivo. Government policy, especially Sau-di Vision 2030 and SFDA regulations emerged as the dominant driver, while employee resistance and upfront cost were the primary inhibitors. Contrary to much prior literature, respondents downplayed technological complexity, arguing that training can offset it. The localisation initiative Saudisation surfaced as a double‑edged sword: essential for national goals yet often slowing digital uptake. We extend the TOE lens by showing how labour‑nationalisation and pandemic experience reshape environmental pressure in emerging markets. Insights guide managers and policymakers striving to improve supply‑chain transparency, efficiency and consumer trust while advancing several UN SDGs.

This study examines how interconnected artificial intelligence (AI) tools can turn early human resource (HR) allocations into evidence for later career decisions. A counterfactual-twin Monte Carlo simulation compares isolated and linked systems, alongside periodic record resets and opportunity equalization. Paired employees share fixed capability and random shocks but receive slightly different initial scores. Linking decisions amplifies this initial difference through opportunity allocation, observed performance and reusable records. Periodic resets reduce final-career sensitivity, while opportunity equalization affects access more strongly than terminal outcomes. Near parity in average group outcomes can coexist with positive sensitivity among matched individuals. Additional tests preserve the direction of the architecture and intervention contrasts across the examined coefficient settings, although their magnitudes vary. The findings motivate evaluating decision lineage, opportunity access and record reuse across HR systems. They identify mechanisms within a synthetic, uncalibrated model and do not estimate effects in real organizations.

This study examines associations between national labor market gender participation parity, the international trade logistics environment, and national transport carbon intensity. The measures describe distinct domains; they do not directly measure women’s inclusion in logistics occupations or freight-only emissions. An unbalanced panel covers seven historical Logistics Performance Index (LPI) editions. Country and wave fixed effects models use the preceding year female-to-male labor force participation ratio and country-clustered standard errors. The LPI model includes 986 observations from 159 economies; the fully adjusted carbon model includes 822 observations from 152 economies. The within-country evidence is inconclusive for overall LPI (β = 0.0034, 95% CI [−0.0024, 0.0092], p = 0.243) and all six components (Benjamini–Hochberg q ≥ 0.369). Pooled and between-country associations are more precisely positive, but limited within-country variation and possible time-varying confounding constrain interpretation. The carbon estimate is imprecise in the fully adjusted model (β = −0.0022, p = 0.616). Common sample comparisons show that both sample composition and added controls contribute to its attenuation. The interaction with LPI is also inconclusive (p = 0.481). These findings neither establish causal policy effects nor demonstrate the absence of longer-term or sector-specific relationships. They support more precise measurement and separate evaluation of social participation, logistics capability, and transport emissions.