This thesis presents the findings from two case study examinations of strategic performance measurement systems within two turbulent environmental contexts: the U.S. security software industry and the U.S. health care industry. Despite a three-­‐decade emphasis on performance measurement research, little empirical work has been carried out inside turbulent settings—contexts characterized by rapid change, high levels of instability and complex configurations among environmental variables. This research targets that gap. Through exploratory case studies from seven security software firms paired with a single in-­‐ depth case investigation within a transforming health care system, this study addresses the question: “how do firms in turbulent environments measure strategic performance?” The research found that in turbulent environments, an effective strategic performance measurement system contains six interrelated elements: management aims, performance objectives, uncertainty areas, decision data, management attention and performance measures. Top managers focus on their aims and performance objectives to meet requirements via a closed-­‐loop approach while monitoring uncertainty areas and gathering decision data in an open-­‐loop way. This union of feedback and feedforward control enables dynamic interaction among the various elements of the system all of which are informed by performance measure data. Effective use is moderated by management’s focus of attention. The research has implications for information processing and management control literature; it extends existing theory to incorporate the use of semi-­‐structures within the framework of the strategic performance measurement system as a means of overcoming the challenges of uncertainty. Further, the research contradicts both extant literature and practice convention that claims strategic performance measurement frameworks need to be balanced to be effective. Practitioners are provided with a strategic performance measurement framework for use in turbulent environments. The framework would benefit from further examination in a variety of different, equally turbulent, contexts.

This thesis presents the findings from two case study examinations of strategic performance measurement systems within two turbulent environmental contexts: the U.S. security software industry and the U.S. health care industry. Despite a three-­‐decade emphasis on performance measurement research, little empirical work has been carried out inside turbulent settings—contexts characterized by rapid change, high levels of instability and complex configurations among environmental variables. This research targets that gap. Through exploratory case studies from seven security software firms paired with a single in-­‐ depth case investigation within a transforming health care system, this study addresses the question: “how do firms in turbulent environments measure strategic performance?” The research found that in turbulent environments, an effective strategic performance measurement system contains six interrelated elements: management aims, performance objectives, uncertainty areas, decision data, management attention and performance measures. Top managers focus on their aims and performance objectives to meet requirements via a closed-­‐loop approach while monitoring uncertainty areas and gathering decision data in an open-­‐loop way. This union of feedback and feedforward control enables dynamic interaction among the various elements of the system all of which are informed by performance measure data. Effective use is moderated by management’s focus of attention. The research has implications for information processing and management control literature; it extends existing theory to incorporate the use of semi-­‐structures within the framework of the strategic performance measurement system as a means of overcoming the challenges of uncertainty. Further, the research contradicts both extant literature and practice convention that claims strategic performance measurement frameworks need to be balanced to be effective. Practitioners are provided with a strategic performance measurement framework for use in turbulent environments. The framework would benefit from further examination in a variety of different, equally turbulent, contexts.

A shift in focus from growing revenues towards managing working capital could be observed in many companies in the recession that followed the financial crisis of 2008. This thesis therefore investigates the relation between working capital management (WCM) and revenue growth by examining 36 Swedish companies within the IT & Telecom, Wholesale, and Manufacturing industries. The results show that there currently is a general gap between the perceived and actual performance regarding WCM and the effects on revenue growth. The studied companies report a belief that no trade-off between WCM and revenue growth exists. However, the actual performance in the studied industries indicates that increases in revenues often are not justifiable in proportion to the increases in net working capital (NWC). The study also shows that responsibility for WCM and implementation of WCM decisions are to a high extent assigned to a centralized organizational level. Recommendations derived from this study are that while companies need a centralized responsibility for WCM decisions, the responsibility also needs to be decentralized for successful implementation. Furthermore, the NWC development in relation to revenue growth needs to be continually monitored.

Within dynamic 21st century knowledge economies, future-building knowledge, that bears capacities to transcend existing boundaries and create something new, is of particular importance. Within the first decade of the new century, new concepts such as "learning from the future" or "self-transcending knowledge" developed within knowledge management. So far, they lacked a theoretical grounding in relevant learning theory as well as a sound acknowledgement and consideration of such knowledge structures' emergence and social embeddedness. Thus, key principles and leverage factors for designing respective knowledge processes were difficult to derive. This dissertation investigates theoretical ground that can provide a basis to explain the creation of future-building knowledge in collective structures. It is guided by the following research question: "How can the emergence of self-transcending knowledge in collective organizational settings be rooted in theories of knowledge, learning and cognition?" Starting from the model of knowledge-based management, the model is expanded by exploring cognitive, creative and social systemic aspects of knowledge creation on a transdisciplinary basis. Research draws on constructivist learning theory, complexity-based approaches in knowledge management and organizational learning, recent accounts in cognitive science (enaction/embodiment) and a creative logic of emergence to derive an integrated model for collective self-transcending knowledge. The model contributes to the integration of knowledge management, organizational learning and cognitive science, expanding knowledge-based management towards attention-based management. The model's three dimensions and three domains form an integrated theoretical basis to derive key principles and leverage factors for steering future-building knowledge processes. Simultaneously, they reveal leverage factors' limited - i.e. enabling, not determining - impact on processes of "organizing future". (author's abstract)