User Experience as Mental Contents Pertti Saariluoma, Jussi P.P. Jokinen, Sari Kuuva University of Jyväskylä, Finland [email protected], [email protected], [email protected] Jaana Leikas VTT Technical Research Centre of Finland [email protected]

Abstract User experience has received a lot of attention in human technology interaction research. Although widely used, it is still a concept, which needs theoretical and methodological clarification. Our study adopts the theoretical framework of mental contents in the investigation of user experience. We study user experience as mental contents by analysing affective participant responses to drinking glasses. Two experiments are reported, the first with pictures of drinking glasses as stimuli and the second with real drinking glasses. Our analysis shows that user experience can be operationalised as dimensions of mental contents, which can be studied using questionnaires containing semantic differential items. Three similar factors emerged in both experiments: beauty, decorativeness, and practicality. Beauty and practicality were shown to be associated with the participant’s willingness to own the glass. The results suggest that our methodology and ontological approach based on taking ‘user experience as mental contents’ has implications for design. KEYWORDS: user experience, mental contents, design thinking

Introduction The notion of user experience (UX) has become important for current human technology interaction (HTI) design thinking (Norman 1995; Law et. al. 2009). This is understandable, as it opens a new dimension for the research: whereas most of the research and design has concentrated on how people should use technology or on how they would be able to use it, user experience is about how people feel – and what they would like to feel – while using technology

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(Hassenzahl & Tractinsky 2006; Jordan 1998). This is an important aspect of HTI research and design, not only because we want to understand the user more deeply but also because it is connected to the general motivation of users to use and buy technology. Although elements of UX research have existed for a long while now, a more programmatic orientation towards the concept emerged in the 1990s. The goal then was to introduce elements beyond task- and work-related usability into the HTI research and design (Hassenzahl & Tractinsky 2006). A central programmatic objective was to focus on the holistic experience of the user. This meant that the investigators of HTI should study the real, lived experience in real environment with real tasks (Hassenzahl & Tractinsky 2006). This knowledge helps in designing technology, which takes human life into account. A study focused on experience entails two theoretical questions with methodological implications: what is experience, and how is it studied? In some studies, UX is implicitly defined as the totality of the subjective assessments about any product with which the user interacts. For example, a study by McKay, Cunningham, and Thomson (2006) implicitly define experience as feelings, thoughts and emotions, which manifest during interaction with a product. The studies of this kind start with the notion of the holisticity of experience, but we feel that they are missing the important theoretical problematisation of what experience is. According to Wright, McCarthy, and Meekison (2004), experience is created through a sensemaking process including anticipating, connecting, interpreting, reflecting, appropriating and recounting. We believe that the goal of a UX study is to define these kinds of elements of experience and the relation between the elements to build an analytical model of UX. To show that UX can be analysed as different dimensions, which can then be shown to be an important part of the total or holistic user experience, UX researchers must be able to procure empirical data, which focuses on an element of experience but takes into account the context of the experience. The central question in empirical UX research is the operationalisation of UX, which means production of valid measurement instruments to capture UX. In order to capture the subjective experience of the user, most UX studies use questionnaires, interviews, and observations as their method of data collection (Bargas-Avila & Hornbaek 2011). This, however, should only be the first step. The second would be to show how the studied experience is structured and how this structure relates to the context of the experience. The goal of this agenda might be called the ‘ontology of user experience’. An ontology is a content theory about the sort of objects, properties of objects, and the relations between objects in a specific domain of knowledge (Chandrasekaran, Josephson, & Benjamins 1999; Rousi, Saariluoma, & Leikas 2010). The relevance of a good research and design thinking ontology is in the clarification of the structure knowledge. A good UX ontology of drinking glass, for example, helps the designer of a drinking glass to take into account how the potential user represents mentally, evaluates, and desires drinking glasses.

