Capturing User Experiences of Mobile Information Technology with the Repertory Grid Technique

An Interdisciplinary Journal on Humans in ICT Environments ISSN: 1795-6889 www.humantechnology.jyu.fi Volume 6 (2), November 2010, 250–268 250 CAPTURING USER EXPERIENCES OF MOBILE INFORMATION TECHNOLOGY WITH THE REPERTORY GRID TECHNIQUE Abstract: We describe the application of the repertory grid technique (RGT) as a tool for capturing the user experience of technological artifacts. In noting the artificiality of assessing the emotional impact of interactive artifacts in isolation from cognitive judgments, we argue that HCI techniques must provide practical solutions regarding how to assess the holistic meaning of users’ interactive experiences. RGT is a candidate for this role. This paper takes the reader step by step through setting up, conducting, and analyzing a RGT study. RGT is a technique on the border between qualitative and quantitative research, unique in that it respects the wholeness of cognition and does not separate the intellectual from the emotional aspects of the user experience. Compared to existing methods in HCI, RGT has the advantage of treating experiences holistically, while also providing a degree of quantitative precision and generalizability in their capture. Keywords: user experiences, mobile HCI, repertory grid, design. INTRODUCTION Adopted from the cognitive psychology of the 1970s and in force until relatively recently, the main theoretical approach to understanding human–computer interaction (HCI) was to view a person interacting with a computer generally as a disembodied information processor. Similarly, the standard methodological practice was to perform various lab-based quantitative experiments to gain empirical insight into the usability of a particular interactive device or environment, typically understood in terms of the specific qualities of the information processing involved. The nature of the users’ experiences during interaction, that is, how he or she felt about it, was not considered or addressed. In the last two decades, many of the limitations of this approach have been well documented within HCI by, for instance, Suchman (1987), Winograd and Flores (1986), Landauer (1991), and others. To a large segment of the HCI community, it has been clear for © 2010 Daniel Fallman and John Waterworth, and the Agora Center, University of Jyväskylä URN: NBN:fi:jyu-201011173094 Daniel Fallman Interactive Institute Umeå and Department of Informatics Umeå University, School of Architecture Sweden John Waterworth Department of Informatics Umeå University Sweden Capturing User Experiences 251 many years that there is more to the interaction between human users and interactive artifacts than information processing, and that methods other than tightly controlled experiments are needed if more experiential aspects of interaction are to be captured. Thus, since the early 1990s, HCI researchers have increasingly explored broader issues to gain an understanding of the relationship between the user and the artifact in terms of, for instance, affective qualities, fun, and playability. In other words, researchers and practitioners are starting to consider the user not just as a processor of information and an experimental subject, but rather as an individual with hopes, desires, expectations, and emotions. During the period when this change of perspective in HCI was gradually taking place, psychological approaches to cognition had already moved on. After a long period in the psychological wilderness, emotion became recognized within mainstream cognitive science as a fundamental component of cognition, of our making sense of the world. As neuroscientists such as Antonio Damasio (1994, 1999) pointed out, not only are our experiences limited without emotion, but we cannot make decisions. Affect is seen as an essential component of reasoning about the world, not an opposing force. Although we may loosely speak of emotion versus reason, both too much and too little emotion will have a negative impact on cognition, with the latter being the more pathological. Understanding the nature and varieties of conscious experience is also a central topic for contemporary cognitive science. For example, huge advances have been made in identifying the neural correlates of a range of subjective states, and relating these to verbal phenomenological reports and behaviors. Experiences and behaviors are viewed as two integrated effects of the same neural events, not as separate things. In attempts to deal with and speak about these new issues in HCI, which are far more complex than the simple human processing and associated usability views they have come to replace, the concept of user experience has become a key concept in recent HCI research. While there is no unified theory about the role and implication of experience to design (Forlizzi & Battarbee, 2004), a number of efforts have been made recently within HCI to establish a better understanding of the role of user experience in interactive systems design (see, e.g., Fallman, 2003, 2006; Forlizzi & Battarbee, 2004; Forlizzi & Ford, 2000; Hassenzahl & Tractinsky, 2006; Ketola & Roto, 2008; Law, Roto, Hassenzahl, Vermeeren, & Kort, 2009; McCarthy & Wright, 2004; Waterworth & Fallman, 2007). A central issue in current user experience research is methodological: Exactly how do we best capture the experiences users have while being exposed to various designs? Purely quantitative measures, such as success rate and reaction time, do not seem to relate directly to users’ experiences even though they may be useful in predicting some aspects