visualization of knowledge domains in the user experience final

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The International Journal of

Visual Design

DESIGNPRINCIPLESANDPRACTICES.COM

VOLUME 6 ISSUE 1

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Visualization of Knowledge Domains inthe User ExperienceCARLOS CRDOBA-CELY AND YADIRA ALATRISTE-MARTNEZ


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THE INTERNATIONAL JOURNAL OF VISUAL DESIGN

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Visualization of Knowledge Domains in the UserExperience

Carlos Crdoba-Cely, University of Nario, Colombia Yadira Alatriste-Martnez, Metropolitan Autonomus University, Mexico

Abstract: This paper explores the research trends on User Experience (Ux) using different techniques of Visualizing Knowledge Domains (VDK). Through the ISI Web of Knowledge(WoK), 9 key authors were selected for Factor Analysis and Pathfinder Network (PFNETs).The goal of this document is to identify the main themes of empirical research in academic journals on Ux among 20052010. The results obtained show the existence of three researchtopics that focus in (1) artifact-oriented hedonic evaluation, (2) artifact-oriented utilitarianevaluation, and (3) user-oriented holistic evaluation. These topics are consistent with the needsof the new Experience Societies. We discuss the results.

Keywords: User Experience (Ux), Experience Design (xD), Experience Society, Visualizing Knowledge Domains (VKD), Author Co-citation Analysis (ACA), Pathfinder Network

(PFNETs), Factor Analysis

INTRODUCTION

User Experience (Ux) studies the interactions of users and technology in order to createa high quality experience in the use of the system (Hassenzahl & Tractinsky, 2006).Ux approach includes much more than the study of the systems instrumental needs,

which takes into account the users internal state, the characteristics of the system,and the environment where the interaction occurs, as shown in Figure 1.

The International Journal of Visual DesignVolume 6, Issue 1, 2013, http://designprinciplesandpractices.com/, ISSN 2325-1581 Common Ground, Carlos Crdoba-Cely, Yadira Alatriste-Martnez, All Rights Reserved, Permissions:[email protected]


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Figure 1: Topics on Ux. (Adapted from Hassenzahl & Tractinsky, 2006)

The Ux research begins with the need to evaluate the usefulness of a system in Human-ComputerInteraction (HCI). At first, this approach was purely instrumental, and usability was the mostimportant indicator of a systems quality (Nielsen, 1993) which came from the TechnologyAcceptance Model by Fred Davis (Davis, 1989; Porat & Tractinsky, 2008). However, this ap-proach was not unanimous among researchers, and from the field of video games, Carroll &Thomas (1988) considered that the fun and enjoyment had a powerful influence on the use of

the system. With these antecedents, Alben (1996) included beauty as an important aspect of the value of technology in what he called quality of experience. Since then, Ux has beendiscussed in conferences and symposia but rarely in academic journals, possibly due to the lackof empirical research (Hassenzahl & Tractinsky, 2006). Thus, the objective of this paper is toidentify the main themes of empirical research on Ux in academic journals in the last five years.It is hoped that these issues help to constitute the state of the art in Ux, and they can identifyresearch topics of the new Experience Societies. (Hassenzahl, 2011). Because Experience Societyfocuses on the pursuit of pleasure and meaning beyond materialism and money, identifyinggeneral themes that include any study on Experience Design to provide artifacts and technologypursuant to these new needs is necessary (Anderson, 2011; Hassenzahl, 2011).

The analysis of academic journals will be done through Author Co-citation Analysis (ACA)and visualization of knowledge domains will be performed by Pathfinder Network. ACA

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technique was chosen because it has examined the cognitive structure in various disciplines-such as Information Sciences (Ma et al., 2009), Knowledge Management (Pilkington & Meredith2009), Ubiquitous Computing (Lee & Chen 2009) and Medical Issues (Raghupathi & Nerur2008) but it has never been used on research in Ux. Moreover, Pathfinder Networks allow to

identify clusters of authors belonging to the same topic of study and visualize them as a networkof relationships. Thus, this paper is organized as follows: Section 2 describes the bibliometrictechnique of Author Co-citation Analysis and the procedure to select a group of key authorsof Ux. Section 3 presents the results obtained using Pathfinder networks, and Section 4 discussesthe relationship between the topics found and those proposed by Hassenzahl & Tractinsky(2006). Finally, Section 5 presents the conclusions of this work.

