PurposeCountry comparative studies especially in Africa on public procurement reforms toward financial control and accountability of public expenditure are limited. Meanwhile, these kinds of studies have potential for providing useful insights on how value for money through public procurement is being ensured across Africa. This paper attempts to provide this. The purpose of this paper is to highlight several policy recommendations for public management aimed at improving public procurement and public financial management (PFM) systems in Africa. Design/methodology/approachThe paper adopts a qualitative case study using secondary data drawn from Global Integrity Index (GII) of the Transparency International and the World Bank’s Country Policy and Institutional Assessments databases to investigate variables that influence public procurement practices in three purposively selected African countries. The comparative approach for presenting some of the experiences of countries in public procurement methods is used in this paper. FindingsThe findings suggest three main variables, namely, government structure and economic variables, complicated by socio-cultural values interact to influence public procurement and PFM systems in the case study countries. Research limitations/implicationsData for the GII indicators used were only available from 2013, which restricted the discussion of those indicators to a short span (2013–2015). Social implicationsThe socio-cultural milieu within which public procurement takes place has implications for how governance structures function to deliver value-for-money public procurement. Originality/valueThis study adds value by comparing three countries within Africa to reveal common variables which influence public procurement and PFM systems.
PurposeAlthough the significance of digital innovation readiness for organizations has been widely acknowledged, the factors that contribute to it remain underexposed and fragmented. Therefore, this scoping review aims to comprehensively map and synthesize the existing literature on factors contributing to digital innovation readiness. Design/methodology/approachThis review uses Arksey and O'Malley's (2005) methodological framework to identify factors that contribute to digital innovation readiness. Five electronic databases were systematically searched, and to validate findings from that literature search and add insights that do not appear in the literature, six semi-structured interviews were conducted among senior digital innovation experts. Thematic analysis with a deductive and inductive approach was used to analyze interview transcripts. FindingsFive factors and 13 sub-factors were identified from these studies. The interviews validated the 13 sub-factors and suggested one more – resource allocation. Thus, five main factors and 14 sub-factors were identified to contribute to digital innovation readiness. Originality/valueFindings from this study offer clarity on factors that contribute to digital innovation readiness.
Purpose This study aims to develop a human-centric Generative AI (GenAI)-enabled decision-support framework for sustainable agroecology supply chains under the Industry 5.0 paradigm. The framework integrates fuzzy Analytic Hierarchy Process (FAHP) and fuzzy C-Means (FCM) clustering to bridge digital innovation with ecological and social sustainability, ensuring participatory and adaptive decision-making in organic and community-based farming systems. Design/methodology/approach The research employs a hybrid FAHP-FCM model supported by ChatGPT as an interpretive reasoning layer. ChatGPT translates qualitative expert judgments and field narratives into structured fuzzy inputs, enhancing contextual adaptability and interpretability. The model is validated through a case study of a long-established agroecological community in India, where ten organic farming alternatives are evaluated across Functional-, Biological-, and Practice-Based Knowledge domains. Findings The integrated framework identified a set of bridging practices that effectively connect scientific innovation with farmers' experiential ecological wisdom. Through interpretive reasoning, ChatGPT uncovered overlaps among diverse knowledge domains, transforming complex numerical outputs into transparent, context-sensitive insights accessible to both farmers and decision-makers. These outcomes highlight how human-AI collaboration can foster adaptive, transparent, and socially inclusive agroecological transformation. Practical implications The framework enables decision-makers to prioritize sustainable agroecological practices by integrating digital, biological, and experiential knowledge. It enhances transparency, adaptability, and participatory governance within sustainable supply chains. Social implications By combining human reasoning with AI interpretability, the framework promotes inclusivity, ecological resilience, and community empowerment. It supports farmers in making informed, context-aware decisions aligned with Industry 5.0 values. Originality/value This research establishes a conceptualization of a GenAI-integrated fuzzy decision-support system that unites scientific, digital, and indigenous knowledge bases. It advances the Industry 5.0 vision of human-centric, participatory, and knowledge-driven management for sustainable agroecology supply chains.
PurposeIt is important to understand the risks, challenges, and adverse effects on Sustainable development goals (SDGs) associated with the accelerated growth of AI. New green initiatives are also evolving to mitigate these challenges. Therefore, it is imperative to analyse the relationship dynamics between the risks involved, their mitigating initiatives, and the SDGs.Design/methodology/approachThis study employed an extensive literature review, discussions with experts, and an examination of causality loops in AI challenges to achieve the SDGs. The stepwise weight assessment ratio analysis (SWARA) was deployed to identify the key challenges, and the analytic hierarchy process (AHP)facilitated an understanding of the relationships between AI challenges and mitigating initiatives.FindingsThe final synthesised outcome from AHP has prioritized the seven challenges from accelerated AI growth. The top three challenges - human inequality and biases, the increased digital divide, and unsustainable energy consumption constitute 79% of the impact of the challenges to the growing AI applications.Research limitations/implicationsThe prioritised factors identified will serve stakeholders in developing relevant policies, investing in the right areas, and facilitating where to focus in the sustainability journey. AHP results have prompted the need for an index-the SDG Achievement Index (SAI). The index is expected to find application in real business.Originality/valueThe application of the AHP methodology to analyse the hierarchy and priorities in AI challenges and green initiatives is the original contribution of this research study. The suggested measure, SAI, is a useful approach for quantifying and tracking the trends of the impact of Green AI initiatives on the SDGs. The current research study is topical, enriching the literature, and insightful to practitioners.
Purpose - This study aims to evaluate the relative efficiency of 12 major Indian ports by applying neutrosophic data envelopment analysis (NDEA), a novel technique that extends traditional DEA by incorporating the dimensions of indeterminacy, falsity and truth. The research addresses the need for uncertainty-informed efficiency assessment in the context of India's synchromodal logistics landscape, where ports serve as critical nodes for flexible modal transitions. Design/methodology/approach - The study employs both classical DEA and NDEA to assess port performance. NDEA is particularly suited for handling the layered uncertainties present in port operations such as fluctuating cargo volumes, imprecise manpower data and variable waiting times. The analysis is theoretically grounded in the dynamic capability theory (DCT) and the natural resource-based view (NRBV). Waiting time is interpreted through the lens of DCT as a reflection of operational agility, while manpower and port capacity are linked to NRBV as sustainable, valuable and inimitable resources. Findings - The comparative analysis reveals that traditional DEA may overestimate efficiency scores by overlooking data ambiguity. In contrast, NDEA offers a more realistic portrayal of efficiency under uncertain conditions. It highlights which ports are operationally resilient and which are more vulnerable to performance variability. These differentiated insights are valuable for logistics planning and infrastructure policy-making in a dynamic and uncertain environment. Originality/value - To the best of the authors' knowledge, this study is among the first to apply neutrosophic DEA in evaluating port performance, introducing a novel methodological contribution to the operations and supply chain management literature.
