  There is a growing interest in utilizing digital services, such as software
apps and cloud-based software services. The utilization of digital services is
increasing more rapidly than any other segment of world trade. The availability
of open data unlocks the possibility of generating market possibilities in the
public and private sectors. Digital service utilization can be improved by
adopting cloud-based software services and open data innovation for service
development. However, open data has no value unless utilized, and little is
known about developing digital services using open data. Evaluation of digital
service development processes to service deployment is indispensable. Despite
this, existing evaluation models are not specifically designed to measure open
data innovation contests. Additionally, existing cloud-based digital service
implications are not used directly to adopt the technology, and empirical
research needs to be included. The research question addressed in this thesis
is: "How can contest-driven innovation of open data digital services be
evaluated and the adoption of digital services be supported to improve the
utilization of digital services?" The research approaches used are design
science research, descriptive statistics, and case study. This thesis proposes
Digital Innovation Contest Measurement Model (DICM-model) and Designing and
Refining DICM (DRD-method) for designing and refining DICM-model to provide
more agility. Additionally, a framework of barriers constraining developers of
open data services from developing viable services is also presented. This
framework enables requirement and cloud engineers to prioritize factors
responsible for effective adoption. Future research possibilities are
automation of idea generation, ex-post evaluation of the proposed artifacts,
and expanding cloud-based digital service adoption from suppliers'
perspectives.

Distributed, Parallel, and Cluster Computing