Journal of Intelligent Manufacturing

, Volume 24, Issue 5, pp 1047–1069 | Cite as

Affective and cognitive design for mass personalization: status and prospect



The prevailing practice of design for mass customization manifests itself through a configure-to-order paradigm, which means to satisfy explicit customer needs (CNs) and built upon legacy design. With pervasive connectivity and interactivity of the Internet and sensor networks, personalization has been witnessed in a number of industry sectors as a promising strategy that makes the market of one a reality. Mass personalization entails a strategy of producing goods and services to satisfy individual customer’s latent needs with values outperforming costs for both customers and producers. This review paper envisions an affective and cognitive design perspective to mass personalization. By exploiting implicit market demand information and revealing latent CNs, mass personalization aspires to assist customers in making better informed decisions, and to the largest extent, to anticipate customer satisfaction and adapt to customer delight. The key dimensions of mass personalization are identified and discussed. By capitalizing on user experience, affective and cognitive design for mass personalization is expected to address individual customer’s latent CNs. The decisions of affective and cognitive design, involving affective and cognitive needs elicitation, affective and cognitive analysis, and affective and cognitive fulfillment, are reviewed with a wide range of interests, including engineering design, human factors and ergonomics, engineering psychology, marketing, and human-computer interaction. Recent trends and future research directions are also speculated to inspire more meaningful research in this area.


Mass customization Mass personalization Affective design Cognitive design User experience 


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  1. Aarts E. (2004) Ambient intelligence: A multimedia perspective. Multimedia, IEEE 11(1): 12–19CrossRefGoogle Scholar
  2. Adam N. R., Atluri V., Huang W.-K. (1998) Modeling and analysis of workflows using petri nets. Journal of Intelligent Information Systems 10(2): 131–158CrossRefGoogle Scholar
  3. Adolphs R., Damasio A. (2001) The interaction of affect and cognition: A neurobiological perspective. In: Forgas J. P. (eds) The handbook of affect and social cognition. Erlbaum, Mahwah, NJ, pp 27–49Google Scholar
  4. Ahn, H., & Picard, R. W. (2005). Affective-cognitive learning and decision making: A motivational reward framework for affective agents. In The 1st international conference on affective computing and intelligent interaction (ACII 2005) (pp. 27–49), Beijing, China.Google Scholar
  5. Allen, C. (2009). Personalization vs. Customization [online]. (Accessed May 5, 2011).
  6. Arakawa, M., Shiraki, W., & Ishikawa, H. (1999). Kansei design using genetic algorithms. In IEEE international conference on systems, man, and cybernetics, Tokyo, JapanGoogle Scholar
  7. Arora N., Dreze X., Ghose A., Hess J., Iyengar R., Jing B., Joshi Y., Kumar V., Lurie N., Neslin S., Sajeesh S., Su M., Syam N., Thomas J., Zhang Z. (2008) Putting one-to-one marketing to work: Personalization, customization, and choice. Marketing Letters 19(3–4): 305–321CrossRefGoogle Scholar
  8. Augusto J. C. (2007) Ambient intelligence: The confluence of ubiquitous/pervasive computing and artificial intelligence. Intelligent computing everywhere. Springer, London, pp 213–234Google Scholar
  9. Bailenson J. N., Pontikakis E. D., Mauss I. B., Gross J. J., Jabon M. E., Hutcherson C. A. C., Nass C., Oliver J. (2008) Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies 66(5): 303–317CrossRefGoogle Scholar
  10. Bause, F., & Kemper, P. (1994). QPN-tool for qualitative and quantitative analysis of queueing petri nets. In The 7th international conference computer performance evaluation modelling techniques and tools, Vienna, Austria.Google Scholar
  11. Blom J., Monk A. (2003) Theory of personalisation of appearance: Why people personalise their mobile phones and PCs. Human-Computer Interaction 18(3): 193–228CrossRefGoogle Scholar
