Puget Sound World Usability Day 2014 Experience Profiles
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Research and design method for getting User Experience right.
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Puget Sound World Usability Day 2014 Experience Profiles
- 1. USING EXPERIENCE PROFILES phillip hunter :: :: phillip.hunter@gmail.com :: :: @designoutloud al youngblood :: :: postmodern@gmail.com :: :: @alyoungblood PUGET SOUND WORLD USABILITY DAY 2014
- 2. Significant emotions are qualities of experience; esthetic quality that rounds out an experience into completeness and unity. John Dewey
- 6. fast fluid compelling award-winning pleasing useful delightful clear fulfilling engaging usable leading intuitive powerful familiar full-featured lightweight
- 10. TECHNOLOGY ≠ Experience was first EXPERIENCE
- 11. INTERFACE ≠ Experience was first EXPERIENCE
- 14. fast fluid award-winning pleasing compelling easy delightful clear fun clean engaging beautiful intuitive powerful familiar full-featured lightweight
- 15. uncooperative apathetic good pleased empowered able irritated clear not able relaxed rushed delighted interested powerful negative frustrated confused
- 16. Experiential qualities must be distinguished from the properties of objects. They are intuited and not subject to error or judgment. CI Lewis
- 17. QUALIA
- 18. Experiential qualities must be distinguished from the properties of objects. They are intuited and not subject to error or judgment. CI Lewis
- 19. fast fluid award-winning pleasing compelling easy delightful clear fun clean engaging beautiful intuitive powerful familiar full-featured lightweight
- 20. uncooperative apathetic good pleased empowered able irritated clear not able relaxed rushed delighted interested powerful negative frustrated confused
- 21. INTERFACE PROPERTIES ≠ Experience was first EXPERIENCE QUALITIES
- 26. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill Experience Profiles detachment engagement leisureliness attention elsewhere inability priority/urgency negative experience strangeness few choices unimportance positive experience familiarity hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 28. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill detachment engagement leisureliness attention elsewhere inability priority/urgency negative experience strangeness few choices unimportance positive experience familiarity hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 29. less cooperative difficult environment insecurity/uncertainty unclear understanding understanding apathy concern ignorance randomness confidence serendipity skill detachment engagement leisureliness attention elsewhere inability unimportance negative experience familiarity few choices priority/urgency positive experience strangeness hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 30. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill detachment engagement leisureliness attention elsewhere inability unimportance negative experience familiarity few choices priority/urgency positive experience strangeness hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 31. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill detachment engagement leisureliness attention elsewhere inability unimportance negative experience familiarity few choices priority/urgency positive experience strangeness hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 32. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill detachment engagement leisureliness attention elsewhere inability unimportance negative experience familiarity few choices priority/urgency positive experience strangeness hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 33. Use Experiences Profiles to… Understand and focus on Qualia, the actual qualities of personal experience Discover and identify Qualia in context Assess Qualia before, during, and after experiences, over time and across changes in context and circumstance
- 34. less cooperative difficult environment insecurity/uncertainty understanding unclear understanding apathy concern ignorance randomness confidence serendipity skill Experience Profiles detachment engagement leisureliness attention elsewhere inability priority/urgency negative experience strangeness few choices unimportance positive experience familiarity hurriedness focus knowledge more choices more cooperative conducive environment thoughtful/purposeful automatic/unconscious
- 35. WE’VE BEEN HERE Experience was first BEFORE
- 36. 1950s Deming and Morita Process Controls and TQM 1964 Osgood Semantic Differential 1965 Mizuno and Akao Quality Function Deployment 1970s Nagamachi Kansei Engineering 1980s NASA – TLX HMI Load Index – Inspired Phillip 1990s Market Research Innovation Voice of the Customer (Griffin, Hauser) Conjoint Analysis (Srinivasan) 1997 Jordan Product Personality Assessment
- 37. The challenge of Human Factors is to look beyond success and task completion; we must address this question holistically Patrick Jordan (1997)
- 38. Product Personality originally used Meyers Briggs but remade as Semantic Scale 209 descriptors 17 dimensions
- 41. People are diverse but fundamentally all people seek a pleasant and emotional satisfaction in life quality Mitsuo Nagamachi
- 42. KANSEI = FEELINGS Experience was first + EXPERIENCE
- 43. KANSEI = Experience was first QUALIA?
