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General Aspects of Science, Design, and Engineering
Published in Tarun Grover, Mugdha Thareja, Science in Design, 2020
Design science research is a practical approach for creating new artifacts to solve defined problems or for redesigning an existing solution to achieve the goal of process improvement. Two basic activities, namely “build” and “evaluate”, are involved, where building is the process of designing an artifact for a specific purpose, and evaluation is the process of determining how well the artifact performs and fits into the design framework [9]. In particular, a process-based approach follows multidisciplinary aspects of design and is evaluated in the following key seven design science principles, which are also listed in Table 1.1.
Framework for examination of software quality characteristics in conflict: A security and usability exemplar
Published in Cogent Engineering, 2020
Bilal Naqvi, Ahmed Seffah, Alain Abran
The approach used for the development of the PoDF is design science research (DSR). Design science research is a research methodology involving the design and investigation of the artifacts in a particular context (Wieringa, 2014). The design science research methodology guides the design of artifacts (patterns) and processes (framework-PoDF). Moreover, the design science research methodology supports the iterative model of development, which means the building of new and evolved processes and artifacts after the communication phase of the last completed iteration (Peffers et al., 2007). The essential aspect to consider during new iteration is the feedback recorded during the last iterations’ communication phase; the feedback should be reflected in the evolved processes and artifacts. As stated earlier, the PoDF is an evolved version and extension of the framework presented in (Naqvi & Seffah, 2019). The key drivers considered while designing an evolved version were the feedback received during the presentation of the framework at the conference.
Designing a blockchain enabled supply chain
Published in International Journal of Production Research, 2021
Yingli Wang, Catherine Huirong Chen, Ahmed Zghari-Sales
This research adopts a qualitative, participative research approach and is particularly informed by design science research methodology (van Aken, Chandrasekaran, and Halman 2016; Peffers, Tuunanen, and Niehaves 2018; Peffers et al. 2007). A typical design science approach follows the structure of problem identification, objective definition, design and development, final demonstration and evaluation (Holmström, Ketokivi, and Hameri 2009). A key role of the academic researcher in design science is to open up the possibility of multiple generative mechanisms as bases for achieving the goals of the design project at hand, for example by challenging and helping to expand the mental models of participants (Hodgkinson and Starkey 2012).
A design-based pedagogical framework for developing computational thinking skills
Published in Journal of Decision Systems, 2021
Samrat Gupta, Amit Anand Tiwari
This research is informed by Design Science Research (DSR) as the different phases of our research map closely with the six activities of the Design Science Research (DSR) process model, viz. problem identification and motivation, objectives of a solution, design and development, demonstration, evaluation and communication (Peffers et al., 2006). First, we are motivated by the relevance of social networks and the problem of community detection in developing CT skills of students. Therefore, our aim is to develop an educational framework based on demonstration of developed solutions to identify communities in social networks. Second, we define the characteristics of a desirable solution through quantitative objectives such as partition density, number of overlapping nodes and number of misassigned nodes. Third, the full-fledged design and development of the variants of aforementioned community detection solutions has been performed in prior research (Gupta & Deodhar, 2021; Gupta & Kumar, 2020; Kumar et al., 2017). Fourth, the community detection solutions are demonstrated and their effectiveness is discussed in the further subsections. Fifth, we iteratively evaluate and compare the community detection results of the solutions in terms of partition density metric. Finally, we communicate the importance of community detection problem, newly developed community detection solutions, rigour behind the design of the solutions, their implications and applications through scholarly publications (Gupta & Deodhar, 2021; Gupta & Kumar, 2020; Kumar et al., 2017), conference presentations (Gupta et al., 2016; Gupta et al., 2019) and teaching postgraduate students of business administration.4