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This paper employs the positivism paradigm, quantitative approach and explanatory research design. It analyses primary data obtained from manufacturing firms via structured questionnaires and uses the partial least square-structural equation modelling technique to establish the effect of individual procurement process risk on procurement performance.

Procurement risks are inevitable in manufacturing procurement process; a situation that could undermine the performance of manufacturing firms if not properly managed. Yet, with procurement accounting of about 14–19% of developing countries gross domestic product, the effects of procurement process risk on performance remain scarce in manufacturing firms in developing countries. Therefore, the paper aims to investigate the effect of procurement process risk on procurement performance of manufacturing firms.

In this paper, five out of the six procurement process risks studied were found to be undermining procurement performance of manufacturing firms significantly. However, the risk threshold effect on the performance differs.

Although this research is geographically/sector bias, several insightful managerial implications can be drawn to manage procurement process risk in manufacturing settings irrespective of the area of operation. The results of this research imply that manufacturing firms' procurement process is risk prone and the effect of risk surrounding each procurement process on procurement performance differs. Hence, the need to identify and analyse the risks surrounding each procurement process before making managerial decision to spend firms limited resources in response to the individual risk to improve procurement performance in the manufacturing sector.

This paper is the first to provide existing and future procuring practitioners/firms with in-depth empirical evidence of the effect of the procurement process risks on procurement performance in manufacturing firms operating in developing economies.

The purpose of this paper is to examine the key risk components (probability and consequence) and their respective thresholds affecting agri-food supply chain operations in Ghana. In addition, it seeks to understand the relationship between the major risk sources and to fathom the risk/disruption impact on agri-food supply chain performance in Ghana.

Cross-sectional survey data were collected using a structured questionnaire. The risks threshold associated with agri-food supply chain were categorized using the risk matrix scale and classification described in the Project Management Body of Knowledge (Project Management Institute, 2013). Next, the Pearson correlation was used to understand the relationship between the various risks and agri-food chain performance. Lastly, to investigate how firms’ supply chain operations have been affected by risks/disruptions, an ordinary least square regression model was employed to quantify the impact of some major risk sources on agri-food chain performance in Ghana.

The results in this paper show variations in risks’ probability, impact and threshold in agri-food supply chain. While risk sources such as periodic change in interest/exchange rate policies and volatility in customer demand are high-rated risks, uncertain land policies/tenure and poor quality control are low rated risks in the operations of the chain. The performance of the agri-food chain significantly but negatively correlates with all the major risks studied. Whereas demand, supply, weather, logistics/infrastructure and financial risk sources significantly undermined the chain’s performance, risks emerging from biological/environmental, management/operational, policy/regulations and political-related issues insignificantly affect the performance of agri-food supply chain in Ghana.

This research is an area biased. However, some insightful managerial implications can be drawn from this paper to manage agri-food chain operations in a similar unstable environment. The result implies that risks are inevitable in agri-food chain but they differ in terms of menace to the chain’s operation. Therefore, to manage agri-food supply chain risks effectively, managers should periodically identify, quantify and categorize risk sources before making risk response decisions. In addition, the results show that risks account for about half of the overall agri-food chain performance in Ghana. This infers that managers/practitioners could improve the performance of the agri-food chain if limited resources are allocated to plan and effectively respond to major risks sources (such as demand, supply, finance, weather and logistical/ infrastructural services-related risks) undermining the performance of the chain.

This research contributes to the agri-food chain risk literature and provides managers/practitioners with empirical evidence of risk thresholds and their corresponding major impact on agri-food chain’s performance. Since risks explained about half of agri-food chain performance in Ghana, this research would prompt decision makers to improve on their risk assessment and responds (e.g. by employing efficient demand, supply and weather forecasting systems, logistic/infrastructure services, hedge to finance, etc.) to improve the chain’s performance.