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The objective of the research, and paper, is to determine first whether or not the accounting abstraction appears to dominate the manager’s perceptions of the physical reality of the firm’s utilization of its physical assets, and second, whether changes in the accounting abstraction (e.g. the addition of Capacity cost management reports and measurements) lead to changes in how managers perceive, and use, their physical assets. Using a cognitive decision-making structure developed by Wagenaar et al. (1995), this study explores the interplay between the structure and nature of capacity reporting (the surface structure of the decision) and the subsequent analysis and choice of managers within the firm (the deep structure of the decision). A five-site field research methodology was used to gather data from companies across a multitude of industry contexts and situations. Results suggest that the nature of capacity measurement and reporting does shape manager’s perceptions of current and potential future performance (the cognitive surface structure), with major implications for the nature and type of decisions and trade-offs made (the deep structure). Specifically, managers appear to make decisions that are illogical when considered in light of the physical reality of their operations based on the representations of this reality (e.g. the capacity measures and reports). Analysis and interpretation of these results suggest that what accounting makes visible appears to drive decision-making and performance in organization.

This study applies theoretical perspectives from urban, environmental, and organization studies to examine if “smart growth” represents an ecological restructuring of the political economy of conventional urban development, long theorized as a “growth machine” (Molotch, H. (1976) The city as growth machine: Toward a political economy of place. American Journal of Sociology, 82, 309–332; Logan & Molotch, 2007); the purpose is to determine if there is a “smart growth machine.”

Nine smart growth projects (SGPs) in four cities in California and Oregon were identified and semistructured interviews were held with the respective developers, architects, and civic officials involved in their implementation process. Comparative, descriptive, and grounded approaches were used to generate themes from interviews and other data sources.

The findings suggest that an ecological modernization of urban political economy occurs through the coordination of entrepreneurial action, technical expertise, and “smart” regulation. Individual and institutional entrepreneurs shift the organizational field of urban development. Technical expertise is needed to make projects sustainable and financially feasible. Finally, a “smart” regulatory framework that balances regulations and incentives is needed to forge cooperative relationships between local governments and developers. This constellation of actors and institutions represents a smart growth machine.

The author questions whether urban growth can become “smart” using an original study of nine SGPs in four cities across California and Oregon.

This chapter is focused on the specification and integration of intercultural variables for human machine systems and the description of content analysis for these variables. Starting with basics of culture-oriented design, these are followed by an approach to machine localization issues and a cost model, then basics of the intercultural design and human machine system engineering process, a definition and specification of intercultural variables, a systematic treatment for their integration into the process, and a description of how to use these variables in the process. Finally, an example of an intercultural variables approach to “information coding” in a human-machine system is presented for China and Germany.

The role of management control has not received sufficient attention in the literature on value creation so far. Therefore, this paper aims to investigate the role of control in value creation in industrial networks. More specifically, the aim is to examine the management and control of interfaces between key resources within and between firms, in the networks surrounding firms, when they attempt to create value. All the firms that take part in a value-creation process have both formal and informal control systems: these firms have budgets, specific routines, reward systems, and sanctioned “ways to behave.” The paper relates the Industrial Marketing and Purchasing (IMP) group's research on interaction, relationships, and networks with control literature, and presents a framework for controlling resource interfaces in a network setting. Two in-depth cases illustrate the role of control in value creation. The first case covers the development of a low-weight newspaper grade that Holmen and its paper mill Hallsta initiated. The second case examines the attempt to develop and commercialize a new, energy efficient pulping technology.

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.