Author Co-citation Analysis (Aca)In many occasions, the knowledge shared by a community can be represented as a scientificnetwork, a social network of co-authorship or citation networks and co-citation of authors(Chen, 2004). These networks of shared knowledge allow the study of conceptual changes inthe paradigm and can be represented by different techniques of Knowledge Domain Visualization(Chen, 2003; Brner, Chen & Boyack, 2003) such as Author Co-citation Analysis. The ACAis a bibliometric technique that involves counting the citations of a body of literature to developstatistical distributions with the purpose of determining the intellectual structure of a discipline(White & Griffith, 1982), assuming that the authors cited are nothing more than the surrogatesfor the concepts they write about (Culnan 1986; Sircar et al., 2001). The ACA approach isbased on the idea that authors who have made contributions to a concept are closely relatedand therefore are more likely to be cited by other researchers thereby determining the cumulativetradition of a field. Thus in a body of literature that contains hundreds of citations, the authorswho have cited two or more documents are grouped together based on their co-citations aswell as on the similarity of their patterns of citations regarding other authors (McCain, 1990).

These groups of authors can provide a fields view (White & Griffith, 1982) of any disciplinein a period of time (Culnan, 1986).

Selection of Authors

To obtain this fields view, the first step was to identify a list of the most cited authors usingthe phrase User Experience in ISI Web of Knowledge and redefining the years of publicationbetween 20052010, time which made the query in the database. Out of a total of 2064 pub-lications found, a pool of 85 seminal authors was selected if they were cited 15 times or moreduring the period stipulated. The cutoff point of 15 citations was chosen based on the fact thatmost items are cited twice per year on average, changing this information positively accordingto the evaluated discipline (Culnan, 1986). Thus we determined that the key authors shouldbe cited at an average of three times per year. From the pool of 85 seminal authors those whoshowed no correlation with any other author, as well as those authors who did not meet thecutoff point were eliminated in the matrix of co-citation analysis because some of their citationsdid not come on paper but another type of document as reviews or abstracts that were notconsidered for this research. The final group was composed of 8 key authors as shown in TableI. Because Ux is a relatively new research theme-as demonstrated by the low co-citation betweenauthors-we decided to include a representative author like Noam Tractinsky with their paperfoundational What is beautiful is usable (Tractinsky, Katz, & Ikar, 2000).

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Table I: Key Authors in Ux, 20052010

Tractinsky, N (72)Wu, JH (90)

Hassenzahl, M (27)Shang, RA (46)

Sanchez-Franco, MJ (27)Bagozzi, RP (33)

Castaeda, JA (25)Zviran, M (25)

Karahanna, E (24)

Co -citation Matrix

Based on the list of key authors, we constructed a matrix of 9 x 9 to count the number of co-citations between each pair of authors. According to previous investigations (Culnan 1986;McCain 1990; Sircar et al., 2001; White and Griffith 1982), the value of the diagonal of thematrix was calculated by adding the three highest co-citations counts for each author and di-viding such result by two. The raw data co-citation matrix was normalized to a Pearson correl-ations matrix for a later analysis of factors. In the case of the Pathfinder Networks has beenused only the raw data matrix and changing the values of the diagonal to zero. Table II showsthe results obtained.

Table II: Matrix of Authors Co-citation in Ux, 20052010

ZviranWu, JH

Tractin-sky

ShangS-Franco

Kara-hanna

Hassen-zahl

CastaedaBagozzi

010001001Bagozzi

021222030Castaeda

0362005,500Hassenzahl

010002021Karahanna

11043,50020S-Franco

1747,540220Shang

005,5400610Tractinsky

060711321Wu, JH

100110000Zviran

Data AnalysisFactor Analysis was obtained with the Pearson correlation of the authors using SPSS, version17.0. PFNETs were derived from the original raw data of co-citations using PCKnot version6.3 for the nodes minimally connected network and nodes among nearest neighbors. UCINETversion 6.0, was used to flow betweenness measure in networks and Pajek version 1.28 wasused to graph visualization. Finally, networks plotting were done using Illustrator CS5 to groupdata and improve the graphics presented in this review.

