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This study aims to provide a meaningful comparison of airports’ performance and better understand the differences observed in the analysed airport performance by presenting a model to analyse the relationship between operational and financial performance and airport characteristics.

This study uses a quantitative analysis approach. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy weight were utilised to analyse 17 airports in three Airports Council International regions: Asia, Europe and North America. Through operational and financial factors, these sample airports identified the most efficiently operated airports from 2016 to 2019.

Overall, Asian airports were superior in operational and financial efficiency. Unlike operating performance, the sample airport’s financial and total performance results show a similar trend. There were no noticeable changes in operational factors. Therefore, differences in financial variables for each airport may affect the total performance.

This study provides insightful implications for airport policymakers to establish a standardised information disclosure foundation for consistent analysis and encourage airports to provide this information.

The adoption of Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) to debt ratio and EBITDA per passenger, which had previously been underutilised in the previous study as financial factors, demonstrated differences between airports for airport stakeholders. In addition, the study presented a model that facilitates producing more intuitive results using TOPSIS, which was relatively underutilised compared to other methodologies such as date envelopment analysis.

The purpose of this study is to identify and evaluate supply chain strategies (SCSs) that drive financial performance to guide practitioners, especially in liquefied natural gas (LNG) networks, to review and adopt SCSs that drive competitiveness and value creation for investors.

Analytical hierarchy process (AHP) was deployed to prioritise SCSs according to their relative impact on financial performance in LNG networks. Interviews with experts were analysed using template analysis to establish latent drivers of financial performance specific to LNG networks.

Results support the significant role of SCSs in improving financial performance. Although findings prioritised collaborative strategy as the most important driver of financial performance in LNG networks, to fully optimise financial outcomes, all the SCSs should be implemented across LNG networks as no single strategy in isolation is a standalone driver of financial performance.

The AHP model provides a novel ranking for SCSs and measures to guide decision-makers. LNG practitioners may exploit the results to make informed decisions.

The study extends previous literature by proposing a framework and a new LNG empirical model that facilitates understanding of how SCSs contribute positively to financial performance and support practitioners in making strategic supply chain decisions.