  12. Bos, D. O. (2008). EEG-based emotion recognition [online]. (Accessed Aug 8, 2009).
  13. Cacioppo J. T., Tassinary L. G. (1990) Inferring psychological significance from physiological signals. American Psychologist 45(1): 16–28CrossRefGoogle Scholar
  14. Carbonara N., Scozzi B. (2006) Cognitive maps to analyze new product development processes: A case study. Technovation 26(11): 1233–1243CrossRefGoogle Scholar
  15. Chan L., Wu M. (2002) QFD: A literature review. European Journal of Operational Research 143(3): 463–497CrossRefGoogle Scholar
  16. Chandler P., Sweller J. (1991) Cognitive load theory and the format of instruction. Cognition and Instruction 8(4): 293–332CrossRefGoogle Scholar
  17. Chellappa R. K., Sin R. (2005) Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information Technology and Management 6(2–3): 181–202CrossRefGoogle Scholar
  18. Chen, L. S. (2000). Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction. PhD thesis, University of Illinois at Urbana-Champaign.Google Scholar
  19. Chen C. H., Khoo L. P., Yan W. (2002) A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network. Advanced Engineering Informatics 16(3): 229–240CrossRefGoogle Scholar
  20. Chen C.-H., Khoo L. P., Yan W. (2006) An investigation into affective design using sorting technique and kohonen self-organising map. Advances in Engineering Software 37(5): 334–349CrossRefGoogle Scholar
  21. Coffey, J. W., & Carnot, M. J. (2003). Graphical depictions for knowledge generation and sharing. In International conference on information and knowledge sharing, Scottsdale, AZ, USA.Google Scholar
  22. Cohen I., Sebe N., Garg A., Chen L., Huang T. S. (2003) Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding 91(1–2): 160–187CrossRefGoogle Scholar
  23. Cosmelli D., Ibáñez A. (2008) Human cognition in context: On the biologic, cognitive and social reconsideration of meaning as making sense of action. Integrative Psychological and Behavioral Science 42(2): 233–244CrossRefGoogle Scholar
  24. Cowie R., Douglas-Cowie E., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W., Taylor J. G. (2001) Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18(1): 32–80CrossRefGoogle Scholar
  25. Crandall B., Klein G., Hoffman R. (2006) Working minds: A practitioner’s guide to cognitive task analysis. The MIT Press, Cambridge, MassachusettsGoogle Scholar
  26. Csikszentmihalyi M. (1990) Flow: The psychology of optimal experience. Harper and Row, New YorkGoogle Scholar
  27. David R., Alla H. (1992) Petri nets and grafcet—tools for modeling discrete event systems. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  28. Dekker S. W. A. (2002) The field guide to human error investigations. Ashgate, LondonGoogle Scholar
  29. Delin J., Sharoff S., Barnes C. J., Lillford S. P. (2007) Linguistic support for concept selection decisions. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2): 123–135CrossRefGoogle Scholar
  30. Desmet P. (2003) Measuring emotions. In: Blythe M. A., Monk A. F., Overbeeke K., Wright P. C. (eds) Funology: From usability to enjoyment. Springer, BerlinGoogle Scholar
  31. Du X., Jiao J., Tseng M. M. (2003) Identifying customer need patterns for customization and personalization. Integrated Manufacturing Systems 14(5): 387–396CrossRefGoogle Scholar
  32. Ekman P. (1982) Methods for measuring facial action. In: Scherer K., Ekman P. (eds) Handbook of methods in non-verbal behavior research. Cambridge University Press, Cambridge, pp 45–90Google Scholar
  33. Ekman P., Friesen W. V. (1978) Facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto, CaliforniaGoogle Scholar
  34. Ellsworth P. C., Scherer K. R. (2003) Appraisal processes in emotion. In: Davidson R. J., Scherer K. R., Goldsmith H. H. (eds) Handbook of affective sciences. Oxford University Press, New York, pp 572–595Google Scholar
  35. Ertay T., Kahraman C. (2007) Evaluation of design requirements using fuzzy outranking methods. International Journal of Intelligent Systems 22(12): 1229–1250CrossRefGoogle Scholar
  36. Essa I. A., Pentland A. P. (1997) Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7): 757–763CrossRefGoogle Scholar
  37. Fairclough S. H. (2009) Fundamentals of physiological computing. Interacting with computers 21(1–2): 133–145CrossRefGoogle Scholar
  38. Fasel B., Luettin J. (2003) Automatic facial expression analysis: A survey. Pattern Recognition 36(1): 259–275CrossRefGoogle Scholar
