Special Issue Editorial –Accumulation and Evolution of Design Knowledge in Design Science Research: A Journey Through Time and Space

Brocke, Jan vom; Winter, Robert; Hevner, Alan R.; Maedche, Alexander

doi:10.17705/1jais.00611

Cited by 154 publications

(174 citation statements)

References 29 publications

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“…Brocke et al [3] provides a short and effective definition of the DSR in general:" The goal of DSR is to generate knowledge on how to build innovative solutions to important problems effectively (p.5)". DSR focuses especially on the construction of socio-technical artifacts, such as the implementation of VRLEs in organizations [18].…”

Section: Design Science Research (Dsr)mentioning

confidence: 99%

“…While mostly used in the information systems research (IS), Design Science Research Methodology (DSR) has also been used in the educational sciences. According to com Brocke et al [3], DSR knowledge can be divided into three main components: problem space, evaluation and solution space ( Figure 1). To gain knowledge on the problem space, context domain and the goodness criteria of the problem needs to be specified.…”

Section: Design Science Research (Dsr)mentioning

confidence: 99%

“…Vom Brocke et al [3] further stresses out that any practical solution has socio-technical aspects when describing the goodness criteria. Therefore, on the goodness criteria -section of the problem space, one has to investigate the criteria related to the technology (e.g.…”

Section: Design Science Research (Dsr)mentioning

confidence: 99%

“…Design Science Research (DSR) is an approach to study effective system solutions [2] and create design knowledge on how to develop them [3]. While it is also based on the same basic concepts as DBR and EDR, DSR has not been widely used in the context of educational sciences.…”

Section: Introductionmentioning

confidence: 99%

“…Central for DSR is investigating the problem space and the "fit" of the proposed solution artifact to the context, including e.g. technology-related considerations as well as socio-technical aspects in order to achieve a satisfactory solution [3]. These include e.g.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Problem Space Identification for Developing Virtual Reality Learning Environments

Lähtevänoja¹,

Holopainen²,

Mattila³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Our study argues that the extant literature on virtual reality-based learning environments (VRLEs) currently lacks proper definitions and context descriptions for a problem space, which is fundamental for conducting design science research (DSR). Without properly conducted problem space identification, the most pivotal problems cannot be identified resulting solutions lacking validity and unreliable evaluations. This is a major challenge for the DSR in the educational field, but also for the research on VRLEs. The purpose of this paper is to introduce a novel DSR method to support rigorous problem space identification, which would allow rigorous and profound problem space analysis. The instantiation of our method is depicted with a VRLE development project. In the problem space identification-process we adopt the concepts of selfdetermination theory and learning path to study and consider individual and a system level of the current VRLE artifact. This theoretical lens enables us to identify the problem space for VRLEs and also suggest how the to-be-developed artifact to be later evaluated. This paper contributes by introducing a general problem space identification for VRLEs and a DSR method to guide the future DSR in the educational field.

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Section: Design Science Research (Dsr)mentioning

confidence: 99%

Section: Design Science Research (Dsr)mentioning

confidence: 99%

Section: Design Science Research (Dsr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Problem Space Identification for Developing Virtual Reality Learning Environments

Lähtevänoja¹,

Holopainen²,

Mattila³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

Model‐based analysis and specification of functional requirements and tests for complex automotive systems

Wiecher,

Mandel,

Günther

et al. 2024

Systems Engineering

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The specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and evolving systems with complex interactions when following traditional development processes. To address this research gap, we propose a technique that starts with the early identification of validation concerns from a stakeholder perspective, which we use to systematically design tests that drive a scenario‐based modeling and analysis of system requirements. To ensure complete and consistent requirements and test specifications in a form that is required in automotive development projects, we develop a Model‐Based Systems Engineering (MBSE) methodology. This methodology supports system architects and test designers in the collaborative application of our technique and in maintaining a central system model, in order to automatically derive the required specifications. We evaluate our methodology by applying it at KOSTAL (Tier1 supplier) and within student projects as part of the masters program Embedded Systems Engineering. Our study corroborates that our methodology is applicable and improves existing requirements and test specification processes by supporting the integrated and stakeholder‐focused modeling of product and validation systems, where the early definition of stakeholder and validation concerns fosters a problem‐oriented, iterative and test‐driven requirements modeling.

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Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning

Esswein

Mayer

Sedneva

et al. 2020

Lecture Notes in Information Systems and Organisation

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Special Issue Editorial –Accumulation and Evolution of Design Knowledge in Design Science Research: A Journey Through Time and Space

Cited by 154 publications

References 29 publications

Problem Space Identification for Developing Virtual Reality Learning Environments

Problem Space Identification for Developing Virtual Reality Learning Environments

Model‐based analysis and specification of functional requirements and tests for complex automotive systems

Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning

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