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Our approach into defining the structure of user experience is user psychology. The goal is to formulate a model of the experiencing user, a model that would be psychologically relevant (Saariluoma 2003; Saariluoma & Oulasvirta 2010). Our problem is thus, how we can explicate the key concepts in user experience discourse in mentally reliable concepts and practices. In this task, the core notion of the user experience ontology we are studying is mental representation (Newell & Simon 1972). On one hand, mental representations have a neural substrate, which serves as the representation vehicle. Its flexibility enables people to have modifiable representations (Newell 1994). On the other hand, representations have also mental contents, which can be sensations, images, memory, thoughts, propositions, beliefs, and emotions (Saariluoma 2003). Mental contents are always about something. They stand for some things and they can be about entities such as objects, people, ideas, action, events, and abstractions. The main notion is that representational contents are about something, i.e., they represent something outside themselves (Saariluoma 2003). It is logical to argue that mental contents can give us knowledge about what people do (Allport 1980; Newell & Simon 1972). If we know how people mentally represent their interaction with objects, we have important knowledge for designers. For example in art design, which is an important dimension of interaction research and design, it is possible to use information about the ways people represent objects in producing experiences (Kuuva 2007). The key is to design products, which take into account the mental contents, which people have while interacting with the products in question. In the conceptual framework of mental contents, experience can generally be seen as the conscious part of mental representations (Rousi, Saariluoma, & Leikas 2010). There are always subconscious parts of representations, which, on their part, are relevant for our understanding of conscious mental representations. Therefore, our analysis should not only concern conscious mental representing, or conscious experience, but also the different forms of tacit knowledge in mental representations. The goal of experimental design is to create circumstances in which relevant mental contents, conscious and tacit, become explicit and observable, and thus allow researchers to analyse human technology interaction as mental contents. The study reported here aims to show how this is accomplished.

General Method How to elicit mental contents from people? The study by Osgood, May, and Miron (1975) serves as a classical example of research on how people use a semantic differential scale consisting of affective word pairs, such as ‘good–bad’, to respond to different stimuli (see also Osgood 1952; Osgood & Luria 1954). Similar methodology was applied in in Kansei-engineering, in which the researcher aims to understand the semantic space, i.e. the relationships of the different expressions, of a product (Nagamashi 2001). In our study, we investigated contents of mental representations by asking test subjects to evaluate drinking glasses with the help of a semantic

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differential scale, which consisted of 30 (experiment 1) or 20 (experiment 2) separate adjective pairs, such as ‘hard–soft’ and ‘ugly–beautiful’. Our general data analysis strategy was to analyse latent UX-related mental contents. This was accomplished with the help of a variety of statistical methods such as multidimensional scaling (MDS) and confirmatory factor analysis (CFA). To increase the power of the analysis with our small sample sizes, the data were pooled to form an observation matrix, in which an appraisal of a single drinking glass by the participant yielded one observation unit. This resulted in a univariate observation matrix (see Osgood, May, & Miron 1975, p. 43; Singer & Willet 2003, p. 22), which was used to analyse and calculate general latent user experience factors. To control the problems of data pooling (see Nesselroade & Molenaar 1999), the reliability of the sum variables for each glass individually was tested using Cronbach’s alpha. Friedman test was used to analyse the difference in medians of the sum variables between the glasses.

Experiment 1 Method Subjects and stimuli. The first experiment was conducted as a pilot study to explore the possibilities of studying user experience as mental contents and using a semantic differential scale to elicit valid data concerning mental contents. Twelve university students participated in the experiment. Six of them were male and six female, and their mean age was 24.00 years (s.d. = 2.13). Eight pictures were chosen as the stimuli. Of the four different types of drinking glasses depicted in those eight pictures, each type was represented by a picture of a coloured glass and a picture of a clear, transparent glass. The glasses used in the experiment were Aino Aalto (by Iittala), Kartio (by Iittala), Ote (by Iittala), and Grcic (by Iittala). The alternative colour option was ultramarine blue for the first two glasses, sand yellow for Ote glass, and grey for Grcic glass. The Iittala glasses were chosen for their classical design and recognisability, and the Ikea glasses were chosen to provide for different, but still typical experience of a drinking glass. The order of the eight pictures was randomised for each participant and displayed on a computer screen. Only one picture was visible at any given time. Procedure. The experiment had two parts for each participant, but we report here only the results of the second part. In the first part, the task of the participants was to touch real drinking glasses, which they could not see, and describe them freely. The glasses were the same in both phases, but obviously the colour was not important in the first task. In the second part, the task of the participants was to appraise the eight drinking glasses with a questionnaire containing 31 semantic differentials. The items of the semantic differential scale were composed from an earlier test, in which test subjects were asked to freely describe drinking glasses, which they could see, and touch (partly reported in Rousi, Saariluoma, & Leikas 2010). The items were adjective pairs,