  39. Favela J., Tentori M., Castro L. A., Gonzalez V. M., Moran E. B., Martínez-García A. I. (2007) Activity recognition for context-aware hospital applications: Issues and opportunities for the deployment of pervasive networks. Mobile Networks and Applications 12(2–3): 155–171CrossRefGoogle Scholar
  40. Fragopanagos N., Taylor J. G. (2005) Emotion recognition in human-computer interaction. Neural Networks 18(4): 389–405CrossRefGoogle Scholar
  41. Frantzidis C. A., Bratsas C., Klados M. A., Konstantinidis E., Lithari C. D., Vivas A. B., Papadelis C. L., Kaldoudi E., Pappas C., Bamidis P. D. (2010) An integrated data-mining-based approach for healthcare applications. IEEE Transactions on Information Technology in Biomedicine 14(2): 309–318CrossRefGoogle Scholar
  42. Fredericks T. K., Choi S. D., Hart J., Butt S. E., Mital A. (2005) An investigation of myocardial aerobic capacity as a measure of both physical and cognitive workloads. International Journal of Industrial Ergonomics 35(12): 1097–1107CrossRefGoogle Scholar
  43. Füller J. (2010) Refining virtual co-creation from a consumer perspective. California Management Review 52(2): 98–122CrossRefGoogle Scholar
  44. Fung R. Y. K., Chen Y., Tang J. (2006) Estimating the functional relationships for quality function deployment under uncertainties. Fuzzy Sets and Systems 157(1): 98–120CrossRefGoogle Scholar
  45. Fung R. Y. K., Popplewell K., Xie J. (1998) An intelligent hybrid system for customer requirements analysis and product attribute targets determination. International Journal of Production Research 36(1): 13–34CrossRefGoogle Scholar
  46. Gilmore J. H., Pine J. B. II (2000) Markets of one. Harvard Business School Press, Boston, MAGoogle Scholar
  47. Grandjean D., Sander D., Scherer K. R. (2008) Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Consciousness & Cognition 17(2): 484–495CrossRefGoogle Scholar
  48. Green P. E., Srinivasan V. (1978) Conjoint analysis in consumer research: Issues and outlook. The Journal of Consumer Research 5(2): 103–123CrossRefGoogle Scholar
  49. Gu, T., Wu, Z., Tao, X., Pung, H. K., & Lu J. (2009). Epsicar: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In IEEE international conference on pervasive computing, Texas, USA.Google Scholar
  50. Gu, Y., Tan, S. L., Wong, K. J., Ho, M.-H.R., & Qu, L. (2010). A GMM based 2-stage architecture for multi-subject emotion recognition using physiological responses. In The 1st augmented human international conference. Megève, France.Google Scholar
  51. Ha S., Suh H.-W. (2008) A timed colored petri nets modeling for dynamic workflow in product development process. Computers in Industry 59(2–3): 193–209CrossRefGoogle Scholar
  52. Han S. H., Yun M. H., Kim K., Kwahk J. (2000) Evaluation of product usability: Development and validation of usability dimensions and design elements based on empirical models. International Journal of Industrial Ergonomics 26(4): 477–488CrossRefGoogle Scholar
  53. Helander M. G. (2005) A guide to human factors and ergonomics. CRC, Boca Raton, FL, USAGoogle Scholar
  54. Helander M. G., Khalid H. M. (2006) Affective and pleasurable design. In: Salvendy G. (eds) Handbook of human factors and ergonomics. Wiley Interscience, New YorkGoogle Scholar
  55. Helander M. G., Tham M. P. (2003) Hedonomics—affective human factors design. Ergonomics 46(13–14): 1269–1272CrossRefGoogle Scholar
  56. Helander, M. G., Khalid, H. M., & Peng, H. (2007). Citarasa engineering for affective design of vehicles. In IEEE International Conference on Industrial Engineering and Engineering Management, Singapore.Google Scholar
  57. Hoffman, R. R., Coffey, J. W., & Ford, K. M. (2000). A case study in the research paradigm of human-centered computing: Local expertise in weather forecasting. Washington, DC: National Technology Alliance, Report on the contract “human-centered system prototype”.Google Scholar
  58. Hoffman R. R., Roesler A., Moon B. M. (2004) What is design in the context of human-centered computing?. IEEE Intelligent Systems 19(4): 89–95CrossRefGoogle Scholar
  59. Hu, D. H., & Yang, Q. (2008). Cigar: Concurrent and interleaving goal and activity recognition. In The 23rd AAAI conference on artificial intelligence (AAAI), Chicago.Google Scholar
  60. Humphreys, P., Samson, A., Roser, T., & Cruz-Valdivieso, E. (2009). Co-creation: New pathways to value an overview. Promise, 1–21. Available from
  61. Inoue, K., Hirokawa, M., Sakai, M., & Kinishita, Y. (2007). Proposal of usability evaluation method by rough set theory. In The 54th annual conference of JSSD, Hong Kong.Google Scholar
