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In recent years, distributed ledger technology and the emerging cryptoeconomic systems (CESs) utilizing this technology have brought about new opportunities to solve great challenges that humanity faces. By improving system properties such as trust or transparency, this technology advances various application domains ranging from supply chains and energy markets to information-sharing scenarios such as in e-health or applications based on the Internet-of-Things. However, due to the large design space of CESs and their emergent properties, it is challenging to make predictions about the impact of design decisions on future system behavior at the time of design. This complicates the development of functional CESs. Especially since few insights into the effects of such decisions have been rigorously investigated in controlled experimental studies. For example, limited knowledge has been collected on the impact of cryptoeconomic incentives on emergent system properties such as (collective) user behavior. This thesis structures the design space of CESs with a conceptual architecture and taxonomy that indicates possible CES configurations. Moreover, the configuration of real systems in this structure is illustrated by a classification that represents the design decisions made in viable systems. In addition, by introducing and applying a novel machine learning-based methodology, a design guide is created that reduces design complexity by clustering system configurations into key design decisions, from which practitioners or researchers can choose to instantiate a system. Furthermore, this work introduces a value-sensitive design methodology for the construction of CESs that ensures both the implementation of an ethical system through the explicit consideration of stakeholder values and the construction of a viable system through the iterative assessment and validation of the constructed system throughout the instantiation process. This methodology is applied to identify design principles of two value-sensitive CESs and to implement them in software artifacts. These are then utilized to investigate the impact of cryptoeconomic incentives in the form of blockchian-based tokens on human behavior. The first software artifact facilitates the creation, collection, and governance of blockchain-based tokens. Among others, the artifact's construction process contributes to design knowledge about the governance and design of tokens in CESs. The second software artifact leverages the first to incentivize the provision of information in an information-sharing community with blockchain-based tokens. This artifact is applied in a 2x2 factorial design experiment with 132 participants. The identified effects of tokens on human behavior confirm findings from the literature and advances knowledge collected thus far by identifying an interaction effect on emerging system properties when tokens are applied simultaneously. These findings inform the construction and theoretical modeling of CESs that utilize tokens. This dissertation thus contributes to three fundamental aspects of cryptoeconomic research: a) to the design of CESs, a taxonomy, conceptual architecture, and a design guide that structure, illustrate, and reduce the complexity of the CES configuration space; b) to the construction of CESs a value-sensitive design science research methodology that facilitates the instantiation of ethical and viable systems; and c) to the impact of CESs, the findings of a controlled experimental studies illustrating the effect of (multiple) token incentives on human behavior. In a nutshell, this thesis informs and supports both practitioners and researchers in the design, construction, and impact assessment of cryptoeconomic systems. |
