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This study aims to examine why tax reforms fail and explores how tax collection can be improved within a developing country context.

Data comprise 28 semi-structured interviews with taxpayers, tax experts and tax authority personnel based in Pakistan. The results are analysed using a combined lens of taxpayer trust and tax agencies’ capabilities.

Tax reforms failed to build taxpayers’ trust and tax agencies’ capabilities. Building trust is challenging and demands extensive ongoing engagement with taxpayers while yielding gradual permanent results. This requires enhancing confidence in government; educating taxpayers; removing complexities; introducing transparency and accountability in tax agencies’ operations and the tax system; promoting procedural and distributive justice; and reversing perceptions of corruption through reconciliation and stakeholder inclusivity. Developing tax agencies’ capabilities requires upgrading outdated technologies, systems and processes; implementing governance and organisational reforms; introducing an oversight board; and recruiting and training skilled professionals.

The findings can assist policymakers and tax collection authorities in understanding why tax reforms fail and identifying potential solutions.

This study contributes to the emerging literature by exploring tax administration failures in developing countries. It contributes to the literature by engaging stakeholders to understand why reforms fail and potential solutions to stimulate tax revenues.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.