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This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

The complexity of property valuation, coupled with valuers’ cognitive limitations, makes some degree of error inevitable in valuations. However, given the crucial role that valuations play in the efficient functioning of the economy, there is a need for continuous improvement in the reliability of reported values by enhancing the quality of the decision-making process. The purpose of this paper is to review previous research on valuation decision-making, with particular interest in examining the approaches to improving the quality of valuation decisions and identifying potential areas for further research.

The paper adopts a narrative approach to review 42 research articles that were obtained from Scopus and Web of Science databases and through author citation searches.

Our findings show that existing literature is skewed towards examining the use of technology in the form of decision support systems (DSS), with limited research attention on non-technological (i.e. behavioural) approaches to improving the quality of valuation decisions. We summarise the non-technological approaches and note that much of the discussions on these approaches often appear as recommendations arising from other studies rather than original investigations in their own rights.

We conclude that studies investigating the effectiveness of the non-technological approaches to improving valuation decision-making are lacking, providing various avenues for further research.

This paper presents the first attempt to provide a comprehensive overview of non-technological approaches to improving the quality of valuation decisions.