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such as ‘traditional – futuristic’, and for each of the 31 adjective pairs, the participants picked one numeric value between 1 (e.g. ‘very traditional’) and 9 (e.g. ‘very futuristic’). Data analysis. Multidimensional Scaling (MDS) is a statistical method, which produces an ndimensional representation of the similarities between the variables in the analysis. MDS is a good way of eliciting knowledge representations of individuals (Oldon & Biolsi 1991), but it can also be used to explore more general inter-individual commonalities. The S-Stress value from the MDS solution can be used to confirm how many dimensions are suitable for representing the data. When the stress value stops decreasing notably, the number of dimensions is acceptable (Kruskal & Wish 1978). In our study, MDS was used to produce a visualisation of the similarities between the semantic differential items. The MDS solution (or ‘configuration’) was obtained using the ALSCAL program in PASW Statistics 18. The program was instructed to create distances from data, using Euclidian distance interval, and the transform values were Z-score standardised for better visualisation. Analysing the resulting configuration, we were able to hypothesise a small number of latent UX factors, seen as clusters of similar items of the semantic differential scale. These factors can be used to represent the user experience as measured using a larger number of individual variables. The internal consistency of the hypothesised user experience factors was calculated using Cronbach’s Alpha (α). The reliability was verified both for the pooled data and for each glass individually. After reliability test, the factors were calculated as the mean of the semantic differential items indicated by the MDS configuration. Friedman test was used to test the hypothesis that the values of the UX factors differed between the glasses.

Results The S-Stress value for the obtained two-dimensional solution was .321, and as its improvement stopped at this point, we can conclude that a two-dimensional solution suffices to represent the semantic space of the items appraised in this experiment. The similarities between the items of the semantic differential scale can be seen in Figure 1, which is the visualisation of the MDS solution or configuration. For the purposes of clarity, only the other side of the differential is shown (for example ‘light’ instead of ‘heavy–light’). In the MDS configuration, similar items are close to each other: for example, ‘light’, ‘warm’, and ‘soft’ are seen here to be similar to each other. This indicates that in appraising the glasses, some contents, which the participants used, had conscious or unconscious relation to each other. Because the amount of individual items (31) and the number of appraised items (8 drinking glasses) were large, and because the resulting solution is obtained from the answers of all 12 participants, it is plausible to argue that at least some of the similarities tell us of the unconscious mechanism of how mental contents are created in an interaction with products.

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Four clusters (or factors) were created to represent the UX of drinking glasses. The naming of the clusters was based on the descriptiveness of the categorical relations, not on statistical analysis. (1) Decorated. This cluster had a pooled (all data together) reliability of α = .842 and, except for one glass, a non-pooled class-specific reliability higher than .700. The exception was clear (non-coloured) Kartio with α = .508. (2) Beautiful. The pooled reliability of this cluster was α = .867, and individual reliabilities for each eight glasses were higher than .700. (3) Cognitive. This cluster had a pooled reliability of α = .734, and unpooled reliabilities were all higher than .600 except for ultramarine Aalto, for which α = .563. (4) Practical. For this cluster, the pooled reliability was α = .772 and non-pooled reliabilities above .600 except for both clear and ultramarine Aalto (α = .526 and α = .560 respectively). Table 1 shows the medians of the four sum variables for each glass. Medians are shown instead of means, because non-parametric tests were used to compare the differences. This was due to the non-normality of some of the sum variables. Friedman tests revealed that there was a statistically significant difference in how different glasses were experienced when measured by these factors.