  62. Ishihara S., Ishihara K., Nagamachi M. (2001) Kansei engineering analysis on car instrument panel. In: Helander M., Khalid H., Tham M. (eds) Proceedings of the international conference on affective human factors design. Asean Academic Press, London, pp 101–108Google Scholar
  63. Jiao, R. J. (2011). Prospect of design for mass customization and personalization. In Proceedings of the ASME 2011 international design engineering technical conferences & computers and information in engineering conference (IDETC/CIE 2011), Washington, DC, USA.Google Scholar
  64. Jiao J., Chen C.-H. (2006) Customer requirement management in product development: A review of research issues. Concurrent Engineering: Research and Applications 14(3): 173–185CrossRefGoogle Scholar
  65. Jiao J., Simpson T. W., Siddique Z. (2007) Product family design and platform-based product development: A state-of-the-art review. Journal of Intelligent Manufacturing 18(1): 5–29CrossRefGoogle Scholar
  66. Jiao J., Zhang Y., Helander M. G. (2006) A Kansei mining system for affective design. Expert Systems with Applications 30(4): 658–673CrossRefGoogle Scholar
  67. Jiao J., Xu Q., Du J., Zhang Y., Helander M. G., Khalid H. M., Helo P., Ni C. (2007) Analytical affective design with ambient intelligence for mass customization and personalization. International Journal of Flexible Manufacturing Systems 19(4): 570–595CrossRefGoogle Scholar
  68. Jin Y. (2003) Advanced fuzzy systems design and applications. Physica-Verlag, New YorkCrossRefGoogle Scholar
  69. John, B. E., & Kieras, D. E. (1994). The GOMS family of analysis techniques: Tools for design and evaluation. Technical Report: CMU-HCII-94-106, Pittsburgh, PA.Google Scholar
  70. Johnson C. M., Turley J. P. (2006) The significance of cognitive modeling in building healthcare interfaces. International Journal of Medical Informatics 75(2): 163–172CrossRefGoogle Scholar
  71. Jordan, P. W. (2000). The four pleasures-a framework for pleasures in design. In Conference on pleasure based human factors design, Groningen, Netherlands.Google Scholar
  72. Juristo N., Moreno A. M., Sanchez-Segura M. I. (2007) Analysing the impact of usability on software design. Journal of Systems and Software 80(9): 1506–1516CrossRefGoogle Scholar
  73. Kano N., Seraku N., Takahashi F., Tsuji S. (1984) Attractive quality and must-be quality. The Japan Society for Quality Control 14(2): 39–48Google Scholar
  74. Karsak E. E. (2004) Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment. Computers & Industrial Engineering 47(2–3): 149–163CrossRefGoogle Scholar
  75. Kasanoff, B. (2009). The personal economy. In The 5th world conference on mass customization and personalization, Helsinki, Finaland.Google Scholar
  76. Kim J., Han S. H. (2008) A methodology for developing a usability index of consumer electronic products. International Journal of Industrial Ergonomics 38(3–4): 333–345CrossRefGoogle Scholar
  77. Kim J., Lee J., Choi D. (2003) Designing emotionally evocative homepages: An empirical study of the quantitative relations between design factors and emotional dimensions. International Journal of Human-Computer Studies 59(6): 899–940CrossRefGoogle Scholar
  78. Kim K.-J., Moskowitz H., Dhingra A., Evans G. (2000) Fuzzy multicriteria models for quality function deployment. European Journal of Operational Research 121(3): 504–518CrossRefGoogle Scholar
  79. Klein G. A., Calderwood R., Macgregor D. (1989) Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man, and Cybernetics 19(3): 462–472CrossRefGoogle Scholar
  80. Komiak S. Y. X., Benbasat I. (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly 30(4): 941–960Google Scholar
  81. Kotler P. (2000) Marketing management. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  82. Kovach J., Cho B. R. (2008) Solving multiresponse optimization problems using quality function-based robust design. Quality Engineering 20(3): 346–360CrossRefGoogle Scholar
  83. Kumar A. (2007) From mass customization to mass personalization: A strategic transformation. International Journal of Flexible Manufacturing Systems 19(4): 533–547CrossRefGoogle Scholar
  84. Kwon K., Cho J., Park Y. (2010) How to best characterize the personalization construct for e-services. Expert Systems With Applications 37(3): 2232–2240CrossRefGoogle Scholar
  85. Lai X., Bai Y., Qiu Y. (2006) Measuring usability: Use HMM emotion method and parameter optimize. Lecture Notes in Computer Science 4221: 241–250CrossRefGoogle Scholar
  86. Laird, J. E. (2008). Extending the soar cognitive architecture. In The 2008 conference on artificial general intelligence, Memphis, TN.Google Scholar
