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Über dieses Buch

This book presents a new, multidisciplinary perspective on and paradigm for integrative experimental design research. It addresses various perspectives on methods, analysis and overall research approach, and how they can be synthesized to advance understanding of design. It explores the foundations of experimental approaches and their utility in this domain, and brings together analytical approaches to promote an integrated understanding. The book also investigates where these approaches lead to and how they link design research more fully with other disciplines (e.g. psychology, cognition, sociology, computer science, management).

Above all, the book emphasizes the integrative nature of design research in terms of the methods, theories, and units of study—from the individual to the organizational level. Although this approach offers many advantages, it has inherently led to a situation in current research practice where methods are diverging and integration between individual, team and organizational understanding is becoming increasingly tenuous, calling for a multidisciplinary and transdiscipinary perspective. Experimental design research thus offers a powerful tool and platform for resolving these challenges.

Providing an invaluable resource for the design research community, this book paves the way for the next generation of researchers in the field by bridging methods and methodology. As such, it will especially benefit postgraduate students and researchers in design research, as well as engineering designers.

Inhaltsverzeichnis

Frontmatter

The Foundations of Experimental Design Research

Frontmatter

Chapter 1. An Introduction to Experimental Design Research

Abstract
Design research brings together influences from the whole gamut of social, psychological, and more technical sciences to create a tradition of empirical study stretching back over 50 years (Horvath 2004; Cross 2007). A growing part of this empirical tradition is experimental, which has gained in importance as the field has matured. As in other evolving disciplines, e.g. behavioural psychology, this maturation brings with it ever-greater scientific and methodological demands (Reiser 1939; Dorst 2008). In particular, the experimental paradigm holds distinct and significant challenges for the modern design researcher. Thus, this book brings together leading researchers from across design research in order to provide the reader with a foundation in experimental design research; an appreciation of possible experimental perspectives; and insight into how experiments can be used to build robust and significant scientific knowledge. This chapter sets the stage for these discussions by introducing experimental design research, outlining the various types of experimental approach, and explaining the role of this book in the wider methodological context.
Philip Cash, Tino Stanković, Mario Štorga

Chapter 2. Evaluation of Empirical Design Studies and Metrics

Abstract
Engineering design is a complex multifaceted and knowledge-intensive process. No single theory or model can capture all aspects of such an activity. Various empirical methods have been used by researchers to study particular aspects of design thinking and cognition, design processes, design artefacts, and design strategies. Research methods include think-aloud protocol analysis and its many variants, case studies, controlled experiments of design cognition, and fMRI. The field has gradually progressed from subjective to objective analyses, requiring well-defined metrics since design of experiments (DOE) involves controlling or blocking particular variables. DOE also requires setting experiment variables at particular levels, which means that each variable needs to be characterized and quantified. Without such quantification, statistical analyses cannot be carried out. This chapter focuses on quantifiable characteristics of designers, targeted users, artefacts, and processes.
Mahmoud Dinar, Joshua D. Summers, Jami Shah, Yong-Seok Park

Chapter 3. Quantitative Research Principles and Methods for Human-Focused Research in Engineering Design

Abstract
Engineering design is increasingly recognised as a complex socio-technical process where the human and social aspects of the system require alignment with those focusing on technical product development. Social science research methods are therefore essential to conduct effective and holistic research into such processes. Accordingly, this chapter provides a grounding in the principles and methods of quantitative social science research. First, the measurement of variables in a reliable and valid manner is considered. Second, scientific principles and the nature of variable relationships are examined, including main effects, mediation effects, and moderation effects. Third, experimental and correlational research designs for exploring the relationships between variables are discussed. Fourth, an overview of statistical methods for analysing quantitative data is provided. Finally, participant sampling, ethical issues, and specialist methods are considered.
Mark A. Robinson

Classical Approaches to Experimental Design Research

Frontmatter

Chapter 4. Creativity in Individual Design Work

Abstract
In order to answer the questions, “Why can humans design?” and further, “Why are human beings the only species capable of design?” this chapter focuses on individual design work. We discuss the features of creativity in the design process, using experimental studies to observe from the microscopic and macroscopic viewpoints, in order to clarify design creativity as a personal activity. First, the basis of design creativity is discussed, and the character of design creativity is delineated—namely the way in which it relies on different modes of searching for the new concept based on the empathy or consideration for other people. Second, from the microscopic perspective, the concept generation phase in the design process is examined through individual designs. Here, keywords of high dissimilarity were found to advance the originality of creative results. In addition, the role of association—in particular the concept of action—was identified. Third, to identify motivations for engaging in long-term creative activity, this chapter considers designers’ process of self-growth to play a role in developing their inner perspective. This chapter also presents a case study of a designer’s process of self-growth, which was conducted as part of a long-term experiment. Finally, the internal and external motivations that activate creativity in the cognitive processes involved in individual design work are comprehensively discussed and clarified.
Yukari Nagai