of user performance under certain conditions. On the other hand, qualitative approaches, such as interviews and questionnaires, often lack any external validation and are limited in terms of generalizability and reliability. What is needed is a hybrid approach that provides a quantifiable and reliable measure, while also capturing subjective aspects of the experiences engendered by specific HCI designs. In this paper, we provide an example of a candidate technique that we believe can be useful for getting insights into users’ experiences of interactive artifacts in a quantitative way. We start from the position that interaction is about finding meaning, and that this involves judgments that result from a highly integrative blending of rational and affective elements, each relying on the other in producing a user’s experience of an artifact. Meaning here refers to the sense individuals make of artifacts; we take things to mean what they are experienced Fallman & Waterworth 252 to be, reflecting the close coupling of rationality and affect. As observers of our own experiences, we cannot separate the two, except perhaps in extreme cases. In what follows, we describe and illustrate what we consider to be a promising technique for capturing the dimensions of meaning that characterize user experiences of technology in a holistic, yet also quantitative, way: the repertory grid technique (RGT). THE REPERTORY GRID TECHNIQUE The repertory grid technique (RGT).is a structured procedure of eliciting a repertoire of conceptual structures and for investigating and exploring them and their interrelations (Bannister & Fransella, 1985; Dalton & Dunnet, 1992; Landfield & Leitner, 1980). It has been found to be a useful technique for eliciting meaning in several different domains, for instance in organizational management, education, clinical psychology, and particularly in the development of knowledge-based systems (Boose & Gaines, 1988; Shaw, 1980; Shaw & Gaines, 1983, 1987). RGT is a methodological extension of Kelly’s (1955) personal construct theory. Kelly argued that we make sense of our world through our own construing of it. That is, we tend to model what we find in the world according to a number of personal constructs that are bipolar in nature and structure our experiences of the world. For instance, according to Kelly, we judge other people through forming personal constructs such as tall–short, light–heavy, handsome– ugly, and so on. A construct is essentially a single dimension of meaning for a person allowing two phenomena to be seen as similar and thereby as different from a third (Bannister & Fransella, 1985). Experiences arise from the interaction of multiple personal constructs. What is a Repertory Grid? While RGT is a technique for eliciting personal constructs, and a repertory grid is the outcome of a successful application of the technique. It is a table, a matrix, whose rows contain constructs and whose columns represent the elements of the phenomena under investigation. Repertory grids also typically embody a rating system used to relate each element quantitatively in relation to the qualitative constructs. An individual repertory grid table is constructed for each subject participating in a RGT study. This construction process, which will be described in detail later in this paper, is fairly straightforward. First, an individual participating in an elicitation session produces her (usage intended to be inclusive) own constructs, that is, what bipolar dimensions of meaning the person sees as the most important for talking about the elements (the investigated phenomena). The construct elicitation process is typically facilitated by the use of triads, through which the participant becomes exposed to sets of three elements at a time and is asked to describe and put a label on what he or she sees as separating one of the elements in the group from the other two. Second, after having provided her own individual, qualitative constructs, the participant is asked to rate the degree to which each element in the study relates to each bipolar construct according to some scale (typically a binary or Likert-type scale). Hence, in RGT, constructs and elements are the two building blocks of each individual’s unique repertory grid table, and which are quantitatively related to each other by the use of some rating system. The constructs represent the qualities the Capturing User Experiences 253 participants use to describe the elements in their own personal words (Fransella & Bannister, 1977). Constructs thus embody the participant’s meaning and experience in relation to the study’s elements. RGT in Human–Computer Interaction RGT has been found to be a useful technique for eliciting people’s experiences and meaning structures in several different domains, including information systems (Tan & Hunter, 2002), education, clinical psychology, and particularly the development of knowledge-based systems (Boose & Gaines, 1988; Shaw, 1980; Shaw & Gaines, 1983, 1987). Despite its popularity in these fields, the interest in RGT from an HCI perspective peaked in the 1980s, with a special issue devoted to the topic in the International Journal of Man-Machine Studies in 1980. Since then, the technique’s appearance in HCI-related literature has been sparse, while not completely nonexistent (see, e.g., Dillon & McNight, 1990; Grose, Forsythe, & Ratner, 1998; Hassenzahl & Wessler, 2000; Tomico, Karapanos, Levy, Mizutani, & Yamanaka, 2009). This lack of popularity may be due to fairly strong association with artificial intelligence and expert systems development in the 1980s, developments that came to