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The constructs of re-deployment and co-deployment have been central to discussions of scope economies in diversified firms. We argue however that these constructs are also significant in the context of single-business firms. Increasingly, changes in technology and demand preferences have provided opportunities for entrants to attack incumbents with a different business model, one that may neutralize the incumbent’s advantage for at least some set of customers (e.g., Netflix vs. Blockbuster). In such a context incumbents often respond by modifying their business model. We note that several of the business model-altering responses of the incumbent can be characterized in terms of co-deployment and re-deployment benefits and costs, where co-deployment benefits/cost apply to the scope economies/diseconomies in running multiple business models within the same firm and re-deployment benefits/costs apply to the implications of moving assets from one business model to another. We then examine the set of strategic choices faced by the incumbent in competing with an entrant with a different business model. We identify five set of factors that are likely to influence the decision to choose between these alternatives – uncertainty spawned by the new business model, market segment targeted by the new model, the within-business-across-business-model co-deployment and re-deployment benefits and costs, the across-business co-deployment and re-deployment benefits and costs, and the incumbent’s prior performance history. Although some of these choices have seen some work, most remain relatively underexplored in the strategy literature. We highlight the potential for research in this area with a set of propositions that identify key conditions that should hold true for a particular strategic choice to be picked by an incumbent.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

This essay seeks to extend the original gambit of this forum, of thinking possible modes of postcolonial sociology, unto a more relational terrain. It takes as its point of departure the vexed status of history in sociology and the hermeneutic suspicion of comparison in postcolonial theory. Any potential rapprochement between postcolonial theory and sociology must engage with the deeply incongruent status of history and comparison across these fields. I attempt to bridge this divide historically by revisiting an anti-imperial internationalist sociology forged in interwar colonial India. I seek thereby to show what Pierre Bourdieu called a “particular case of the possible” and to participate in ongoing efforts to “provincialize” sociology.