Chapter 5. Methods for Studying Collaborative Design Thinking

Abstract
When discussing the performance of design teams, researchers repeatedly stress the key role of team cognition, which refers to collective cognitive structures and processes relating to product conceptualization and realization. The perspective taken in this chapter is that individual team member knowledge contributes to team cognition, and the quality of the aggregation of knowledge explains variance in knowledge-intensive activities such a design. We will describe two methods that together assess the structure and processes of team cognition and their impact on design team performance. Taken together, these methods provide a way to assess team cognition over time so as to account for variance in team performance based upon the quality of their knowledge practices.
Andy Dong, Maaike Kleinsmann

Chapter 6. The Integration of Quantitative Biometric Measures and Experimental Design Research

Abstract
In design research, recently an increasing number of experiments have been conducted that successfully applied quantitative biometric measurement methods to investigate design-related research questions. These methods are heart rate variability (HRV), skin conductance response (SCR), electroencephalography (EEG), functional magnetic resonance imaging (fMRI) as well as remote and mobile eye tracking (ET). Within the scope of these experiments, a variety of different biometric measurement systems have been used, each able to record specific raw data and each using characteristic measures to detect and specify particular patterns of human behaviour. This chapter explores how these biometrical measurement systems work, what exactly they measure, and in which ways collected raw data can be analysed to obtain meaningful results. By using the example of selected design studies, the benefits as well as the limitation of the aforementioned biometric measurement methods are discussed and reflected in regard to their present and future role in experimental design research.
Quentin Lohmeyer, Mirko Meboldt

Chapter 7. Integration of User-Centric Psychological and Neuroscience Perspectives in Experimental Design Research

Abstract
This chapter deals with the experimental investigation of user-centred, rather than technology-centred, perspectives on engineering design. It explores how experimental approaches can be used to assess and capture the cognitive as well as emotional mechanisms that underlie the perception of human–product interaction and other facets of design cognition. The focus of the chapter is on the experimental research of product design, exploring key features of methods based on applied psychological and neuroscientific theories, concepts, methods and data.
Claus-Christian Carbon

Computation Approaches to Experimental Design Research

Frontmatter

Chapter 8. The Complexity of Design Networks: Structure and Dynamics

Abstract
Why was the $6 billion FAA air traffic control project scrapped? How could the 1977 New York City blackout occur? Why do large-scale engineering systems or technology projects fail? How do engineering changes and errors propagate, and how is that related to epidemics and earthquakes? In this chapter, we demonstrate how the emerging science of complex networks provides answers to these intriguing questions.
Dan Braha

Chapter 9. Using Network Science to Support Design Research: From Counting to Connecting

Abstract
A network-based perspective on designing permits research on the complexity of product, process, and people interactions. Strengthened by the latest advances in information technologies and accessibility of data, a network-based perspective and use of appropriate network analysis metrics, theories, and tools allow us to explore new data-driven research approaches in design. These approaches allow us to move from counting to connecting, meaning to explicitly link disconnected pieces of data, information, and knowledge, and thus to answer far-reaching research questions with strong industrial and societal impact. This chapter contributes to the use of network science in empirical studies of design organisations. It focuses on introducing a network-based perspective on the design process and in particular on making use of network science to support design research and practice. The main contribution of this chapter is an overview of the methodological challenges and core decision points when embarking on network-based design research, namely defining the overall research purpose and selecting network features. We furthermore highlight the potential for using archival data, the opportunities for navigating different levels of the design process that network analysis permits, what we here call zooming in and out, and the use of network visualisations. We illustrate the main points with a case from our own research on engineering communication networks. In this case, we have used more than three years of archival data, including design activity logs and work-related email exchanges from a recently completed large-scale engineering systems project of designing and developing a renewable power plant.
Pedro Parraguez, Anja Maier

