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Affective and cognitive design for mass personalization: status and prospect

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Abstract

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.

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References

  • Aarts E. (2004) Ambient intelligence: A multimedia perspective. Multimedia, IEEE 11(1): 12–19

    Article  Google Scholar 

  • Adam N. R., Atluri V., Huang W.-K. (1998) Modeling and analysis of workflows using petri nets. Journal of Intelligent Information Systems 10(2): 131–158

    Article  Google Scholar 

  • 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 27–49

    Google Scholar 

  • 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.

  • Allen, C. (2009). Personalization vs. Customization [online]. www.allen.com (Accessed May 5, 2011).

  • Arakawa, M., Shiraki, W., & Ishikawa, H. (1999). Kansei design using genetic algorithms. In IEEE international conference on systems, man, and cybernetics, Tokyo, Japan

  • 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): 305–321

    Article  Google Scholar 

  • Augusto J. C. (2007) Ambient intelligence: The confluence of ubiquitous/pervasive computing and artificial intelligence. Intelligent computing everywhere. Springer, London, pp 213–234

    Google Scholar 

  • 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): 303–317

    Article  Google Scholar 

  • 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.

  • Blom J., Monk A. (2003) Theory of personalisation of appearance: Why people personalise their mobile phones and PCs. Human-Computer Interaction 18(3): 193–228

    Article  Google Scholar 

  • Bos, D. O. (2008). EEG-based emotion recognition [online]. http://hmi.ewi.utwente.nl/verslagen/capita-selecta/CS-Oude_Bos-Danny.pdf (Accessed Aug 8, 2009).

  • Cacioppo J. T., Tassinary L. G. (1990) Inferring psychological significance from physiological signals. American Psychologist 45(1): 16–28

    Article  Google Scholar 

  • Carbonara N., Scozzi B. (2006) Cognitive maps to analyze new product development processes: A case study. Technovation 26(11): 1233–1243

    Article  Google Scholar 

  • Chan L., Wu M. (2002) QFD: A literature review. European Journal of Operational Research 143(3): 463–497

    Article  Google Scholar 

  • Chandler P., Sweller J. (1991) Cognitive load theory and the format of instruction. Cognition and Instruction 8(4): 293–332

    Article  Google Scholar 

  • 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): 181–202

    Article  Google Scholar 

  • 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.

  • 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): 229–240

    Article  Google Scholar 

  • 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): 334–349

    Article  Google Scholar 

  • 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.

  • 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): 160–187

    Article  Google Scholar 

  • 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): 233–244

    Article  Google Scholar 

  • 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): 32–80

    Article  Google Scholar 

  • Crandall B., Klein G., Hoffman R. (2006) Working minds: A practitioner’s guide to cognitive task analysis. The MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Csikszentmihalyi M. (1990) Flow: The psychology of optimal experience. Harper and Row, New York

    Google Scholar 

  • David R., Alla H. (1992) Petri nets and grafcet—tools for modeling discrete event systems. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Dekker S. W. A. (2002) The field guide to human error investigations. Ashgate, London

    Google Scholar 

  • 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): 123–135

    Article  Google Scholar 

  • Desmet P. (2003) Measuring emotions. In: Blythe M. A., Monk A. F., Overbeeke K., Wright P. C. (eds) Funology: From usability to enjoyment. Springer, Berlin

    Google Scholar 

  • Du X., Jiao J., Tseng M. M. (2003) Identifying customer need patterns for customization and personalization. Integrated Manufacturing Systems 14(5): 387–396

    Article  Google Scholar 

  • 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 45–90

    Google Scholar 

  • Ekman P., Friesen W. V. (1978) Facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto, California

    Google Scholar 

  • 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 572–595

    Google Scholar 

  • Ertay T., Kahraman C. (2007) Evaluation of design requirements using fuzzy outranking methods. International Journal of Intelligent Systems 22(12): 1229–1250

    Article  Google Scholar 

  • 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): 757–763

    Article  Google Scholar 

  • Fairclough S. H. (2009) Fundamentals of physiological computing. Interacting with computers 21(1–2): 133–145

