Improving the Utilization of Digital Services - Evaluating Contest - Driven Open Data Development and the Adoption of Cloud Services

TL;DR

This paper introduces a new measurement model and framework for evaluating and supporting the development and adoption of open data-driven digital services, especially in contest-driven innovation contexts.

Contribution

It proposes the DICM-model and DRD-method for evaluating open data digital service contests and presents a barrier framework to enhance adoption and development.

Findings

The DICM-model effectively measures open data digital service innovation contests.

The DRD-method improves the agility of the evaluation process.

A barrier framework helps prioritize factors for better adoption of cloud-based services.

Abstract

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.