  87. Lamoureux, T. M., Bandali, F., Bruyn Martin, L. M., & Li, Z. (2006). Team modelling: Review of experimental scenarios and computational models. Technical Report: CR2006-092, Toronto.Google Scholar
  88. Lanzotti A., Tarantino P. (2008) Kansei engineering approach for total quality design and continuous innovation. The TQM Journal 20(4): 324–327CrossRefGoogle Scholar
  89. Larsen R. J., Fredrickson B. (1999) Measurement issues in emotion research. In: Kahneman D., Diener E., Schwarz N. (eds) Well-being: The foundations of hedonic psychology. Russell Sage Foundation, New York, pp 40–60Google Scholar
  90. Lee C. M., Narayanan S. S. (2005) Toward detecting emotions in spoken dialogs. IEEE Transactions on Speech Audio Process 13(2): 293–303CrossRefGoogle Scholar
  91. Lekkas, Z., Tsianos, N., Germanakos, P., Mourlas, C., & Samaras, G. (2008). The role of emotions in the design of personalized educational systems. In The eighth IEEE international conference on advanced learning technologies, Santander, Cantabria.Google Scholar
  92. Leont’ev A. N. (1977) Activity and consciousness (N. Schmolze, trans.). Progress Press, MoscowGoogle Scholar
  93. Lewin K. (1951) Field theory in social science. Harper, New YorkGoogle Scholar
  94. Li, L., & Chen, J.-H. (2006). Emotion recognition using physiological signals from multiple subjects. In Proceedings of the 2006 international conference on intelligent information hiding and multimedia signal processing Pasadena, California, USA.Google Scholar
  95. Li M., Chai Q., Teo K., Wahab A., Abut H. (2009) Eeg emotion recognition system. In: Takeda K., Erdogan H., Hansen J. H. L., Abut H. (eds) In-vehicle corpus and signal processing for driver behavior. Springer, New York, US, pp 125–136CrossRefGoogle Scholar
  96. Lisetti, C. L., & Nasoz, F. (2002). MAUI: A multimodal affective user interface. In The tenth ACM international conference on multimedia, Juan-les-Pins, France.Google Scholar
  97. Liu Y. (2003) Engineering aesthetics and aesthetic ergonomics: Theoretical foundations and a dual-process research methodology. Ergonomics 46(13-14): 1273–1292CrossRefGoogle Scholar
  98. Liu Z. Q., Satur R. (1999) Contextual fuzzy cognitive map for decision support in geographic information systems. IEEE Transactions on Fuzzy Systems 7(5): 495–507CrossRefGoogle Scholar
  99. Liu C., Conn K., Sarkar N., Stone W. (2008) Physiology-based affect recognition for computer-assisted intervention of children with autism spectrum disorder. International Journal of Human-Computer Studies 66(9): 662–677CrossRefGoogle Scholar
  100. Maffiolo V., Chateau N. (2003) The emotional quality of speech in voice services. Ergonomics 46(13/14): 1375–1385CrossRefGoogle Scholar
  101. Mandryk R., Atkins M. (2007) A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies 65(4): 329–347CrossRefGoogle Scholar
  102. Marinier R., Laird J. E., Lewis R. L. (2008) A computational unification of cognitive behavior and emotion. Journal of Cognitive Systems Research 10(1): 48–69CrossRefGoogle Scholar
  103. Mazur, G. H. (2005). Life qfd: Incorporating emotional appeal in product development. In 17th symposium on quality function deployment, Portland.Google Scholar
  104. Mcgraw K. L. (1992) Designing and evaluating user interfaces for knowledge based systems. Ellis Horwood, New YorkGoogle Scholar
  105. Mckay M. T., Fischler I., Dunn B. R. (2003) Cognitive style and recall of text: An eeg analysis. Learning and Individual Differences 14(1): 1–21CrossRefGoogle Scholar
  106. Miao Y., Liu Z.-Q. (2000) On causal inference in fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems 8(1): 107–119CrossRefGoogle Scholar
  107. Militello L. G., Hutton R. J. B. (1998) Applied cognitive task analysis (ACTA): A practitioner’s toolkit for understanding cognitive task demands. Ergonomics 41(11): 1618–1641CrossRefGoogle Scholar
  108. Montgomery A. M., Smith M. D. (2009) Prospects for personalization on the internet. Journal of Interactive Marketing 23(2): 130–137CrossRefGoogle Scholar
  109. Morris J. D., Woo C., Geason J. A., Kim J. (2002) The power of affect: Predicting intention. Journal of Advertising Research 42(3): 7–17Google Scholar
  110. Mower E., Mataric M. J., Narayanan S. (2009) Human perception of audio-visual synthetic character emotion expression in the presence of ambiguous and conflicting information. IEEE Transactions on Multimedia 11(5): 843–855CrossRefGoogle Scholar
  111. Murata T. (1989) Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4): 541–580CrossRefGoogle Scholar