Chapter 10. Computational Modelling of Teamwork in Design

Abstract
Computational simulation has been long established across research areas for modelling the behaviour of complex, multivariable and socio-technical systems. In the computational study of teamwork in design, the focus is set on capturing dynamic interactions between the individual team members within their environment using multi-agent systems. Agent-based simulation (ABS) provides a platform to inductively develop and examine theories on human behaviour in design that have the potential to inform experimental research. This chapter aims to outline the role of agent-based simulation in design drawing from a multidimensional framework for computational modelling. This research approach is applied to examine group support at the time of creative breakthroughs. The chapter concludes with guidelines for the use of agent-based simulation in design research.
Ricardo Sosa

Chapter 11. Human and Computational Approaches for Design Problem-Solving

Abstract
Human and computational approaches are both commonly used to solve design problems, and each offers unique advantages. Human designers may draw upon their expertise, intuition, and creativity, while computational approaches are used to algorithmically configure and evaluate design alternatives quickly. It is possible to leverage the advantages of each with a human-in-the-loop design approach, which relies on human designers guiding computational processes; empirical design research for better understanding human designers’ strengths and limitations can inform the development human-in-the-loop design approaches. In this chapter, the advantages of human and computational design processes are outlined, in addition to how they are researched. An empirical research example is provided for conducting human participant experiments and simulating human design problem-solving strategies with software agent simulations that are used to develop improved strategies. The chapter concludes by discussing general considerations in human and computational research, and their role in developing new human-in-the-loop design processes for complex engineering applications.
Paul Egan, Jonathan Cagan

Building on Experimental Design Research

Frontmatter

Chapter 12. Theory Building in Experimental Design Research

Abstract
As an introduction, a brief overview of the types and process of experimental research is provided and the concept of research phenomenon is discussed. Then, various kinds of theories, such as: (i) explorative, (ii) descriptive, (iii) explanatory, (iv) predictive, and (v) regulative theories, are considered as milestones of progression of knowing, and some philosophical stances and approaches of scientific theorizing are deliberated. Historically, there has been a move from empiricist and positivist approaches to pragmatist, interpretivist, and instrumentalist approaches of theorizing, which recognized the socially constructed nature of scientific knowledge. These approaches are concisely reviewed and, after that, a systematic procedure of theory building and testing is proposed, which harmonizes with the epistemological and methodological objectives of experimental research. It consists of an exploratory part, which includes knowledge aggregation, assumptions on conducting data generation, and deriving a specific theory, and a confirmative part, which includes justification, validation and consolidation of the proposed theory. As an exemplification, the differences of probabilistic theory building in various manifestations of experimental research are summarized. Finally, some propositions are made.
Imre Horváth

Chapter 13. Synthesizing Knowledge in Design Research

Abstract
This chapter discusses knowledge synthesis in Design Research, bringing together the perspectives of experimental Design Research, or Research in Design Context that is treated extensively elsewhere in this book, with Design Inclusive Research as well as Practice-Based Design Research. Specific attention is paid to the question of how practice-based or problem-driven Design Research processes can be rigorous and yield contributions to knowledge. The main argument in this chapter is that a key to knowledge synthesis and scientific contribution is setting explicit design propositions that are instantiated within design artefacts and evaluated rigorously. This chapter starts with a discussion of knowledge creation and synthesis within Design Research. Following this, the chapter moves on to focus on setting a methodological framework for deriving design propositions. Lastly, this chapter elaborates on empirical aspects of evaluation of design artefacts and propositions and the associated knowledge claims.
Kalle A. Piirainen

Chapter 14. Scientific Models from Empirical Design Research

Abstract
For many, designing is an unknowable mystery. Science is founded on the axiom that things are knowable. Can designing be “known” through science? Science takes the approach that there are observables called phenomena that can be represented separately from the phenomena themselves and that these phenomena exhibit regularities. Further, science assumes that these phenomena can in some sense be measured. Hypotheses are conjectures about the regularities of these phenomena that can be tested against the data acquired through measurement. Tested hypotheses form the basis of the construction of models that can be used both to describe the regularities and to make predictions about the phenomena underlying those regularities. It has been argued that since the result of designing is a unique design, i.e., when you carry out the same design task again you produce a different design, where is the regularity that is required for science to apply to designing? The regularity in designing is not necessarily in the resultant design but in the process that produces that design—designerly behaviour. Using science to study designerly behaviour results in scientific models describing designerly behaviour based on empirical evidence rather than on personal experience. The remainder of this chapter will introduce a method for the capture of empirical data on designing. This is followed by the description of an ontology of designing that maps onto the phenomena of designing that are capturable. The rest of the chapter describes some of the scientific models that can be produced from this empirically grounded data.
John S. Gero, Jeff W. T. Kan
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