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Modeling Users' Experiences with Interactive Systems 2013 ed. [Kõva köide]

  • Formaat: Hardback, 164 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XII, 164 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 436
  • Ilmumisaeg: 09-Aug-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642309992
  • ISBN-13: 9783642309991
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Modeling Users' Experiences with Interactive Systems 2013 ed.Suurem pilt
  • Formaat: Hardback, 164 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XII, 164 p., 1 Hardback
  • Sari: Studies in Computational Intelligence 436
  • Ilmumisaeg: 09-Aug-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642309992
  • ISBN-13: 9783642309991
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642309991.html
Märksõnad:
Over the past decade the field of Human-Computer Interaction has evolved from the study of the usability of interactive products towards a more holistic understanding of how they may mediate desired human experiences. This book identifies the notion of diversity in users' experiences with interactive products and proposes methods and tools for modeling this along two levels: (a) interpersonal diversity in users? responses to early conceptual designs, and (b) the dynamics of users' experiences over time.The Repertory Grid Technique is proposed as an alternative to standardized psychometric scales for modeling interpersonal diversity in users' responses to early concepts in the design process, and new Multi-Dimensional Scaling procedures are introduced for modeling such complex quantitative data.iScale, a tool for the retrospective assessment of users' experiences over time is proposed as an alternative to longitudinal field studies, and a semi-automated technique for the analysis of the elicited experience narratives is introduced.Through these two methodological contributions, this book argues against averaging in the subjective evaluation of interactive products. It proposes the development of interactive tools that can assist designers in moving across multiple levels of abstraction of empirical data, as design-relevant knowledge might be found on all these levels. Foreword by Jean-Bernard Martens and Closing Note by Marc Hassenzahl.

This book presents models for users' experiences with interactive systems. It examines how to incorporate aesthetic, emotional, and social aspects of a product while designing interactive systems.

Arvustused

From the reviews:

The book is actually a report of the authors research explorations. Each chapter sets specific goals intended to highlight different aspects related to modeling, measuring, and analyzing user experience over time. this book is a constructive source of information for both novice and experienced researchers in the field of human-computer interaction, as well as for usability practitioners and designers of interactive products. (Evangelia Kavakli, ACM Computing Reviews, March, 2013)

