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Recently the route connecting the trans-Korean railway and the trans-Siberian railway has become of particular interest for many academics and policy-makers in East Asian countries. The extensive review of previous studies, however, reveals that literature on the subject is lacking solid analytical framework. Most studies are one-sided, focusing on the political aspects of the issue or paying little attention to the economic aspects of the problem.

This study intends to develop an analytical framework through which the most efficient route among four major alternative routes connecting the trans-Korean and trans-Siberian railways can be identified. It attempts to assign priorities to the four alternative routes according to their level of economic efficiency.

This study utilizes a simple cost-benefit analysis in evaluating the four routes. Cost side, transportation time, effectiveness of customs procedures, and gauge difference are selected as the main economic factors. The volume of cargo, industrial production in adjacent regions, access to natural resources, and market size and foreign investment climate are used to evaluate the benefits of the routes.

The study concludes that Route 3, which connects ‘Busan - Seoul (South Korea) –Pyongyang -Sinuiju (North Korea) –Shenyang –Beijing - Erenhot (China) –Ulaanbaatar (Mongolia) –Ulan-Ude - Moscow (Russia)’ is the most efficient route.

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.