    Article  Google Scholar 

  • Fasel B., Luettin J. (2003) Automatic facial expression analysis: A survey. Pattern Recognition 36(1): 259–275

    Article  Google Scholar 

  • 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): 155–171

    Article  Google Scholar 

  • Fragopanagos N., Taylor J. G. (2005) Emotion recognition in human-computer interaction. Neural Networks 18(4): 389–405

    Article  Google Scholar 

  • 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): 309–318

    Article  Google Scholar 

  • 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): 1097–1107

    Article  Google Scholar 

  • Füller J. (2010) Refining virtual co-creation from a consumer perspective. California Management Review 52(2): 98–122

    Article  Google Scholar 

  • 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): 98–120

    Article  Google Scholar 

  • 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): 13–34

    Article  Google Scholar 

  • Gilmore J. H., Pine J. B. II (2000) Markets of one. Harvard Business School Press, Boston, MA

    Google Scholar 

  • 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): 484–495

    Article  Google Scholar 

  • Green P. E., Srinivasan V. (1978) Conjoint analysis in consumer research: Issues and outlook. The Journal of Consumer Research 5(2): 103–123

    Article  Google Scholar 

  • 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.

  • 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.

  • 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): 193–209

    Article  Google Scholar 

  • 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): 477–488

    Article  Google Scholar 

  • Helander M. G. (2005) A guide to human factors and ergonomics. CRC, Boca Raton, FL, USA

    Google Scholar 

  • Helander M. G., Khalid H. M. (2006) Affective and pleasurable design. In: Salvendy G. (eds) Handbook of human factors and ergonomics. Wiley Interscience, New York

    Google Scholar 

  • Helander M. G., Tham M. P. (2003) Hedonomics—affective human factors design. Ergonomics 46(13–14): 1269–1272

    Article  Google Scholar 

  • 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.

  • 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”.

  • Hoffman R. R., Roesler A., Moon B. M. (2004) What is design in the context of human-centered computing?. IEEE Intelligent Systems 19(4): 89–95

    Article  Google Scholar 

  • Hu, D. H., & Yang, Q. (2008). Cigar: Concurrent and interleaving goal and activity recognition. In The 23rd AAAI conference on artificial intelligence (AAAI), Chicago.

  • Humphreys, P., Samson, A., Roser, T., & Cruz-Valdivieso, E. (2009). Co-creation: New pathways to value an overview. Promise, 1–21. Available from http://www.promisecorp.com/newpathways.

  • 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.

  • 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 101–108

    Google Scholar 

  • 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.

  • Jiao J., Chen C.-H. (2006) Customer requirement management in product development: A review of research issues. Concurrent Engineering: Research and Applications 14(3): 173–185

    Article  Google Scholar 

  • 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): 5–29

    Article  Google Scholar 

  • Jiao J., Zhang Y., Helander M. G. (2006) A Kansei mining system for affective design. Expert Systems with Applications 30(4): 658–673

    Article  Google Scholar 

  • 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): 570–595

    Article  Google Scholar 

  • Jin Y. (2003) Advanced fuzzy systems design and applications. Physica-Verlag, New York

    Book  Google Scholar 

  • 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.

  • Johnson C. M., Turley J. P. (2006) The significance of cognitive modeling in building healthcare interfaces. International Journal of Medical Informatics 75(2): 163–172

    Article  Google Scholar 

  • Jordan, P. W. (2000). The four pleasures-a framework for pleasures in design. In Conference on pleasure based human factors design, Groningen, Netherlands.

  • 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): 1506–1516

    Article  Google Scholar 

  • Kano N., Seraku N., Takahashi F., Tsuji S. (1984) Attractive quality and must-be quality. The Japan Society for Quality Control 14(2): 39–48

    Google Scholar 

  • Karsak E. E. (2004) Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment. Computers & Industrial Engineering 47(2–3): 149–163

    Article  Google Scholar 

  • Kasanoff, B. (2009). The personal economy. In The 5th world conference on mass customization and personalization, Helsinki, Finaland.