- 45. KANSEI AFFECTIVE Experience was first ENGINEERING
- 46. Experience was first KAE
- 49. GET KANSEI DESIGN SD SCALE ASSIGN TO PRODUCT Experience was first TEST WITH CUSTOMER INTERPRET DESIGN
- 50. PCA + PLS Experience was first Principal Component Analysis + Partial Least Squares
- 51. EXPERIENCE PROFILES Experience was first WORK IN PROGRESS
- 52. GENERATIVE OR Experience was first PRESCRIPTIVE
- 53. GENERATIVE OR Experience was first PRESCRIPTIVE
- 54. LARGE SEMANTIC SET FILTER BY INSPECTION TEST Experience was first INTERPRET DESIGN
- 55. Experience was first SIMPLE VERSION
- 56. Experience was first FACTOR ANALYSIS
- 57. Experience was first HARD VERSION
- 58. CLUSTER ANALYSIS (K-MEANS) Experience was first PCA + VERIMAXROTATION
- 59. THIS IS JUST MODEST Experience was first START
- 60. THANK YOU!! phillip hunter :: :: phillip.hunter@gmail.com :: :: @designoutloud al youngblood :: :: postmodern@gmail.com :: :: @alyoungblood
Hinweis der Redaktion
- Experience profile assessment Semantic differential
- “Kansei” is a Japanese word corresponding to “feelings” or “impression.” Kansei engineering originated with Nagamachi about 30 years ago and is a method to convert customers’ ambiguous ideas about products into a detailed product design and thereby to assist designers by providing guidance for product development that is in tune with customers’ Kansei. It also helps customers choose from a variety of products those that fit their Kansei. Kansei engineering procedures are based on psychological evaluation and multivariate analyses. This technique has advanced by incorporating artificial intelligence approaches, such as neural networks, genetic algorithms, and rough set theory, and by including various computer graphics (CG) techniques such as 3D CG and virtual reality (VR). Kansei engineering has been introduced into many industries worldwide, such as the manufacture of automobiles (e.g., Mazda Miata, MX5 in Europe), construction machines (KOMATSU), forklifts (BT industries), electric home appliances (SANYO), welfare, and home products (PANASONIC) (Nagamachi et al. [1–7]).
- Qualia, the actual qualities of personal experience “Kansei” is a Japanese word corresponding to “feelings” or “impression.”
- “Kansei” is a Japanese word corresponding to “feelings” or “impression.” Kansei engineering originated with Nagamachi about 30 years ago and is a method to convert customers’ ambiguous ideas about products into a detailed product design and thereby to assist designers by providing guidance for product development that is in tune with customers’ Kansei. It also helps customers choose from a variety of products those that fit their Kansei. Kansei engineering procedures are based on psychological evaluation and multivariate analyses. This technique has advanced by incorporating artificial intelligence approaches, such as neural networks, genetic algorithms, and rough set theory, and by including various computer graphics (CG) techniques such as 3D CG and virtual reality (VR). Kansei engineering has been introduced into many industries worldwide, such as the manufacture of automobiles (e.g., Mazda Miata, MX5 in Europe), construction machines (KOMATSU), forklifts (BT industries), electric home appliances (SANYO), welfare, and home products (PANASONIC) (Nagamachi et al. [1–7]).
- “Kansei” is a Japanese word corresponding to “feelings” or “impression.” Kansei engineering originated with Nagamachi about 30 years ago and is a method to convert customers’ ambiguous ideas about products into a detailed product design and thereby to assist designers by providing guidance for product development that is in tune with customers’ Kansei. It also helps customers choose from a variety of products those that fit their Kansei. Kansei engineering procedures are based on psychological evaluation and multivariate analyses. This technique has advanced by incorporating artificial intelligence approaches, such as neural networks, genetic algorithms, and rough set theory, and by including various computer graphics (CG) techniques such as 3D CG and virtual reality (VR). Kansei engineering has been introduced into many industries worldwide, such as the manufacture of automobiles (e.g., Mazda Miata, MX5 in Europe), construction machines (KOMATSU), forklifts (BT industries), electric home appliances (SANYO), welfare, and home products (PANASONIC) (Nagamachi et al. [1–7]).
- stats on the data set- principal component Analysis (PCA) with factor analysis, and Partial Least squares (PLS) to identfy reevant emotions; intperpretation: PLA will tell use what kansei words have what kinds of design specificaation. , Factor anaysis tels us about what semantic kensei hand well together; KAE engineer talks to designer about interpretation; Evaluation of new design with customer using satisfaction measures. Can use PCA with verimax rotation
- Discovery Phase: -generative: full kansei to use cluster analysis to filter out words organically -prescriptive: begin with large set of 200-400 semantic items, then filter by inspection using experts or other live users, then pair down to about 50 items, then use cluste
- Discovery Phase: -generative: full kansei to use cluster analysis to filter out words organically -prescriptive: begin with large set of 200-400 semantic items, then filter by inspection using experts or other live users, then pair down to about 50 items, then use cluste
- Our process is relatively simple but needs to be tested If you are interested, join us!
- There are two stats methods we can think of
- But there is harder and more precise – Tradeoff between hard and simple – precision versus accuracy
- PCA is a descriptive statistical model K-means clustering (cluster indicators) is given by PCA C. Ding and X. He. "K-means Clustering via Principal Component Analysis". Proc. of Int'l Conf. Machine Learning (ICML 2004), pp 225–232. July 2004
- If you’d like to join our project, we would love to have you! Thanks