epitomize the cognitivist viewpoint from which many HCI researchers were intent on distancing themselves. Tan & Hunter (2002) recommend RGT as a means of studying the cognition of professionals and users of information systems in organizational settings, and review four examples of previous work focusing on its use for knowledge modeling. The emphasis of this kind of work is more on identifying experts’ cognitive rules than on the nature of subjective experiences with technology. But recently, there has been a modest resurgence of interest in RGT as a means of capturing dimensions of user experiences with technology, as shown in research on loudspeaker array design (Berg, 2002) and subjective aspects of immersive virtual reality (Steed & McDonnell, 2003); and, more recently, to help understand cross-cultural differences in the experience of different designs of writing pen (Tomico et al., 2009). The intention of the present paper is to further explore the potential of RGT, and to bring it to the attention of the HCI community as a possible integrative approach to understanding user experiences in HCI. This approach assumes that emotion and reason are essential and interrelated parts of making sense of the world, and provides results that are both subjective and quantitative. The following sections take the reader step by step through the setting up and carrying out of an HCI study using RGT in the context of mobile interaction devices. USING RGT TO CAPTURE THE EXPERIENCE OF USING MOBILE INFORMATION TECHNOLOGY In the study described below, we were interested in how people experience mobile information technology, as embodied in existing products and newly developed research prototypes. In addition to a general interest in how people relate to this kind of technology, we wanted particularly to gain empirical insight into what kinds of meanings people ascribed to the different styles of interaction these various devices embodied. The study involved existing off-the-shelf devices, as well as a number of research prototypes that represent a range of alternative means of interaction. Fallman & Waterworth 254 Participants The empirical data collection process was carried out over a period of 3 weeks. In total, 18 participants took part in the study, all of which had previously volunteered by signing up for a scheduled time slot. Of the total number of participants, 14 (78%) were males and 4 (22%) were females. Eight of the participants (44%) were in the age span of 20–29, seven (39%) were 30–39 years of age, two (11%) were 40–49, and one (6%) was 50–59 years. As assessed by a preparatory questionnaire, three participants (16%) rated themselves as 3 on a 5-graded scale of self-estimated computer literacy, 14 (78%) rated themselves 4, while only one (6%) indicated 5. On a similar scale from 1 to 5, when asked to rate their previous exposure to mobile information technology, one participant (6%) responded with a 2, six (33%) rated themselves as 3, nine participants (50%) rated themselves 4, while two (11%) considered themselves to be 5 out of 5. As a sign of appreciation for their participation in the study, participants were provided cinema tickets. Each session lasted from 45 minutes to two hours, averaging slightly more than an hour. All participants took part in the study individually, with only the participant and the experimenter in the room. With the exception of a single native English speaker, the other 17 participants were native Swedish speakers. The study was carried out in each participant’s native language and carefully translated for this paper. Step 1: Element Familiarization All 18 sessions began with the participant being exposed to seven different mobile information technology devices. Three of them were examples of existing devices; a Compaq iPaq H3660 personal digital assistant (PDA, known in the study as E0), a Canon Digital Ixus 300 digital camera (E1); and a Sony Ericsson T68i mobile phone (E2). Four research prototypes were also part of the study (see Figure 1, a–d). The ABB Mobile Service Technician (E5, Figure 1a) is a wearable support tool for service technicians in vehicle manufacturing (Fallman, 2002). The Dupliance prototype (E4, Figure 1b) is a physical/virtual communication device for preschool aged children (Fallman, Andersson, & Johansson, 2001). The Slide Scroller (E3, Figure 1c) combines a PDA with an optical mouse to form a novel way of interacting with Web pages on palmtop-size displays (Fallman, Lund, & Wiberg, 2004). Finally, the Reality Helmet (E6, Figure 1d) is a wearable interactive experience that alters its user’s perceptual experience (Fallman, Jalkanen, Lörstad, Waterworth, & Westling, 2003; Waterworth & Fallman, 2003). Each session started with the seven devices being presented, one by one, to the participant. We provided brief (3–5 minutes each) introductions to the different contexts of the four research prototypes and the projects from which they originated. The participant was then able to try out each device for as long as necessary in order to become familiar with it. The session organizer was always available during the session and willing to answer any questions posed by the participants. Step 2: Construct Elicitation After the preparatory questionnaire had been completed, the elicitation of a participant’s constructs for the seven elements (devices) began. Each participant sat at a table opposite to the Capturing User Experiences 255 Figure 1. The four research prototypes that, together with three existing devices, were part of this study. experimenter. On the table, seven palm-sized cards where displayed. Every card contained