Advisor

Antonie Jetter

Date of Award

6-8-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management

Department

Engineering and Technology Management

Physical Description

1 online resource (ix, 320 pages)

Abstract

Over the course of three to four decades, most well-established companies lose their dominating position in the market or fail entirely. Their failure occurs even though they have resources for sensing shifting market trends, skills and assets to develop next-generation technologies, and the financial means to fill skill gaps and afford risky investments. Nevertheless, incumbents obviously find it very difficult to invest in innovation that takes attention and resources away from a highly successful core business. A solution to this "innovator's dilemma" is the concept of "organizational ambidexterity," which has garnered considerable attention among researchers in organization and innovation. According to empirical findings and emergent theory, companies can improve their financial performance and ensure their long-term survival by balancing their innovation activities, so that they are equally focused on exploratory (discontinuous) and exploitative (incremental, continuous) innovations. But how can such a balance be achieved? The literature on the organizational theory and related fields (product innovation, knowledge management, creativity, etc.) identifies more than 300 contributing factors to innovation and ambidexterity: many are interdependent so that their impacts compound or cancel each other. Moreover, for many factors, there is limited empirical data and the size of impacts is unknown. To understand which managerial actions lead to ambidexterity, this dissertation develops a novel approach to the study and analysis of complex casual systems with high uncertainty: exploratory fuzzy cognitive mapping.

Fuzzy Cognitive Mapping (FCM) is a semi-quantitative system modeling and simulation technique. It is used to represent qualitative information about complex systems as networks of casual relationships that can be studied computationally. Exploratory modeling and analysis (EMA) is a new approach to modeling and simulation of complex systems when there is high uncertainty about the structural properties of the system. This work is the first to combine both approaches.

The work makes several contributions: First, it shows that only a small fraction of management interventions will actually lead to ambidexterity while most will, at best, improve one type of innovation at the expense of the other. Second, it provides a simulation tool to management researchers and practitioners that allows them to test ideas for improving ambidexterity against a model that reflects our current collective knowledge about innovation. And third, it develops a range of techniques (and software code) for exploratory FCM modeling, such as methods for transforming qualitative data to FCM, for exploratory simulation of large and complex FCM models, and for data visualization. They can be utilized to study other similarly complex and uncertain systems.

Persistent Identifier

https://archives.pdx.edu/ds/psu/25570

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