Thinking about design experience: A semantic network approach by Donna Wheatley [Design Research Society]

links 1 + 2 

Abstract
 Social network analysis software has been used in this study to reveal individual and collective perceptions of space from different perspectives. The paper outlines how to analyse an ‘environment-response’ semantic network of a user group and that of the architect. The semantic network of the designer was found to be quite 
different from the users of their designs, a starting point from which to question how far designers of space are able to anticipate what impressions and reactions their designs elicit in users.
Determining what thematic clusters or topics emerge (called ‘metatopics’ in the study) from the networks is a primary aim. The networks usually contain 4-7 metatopics. A range of network analysis algorithms, calculating measures such as centrality and proportional
strength of ties are applied to identify important constructs and
help identify metatopics.
These metatopics can also themselves be ranked and compared 
through network analysis indicators. Through these tools, new observations on the structure of collective mental representations of 
built environments are gathered.
Key words Semantic networks, network analysis, perception,
designed environments, interviews. The assumptions that designers
make about the effect their designs have on end users is a
vital part of the design process as well as a key part of the 
design. Social network analysis software ‘Pajek’ has been used in 
this study to visualize and analyse the mental constructs toward a
particular environment. How people respond to a designed
environment has not previously been explored as a network of 
thoughts, yet this could be a productive use of network analysis 
given that human relationships with the environment (natural and
built) is gaining prominence as a research topic.
 The paper discusses two ‘environment-response’ networks that were derived from a new workplace: one for the user group and the other for the architect. The network of the designer is quite different 
from that of the user group, thus calling into question in how far
designers of space understand what effect their designs will have
on users.
 The paper firstly outlines why network analysis can be a useful 
and valid research tool in investigating human responses to the 
environment. The middle section describes the data used for 
deriving the networks. The final section, using a case study, 
discusses analysis of the environment-response user group 
(consensus) network before showing the designer’s
environment-response network for brief comparison. Results of this method could be used contribute to discussions on how and why designers conceptualise space differently to users Page 2 of 16
(Cross 2006; Lawson 1997) and is an aim of the larger study. This
paper refrains from making generalisations of theory in order to 
present one worked example of a new design research method. The 
intention is to indicate the utility that social network analysis 
offers to researchers interested in understanding the thought 
structure that individuals and groups of people have about a 
designed environment.
 Theorising an environment-response network
 This section of the paper provides an overview of key theories that provide a rationale for
 developing an environment-response network: environmental psychology confirms the
 existence of a human environmental response; the science of semantic networks explains
 that thoughts exist as a type of network; an overview of networks as an abstracted set of
 elements and connections that can be mathematically and algorithmically analysed and
 represented; and finally, the reasons for applying an environment-response network to a
 workplace setting.
 Environmental psychology
 Any environment has a psychological effect on users (Norberg-Schulz, 1980; Rasmussen
 1964). Many researchers have attempted to understand what elements of the environment
 can enhance quality of life (Alexander, Ishikawa & Silverstein, 1977; Gifford, 2007; Zeisel,
 1984). The hope is that designers armed with this knowledge could create environments that
 reduce stress, enhance mental acuity and emotional response and any other desirable
 outcomes. However, research in this area is generally inconclusive, based on intuition or
 small-scale studies and has not kept up with scientific advances in other fields.
 This year Oxford University Press published Brain Landscape: The Coexistence of
 Neuroscience and Architecture to “challenge neuroscientists to study how architecture
 affects the brain” (Eberhard, 2009, p.xii). The emphasis the book presents to readers is that
 the brain, which controls behaviour, is influenced by the environment. Neuroscientists have
 found that connections between neurons in the brain to continuously ‘re-wire’ to adapt to
 environmental stimuli all through our adult lives (Gordon, 2000, p.72). Architecture, it is
 supposed, can change our brains and our behaviour.
 Semantic networks
 Following on from studies on the network structure of neurons in the brain is a notion that
 thoughts would also follow a network-like structure. Thoughts about a subject or object (such
 as architecture), have been considered a type of network in order to “create abstract
 representations of the general features of input data” (Spitzer, 1999). Spitzer describes
 these ‘semantic networks’ as relating to associations between words as a form of knowledge
 representation. Semantic networks were first studied as a concept called associationist
 psychology (John Locke and David Hume) and later free association (Sigmund Freud, Sir
 Francis Galton and Carl-Gustav Jung). Later, it was theorised that words themselves are
 stored in a network like structure in the brain (Collins and Loftus, 1975). The use of semantic
 networks as a tool used to examine how thoughts occur has also been used in the context of
 developing artificial intelligence (Sowa and Borgida, 1991). Concepts underlying semantic
 networks are relevant to this research project, in which word-associations, extracted from
 interview transcripts, generate abstract semantic networks that represent respondents’ Page 3 of 16
 concepts of architectural spaces. It takes as a starting point the notion that the abstract
 structural type of the network are tied closely to how subjective ideas about objects such as
 architectural environments are represented in the brain.
