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In this chapter, I focus on how MNCs are justifying the responsibility of their vision and technologies for the sustainability of agri-food systems. I pay close attention to how responsibility is distributed between actors in the institutional arrangements and which instruments are used to govern the responsibility of actors. This case is of interest to RES-AGorA because it explores how large, multi-national, private businesses are addressing issues of responsible research and innovation. These characteristics strategically position MNCs as critical organisations within the existing rri landscape as they are both highly invested in conducting research and innovation in the agricultural sector and are also carrying this research through an innovation process to introduce new products and technologies to markets from within a single organisational environment. We explore three different MNCs – two of the leading food manufacturers (Nestlé and Unilever) and one of the leading agricultural input manufacturers (Syngenta). These three organisations are among the leaders in their sectors and have each made 'responsibility' a fundamental aspect of their innovation agenda. Due to time and access constraints, this case study does not go into detail of each individual case. Rather, it focuses on a cross-comparison that identifies common governance instruments across the three companies. These instruments are currently serving as de facto rri governance mechanisms within private-sector research on agri-food systems. This comparative approach helps us to better understand the conditions under which 'shared understandings of responsibilities' are developed within and between organisations.

An essential part of any manufacturing company's set of decisions is its distribution policy. This is very often referred to in the marketing literature as the "place" or "distribution mix" decision. This set of decisions might in turn, be divided into two subsets—channel decisions and physical distribution (logistics) decisions. Channel decisions deal with the choice of intermediaries between the producer of a good and the final user. Physical distribution decisions deal with the arrangements for "locating, stocking and shipping a company's goods to meet the service requirements of the marketplace".

PurposeThe aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential of AI to address emerging challenges, such as talent shortages and applicant surges in specific regions, has been anecdotally highlighted, there is limited empirical evidence regarding its effective deployment and adoption in TA. As a result, this paper endeavors to develop a theoretical model that delineates the motives, barriers, procedural steps and critical factors that can aid in the effective adoption of AI in TA within MNCs.Design/methodology/approachGiven the scant empirical literature on our research objective, we utilized a qualitative methodology, encompassing a multiple-case study (consisting of 19 cases across seven industries) and a grounded theory approach.FindingsOur proposed framework, termed the Framework on Effective Adoption of AI in TA, contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA.Research limitations/ implicationsThis paper contributes to literature on effective adoption of AI in TA and adoption theory.Practical implicationsAdditionally, it provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.Originality/valueTo the best of the authors' knowledge, this study is unparalleled, being both grounded in theory and based on an expansive dataset that spans firms from various regions and industries. The research delves deeply into corporations' underlying motives and processes concerning the effective adoption of AI in TA.