提高数字服务的利用率-评估竞赛驱动的开放数据开发和云服务的采用
Improving the Utilization of Digital Services - Evaluating Contest - Driven Open Data Development and the Adoption of Cloud Services
摘要：
人们对利用数字服务的兴趣越来越大，例如软件应用程序和基于云的软件服务。数字服务的使用增长速度超过世界贸易的任何其他部分。开放数据的可用性开启了在公共和私营部门创造市场机会的可能性。通过采用基于云的软件服务和开放式数据创新进行服务开发，可以提高数字服务的利用率。然而，开放数据没有价值，除非使用，并很少有人知道如何使用开放数据开发数字服务。对数字服务开发流程到服务部署的评估是必不可少的。尽管如此，现有的评估模型并不是专门为衡量开放数据创新竞赛而设计的。此外，现有的基于云的数字服务的影响不直接用于采用该技术，需要包括实证研究。本论文的研究问题是：“如何评估开放数据数字服务的创新，并支持数字服务的采用，以提高数字服务的利用率？“研究方法包括设计科学研究、描述性统计及个案研究。本论文提出数位创新竞赛衡量模型（DICM-model）与数位创新竞赛设计与改善方法（DRD-method），以设计与改善数位创新竞赛模型，使其更具弹性。此外，还提出了一个框架的障碍，限制开发人员的开放数据服务开发可行的服务。该框架使需求和云工程师能够优先考虑有效采用的因素。未来的研究可能是想法生成的自动化，对提议的工件进行事后评估，以及从供应商的角度扩大基于云的数字服务的采用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