 Network analysis
 A network refers to any collection of interacting parts, which satisfies certain laws of form
 and organisation (Buchanan, 2002, p.18). The architecture of networks can be analysed
 meaningfully and patterns observed where previously none could be seen. To analyse a
 network its ‘parts’ are understood as a set of discrete elements (vertices) and connections
 (edges) between them. Extracting these features provides clarity to a question that would be
 impossible to answer, were all details to remain (Newman, Barabási & Watts, 2006, p.4). In
 addition to powerful visualisation capabilities of software, network analysis techniques allows
 for mathematically rigid measurement of the structure of the connectedness of a network
 independently of its content.
 Sociologists and statisticians have been advancing the field of network analysis since the
 1930s, but software for analysing networks really only began appearing in the 1990s when
 computer scientists became interested in modelling increasingly complex domains. Fields
 that use network analysis include information technology (including computer networks such
 as World Wide Web), biology, and the social sciences.
 Why look at workplace settings
 Many people spend a significant part of their lives at a workplace. Robert Gifford, eminent
 environmental psychologist, says that “the physical environment at work is crucial to
 employees’ performance, satisfaction, social relations and health” (Gifford, 2007, p.372).
 Research into people’s response to workplace settings focuses on employee productivity
 and satisfaction (Brill, 1984; Becker & Steele, 1995), but other important areas are to be
 investigated, especially in an increasingly knowledge-based work environment:
 collaboration, interaction, social behaviour, being part of a team, control over environment,
 flexibility, comfort and health (Worthington, 2006). To map out the human response to the
 workplace environment, Gifford (2007) draws on extensive research to find that five
 variables of workplace settings primarily contribute to behavioural response: 1) sound, noise,
 and music, 2) indoor climate, 3) air, 4) light, colour and windows and 5) density and
 arrangement of space.
 1
 John Eberhard, author of Brain Landscape, also devotes a chapter
 to workplace design (2009, pp. 135-153). His purpose is to identify areas for further
 research. It can be concluded that this architectural setting has elicited limited empirical
 research on the user response, yet is an important architectural type to investigate in terms
 of its psychological impact on society.
1
 These variables can be observed in the environment-response network, but so do other features,
 such as technological devices that enable free and easy movement as shown in the later graphs.
 The usefulness of an environment-response network is that it captures variables in total, so that
 their relative importance can also be examined. Page 4 of 16
 Fig 1. An atrium inside case study workplace x. The environment-response network will reflect the
 thoughts and feelings of employees that use the space, positively and negatively. (Photo: author)
 Data in the environment-response networks
 The data was collected from interviews, coded into spreadsheets then transformed into a
 Pajek readable file.
 Data Collection
 Data was gathered from in-depth interviews at a series of workplaces (this paper focuses on
 one, workplace x, as an example). Nine to fifteen employees at each case study workplace
 were asked to talk about their experience of their physical surrounds using metaphors.
 Metaphors involve understanding one thing in terms of another and are assumed to shape
 new thoughts (Gibbs, 1992). This technique has been used in the field of psychotherapy to
 help patients make unconscious experiences more conscious and communicable (Kopp,
 1994) and in consumer market research (Zaltman & Coulter, 1995). The use of metaphors is
 stimulated by a range of images provided to interviewees to help illustrate their thoughts.
 This technique was used to collect data for the environment-response network as it offers a
 way to go beyond the interviewee’s conscious self-perception of their response to their
 workplace environment. Once a metaphor, thought or feeling has been stated by a
 participant they were then asked to elaborate on it and to connect it to an aspect of the
 workplace that most strongly gave them that response.
 Coding the data
 Data in the interview transcripts are coded in environment-response pairs, responseresponse pairs and occasionally, environment-environment pairs. When a thought or feeling
 is mentioned, its link to the workplace environment or other thoughts or feelings is recorded.
 Between 40 and 80 pairs are identified for each respondent.