  • 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): 333–345

    Article  Google Scholar 

  • 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): 899–940

    Article  Google Scholar 

  • Kim K.-J., Moskowitz H., Dhingra A., Evans G. (2000) Fuzzy multicriteria models for quality function deployment. European Journal of Operational Research 121(3): 504–518

    Article  Google Scholar 

  • Klein G. A., Calderwood R., Macgregor D. (1989) Critical decision method for eliciting knowledge. IEEE Transactions on Systems, Man, and Cybernetics 19(3): 462–472

    Article  Google Scholar 

  • Komiak S. Y. X., Benbasat I. (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly 30(4): 941–960

    Google Scholar 

  • Kotler P. (2000) Marketing management. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Kovach J., Cho B. R. (2008) Solving multiresponse optimization problems using quality function-based robust design. Quality Engineering 20(3): 346–360

    Article  Google Scholar 

  • Kumar A. (2007) From mass customization to mass personalization: A strategic transformation. International Journal of Flexible Manufacturing Systems 19(4): 533–547

    Article  Google Scholar 

  • Kwon K., Cho J., Park Y. (2010) How to best characterize the personalization construct for e-services. Expert Systems With Applications 37(3): 2232–2240

    Article  Google Scholar 

  • Lai X., Bai Y., Qiu Y. (2006) Measuring usability: Use HMM emotion method and parameter optimize. Lecture Notes in Computer Science 4221: 241–250

    Article  Google Scholar 

  • Laird, J. E. (2008). Extending the soar cognitive architecture. In The 2008 conference on artificial general intelligence, Memphis, TN.

  • 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.

  • Lanzotti A., Tarantino P. (2008) Kansei engineering approach for total quality design and continuous innovation. The TQM Journal 20(4): 324–327

    Article  Google Scholar 

  • 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 40–60

    Google Scholar 

  • Lee C. M., Narayanan S. S. (2005) Toward detecting emotions in spoken dialogs. IEEE Transactions on Speech Audio Process 13(2): 293–303

    Article  Google Scholar 

  • 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.

  • Leont’ev A. N. (1977) Activity and consciousness (N. Schmolze, trans.). Progress Press, Moscow

    Google Scholar 

  • Lewin K. (1951) Field theory in social science. Harper, New York

    Google Scholar 

  • 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.

  • 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 125–136

    Chapter  Google Scholar 

  • Lisetti, C. L., & Nasoz, F. (2002). MAUI: A multimodal affective user interface. In The tenth ACM international conference on multimedia, Juan-les-Pins, France.

  • Liu Y. (2003) Engineering aesthetics and aesthetic ergonomics: Theoretical foundations and a dual-process research methodology. Ergonomics 46(13-14): 1273–1292

    Article  Google Scholar 

  • Liu Z. Q., Satur R. (1999) Contextual fuzzy cognitive map for decision support in geographic information systems. IEEE Transactions on Fuzzy Systems 7(5): 495–507

    Article  Google Scholar 

  • 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): 662–677

    Article  Google Scholar 

  • Maffiolo V., Chateau N. (2003) The emotional quality of speech in voice services. Ergonomics 46(13/14): 1375–1385

    Article  Google Scholar 

  • 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): 329–347

    Article  Google Scholar 

  • Marinier R., Laird J. E., Lewis R. L. (2008) A computational unification of cognitive behavior and emotion. Journal of Cognitive Systems Research 10(1): 48–69

    Article  Google Scholar 

  • Mazur, G. H. (2005). Life qfd: Incorporating emotional appeal in product development. In 17th symposium on quality function deployment, Portland.