the following: a photograph of one of the devices; a label on which the name of the device was printed; and the identification number used for organizing the study (i.e., E0 to E6). In each session, the participant was exposed to the seven devices in groups of three; this is known as triading in RGT’s technical language. Each triad was chosen from a list randomized prior to the study. (a) (b) (c) (d) Fallman & Waterworth 256 On a paper-based form designed especially for this study, the experimenter put down three identification numbers taken from a pre-prepared list, for instance E0, E4, and E5. The experimenter and the participant then together found the corresponding cards on the table and grouped them in front of the participant, while the remaining four cards were put aside. The participant was then asked to think of a property or quality that she considered notable enough to single out one of the three elements (devices) in the triad, and to put a name or label to that property. For instance, among a group of E1, E2, and E3, Participant 10 singled out E1, and labeled her experience as ―warm.‖ The participant was then asked to put a name or label on the property or quality that the other two devices in the triad shared in relation to the experience of E1. Participant 10 decided to collectively label E2’s and E3’s shared quality as ―cold.‖ Some of the participants were fairly quick in finding what they saw as appropriate labels to put on their experiences; others could remain silent for quite some time, thinking carefully to themselves, while a few others discussed loudly and in detail their thoughts and ideas with the experimenter. Although the experimenters tried to answer questions and generally took part in discussions initiated by the participants, we were careful not to generate or imply properties or concepts, in order to avoid putting our words into the participant’s mouth. To be able to keep the relation between construct and originator throughout the study, the suffix (S10) was added to each construct elicited from Participant 10. Hence, in this case, the elicited personal construct was Warm (S10)–Cold (S10). On the form there was also a preprinted table containing the elements, each with its own 7- grade Likert-type scale. After the triading session, the form was handed over to the participant with the instruction to grade each of the seven elements according to the bipolar scale that had just been constructed from the participant’s own concepts. That is, for each element of the study as a whole—including those that did not appear in the specific triad from which a particular construct pair was established—the participant was asked to rate or grade that element on a 7- point scale, 1 would represent a high degree of the property found to be embodied in a singled out device (e.g., in the case of Participant 10, ―warm‖), 7 would represent a high degree of the property embodied by the two other devices in the specific triad (i.e., ―cold‖). The Likert scale is the most widely used scale in survey research for measuring attitudes in which respondents are asked to express their strength of agreement, typically using an odd number of response options. For this study, we chose to apply a 7-grade scale, for two primary reasons. First, compared to an even-grade (a so-called forced choice scale), a scale with an odd number of choices does not force people to make choices that might not reflect their true positions). A grade 4 out of 7 thus indicates, statistically, that a construct has no particular meaning for a given element. This is important since the constructs in a repertory grid are constructed from triads in which only three out of seven elements appear. Second, because some people do not like making extreme choices (i.e., 1 or 7 out of 7), the 7-grade scale provides richer data than, for instance, 3- or 5-grade scales. Thus, for each triad exposed to a participant, two kinds of data were collected. First, a personal construct was elicited (i.e., a one-dimensional semantic space that the participant thought meaningful and important for discussing and differentiating between the elements of a triad). This process provided the study with qualitative data: insight into the participant’s own meaning structures, values, and preferences. Second, since each elicited personal bipolar construct was then used as the scale by which the participant rated all seven elements in the study using a 7-point Likert scale, data were also gathered about the degree to which Capturing User Experiences 257 participants thought their construct had relevance to a specific element. This provided the study with quantitative data used to find out how the different elements compare and relate to each other and to the constructs, described in detail below. This analysis reveals, or at least suggests, whether or not, for example, Participant 10’s construct ―warm–cold‖ is purely literal (i.e., referring to the actual temperature of the artifact) or metaphorical (i.e., referring to the emotional effect the artifact has on the participant). The same kind of statistical analysis would not have been possible if we had asked the participants to rank rather than rate the elements. To keep the length of the sessions roughly equal and in order not to make our participants weary, we decided to limit each session to 10 triads. Thus, from the 18 participants we elicited 180 pairs of personal constructs (i.e., 360 different concepts the participants thought meaningful and relevant) for describing their experiences of mobile information technology. At this point, it should be noted that a specific advantage