 Linguistic research in syntax, the study of the rules of language and sentence structure, is
 beginning to examine the basis of language as links between words and actions when they Page 5 of 16
 share an object of reference. Richard Solé uses this notion to turn linguistic constructs into
 networks: “two words are linked if they have been syntactically combined in a collection of
 sentences. Different languages share the same scale-free structure, with most words having
 few syntactic links and a few of them being connected to many others” (2005). While
 linguistic theories are being developed, there is not much literature on research methods that
 translate speech into a network model. What has been done in this study is to assemble
 networks from meaningful constructs that are connected by association within in a related
 set of sentences.
 Structure of the network data
 Each analysed network of a user group has approximately around 100-120 vertices
 representing meaningful constructs and 600-1000 edges (comprised of 40-80 edge pairs
 from each individual interviewee). Vertices are either thoughts or feelings about an
 environment (shown as circles in the network) or workplace environment features (shown as
 squares). The number of duplicate connections (connections made by several individuals)
 provides a weighting to an edge. In the visualisations the vertices are sized according to the
 number of participants mentioning the corresponding construct. The graphs are also
 undirected as the directions of connections between constructs in interviews are not able to
 be coded with certainty due to language syntax complexities.
 In the consensus network one-off responses are removed so that it only reflects connections
 about which there is a certain consensus within the respondent group. This consensus data,
 displayed as a network, provides visual information about the pattern of perception and
 emotional response to space for the group.
 A key feature of the environment-response network is that it demonstrates links between
 workplace environment features and thoughts and feelings. This allows tracing of how
 particular responses were generated. While the networks have been abstracted, they
 demonstrate that responses to any given environment are complex and interrelate with many
 different parts of the environment. Social network analysis tools enable unpacking and
 interpreting of that complexity. In reading the networks, caution must be exercised towards
 the generalisation that certain features will, if installed in a different environment, lead to
 similar responses. The aim of this paper is instead to demonstrate the potential of analysing
 the overall environment-response network to observe perspectives on the one environment
 by different involved groups of people.
 Analysing environment-response networks
 Analysis of environment-response networks takes, using social network analysis (SNA)
 terminology, a ‘sociocentric approach’, in which the structure of the entire network is
 analysed as opposed to focusing on the position of one of the constructs of the network, or
 an ‘ego-centered’ approach, although this can be done on a case-by-case basis when much
 more information specific about a case is desired. In addition to the visualisation capabilities
 of network analysis software, the network analysis operations used for the environmentresponse network are proportional strength of ties, removal of edges, centrality measures
 (degree centrality and closeness centrality), cut points and bi-components. Page 6 of 16
 To analyse the environment-response network it was first considered what analysis was
 desired from the network. Determining what thematic clusters or topics emerge (which we’ve
 called ‘metatopics’) from the networks is a primary finding. The first step is to reduce the
 number of nodes on the user group network by removing one-off responses. The reduced
 network is then visualized by drawing vertices with strong connections close together.
 Important vertices are identified. These include the most highly connected workplace environment features and thought or feeling responses, and the most central workplace
 environment features and thought and feeling responses. The vertices that, if removed,
 break the network are found, as they are important links in the overall perception of the
 workplace as well as possibly indicating the location of clusters in the network. The
 identification of metatopics follows on from the above steps and through domain knowledge.
 These clusters can be ranked and compared through number, sizes, values and density of
 their nodes and the location of the cluster in the network. Highlighting metatopics reveals
 additional pivotal vertices in the network, such as vertices that connect clusters together, but
 whose importance as a link may not been so clearly seen without domain metatopic
 clustering. This section presents these steps using the data gathered from interviewing a
 user group of case study workplace x.
 The automatic layout to generate all depictions of networks uses the Kamada-Kawai
 algorithm. For small networks that have less than 500 vertices it produces regularly spaced
 and stable results (Nooy, Batagelj & Nooy, 2005, p.17).
 Reducing and drawing the network
 The vertices are sized proportionally by the number of respondents mentioning it and the
 edges thickness by the number of respondent mentioning the link. Drawing all the connected
 constructs or vertices, totalling around 100-120, makes for a very difficult to read graph (Fig.
 2). Removing one-off responses makes the graph more valid and helps to focus on items
 that are important across individual conversations. Page 7 of 16
 Fig 2. The complete environment-response network for the group of users interviewed in workplace
 x. The network is difficult to read when all the coded responses are included.
