A novel systematic approach for analysing exploratory design ideation

ABSTRACT Two kinds of design ideation process may be distinguished in terms of the problems addressed: (i) solution-focused, i.e. generating solutions to address a fixed problem specifying a desired output; and (ii) exploratory, i.e. considering different interpretations of an open-ended problem and generating associated solutions. Existing systematic analysis approaches focus on the former; the literature is lacking such an approach for the latter. In this paper, we provide a means to systematically analyse exploratory ideation for the first time through a new approach: Analysis of Exploratory Design Ideation (AEDI). AEDI involves: (1) open-ended ideation tasks; (2) coding of explored problems and solutions from sketches; and (3) evaluating ideation performance based on coding. We applied AEDI to 812 concept sketches from 19 open-ended tasks completed during a neuroimaging study of 30 professional product design engineers. Results demonstrate that the approach provides: (i) consistent tasks that stimulate problem exploration; (ii) a reliable means of coding explored problems and solutions; and (iii) an appropriate way to rank/compare designers’ performance. AEDI enables the benefits of systematic analysis (e.g. greater comparability, replicability, and efficiency) to be realised in exploratory ideation research, and studies using open-ended problems more generally. Future improvements include increasing coding validity and reliability.

[1]  Alex H. B. Duffy,et al.  The neural underpinnings of creative design , 2019 .

[2]  Ricardo Sosa,et al.  Metrics to select design tasks in experimental creativity research , 2018, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

[3]  Klaus Krippendorff,et al.  Answering the Call for a Standard Reliability Measure for Coding Data , 2007 .

[4]  Sonit Bafna,et al.  How architectural drawings work — and what that implies for the role of representation in architecture , 2008 .

[5]  Scarlett R. Miller,et al.  Choosing creativity: the role of individual risk and ambiguity aversion on creative concept selection in engineering design , 2016, Research in Engineering Design.

[6]  Masaki Suwa,et al.  Unexpected discoveries and S-invention of design requirements , 2000 .

[7]  Juan A. Carretero,et al.  Comparing Cognitive Efficiency of Experienced and Inexperienced Designers in Conceptual Design Processes , 2014 .

[8]  Jef R. Peeters,et al.  Effectiveness of the PAnDA ideation tool , 2010 .

[9]  Barbara Tversky,et al.  Finding Creative New Ideas: Human-Centric Mindset Overshadows Mind-Wandering , 2017, CogSci.

[10]  John L. Campbell,et al.  Coding In-depth Semistructured Interviews , 2013 .

[11]  P. Grant,et al.  Dopaminergic foundations of schizotypy as measured by the German version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE)—a suitable endophenotype of schizophrenia , 2013, Front. Hum. Neurosci..

[12]  Jef R. Peeters,et al.  Refinements to the variety metric for idea evaluation , 2013 .

[13]  Nigel Cross,et al.  Creativity in the design process: co-evolution of problem–solution , 2001 .

[14]  David W. Rosen,et al.  Refined metrics for measuring ideation effectiveness , 2009 .

[15]  Jonathan Stephen Fish,et al.  Amplifying the Mind’s Eye: Sketching and Visual Cognition , 1990 .

[16]  Klaus Krippendorff,et al.  Agreement and Information in the Reliability of Coding , 2011 .

[17]  Arnold P. O. S. Vermeeren,et al.  Measuring and comparing novelty for design solutions generated by young children through different design methods , 2016 .

[18]  Todd Lubart,et al.  Children's Original Thinking: An Empirical Examination of Alternative Measures Derived From Divergent Thinking Tasks , 2001, The Journal of genetic psychology.

[19]  Steven M. Smith,et al.  Metrics for measuring ideation effectiveness , 2003 .

[20]  A. Abraham,et al.  The promises and perils of the neuroscience of creativity , 2013, Front. Hum. Neurosci..

[21]  Alex H. B. Duffy,et al.  A systematic review of protocol studies on conceptual design cognition: Design as search and exploration , 2017, Design Science.

[22]  Steven M. Smith,et al.  Creative Cognition: Theory, Research, and Applications , 1996 .

[23]  Jakob F. Maier,et al.  Model granularity in engineering design – concepts and framework , 2017, Design Science.

[24]  Lorenzo Fiorineschi,et al.  ISSUES RELATED TO MISSING ATTRIBUTES IN A-POSTERIORI NOVELTY ASSESSMENTS , 2018 .

[25]  Klaus Krippendorff,et al.  Content Analysis: An Introduction to Its Methodology , 1980 .

[26]  Jef R. Peeters,et al.  Refined Metrics for Measuring Novelty in Ideation , 2010 .

[27]  Colleen M. Seifert,et al.  Evidence of problem exploration in creative designs , 2018, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[28]  Lorenzo Fiorineschi,et al.  A-POSTERIORI NOVELTY ASSESSMENTS FOR SEQUENTIAL DESIGN SESSIONS , 2018 .

[29]  Shanna R. Daly,et al.  Design by taking perspectives: How engineers explore problems , 2019, Journal of Engineering Education.

[30]  Klaus Krippendorff,et al.  On the reliability of identifying design moves in protocol analysis , 2013 .

[31]  John S. Gero,et al.  Patterns of Cortical Activation When Using Concept Generation Techniques of Brainstorming, Morphological Analysis, and TRIZ , 2018 .

[32]  Mary Lou Maher,et al.  Co-evolution as a computational and cognitive model of design , 2003 .

[33]  David W. Rosen,et al.  The effects of biological examples in idea generation , 2010 .

[34]  Gül E. Okudan Kremer,et al.  The Impact of Team-Based Product Dissection on Design Novelty , 2014 .

[35]  John S. Gero,et al.  An approach to the analysis of design protocols , 1998 .

[36]  Jonathan Cagan,et al.  A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli , 2019, Design Studies.