A DESIGN RATIONALE ANALYSIS METHOD TOWARDS ROBUST ARTIFACT DESIGN

To design a more robust artifact, an artifact design based on a design rationale analysis is pivotal. Errors in previous design rationales that led to the degradation of artifact robustness in the past provide valuable knowledge towards improving the robust design. However, methods for exposing and analysing errors in design rationale remain unclear. This paper proposes a structured method for a design rationale analysis based on logical structuring. This method provides a well-constructed means of identifying and analysing errors in design rationale from the perspective of knowledge operation.

Utilizing, producing, and contributing design knowledge in DSR projects

© Springer International Publishing AG, part of Springer Nature 2018. We distinguish several design knowledge types in IS research and examine different modes of utilizing and contributing design knowledge that can take place during design science research (DSR) projects. DSR projects produce project design knowledge, which is project-specific, possibly untested, conjectural, and temporary; thus, distinct from the more stable contributions to the propositional and prescriptive human knowledge bases. We also identify solution design knowledge as distinct from solution design entities in the prescriptive knowledge base. Each of the six modes of utilizing or contributing knowledge (i.e. design theorizing modes) we examine draws on different knowledge types in a different way to inform the production of project design knowledge (including artifact design) in a DSR project or to grow the human knowledge bases in return. Design science researchers can draw on our design theorizing modes and design knowledge perspectives to utilize the different extant knowledge types more consciously and explicitly to inform their build and evaluation activities, and to better identify and explicate their research’s contribution potential to the human knowledge bases.

Utilizing, producing, and contributing design knowledge in DSR projects

© Springer International Publishing AG, part of Springer Nature 2018. We distinguish several design knowledge types in IS research and examine different modes of utilizing and contributing design knowledge that can take place during design science research (DSR) projects. DSR projects produce project design knowledge, which is project-specific, possibly untested, conjectural, and temporary; thus, distinct from the more stable contributions to the propositional and prescriptive human knowledge bases. We also identify solution design knowledge as distinct from solution design entities in the prescriptive knowledge base. Each of the six modes of utilizing or contributing knowledge (i.e. design theorizing modes) we examine draws on different knowledge types in a different way to inform the production of project design knowledge (including artifact design) in a DSR project or to grow the human knowledge bases in return. Design science researchers can draw on our design theorizing modes and design knowledge perspectives to utilize the different extant knowledge types more consciously and explicitly to inform their build and evaluation activities, and to better identify and explicate their research’s contribution potential to the human knowledge bases.

Design Drives Discovery in Causal Learning

We assessed whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults’ ( N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact’s design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.

Design Facilitates Discovery in Causal Learning

We assess whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activate a machine) using a machine with one of two designs. In the standard-design condition, pairs were placed on top; in the relational-design condition, blocks were placed into openings on either side. Experiment 2 assessed whether this design cue could facilitate adults’ (N=102) inference of a distinct, conjunctive cause (i.e., that two blocks, together, activate the machine). Results of both experiments demonstrate that causal inference is sensitive to design: participants in the design conditions were more likely to infer the a priori unlikely rules. Findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses, but that artifact design aids causal inference, with clear implications for creating intuitive learning environments.