 The reduced consensus network is drawn by positioning vertices with strong connections
 close together using proportional strength of ties (Fig. 3). Visualising proportional strength of
 ties helps identify categories of themes later, but is shown now so that the same graph
 layout may be used to demonstrate all the following analysis steps. The calculation
 considers the importance (or exclusivity) of an edge. It is calculated as the edge weight of a
 tie to a vertex divided by the sum of all ties incident to that vertex. A tie is not very important
 for a vertex if it has a low edge weight and is just one of many. For each edge there are two
 results depending on the vertex the strength is calculated relative to. For instance, in Fig. 3,
 the ties between sense of having a choice and fluid organic space have two values. The
 vertex sense of having a choice is one of three ties that fluid organic space has and the
 connection has a value of 0.33. In the opposite direction the tie is much weaker with a value
 of 0.08 as sense of having a choice has many more ties, several of them also having
 stronger edge values. Page 8 of 16
 Fig 3. A consensus environment-response network for a user group in workplace x. One-off
 responses are removed and vertices with strong connections are drawn closely together. The
 squares are workplace environment features and circles thoughts and feelings.
 Most highly connected vertices
 Highly connected environmental features control a constellation of feelings about the
 workplace, and highly connected thoughts or feelings are quite permanent and strong. A
 vertex ‘degree’ is simply the number of lines incident with it. They are likely to be found in
 dense sections of the network.
 The most highly connected workplace environment features (squares) are shown in grey in
 Fig. 4. These three elements of the workplace (understated design, wireless
 headphones/laptops, no rules about where to work) have the most control and influence over
 how the user group thinks and feels about the experience of the workplace. Removing or
 changing these features will have a major impact on the structure of the network, thus
 perception of the workplace. Page 9 of 16
 Fig 4. Highly connected vertices (degree centrality). These show the most controlling/influential
 vertices on the environment response network.
 The most highly connected thought or feeling responses to the workplace (circles) are
 shown in a darker grey in Fig. 4. These thoughts are quite permanent and strong. Many
 workplace changes are needed to change these thought responses. For example, the
 feeling that the workplace is refreshing is connected to five other constructs. Interestingly,
 only one, sense of always having a choice, is directly connected to one of these influential
 workplace features. The degree average for all vertices in the network is 2.18.
 Most central vertices
 These are the most central or embedded vertices within the network. Their centrality is
 brought on by often distant nodes in the network, making it difficult to determine the impact
 changing a workplace feature (square) will have, or what features effect a central thought or
 feeling (circle). They are found using a closeness centrality algorithm. This is based on the
 total distance between one vertex and all the other vertices, where larger distances yield
 lower closeness centrality scores. The closer a vertex is to all other vertices the higher its
 centrality. The degree centralities for all vertices are given in Fig. 5, with the most central
 ones in grey. The closeness centrality calculation results in continuous rather than discrete
 scores, which enables Pajek to draw the vertices according to its closeness centrality value.
 The arithmetic mean for closeness centrality across all constructs is 0.186 (from a range of
 0.035 to 0.296). Page 10 of 16
 Fig 5. Central vertices (closeness centrality). Vertices are sized according to their closeness
 centrality values.
 Vertices that break the network
 Identifying cut-vertices and bi-components can be helpful in locating clusters in the network.
 Cut-vertices are all vertices whose removal, and all edges incident with it, breaks the
 network into more than one component. The numbers on the vertices in Fig. 6 refer to the
 resulting number of components should the vertex and its incident lines be removed from the
 network. It identifies vertex constructs that are necessary to link constructs (in particular
 chains or subnetworks of constructs) to the bigger picture. These linking vertices control the
 flow from one component of the network to another.
 Bi-component operations are a subset of cut-vertices. It identifies those vertices that break a
 network into components whereby each vertex in the subnetwork connects to at least two
 vertices. They can indicate a more strongly clustering of vertices within the network. No
 positive result occurs in this example network. Page 11 of 16
 Fig 6. Vertices that break the network (Cut vertex). The vertex values show the number of
 components that would result should the vertex and its incident lines be deleted.
 Metatopics
 All previous network analysis steps help to understand where clusters of themes or topics
 may emerge from the network. But separating vertices into thematic clusters also requires
 domain knowledge of which vertices can be grouped together in a meaningful way, and to be
 able to define and name metatopics represented by the clusters.
 In the users’ environment-response network (Fig. 7) there are six metatopics, labelled
 creative, stylish and professional, social, freedom and choice, team and, separate from
 network, serious. The most important metatopics are identified by observing the number of
 nodes, sizes of the nodes, how closely they are drawn together, their interconnectedness (or
 density), the centrality values of its nodes, the location of the cluster within the network (does
 it take a central position by having connections with other clusters?) and the connectedness
 of the cluster to other clusters. Page 12 of 16
 Fig 7. Metatopics for the user group of workplace x.