Discussion The reliability of the constructed appraisal dimensions was sufficient for all of the individual drinking glasses to support the claim that there are general content dimensions, which manifest themselves as individual appraisals on a semantic differential scale. The important dimensions were Decorated, Beautiful, Cognitive, and Practical. The medians of the sum variables representing these dimensions differed for the drinking glasses, which supports the hypothesis that the user experience measured as latent factors is affected by the stimuli and as such is a valid representation of the affective experience of the interaction situation. The main result of the study was that it is possible to discriminate the latent dimensions of mental contents of user experience by asking people to appraise products using a semantic differential scale. The method can in theory be used to study the user experience of any technological object from everyday items such as drinking glasses to more complex technologies such as industrial interfaces. What is important is the choice of relevant semantic differential items: what is an important quality of a drinking glass may not be such for a computer program, for example.

Experiment 2 The pilot experiment revealed that it is possible to obtain factors of user experience by asking subjects to assess drinking glasses, using a semantic differential questionnaire. The goal of the following experiment was to repeat the general result of the pilot experiment and offer deeper understanding for the analysis of user experience as mental contents.

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Method Subjects, stimuli, and procedure. Of the 40 university students recruited for the tests, 13 were male and 27 female. Their mean age was 24.88 (s.d. = 4.43). Four drinking glasses were presented to the participants in a counterbalanced order (different order of glasses for different participants), and the participants were asked to fill in the semantic differential questionnaire for each glass. The glasses were Kartio by Iittala, Godis by Ikea, Verna by Iittala, and Rättvik by Ikea. Instead of pictures of drinking glasses, we wanted the participants to appraise real drinking glasses, which they could both see and touch. We also made two changes in the questionnaire. First, we reduced the number of semantic differential items from 31 to 20 by removing, among others, the cognitive attributes, as they are self-evident: it is not possible to evaluate an object without sensory contents of it. The goal was to see whether we still could get similar latent mental contents with respect to the three other content attributes, i.e., beauty, practicality, and decorativeness. Second, as we also wanted to analyse the desirability of each glass, we added a measure of ‘willingness to own a glass’ in the questionnaire. The scale was the same as in the semantic differential items: from 1 (‘very unwilling to own’) to 9 (‘very willing to own’). Data analysis. The beginning of the analysis was similar to that in the pilot experiment, and will not be repeated here in detail. The new addition to the second experiment was the use of Confirmatory Factor Analysis (CFA), which can be used to analyse the concept validity of statistical measures (Bryant, King, & Smart 2007). In our analysis, CFA was used to confirm that the variables suggested by the MDS were true instances of a latent factor. Factor loadings more than .500 and statistically non-significant Goodness-of-Fit test results were taken to support the hypothesis that the individual appraisals are instances of an underlying dimension measures (Bryant, King, & Smart 2007). To analyse the relation between the extracted user experience factors and the participants’ willingness to own the drinking glasses, we conducted Linear Regressions both to the pooled data (all glasses together) and separately for each of the glass.

Results The S-Stress value for the two-dimensional MDS solutions was .153, which was an improvement from the .321 of the first experiment, probably due to the smaller number of semantic differential items (20 instead of 31) and glasses (four instead of eight). The visualisation of the MDS solutions is shown in Figure 2. Based on it, latent user experience factors were created as sum variables. Their naming was based on the descriptiveness of the name, not on statistical analysis. (1) Beautiful. The reliability of this scale was α = .883, and the reliability of the measure for individual glasses was also over .800. (2) Decorated. The reliability of this scale was α = .838. For each glass, the reliability of this measure was over .700. (3) Practical. The reliability of this scale was α = .814. For each glass, the reliability was over .600 except for Kartio, for which it was α = .521.

