scholarly journals The DevOps Reference Architecture Evaluation : A Design Science Research Case Study

Author(s):  
Georges Bou Ghantous ◽  
Asif Gill
2015 ◽  
pp. 1116-1133 ◽  
Author(s):  
Aline Dresch ◽  
Daniel Pacheco Lacerda ◽  
Paulo Augusto Cauchick Miguel

Author(s):  
Philip Huysmans ◽  
Jan Verelst

In this paper, the authors present the results of a design science research project to develop a method for the evaluation of enterprise architecture projects. The methodology is based on the SAAM methodology, and applies concepts from the Normalized Systems theory to provide a more systematic way of performing architectural evaluations. They first discuss the problem statement, the objectives of our solution and the design of the method. The authors then demonstrate how the method has been applied in a real-life organization. Finally, they evaluate the proposed method using the criteria formulated in our problem statement.


Author(s):  
Nadhmi Gazem ◽  
Azizah Abdul Rahman ◽  
Faisal Saeed ◽  
Noorminshah A. Iahad

This article contends that design science research (DSR) has emerged as an important approach in information systems (IS) research. The design science research roadmap (DSRR) model describes the process of using the DSR in IS in great detail. Unfortunately, the existing literature does not address the task of demonstrating the use of the DSRR in detail by conducting a real case study. This article aims to examine the implementation of the DSRR with real IS research activities. The construction of a systematic innovation framework to solve problems for small and medium enterprises (SMEs) is used as a case study for demonstration purposes. This article shows that the DSRR provides very useful guidance, since it covers almost all the necessary steps to conduct DSR in the information systems field. The illustrations provided with each step of the DSRR in this article will help other researchers, especially novice researchers, to gain a comprehensive understanding of the use of the DSRR model.


2021 ◽  
Vol 16 (4) ◽  
pp. 937-958
Author(s):  
Yiwei Gong ◽  
Sélinde van Engelenburg ◽  
Marijn Janssen

Companies increasingly tender knowledge-intensive tasks using crowdsourcing platforms to gain access to scarce knowledge and skills otherwise out of reach, and in this way, gaining competitive advantage. Despite its potential, existing crowdsourcing platforms encounter several challenges, including (1) fragmentation of expertise, as there are many platforms, (2) distrust between task providers and crowdsourcing participants, as identity and past performance are often not known, and (3) inability to learn from experience due to a lack of openness. A reference architecture for blockchain-based knowledge-intensive crowdsourcing platforms to mediate transactions between demand and supply of knowledge is designed in this paper to overcome these challenges. A design science research method is followed to develop the architecture. The reference architecture shows how blockchain and smart contract components can be integrated to support and coordinate knowledge-intensive crowdsourcing activities. By removing traditional e-commerce intermediaries, blockchain reduces search friction, knowledge transfer costs, and cheating by task providers or crowdsourcing participants.


2022 ◽  
Vol 34 (4) ◽  
pp. 0-0

This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.


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