  112. Nagamachi M. (1995) Kansei engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics 15(1): 3–11CrossRefGoogle Scholar
  113. Nagamachi M., Okazaki Y., Ishikawa M. (2006) Kansei engineering and application of the rough sets model. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering 220(I8): 763–768CrossRefGoogle Scholar
  114. Nielsen J. (1994) Usability engineering. Morgan Kaufmann, San FranciscoGoogle Scholar
  115. Niu, F., & Abdel-Mottaleb, M. (2004). View-invariant human activity recognition based on shape and motion features. In Proceedings of the IEEE sixth international symposium on multimedia software engineering.Google Scholar
  116. Norman D. A. (2004) Emotional design: Why we love (or hate) everyday things. Basic Books, New YorkGoogle Scholar
  117. Norman, D. A., Draper, S. W. (eds) (1986) User-centered system design: New perspectives on human-computer interaction. Lawrence Earlbaum Associates, Hillsdale, NJGoogle Scholar
  118. Novak, J. D., & Cañas, A. J. (2006). The theory underlying concept maps and how to construct them. Technical Report: IHMC CmapTools 2006-01, Pensacola Fl.Google Scholar
  119. Nuseibeh, B., & Easterbrook, S. (2000). Requirements engineering: A roadmap. In Conference on The future of software engineering, Limerick, Ireland.Google Scholar
  120. Osgood C. E., Suci G. J., Tannenbaum P. H. (1957) The measurement of meaning. University of Illinois Press, Urbana, USAGoogle Scholar
  121. Pantic M., Rothkrantz L. J. M. (2000) Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12): 1424–1445CrossRefGoogle Scholar
  122. Pantic M., Rothkrantz L. J. M. (2003) Toward an affect-sensitive multimodal human-computer interaction. Proceedings of the IEEE 91(9): 1370–1390CrossRefGoogle Scholar
  123. Parasuraman R., Caggiano D. (2005) Neural and genetic assays of mental workload. In: Mcbride D., Schmorrow D. (eds) Quantifying human information processing. Rowman & Littlefield, Lanham, MD, pp 123–155Google Scholar
  124. Parrott G., Sabini J. (1989) On the “emotional” qualities of certain types of cognition: A reply to arguments for the independence of cognition and affect. Cognitive Therapy and Research 13(1): 49–65CrossRefGoogle Scholar
  125. Pentney, W., Popescu, A.-M., Wang, S., Kautz, H., & Philipose, M. (2006). Sensor-based understanding of daily life via large-scale use of common sense. In Proceedings of the 21st national conference on artificial intelligence (Vol. 1), Boston, Massachusetts.Google Scholar
  126. Peppers D., Rogers M. (1997) The one-to-one future. Double Day Publications, New YorkGoogle Scholar
  127. Perkowitz, M., Philipose, M., Fishkin, K., & Patterson, D. J. (2004). Mining models of human activities from the web. In Proceedings of the 13th international conference on world wide web, New York, NY, USA.Google Scholar
  128. Perry A., Crisp H. E., Mckneely J. A., Wallace D. F. (1999) The solution for future command and control: Human-centered design. In: Hamburger P. (eds) Proceedings of SPIE, office of naval research manning and affordability initiative: Vol 4126. Integrated command enviroments. SPIE, Belligham, WA, pp 42–53Google Scholar
  129. Perusich K. (2008) Using fuzzy cognitive maps to identify multiple causes in troubleshooting systems. Integrated Computer-Aided Engineering 15(2): 197–206Google Scholar
  130. Peter C., Herbon A. (2006) Emotion representation and physiology assignments in digital systems. Interacting with Computers 18(2): 139–170CrossRefGoogle Scholar
  131. Petrushin, V. A. (1998). How well can people and computers recognize emotionsn in speech? In The 1998 AAAI fall symp, Orlando, FloridaGoogle Scholar
  132. Pfautz J. R. E., Roth E. (2006) Using cognitive engineering for system design and evaluation: A visualization aid for stability and support operations. International Journal of Industrial Ergonomics 36(5): 389–407CrossRefGoogle Scholar
  133. Picard R. W. (1997) Affective computing. The MIT Press, Cambridge, MassachusettsGoogle Scholar
  134. Picard R. W., Klein J. (2002) Computers that recognise and respond to user emotion: Theoretical and practical implications. Interacting with computers 14(2): 141–169CrossRefGoogle Scholar
  135. Picard R. W., Vyzas E., Healey J. (2001) Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10): 1175–1191CrossRefGoogle Scholar
  136. Pidd M. (1996) Tools for thinking modeling management science. Wiley, ChichesterGoogle Scholar
  137. Pine, J., II. (1993). Mass customization-The new frontier in business comptetition. Boston: Harvard Business School Press.Google Scholar