of the RGT approach is that it is not necessary for the experimenter to share the specific meaning structures a participant holds in relation to an elicited construct at the time of elicitation. These are revealed during analysis by comparing the data connected with elicited constructs to data connected with other groups of elicited constructs. ANALYSIS OF REPERTORY GRID DATA While RGT is an open approach that results in a number of highly individual repertory grid tables, some basic structures are shared among the participants. Each table in this study consisted of a number of bipolar constructs; a fixed number of elements (7); and a shared rating system (a scale of 1 to 7). From this setup, there are at least two basic ways in which different people’s repertory grid tables may be compared and analyzed interpersonally (i.e., to compare different people’s repertory grids in different ways). First, the finite number of elements and the shared rating system provide the basis for applying statistical methods that search for variations, similarities, and other kinds of patterns in the series of numbers occurring in the numerical data (the ratings). Using relational statistical methods, it becomes possible to compare and divide all constructs from all participants into groups of constructs showing some degree of similarity. This may result in interesting and unexpected correlations between constructs whose relation would most likely have remained unnoticed if one were only looking for semantic similarity. This method may hence be called semantically blind, since it is driven primarily by each construct pair’s quantitative data in relation to elements. Second, several seemingly semantically related and overlapping groups of construct pairs appeared across the study’s participants. Some similar bipolar scales, for instance, young–old, appliance–multifunctional, and work–leisure, can be spotted among the responses from several of the participants. It would be possible to go through the list of all participants’ constructs and gather in groups those that bear semantic resemblance to each other, and analyze these groups (e.g., using discourse analysis). This approach could be regarded as statistically blind, since it is driven by an interpretation of the semantic content of the constructs, not taking the numerical ratings into account. Both of these approaches would result in a number of groups of constructs. In this particular study, we were primarily interested in finding correlations between different Fallman & Waterworth 258 constructs that may or may not seem by semantic resemblance to belong together, but which according to their ratings do. From this, it appeared that a semantically blind statistical approach that compares ratings would be the best choice for exploring the data set. Step 3: Participant-Level Analysis The manually collected data from the 18 participants was compiled and put into the WebGrid- III application, a frequently used and feature-rich tool for collecting, storing, analyzing, and visually representing repertory grid data (Gaines & Shaw, 1980, 1993, 1995). Each participant’s repertory grid table was used as the basis for three different ways of presenting the data graphically, increasingly driven by and dependent on statistical methods of analysis. First, a Display Matrix was generated. As the most basic way of presenting a repertory grid, this table simply lays out the numerical results of all constructs for all elements. Second, a FOCUS Graph was constructed for each participant. Here, both elements and constructs are sorted using the FOCUS algorithm (Gaines & Shaw, 1993, 1995; Hassenzahl & Wessler, 2000) so that similar ones are grouped together. Third, the PRINCOM Map provides principal component analysis of the repertory grid data. The grid is rotated and visualized in a vector space to facilitate maximum separation of elements in two dimensions (Gaines & Shaw, 1980; Slater, 1976). For more detailed information and discussion about these common ways of analyzing and visualizing repertory grid data, see Gaines & Shaw (1993, 1995), Shaw (1980), and Shaw & Gaines (1998). Step 4: Statistical Analysis of Multiparticipant Data For our study, we were interesting in seeing if any patterns or other kinds of relationships between different participants’ repertory grids could be derived. But how could these highly individual and subjective personal constructs be compared with each other in practice? To be able to perform statistical analysis on multiparticipant data, all 180 bipolar constructs of the participants were put into the same, very large repertory grid. This huge grid then became subject to various kinds of analyses similar to those applied to each individual participant’s repertory grid. Hence, a DISPLAY matrix, a FOCUS graph, and a PRINCOM map were constructed from the WebGrid-III application using all the data. These diagrams are immense and unstructured, so the task at this point became to refine and bring order into the data set. Statistical analysis may be performed on repertory grid data to find similarities and other kinds of patterns among the constructs elicited from different participants. Finding constructs that share a rating pattern indicate that they, mathematically, belong to the same group. This suggests that the coherence in rating also reflects coherence in experience, but one which may have been expressed differently in the semantic terms used. A group whose constructs share a unique topology in ratings thus becomes seen as a specific dimension of meaning in relation to the elements of the study. The part played by the researcher in