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Confirmatory factor analysis was conducted for each three factor. The results are shown in Table 2. For each factor, the factor loadings of individual variables were more than .500. Goodness-offit test was statistically non-significant (p > .05) for the first and the second factor but significant for the third factor, p < .001. The results are consistent with the reliability measures above. Friedman tests were conducted to analyse the difference of medians of the user experience factors between the glasses. Each three factors had statistically significant difference in medians between the four glasses. Table 3 shows the medians of the three factors for each glass and the results of Friedman tests. Linear regression for predicting the desirability of the glass for the pooled data matrix produced a model, which is shown in Table 4. Model adjusted R² = 613 and F(3) = 85.030, p < .001. The results indicate that the first and the third factor, i.e., beauty and practicality, predict very well the desirability of the drinking glass. Beauty, however, affected the desirability over three times as much as practicality. How decorated a glass was appraised did not predict the desirability of that glass.

Discussion We were able to repeat the general result of the first experiment, i.e., that user experience can be analysed as latent factors. Of course, the cognitive dimension was unobserved, as we had no questions relevant in it. However, the significantly smaller stress value for the MDS solution suggests that lowering the number of items in the semantic differential results in a more coherent representation of the affective experience, and perhaps it is not necessary to include the selfevident cognitive factor into questionnaires of this type. The other three factors were very similar in both of the experiments, which suggests that the reported factors reflect more general dimensions of UX, at least for drinking glasses. A noteworthy result was also that beauty and decorativeness are different mental contents. A common sense approach might take these to be very closely associated, but our results show that this is not the case with the drinking glasses we chose for the study. The result is in line with the basic philosophy of Scandinavian design movement, which emphasizes simplicity. Being decorated is not the requirement of being beautiful, and it seems that the users have internalised this way of evaluating drinking glasses. Confirmatory factor analysis revealed that the sum variables had statistical construct validity. This gives validity to our general assumption that UX can be operationalised as factors, which can further be operationalised as multiple items on a semantic differential scale. By a deeper analysis of the results of CFA, it becomes possible to analyse how the underlying UX factors are captured by the individual semantic differential appraisals. For example, being playful (and not grave) does not have as good an association with the ‘decorated’ factor, as being creative (and not unimaginative) has. This helps the designer of a drinking glass to understand what meaningful contents create the feeling about decorativeness of a drinking glass: the more loading the affective content gets, the more important it is if the

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designer sets this factor of user experience as user experience target for the object which is being designed. We were also able to confirm the hypothesis that the user experience factors can be used to predict the desirability of a glass. Especially appraisals of beauty, but also practicality, were shown to affect the subjects’ willingness to own the glass. This is an important result because it further validates our methodology: the proposed user experience factors can be used to predict attitudes toward the product. It seems, for example, that although practicality is a meaningful and important part of an experience about a drinking glass, beauty is the most important factor and should be targeted in UX-focused design of drinking glasses.

General Discussion Mental contents, i.e., the information contents of human mental representations, can be explicated by verbalisations. There are numerous ways one can carry out this process of investigating mental contents. It can take place by means of protocol analyses (Ericsson & Simon 1984), by means of focus groups (Leikas 2007), and by means of observing behaviour and interviewing subjects (Piaget 1976), but it can also be done by using questionnaires. All methods have their strengths and weaknesses. Questionnaires give information about issues, which are present in the questions. Thus, free interviews or protocols give information about unexpected aspects of mental contents more readily. On the other hand, questionnaires can be informative with respect to the latent structures in mental contents, that is, they enable researchers to get a glimpse about subconscious layers of mental contents. In both experiments, we found a number of similar and coherent dimensions of experience. ‘Beautiful’, ‘decorated’, and ‘practical’ were present in the analyses of both experiments. In addition, in the first experiment a cognitive dimension became explicit. These dimensions of mental contents seem quite valid, as they are typical for Scandinavian design, and all the glasses in this sample belonged to this style. This design is characterized by simple and practical forms, but still the objects are carefully designed. Our analysis thus illustrates two aspects of mental contents relevant for user experience. Firstly, people generate their conscious responses to stimuli. This means people respond on the ground of their conscious mental contents or experiences. Questionnaires provide them an opportunity to assess how they have consciously experienced or how they are experiencing design objects. However, the analysis of associations between individual questionnaire items provides information about latent and subconscious associative conceptual memory systems between elements. The analysis illustrates that, below experience, subjects have subconscious dimensions' mental contents, which give structure for the mental representations. Researchers can verbally label these structures to make them explicit.