  138. Pine J. II, Gilmore J. (1999) The experience economy. Harvard Business School Press, BostonGoogle Scholar
  139. Prasad B. (1998) Review of qfd and related deployment techniques. Journal of Manufacturing Systems 17(3): 221–234CrossRefGoogle Scholar
  140. Readinger, W. O. (2004). How they really think: Capturing the context of consumer decision-making. Quirk’s Marketing Research Review,
  141. Riemer, K., & Totz, C. (2001). The many faces of personalization? An integrative economic overview of mass customization and personalization. In The world conference on mass customization, personalization, and co-creation, Hong Kong, China.Google Scholar
  142. Rosenberg E. L., Ekman P. (1994) Coherence between expressive and experiential systems in emotion. Cognition and Emotion 8(3): 201–229CrossRefGoogle Scholar
  143. Rugg G., Mcgeorge P. (1995) Laddering. Expert Systems with Applications 12(4): 279–291Google Scholar
  144. Russell J. A. (1980) A circumplex model of affect. Journal of Personality and Social Psychology 39(6): 1161–1178CrossRefGoogle Scholar
  145. Sackmann S., Strüker J., Accorsi R. (2006) Personalization in privacy-aware highly dynamic systems. Communications of the ACM 49(9): 32–38CrossRefGoogle Scholar
  146. Salvucci, D. D., Zuber, M., Beregovaia, E., & Markley D. (2005). Distract-r: Rapid prototyping and evaluation of in-vehicle interfaces. In The SIGCHI conference on human factors in computing systems, Portland, Oregon, USA.Google Scholar
  147. Schütte S., Eklund J., Alxelsson J., Nagamachi M. (2004) Concepts, methods and tools in kansei engineering. Theoretical Issues in Ergonomics Science 5(3): 214–231CrossRefGoogle Scholar
  148. Scheiberg S. L. (1990) Emotions on display: The personal decoration of workspace. American behavioral Scientist 33(3): 330–338CrossRefGoogle Scholar
  149. Scheirer J., Fernandez R., Klein J., Picard R. W. (2002) Frustrating the user on purpose: A step toward building an affective computer. Interacting with computers 14(2): 93–118CrossRefGoogle Scholar
  150. Schraagen J. M., Chipman S. F., Shalin V. L. (2000) Cognitive task analysis. Lawrence Erlbaum Associates, LondonGoogle Scholar
  151. Sedgwick, J., Henson, B., & Barnes, C. (2003). Sensual surfaces: Engaging consumers through surface textures. In International conference on Designing pleasurable products and interfaces, Pittsburgh, PA, USA.Google Scholar
  152. Shen X. X., Tan K. C., Xie M. (2001) The implementation of quality function deployment based on linguistic data. Journal of Intelligent Manufacturing 12(1): 65–75CrossRefGoogle Scholar
  153. Shepherd A. (2000) Hierarchical task analysis. Taylor & Francis, New YorkGoogle Scholar
  154. Simpson T. W., Siddique Z., Jiao J. (2005) Product platform and product family design: Methods and applications. Springer, New YorkGoogle Scholar
  155. Stanton N. A. (2006) Hierarchical task analysis: Developments, applications, and extensions. Applied Ergonomics 37(1): 55–79CrossRefGoogle Scholar
  156. Storbeck J., Clore G. L. (2007) On the interdependence of cognition and emotion. Cognition Emotion 21(6): 1212–1237CrossRefGoogle Scholar
  157. Suh N. (2001) Axiomatic design: Advances and applications. Oxford University Press, New YorkGoogle Scholar
  158. Sul C., Lee K., Wohn K. (1998) Virtual stage: A location-based karaoke system. Multimedia 5(2): 42–52CrossRefGoogle Scholar
  159. Sundin E., Sakao T., Lindahl M., Shimomura Y., Comstock M. (2009) Achieving mass customisation through servicification. International Journal of Internet Manufacturing and Services 2(1): 56–75CrossRefGoogle Scholar
  160. Tague N. R. (2004) The quality toolbox. ASQ Quality Press, Milwaukee, WIGoogle Scholar
  161. Takeshi E., Midori M., Sadao H. (1998) Usability evaluation applied cognitive task analysis on MS-excel 95 and word 95. Japanese Journal of Ergonomics 34(2): 428–429Google Scholar
  162. Trejo L. J., Knuth K., Prado R., Rosipal R., Kubitz K., Kochavi R., Matthews B., Zhang Y. (2007) EEG-based estimation of mental fatigue: Convergent evidence for a three-state model. In: Schmorrow D. D., Reeves L. M. (eds) Augmented Cognition, HCII, LNAI 4565. Springer, Berlin, pp 201–211CrossRefGoogle Scholar
  163. Tseng M. M., Jiao J. (1996) Design for mass customization. CIRP Annals-Manufacturing Technology 45(1): 153–156CrossRefGoogle Scholar
  164. Tseng M. M., Jiao J. (2001) Mass customization. In: Salvendy G. (eds) Handbook of industrial engineering, technology and operation management. Wiley, New YorkGoogle Scholar