this process is, through semantic analysis of the constructs that make up such groups, to establish what conceptual similarity they share. Capturing User Experiences 265 On the other hand, RGT is more efficient and less time-consuming than most other fully open approaches, such as unstructured interviews and explorative ethnography. And, because the personal constructs elicited from participants constitute the study’s data, it follows that using the RGT significantly reduces the amount of data that needs to be analyzed, compared with transcribing and analyzing unstructured interviews or ethnographic records. Specific Issues Regarding the Elicitation Process Two potential problems regard the actual conduct of constructing repertory grids. While these are generally not unique to RGT, they are worth noting. First, for various reasons, participants may feel inclined to provide the experimenter with socially desirable responses. In other words, a participant may experience a sense of social pressure during the elicitation session that makes her try to give the experimenter the ―right answer.‖ Second, some participants may, again for various reasons (e.g., that they feel uncomfortable in the situation, do not really have the time for the session, do not want to or cannot concentrate, do not really understand the purpose or doubt the study’s usefulness, etc.), come to develop a habit of consistently providing moderate answers, or always either fully agreeing or disagreeing with their own constructs. CONCLUSIONS In this paper we have commented on the artificiality of assessing the emotional impact of interactive artifacts in isolation from cognitive judgments. We stressed that both emotion and reason are inherently part of any cognitive appraisal, and underlie the user’s experience of an artifact. We suggested that studying the one without the other is – literally – meaningless. What HCI needs are techniques that recognize this and that provide practical solutions to the problem of how to assess the holistic meaning of users’ interactive experiences. In this light, a candidate method, the repertory grid technique (RGT), may partly fill this need, and has been presented, discussed, empirically exemplified, and explored. RGT was found to be an open and dynamic technique for qualitatively eliciting people’s experiences and meanings in relation to technological artifacts, while at the same time providing the possibility for data to be subjected to modern methods of statistical analysis. The RGT may as such best be described as a research method on the border between qualitative and quantitative research. An example from the area of mobile HCI was used to take the reader step by step through the setting up, conducting, and analyzing of an RGT study. How should a designer of interactive experiences think about the 10 dimensions of mobile technologies found in this study? Are they only relevant to this study and these devices, or are they general enough to provide a sound understanding of users’ experience mobile information technology? The answer probably lies somewhere between these two possibilities. Since RGT relies on comparisons between different elements, all results—such as the 10 unique dimensions surfaced in this study—must be regarded as relative to the group of elements that were included in the study. The 10 dimensions speak of something that is specifically about the seven technology designs provided to the participants. In a statistical sense, the resulting dimensions are relational to these seven devices. There is no way of Fallman & Waterworth 266 knowing whether they would change dramatically if an eighth device were to be added, without doing such an extended study. But this limitation was to some extent addressed in the study by including already existing mobile information technology devices to which the new research prototypes can be related. Doing so provided a result that, while still not absolute, nevertheless has become more situated. It would not do justice to the study and the effort put into it by the participants to argue that the results are only valid within the study itself. On the contrary, we believe that the results from this study and the approach it illustrated could be useful for designers of mobile information technology, not the least as a tool for design. Given that a team of designers wants to provide form and content to a mobile device that should embody certain characteristics, there are at least two ways in which this study can be used to guide the process. First, they may take the three existing devices as a basis and consider the four prototypes to provide a large number of alternative design dimensions. If they want their design to provide its users with a sense of mysteriousness, for instance, then aspects of the Reality Helmet may be taken as influence. Second, designers may use this study as the basis for designing and conducting their own studies in similar ways. If they want to find out whether their design really is experienced as mysterious, they can set up and conduct their own repertory grid study in a similar fashion, perhaps even using the same existing devices as were used here. Such comparisons can at least provide some hints and traces of meaning that may be very useful for further design work. The design team may also wish to embed small repertory grid studies throughout the production cycle to monitor designs against some sought-after set of qualities of user experience: These grids could become a recurring element in organizing the process of interactive artifact design. 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