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The results are valuable for designers, as they reveal information about how people mentally represent design objects. The designers can learn what kinds of attributes belong together and what kinds of attributes are meaningful for the audience. If there are different audiences, the designer can use the empirical methods used here to analyse the different groups and their relations to each other. This helps them in creating emotional experiences and aids them in testing and assessing their plans.

References Allport, D. A. (1980). Patterns and actions: cognitive mechanisms are content specific. In G. Claxton (ed.), Cognitive psychology: new perspectives. London: Routledge and Kegan Paul. Bargas-Avila, J. A., & Hornbaek, K. (2011). Old wine in new bottles or novel challenges: a critical analysis of empirical studies of user experience. Proceedings of the 2011 annual conference on Human factors in computing systems (pp. 2689–2698). Bryant, F., King, S., & Smart, C. (2007). Multivariate statistical strategies for construct validation in positive psychology. In A. Ong & M. Dulmen (eds.), Oxford Handbook of methods in positive psychology (pp. 61–82). Oxford: Oxford university press. Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (1999). What are ontologies, and why do we need them? Intelligent Systems and Their Applications, 14(1). 20–26. Ericsson, K., & Simon, H. (1984). Protocol Analysis. Cambridge, Mass: MIT press. Hassenzahl, M., & Tractinsky, N. (2006). User experience-a research agenda. Behaviour & Information Technology, 25(2). 91–97. Jordan, P. W. (1998). Human factors for pleasure in product use. Applied Ergonomics, 29(1). 25–33. Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, California: SAGE Publications. Kuuva, S. (2007). Content-Based Approach to Experiencing Visual Art. Jyväskylä Studies in Computing 76. Jyväskylä: Jyväskylä University Printing House. Law, E., Roto, V., Hassenzahl, M., Vermeeren, A., & Kort, J. (2009). Understanding, scoping and defining user experience: a survey approach. Proceedings of the 27th international conference on Human factors in computing systems (pp. 719–728). Leikas, J. (2007). Idea movement of aging citizens: Lessons-learnt from innovation workshops. In C. Stephanidis (ed.), Universal access in HCI (pp. 923-931). Berlin: Springer. Nagamachi, M. (2001). Workshop 2 on Kansei Engineering. Proceedings of International Conference on Affective Human Factors Design, Singapore, 2001. Nesselroade, J. & Molenaar, P. 1999. Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor Analysis. In R. H. Hoyle (ed.), Statistical Strategies for Small Sample Research (pp. 223–250). California : SAGE Publications. Newell, A. (1994). Unified theories of cognition. Cambridge, Mass.: Harvard University Press. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, N.J.: Prentice-Hall.