Factor Analysis

The factor analysis technique is used to explain the relationships between different factorsthrough the creation of a smaller number of subsets in which the key authors are grouped intoone specific discipline. Each factor may be observed as an intellectual perspective representedby the authors who load on it (Sircar et al., 2001). All authors have a load greater than 0.4,

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but for the interpretation of each factor only loads greater than 0.7 are considered. The resultsof the main components using Varimax rotation and Kaiser normalization are shown in TableIII. The results suggest that the first three factors accounted for 81.2% of the variance.

Table III: Factor Analysis of Ux, 20052010Factor 3Factor 2Factor 1

0.527Bagozzi0.722Tractinsky0.821S. Franco

0.719Castaeda7.21Zviran

0.689Hassenzahl0.719Wu, JH

0.599Karahanna0.709Shang

Eigenvalue

1.7312.4043.173

% Total Variance

19.23526.70635.289

Acumulative % Variance

81.22961.99535.289

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

Authors loaded on factor 1 based their research on users motivational models in differentcontexts. This group includes the following investigations: Sanchez-Franco & Roldans (2005)study of the flow of user web navigation; Zviran, Glezer & Avnis (2006) study on the effectof design in different commerce websites; Wu, JH & Wangs (2005) study of the perceived riskin the acceptance of mobile commerce; and, Shang Chen & Shens (2005) study on the intrinsicand extrinsic motivations of consumers when shopping online. Authors loaded under factor 2based their research on two different themes. Tractinsky, Ikary, & Katz (2000), and Hassenzahl(2004) who studied the influence of beauty and aesthetics in the evaluation of the system byuser are in the first group. On the other hand, Castaeda Munoz & Luque (2007), and Kara-hanna, Agarwal & Angst (2006) who studied different moderating effects of user, such as theexperience and the intension of use, are in the second group. Finally, Bagozzi & Dholakia(2006) studied the involvement of user groups of LINUX based on three different group-context:the group with a cognitive social identity, the group with a social affective identity and thegroup with an evaluative social identity. Although Bagozzi & Dholakia (2006) do not have aload greater than 0.7, their research is a good example of comparative evaluation in different

collaborative work environments.

Pathfinder Networks (PFNETS)

The scaling algorithm Pathfinder Network (PFNET) is a technique to extract the underlyingpatterns in proximity data and represent them spatially through interconnected networks. ThePFNET algorithm allows prune, a complex network of multiple paths using the minimumweight of a path between nodes (White 2003b). The weight of the paths can be determined bythe raw data matrix co-citation or Pearson correlation (Chen & Lee 2006; De Nooy 2005;White 2003a). In this study, the weight of a path is the number of co-citations between authorsand the nodes are the authors themselves as shown in Figure 2. Because the distance from onenode to itself is zero, the diagonals of the original matrix of co-citations were changed to zero

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to meet the third condition of the PFNET algorithm (Chen 1998). To prune the network of TAM was q = n-1 and r = , resulting in a new network in which all authors are connected.The q parameter limits the minimum cost of a path, and the parameter r defines the distanceof a path through the Minkowski metric. Of a total of 16 initial links, 8 lines were obtained

from authors who were graphed using the Kamada-Kawai spring embedder in Pajek such asshown in Figure 3.

Figure 2: PFNET of Authors on Ux, 20052010

For interpret the PFNETs, the thickness of the lines was varied according to the number of co-citations between authors displayed in italics. The size of the nodes in gray was varied accordingto the flow betweenness. The flow betweenness measures the centrality of actors in a networkusing the sum of all independent paths between two points in the network (Nooy, Mrvar, &Batagelj, 2005; Freeman, Borgatti, White, 1991). With this measure it is possible to speak of the influence of information of an actor in the net if is assumed that actors will use all pathwaysthat connect them, proportionally to the length of the pathways (Hanneman & Riddle, 2005).

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Figure 3: Pruned PFNET of Authors on Ux

Figure 3 clearly shows the existence of four clusters: Users Intrinsic Motivation, Systems Ex-trinsic Evaluation and Systems Hedonic Evaluation. Bagozzis work appears alone in a fourthresearch cluster on the context. These four clusters are consistent with data previously obtainedin the factor analysis. Below the results are discussed.

