Organizational Ambidexterity and Performance: Assessment Approaches and Empirical Evidence

  • Yury DranevEmail author
  • Alisa Izosimova
  • Dirk Meissner


Three approaches are developed for assessment of different types of organizational ambidexterity proposed in the relevant literature. The new model for measurement of organizational ambidexterity using data envelopment analysis (DEA) is introduced. The DEA score based on innovation activity inputs and two different performance outputs acts as a proxy for organizational ambidexterity. Sustainability goals and product ambidexterity are also analyzed as the key characteristics of ambidextrous behavior. The introduced three approaches are tested for their aptness to complement each other as well as to support a strategic decision-making. Empirical examples from energy and pharma sectors associate organizational ambidexterity with firms’ performance. We measured the organizational ambidexterity of energy and pharma companies by (1) pursuing long-term versus short-term organizational performance measured as a DEA two-output efficiency score; (2) the share of disruptive products in a company’s activities assessed through the proportion of R&D expenditure or sales; and (3) sustainability versus financial performance of the company, where the Green ranking and participation in innovative financing programs were used as proxies for sustainable development. Positive relation between performance and organizational ambidexterity for energy sector are discovered. At the same time, orientation towards sustainability disrupts performance of pharmaceutical companies. Results of the OA impact on performance are highly industry-sensitive and depend on the methods used in empirical assessment. Our findings suggest that the scarcity of data sources make all three approaches complementary and mainly functional for strategic decision-making.


Organizational ambidexterity Organizational performance Innovation strategy Sustainable development Data envelopment analysis 



The research leading to results presented in this paper has received funding from the Ministry of Science and Higher Education of the Russian Federation in 2017-2018 (project ID: RFMEFI60117X0015)

Supplementary material

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Statistical Studies and Economics of KnowledgeNational Research University Higher School of EconomicsMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia

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