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Norman, D. A. (2004). Emotional design: why we love (or hate) everyday things. New York: Basic Books. McKay, D., Cunningham, S. J., & Thomson, K. (2006). Exploring the user experience through collage. Proceedings of the 7th ACM SIGCHI New Zealand chapter’s international conference on Computer-human interaction: design centered HCI (pp. 109–115). Oldon, Judith, & Kevin Biolsi. 1991. Techniques for representing expert knowledge. In A. Ericsson, & J. Smith (Eds.), Toward a general theory of expertise: prospects and limits (pp. 240-285). Cambridge: Cambridge University Press. Osgood, C. (1952). The nature and measurement of meaning. Psychological Bulletin, 49. 197–237. Osgood, C. & Luria, Z. (1954). A blind analysis of a case of triple personality using the semantic differential. Journal of Abnormal and Social Psychology, 49. 579–591. Osgood, C., May, W., & Miron, M. (1975). Cross-Cultural Universals of Affective Meaning. Chicago: University of Illinois Press. Piaget, J. (1976). The grasp of consciousness. Cambridge: Mass. Harvard University Press. Singer J., & Willet, B. 2003. Applied Longitudinal Data Analysis. Oxford: Oxford University Press. Rousi, R., Saariluoma, P. & Leikas, J. (2010). Mental Contents in User Experience. In Proceedings of MSE2010 V.II 2010 International conference on Management and Engineering, October 17-18, 2010, Wuhan, China (pp. 204–06). Hong Kong: ETP Engineering Press. Saariluoma, P. (2003). Apperception, content-based psychology and design. In U. Lindeman (Ed.), Human behaviour in design (pp. 72–78). Berlin: Springer. Saariluoma, P., & Oulasvirta, A. (2010). User psychology: Re-assessing the boundaries of a discipline. Psychology, 1(5). 317–328. Schutte, S. (2005). Engineering Emotional Values in Product Design. Kansei Engineering in Development. Linköping: UniTryck. Wright, P., McCarthy, J. & Meekison, L. (2004): Making sense of experience. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology: From usability to enjoyment (pp. 43–53). Norwell, Ma.: Kluwer Academic Publishers.

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Figure 1: MDS solution for pooled glass appraisals. The ellipses represent the four chosen clusters of user experience about drinking glasses.

Table 1: The medians of the four user experience factors between the drinking glasses, and Friedman test results. Factor

Median for glass

Friedman

CA

CK

CO

CG

UA

UK

SO

GG

χ2(7)

1 Decorated

5.92

5.67

2.50

4.00

5.33

4.92

5.83

4.33

28.97 (.000)

2 Beautiful

5.36

5.50

5.29

6.07

4.79

4.36

6.07

6.21

21.67 (.003)

3 Cognitive

4.00

3.20

4.30

4.40

5.70

5.60

4.40

4.60

21.88 (.003)

4 Practical

5.30

5.00

7.40

6.30

4.80

5.00

6.30

6.00

28.77 (.000)

Note. CA = Clear Aalto, CK = Clear Kartio, CO = Clear Ote, CG = Clear Grcic, UA = Ultramarine Aino Aalto, UK = Ultramarine Kartio, SO = Sand Yellow Ote, GG = Grey Grcic.

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Figure 2: MDS solution for pooled glass appraisals. The ellipses represent the three chosen clusters of user experience about drinking glasses.

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Table 2. Factor loadings and communalities for affects in confirmatory factor analysis. Three confirmatory factor analyses were run separately. The name of each factor is bolded. Variable

Factor loading

Communality

inviting

.897

.805

beautiful

.819

.671

pleasant

.793

.628

interesting

.750

.563

experiential

.880

.774

creative

.770

.592

decorated

.733

.537

luxurious

.643

.413

playful

.578

.334

ordinary

.762

.581

simple

.760

.578

general-purpose

.749

.560

practical

.638

.408

Table 3: Median of the three user experience factors for the glasses and the result of Friedman test on the median differences between the glasses. Factor

Median for glass

Friedman

K

G

V

R

χ2(3)

1 Beautiful

6.63

5.50

6.75

6.75

22.90 (.000)

2 Decorated

3.60

3.00

4.80

5.70

45.31 (.000)

3 Practical

8.00

7.38

6.50

5.38

57.08 (.000)

Note. K = Kartio, G = Godis, V = Verna, R = Rättvik

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Table 4: Linear regression analysis for the pooled data matrix predicting the desirability of the glass as measured by the willingness-to-own-the-item scale. Model adjusted R² = 613 and F(3) = 85.030, p < .001 Factor

B

β

sig.

(Constant)

-4.268

1 Beautiful

1.1216

.762

.000

2 Decorated

.096

.062

.469

3 Practical

.366

.244

.001

.000

Note. B = Unstandardised coefficient. β = Standardised coefficient.

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