DiscussionFactor analysis shows some variations between the factors found and themes proposed byHassenzahl and Tractinsky (2006). Initially, factors 1 and 3 are adapted to the model. Thus,the authors under factor 1 might be associated with topics on the users internal state with thethemes on flow, enjoyment and confidence. However, the author under factor 3 could be asso-ciated with themes about the context where interaction occurs. Conversely, factor 2 is composedby two different types of authors: Castaneda and Karahanna who worked on utilitarian features,and Tractinsky and Hassenzahl who worked on the systems hedonic characteristics and whoare closer to studies on users internal state. On the other hand, PFNET on Ux shows the sameseparation of clusters. Thus, while systems extrinsic evaluation focuses on system utility, systemshedonic evaluation focuses on aesthetic and emotional components. Similarly, the lack of research

on the environment where interaction occurs may be due to the fact that all the consulted em-pirical studies have been constructed from the particular characteristics of the context wherethe valuation is intended to apply the system. From this perspective, all the authors presentedhere, belong to the thematic context studies. Figure 4 shows the variations between the modelHassenzahl & Tractinsky (2006), and the themes found in this study represented by coloredcircles. The diameters of the circles are determined from the average flow betweenness authorsbelonging to each cluster. Their location in space is given by the observations exposed above.

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Figure 4: Topics Proposed by Hassenzahl and Tractinsky vs Topics Obtained in this Study

Moreover, it is important to stress that Figure 4 also shows the evolution of the user experience.While extrinsic evaluation, focuses on issues of usability and function of the system (Nielsen,1993), intrinsic motivation goes a step further and focuses on the emotions of the user (Norman,2004). Finally, it appears the hedonic evaluation of the system, which includes indicators of the previous two approaches, to propose a broader notion of experience, called ExperienceDesign (Hassenzahl, 2011). The Experience Design (xD), represents the evolution of hierarch-ical responsive to the needs of the new Society of Experience (Anderson, 2011). Thus, xD mustcontain utilitarian and emotional indicators that tend to enjoy the experience.

ConclusionsFrom the results obtained in this study, the following conclusions can be drawn: (1) taking intoaccount the low co-citation between authors who write in academic journals, it can be said

that the Ux research topics are still new to the researchers and there is still short empirical workon the subject. (2) The Ux research topics focus on the users intrinsic motivation, the systemsextrinsic evaluation; and the systems hedonic evaluation. The users intrinsic motivation isfocuses on determining the levels of enjoyment of the system, while the systems extrinsicevaluation focuses on determining the usefulness of the system. Finally, hedonic evaluation isfocuses on the users aesthetic and emotional evaluations when the system is used. (3) Currently,the Ux thematic focuses on evaluating the technological artifact and evaluate the user separately.In turn, the technological artifact can be evaluated from a utilitarian perspective and from ahedonic perspective, while the user is considered from a holistic perspective as a total experienceobtained by interacting with the system. Thus, the search for a total experience is the funda-mental objective of Experience Design (xD). The next step is in user experience. Figure 5 showsthe topics proposed for this research theme.

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Figure 5: Research Topics of xD

(4) This model is thought to be used as guidance to researchers who decide to carry out empir-ical work on xD and wishes to obtain valid indicators of previous investigations. It is clear thatall research on xD must contain at least three types of theoretical constructs: Utility, Aestheticsand Enjoyment. These three constructs have their epistemological origins in the technologyacceptance model in the theory of fun, and the systems quality evaluation through the usersexperience.

.

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ABOUT THE AUTHORS

Prof. Carlos Crdoba-Cely: Associate professor in the design department at the University of Nario (Colombia). Currently doing doctoral studies at the Polytechnic University of Catalonia(Spain) in multimedia engineering about user experience in learning systems.

Prof. Yadira Alatriste-Martnez: Professor of design department at the Metropolitan Autonomus

University, Mxico City (Mexico). Currently doing doctoral studies at the Polytechnic Universityof Catalonia (Spain) in multimedia engineering about living lab and user experience.

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The International Journal of Visual Design is oneof six thematically focused journals in the familyof journals that support the Design Principles andPractices knowledge communityits journals, bookseries, conference and online community. It is a sectionof Design Principles and Practices: An International

Journal.

The journal explores processes and practices ofrepresentation and communication using the mediumof the image. Areas of interest include communicationsdesign, visual arts, illustration, photography, lm andvideo, graphic design, typography, interface design,internet design, animation and computer simulations.

As well as papers of a traditional scholarly type, this journal invites presentations of practiceincludingdocumentation of visual designs accompanied byexegeses analyzing visual design purposes, processesand effects.

The International Journal of Visual Design is a peer-reviewed scholarly journal.

ISSN 2325-1581

visualization of knowledge domains in the user experience final

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