1 Introduction
1(16)
1.1 From Usability to Experience
2(1)
1.2 Two Distinct Approaches in User Experience Research
3(5)
1.2.1 Reductionist Approaches
4(2)
1.2.2 Holistic Approaches
6(2)
1.3 Diversity in User Experience
8(3)
1.3.1 A Framework of Diversity in Subjective Judgments
8(2)
1.3.2 Four Sources of Diversity in User Experience
10(1)
1.4 Methodological Issues in Accounting for Diversity
11(4)
1.4.1 Understanding Interpersonal Diversity through Personal Attribute Judgments
13(1)
1.4.2 Understanding the Dynamics of Experience through Experience Narratives
14(1)
1.5 Manuscript Outline
15(2)
2 Personal Attribute Judgments
17(24)
2.1 Introduction
17(3)
2.2 The Repertory Grid Technique
20(1)
2.3 The Quantitative Side of Repertory Grid - Some Concerns
21(4)
2.3.1 Are We Really Interested in Idiosyncratic Views?
21(1)
2.3.2 On Bipolarity
22(3)
2.3.3 On the Measurement of Meaning
25(1)
2.4 Analyzing Personal Attribute Judgments - An Initial Exploration
25(2)
2.5 The Study
27(2)
2.5.1 Method
27(2)
2.6 Analysis Procedure
29(6)
2.6.1 Identifying Homogeneous User Groups in the User Segmentation Map
29(1)
2.6.2 Classifying Attributes for Interpersonal Analysis
29(2)
2.6.3 Charting Perceptual Maps for Homogeneous Groups of Users
31(4)
2.7 Discussion
35(3)
2.8 Conclusion
38(3)
3 Analyzing Personal Attribute Judgments
41(16)
3.1 Introduction
41(1)
3.2 The Study
42(1)
3.3 A Multi-dimensional Scaling Approach to Account for Diversity
42(11)
3.3.1 Identifying the Different Views
44(1)
3.3.2 Defining Goodness-of-Fit Criteria
44(1)
3.3.3 Two Diverse Views for One Participant
45(3)
3.3.4 Assessing the Similarity between Different Views
48(1)
3.3.5 Grouping the Homogeneous Views
49(1)
3.3.6 How Do the Diverse Views Compare to the Average View?
50(3)
3.4 Discussion
53(3)
3.5 Conclusion
56(1)
4 User Experience Over Time
57(28)
4.1 Introduction
57(1)
4.2 Background on Experience and Temporality
58(2)
4.2.1 Temporal Aspects in Frameworks of Experience
59(1)
4.2.2 Beauty, Goodness and Time
59(1)
4.3 Study 1
60(6)
4.3.1 Method
60(1)
4.3.2 Results
61(3)
4.3.3 Discussion
64(1)
4.3.4 Limitations of the Study
65(1)
4.4 Study 2
66(15)
4.4.1 The Study
67(2)
4.4.2 Data Analysis
69(1)
4.4.3 Findings
70(8)
4.4.4 Discussion
78(1)
4.4.5 Implications for Design
79(2)
4.5 Discussion
81(1)
4.6 Conclusion
82(3)
5 iScale: Studying Long-Term Experiences through Memory
85(30)
5.1 Introduction
85(4)
5.2 Reconstructing Experiences from Memory
89(5)
5.2.1 The Constructive Approach
89(1)
5.2.2 The Value-Account Approach
90(1)
5.2.3 Graphing Affect as a Way to Support the Reconstruction of Experiences
91(1)
5.2.4 iScale
91(3)
5.3 Study 1: Understanding Graphing as a Tool for the Reconstruction of Experiences
94(8)
5.3.1 Method
94(2)
5.3.2 Analysis and Results
96(5)
5.3.3 Discussion
101(1)
5.4 Study 2: Benefits and Drawbacks of the Constructive and the Value-Account Version of iScale
102(8)
5.4.1 Method
102(3)
5.4.2 Analysis and Results
105(3)
5.4.3 Discussion
108(2)
5.5 Conclusion and Future Work
110(3)
5.6 Appendix - Temporal Transformation
113(2)
6 A Semi-Automated Approach to the Content Analysis of Experience Narratives
115(22)
6.1 Introduction
115(2)
6.2 Automated Approaches to Semantic Classification
117(3)
6.2.1 The Latent-Semantic Analysis Procedure
117(2)
6.2.2 Limitations of Latent-Semantic Analysis in the Context of Qualitative Content Analysis
119(1)
6.3 A Semi-automated Approach to Content Analysis
120(9)
6.3.1 Incorporating Existing Domain-Specific Knowledge
120(1)
6.3.2 Iterative Open Coding
121(5)
6.3.3 Computing Narrative Similarity
126(1)
6.3.4 Hierarchical Clustering
127(1)
6.3.5 Visualizing Insights
127(2)
6.4 Validation of the Proposed Approach
129(5)
6.4.1 Preparing the Dataset
129(1)
6.4.2 Concept Analysis
130(1)
6.4.3 Latent-Semantic Analysis on Restricted Terms
131(1)
6.4.4 Traditional Latent-Semantic Analysis
132(1)
6.4.5 Cluster Analysis on Dissimilarity Matrices
132(2)
6.5 Discussion
134(2)
6.6 Conclusion
136(1)
7 Conclusions
137(14)
7.1 Contributions of This Work
137(5)
7.1.1 Conceptualizing Diversity in User Experience
138(1)
7.1.2 Establishing Empirical Evidence for the Prevalence of Diversity in User Experience
138(1)
7.1.3 Proposing Methodological Tools for the Study of Diversity
139(3)
7.2 Implications for the Product Creation Process
142(2)
7.2.1 Integrating Subjective and Behavioral Data
142(2)
7.2.2 The End of Specifications?
144(1)
7.3 Avenues for Future Research
144(7)
7.3.1 Leveraging Insights across Different Exploratory Studies
144(1)
7.3.2 Computational Tools for Making Survey Research Scalable
145(1)
7.3.3 Empirical Knowledge Bases for Forming Design Goals
146(1)
7.3.4 A New Basis for User Insights?
146(5)
References 151