  165. Tseng M. M., Piller F. (2003) The customer centric enterprise: Advances in mass customization and personalization. Springer, New York/BerlinCrossRefGoogle Scholar
  166. Tseng M. M., Jiao R. J., Wang C. (2010) Design for mass personalization. CIRP Annals—Manufacturing Technology 59(1): 175–178CrossRefGoogle Scholar
  167. Tsuchiya T., Maeda T., Matsubara Y., Nagamachi M. (1996) A fuzzy rule induction method using genetic algorithm. International Journal of Industrial Ergonomics 18(2–3): 135–145CrossRefGoogle Scholar
  168. Venasen J. (2007) What is personalization. A conceptual framework. European Journal of Marketing 41(5–6): 409–418Google Scholar
  169. Ververidis D., Kotropoulos C. (2006) Emotional speech recognition: Resources, features, and methods. Speech Communication 48(9): 1162–1181CrossRefGoogle Scholar
  170. Wansink B. (2003) Using laddering to understand and leverage a brand’s equity. Qualitative Market Research 6(2): 111–118CrossRefGoogle Scholar
  171. Ward J. A., Lukowicz P., Troster G., Starner T. E. (2006) Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10): 1553–1567CrossRefGoogle Scholar
  172. Wickens C. D., Hollands J. G. (1999) Engineering psychology and human performance. Prentice Hall, New JerseyGoogle Scholar
  173. Wiendahl H. P., Elmaraghy H. A., Nyhuis P., Zäh M., Wiendahl H. H., Duffie N., Kolakowski M. (2007) Changeable manufacturing: Classification, design and operation. Annals of the CIRP 56(2): 783–809CrossRefGoogle Scholar
  174. Woods D., Roesler A. (2008) Connecting design with cognition at work. In: Schifferstein H. N. J., Hekkert P. (eds) Product experience. Elsevier, New York, pp 199–213CrossRefGoogle Scholar
  175. Woods D. D., Roth E. M. (2006) Joint cognitive systems: Patterns in cognitive systems engineering. Taylor & Francis, Boca Raton, FLCrossRefGoogle Scholar
  176. Xirogiannis G., Stefanou J., Glykas M. (2004) A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26(2): 257–268CrossRefGoogle Scholar
  177. Yan H.-B., Huynh V.-N., Murai T., Nakamori Y. (2008) Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis. Information Sciences 178(21): 4080–4093CrossRefGoogle Scholar
  178. Yan W., Chen C.-H., Shieh M.-D. (2006) Product concept generation and selection using sorting technique and fuzzy c-means algorithm. Computers and Industrial Engineering 50(3): 273–285CrossRefGoogle Scholar
  179. Yeung C. W. M., Wyer R. S. (2004) Affect, appraisal and consumer judgment. Journal of Consumer Research 31(2): 412–424CrossRefGoogle Scholar
  180. Yun, D. K., Kim, K. Y., & Ko, H. S. (2005). Customer expectation level in mobile data services. In The 7th international conference on Human computer interaction with mobile devices and services, Salzburg, Austria.Google Scholar
  181. Zajonc R. B. (1980) Feeling and thinking: Preferences need no inferences. American Psychologist 35: 151–175CrossRefGoogle Scholar
  182. Zhai L.-Y., Khoo L.-P., Zhong Z.-W. (2007) A dominance-based rough set approach to kansei engineering in product development. Expert Systems with Applications 36(1): 393–402CrossRefGoogle Scholar
  183. Zhai L.-Y., Khoo L.-P., Zhong Z.-W. (2009) A rough set based qfd approach to the management of imprecise design information in product development. Advanced Engineering Informatics 23(2): 222–228CrossRefGoogle Scholar
  184. Zhai L. Y., Khoo L. P., Zhong Z. W. (2008) A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology 37(5–6): 613–624CrossRefGoogle Scholar
  185. Zhang Y., Feick L., Price L. J. (2006) The impact of self-construal on aesthetic preference for angular versus rounded shapes. Personality and Social Psychology Bulletin 32(6): 794–805CrossRefGoogle Scholar
  186. Zhou F., Xu Q., Jiao R. (2011) Fundamentals of product ecosystem design for user experience. Research in Engineering Design 22(1): 43–61CrossRefGoogle Scholar
  187. Zhou F., Jiao J., Chen S., Zhang D. (2011) A case-driven ambient intelligence system for elderly in-home assistance applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 41(2): 179–189CrossRefGoogle Scholar
  188. Zhou F., Jiao J. R., Schaefer D., Chen S. (2010) Hybrid association mining and refinement for affective mapping in emotional design. Journal of Computing and Information Science in Engineering 10(3): 0310101–0310109CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.The G.W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Mechanical Engineering, Industrial Engineering CenterZhejiang UniversityHangzhouChina

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