 In this network the cluster freedom and choice is the most prominent cluster by these
 measures. Although it is not the cluster with the most vertices it contains the highest scoring
 vertex on both centrality measures (sense of always having a choice), a central workplace
 environment feature (no rules about where/when to work) and a high value closeness
 centrality vertex (able to select appropriate desk). It directly connects to two other metatopics
 and another indirectly, and its vertices connect to each other with multiple weighted edges.
 At the opposite end, the metatopic serious takes a minor role in the user response. While it
 is a recurring subject in user responses, it takes no influential role on the other thoughts and
 feelings that the users have.
 One of the most pivotal vertices in the network is fluid organic space. It connects to the
 metatopics social, freedom and choice and team, and at the same time is not a part of any of
 these categories. While its degree centrality is average, its three edges are key in
 connecting metatopics. Its significance is better indicated by its closeness score, which
 reflects its average distance to all other vertices. The closeness centrality score is 0.28, the
 second highest in the network. Although the feeling that the workplace is a fluid organic
 space is a key finding in the network analysis, this certainly is not something that can be
 easily seen when visiting the workplace. It would also be a difficult property to be asked to
 ‘design’ into an environment. Another notable element is the workplace environment feature
 wireless headphones/laptops. It only connects two categories, but it does rate highly on both
 centrality measures. This indicates it is influential as to how the workplace is thought about.
 Comparing the users’ network with the designer’s
 All the steps mentioned above, involved in the network analysis of the user group, also took
 place to define the clusters of thoughts or metatopics that the designer has towards Page 13 of 16
 workplace x (apart for step one because it is an individual rather than consensus network).
 The same colour coding from the user’s network (Fig. 7) is used in the architect's (Fig. 8) to
 allow identification of similar metatopics, with a dotted grey outline used to indicate themes
 the architect does not share with the user group. Both networks exhibit themes of serious,
 team and social. Interestingly in the designer’s environment-response network serious has
 become one of the more significant metatopics. In this network it contains a greater number
 of vertices, with increased density (interconnectedness), and it is, albeit at some distance
 from the rest of the network, now connected to the network. The categories of team and
 social match the user group network reasonably well. The architect does not include the
 themes of creative, stylish and professional, or freedom and choice. Instead, categories of
 uplifting, informal, cutting edge and easy are added. The fact there is no metatopic
 corresponding the one the users mostly use to categorise their environment, freedom and
 choice, is quite surprising. In this case the users see the workplace generally more positively
 than the designer does. Further studies may help to clarify what factors contribute to a
 discrepancy in this direction.
 Fig 7. Metatopics for the designer of workplace x. The grey clusters indicate metatopics shared
 with the users’ and the dotted clusters are metatopics unique to the designer.
 There is no pivotal thought or feeling characterising the environment for the designer. In fact,
 the construct fluid organic space that was so prominent in the user network does not register Page 14 of 16
 at all in the designer’s response. On the other hand, the workplace environment feature
 wireless headphones/laptops connects, yet stands apart from, two categories in both
 networks. It also exhibits high centrality within the network of the architect’s responses.
 Also interesting is that many of the vertices (workplace features or thought constructs)
 correspond to similar ones in the user group network, but forming different combinations,
 and thus metatopics. For example, in the user network the workplace feature overall open
 plan layout is allocated to the category of stylish and professional, but in the designer
 network it is connected to very different vertices and is allocated to the metatopic serious. In
 the user network the social hub is perceived as somewhat marginal, but for the designer it is
 an important feature (and has higher centrality values in this network).
 It might be expected that workplace environment features would take central positions in the
 designer’s network due to their focus on being on the physical elements of the workplace,
 with thought constructs taking secondary roles. But this is not the case. In both networks the
 central elements are roughly equally divided between workplace environment elements and
 thoughts and feelings.
 Conclusion
 The application of network analysis to interviewee response constructs demonstrates a way
 in which networks can be used to visualise ‘group think’. The significance of being able to
 analyse centrality of constructs and identify clusters using network analysis is highlighted in
 this case by the fact that the user environment-response network is quite different from that
 of the architect who is professionally expected to predict the implications of design decisions
 on the users’ perception of space.
 By using network analysis to help identify central vertices (constructs that control a
 constellation of themes) and metatopics (collective orientations or themes), observations are
 gathered on the structure of collective mental representation of built environments. This is
 considered an initial step towards further research into interaction between designer and
 users.
 Acknowledgements
 Many thanks to Jörg Krämer (Dipl. Ing.) for providing essential programming skills and
 helpful comments.
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