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Projekt Druckansicht

Die institutionelle Komplexität der Energiewende, Eine Längsschnittstudie zu Framing und Aushandlungsprozessen der Energiewende (1990-2016)

Antragsteller Dr. Stephan Bohn
Fachliche Zuordnung Accounting und Finance
Förderung Förderung von 2016 bis 2020
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 315217379
 
Erstellungsjahr 2020

Zusammenfassung der Projektergebnisse

Recent research has shown the strength of the institutional complexity concept in better understanding organisational response in different institutional settings. However, the basis of the organisational response — the institutional complexity on the field level — has attracted relatively little attention, which is reflected for example in the fact that fields were often predefined by specific logics and singular conflicts. This is surprising as fields are not only a key theoretical concept in organisation studies, but also the critical context that enables specific organisational action, or as Friedland and Alford (1991) put it, “it is not possible to understand individual or organisational behaviour without locating it in a social context“. In this project, we demonstrate the benefits of a bottom-up approach to institutional logics, which explicitly addresses the dynamics, complexity and heterogeneity of fields in their interaction with organisations and in general forms of organising. To further strengthen this perspective, we also incorporated the work about framing and legitimacy. Drawing on two cases in relation to sustainability transitions (energiewende and electric mobility), we especially extend theory about the interaction of organisations and fields. From an organisational perspective, we analyse the responses to institutional complexity and identified a playing off strategy, which is used to reject institutional demands but at the same time avoid a loss of legitimacy. From a field-level perspective, we show that conflicts between institutional logics constantly challenge and may ultimately shatter fields. We additionally contribute to theory by distinguishing two processes of increasing complexity — incremental and escalating complexity — which has serious consequences for the organisational response and the field itself. Our findings not only raise further questions about the breakup and demise of fields but has also implications for the organisational interest in sustainability transitions. Furthermore, we show on the case of electric mobility how the electric vehicle has overcome the initial lack of legitimacy and illuminate largely neglected frame mechanism—especially confrontational frames like demarcation and devaluation. Thereby, this work offers implications for the innovation and strategy literature about the framing and legitimacy of novel technologies in markets with strong lock-in effects such as the fossil economy. Finally, this project should not least be a source of inspiration for further research that uses the promising character of big data techniques like topic modeling and in general mixed methods machine learning approaches to capture field-level change, compare innovations and in general to open the potential of new data to ask novel questions and look at unresolved phenomenon from different perspectives.

Projektbezogene Publikationen (Auswahl)

  • (2017). Refusing, connecting and playing off conflicting institutional demands. A longitudinal study on the organizational handling of the end of nuclear power, climate protection, and the energy turnaround in Germany. In C. Mazza, R. E. Meyer, G. Krücken, & P. Walgenbach (Eds.), New Themes in Institutional Analysis: Topics and Issues from European Research. (pp. 162-193). Cheltenham, UK; Northampton, MA, USA
    Bohn, S., & Walgenbach, P.
    (Siehe online unter https://doi.org/10.4337/9781784716875.00011)
  • (2018). Komplexität, Heterogenität und big data – (oder) Wie Machine Learning Algorithmen die explorative Analyse institutioneller Vielfalt erlauben. Munich
    Bohn, S.
    (Siehe online unter https://doi.org/10.14459/2018md1463435)
  • (2018). Topic Modeling and Management Research. Mapping Fields with Big Data. AOM Big Data and Managing in a Digital Economy Proceedings, Surrey
    Bohn, S., Perendija, A., & Jha, H. K.
  • (2019). Field-configuring projects? A machine learning approach about how projects shape the meaning of electronic mobility in Germany. Academy of Management Proceedings, Boston
    Bohn, S., & Braun, T.
    (Siehe online unter https://doi.org/10.5465/AMBPP.2019.17505abstract)
 
 

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