  • Mcgraw K. L. (1992) Designing and evaluating user interfaces for knowledge based systems. Ellis Horwood, New York

    Google Scholar 

  • Mckay M. T., Fischler I., Dunn B. R. (2003) Cognitive style and recall of text: An eeg analysis. Learning and Individual Differences 14(1): 1–21

    Article  Google Scholar 

  • Miao Y., Liu Z.-Q. (2000) On causal inference in fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems 8(1): 107–119

    Article  Google Scholar 

  • 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): 1618–1641

    Article  Google Scholar 

  • Montgomery A. M., Smith M. D. (2009) Prospects for personalization on the internet. Journal of Interactive Marketing 23(2): 130–137

    Article  Google Scholar 

  • Morris J. D., Woo C., Geason J. A., Kim J. (2002) The power of affect: Predicting intention. Journal of Advertising Research 42(3): 7–17

    Google Scholar 

  • 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): 843–855

    Article  Google Scholar 

  • Murata T. (1989) Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4): 541–580

    Article  Google Scholar 

  • Nagamachi M. (1995) Kansei engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics 15(1): 3–11

    Article  Google Scholar 

  • 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): 763–768

    Article  Google Scholar 

  • Nielsen J. (1994) Usability engineering. Morgan Kaufmann, San Francisco

    Google Scholar 

  • 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.

  • Norman D. A. (2004) Emotional design: Why we love (or hate) everyday things. Basic Books, New York

    Google Scholar 

  • Norman, D. A., Draper, S. W. (eds) (1986) User-centered system design: New perspectives on human-computer interaction. Lawrence Earlbaum Associates, Hillsdale, NJ

    Google Scholar 

  • 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.

  • Nuseibeh, B., & Easterbrook, S. (2000). Requirements engineering: A roadmap. In Conference on The future of software engineering, Limerick, Ireland.

  • Osgood C. E., Suci G. J., Tannenbaum P. H. (1957) The measurement of meaning. University of Illinois Press, Urbana, USA

    Google Scholar 

  • 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): 1424–1445

    Article  Google Scholar 

  • Pantic M., Rothkrantz L. J. M. (2003) Toward an affect-sensitive multimodal human-computer interaction. Proceedings of the IEEE 91(9): 1370–1390

    Article  Google Scholar 

  • 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 123–155

    Google Scholar 

  • 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): 49–65

    Article  Google Scholar 

  • 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.

  • Peppers D., Rogers M. (1997) The one-to-one future. Double Day Publications, New York

    Google Scholar 

  • 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.

  • 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 42–53

    Google Scholar 

  • Perusich K. (2008) Using fuzzy cognitive maps to identify multiple causes in troubleshooting systems. Integrated Computer-Aided Engineering 15(2): 197–206

    Google Scholar 

  • Peter C., Herbon A. (2006) Emotion representation and physiology assignments in digital systems. Interacting with Computers 18(2): 139–170

    Article  Google Scholar 

  • Petrushin, V. A. (1998). How well can people and computers recognize emotionsn in speech? In The 1998 AAAI fall symp, Orlando, Florida

  • 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): 389–407

    Article  Google Scholar 

  • Picard R. W. (1997) Affective computing. The MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Picard R. W., Klein J. (2002) Computers that recognise and respond to user emotion: Theoretical and practical implications. Interacting with computers 14(2): 141–169

    Article  Google Scholar 

  • 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): 1175–1191

    Article  Google Scholar 

  • Pidd M. (1996) Tools for thinking modeling management science. Wiley, Chichester

    Google Scholar 

  • Pine, J., II. (1993). Mass customization-The new frontier in business comptetition. Boston: Harvard Business School Press.

  • Pine J. II, Gilmore J. (1999) The experience economy. Harvard Business School Press, Boston

    Google Scholar 

  • Prasad B. (1998) Review of qfd and related deployment techniques. Journal of Manufacturing Systems 17(3): 221–234

    Article  Google Scholar 

  • Readinger, W. O. (2004). How they really think: Capturing the context of consumer decision-making. Quirk’s Marketing Research Review, http://klein-inc.com/whatwedo/HowTheyReallyThink.pdf.

  • 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.

  • Rosenberg E. L., Ekman P. (1994) Coherence between expressive and experiential systems in emotion. Cognition and Emotion 8(3): 201–229

    Article  Google Scholar 

  • Rugg G., Mcgeorge P. (1995) Laddering. Expert Systems with Applications 12(4): 279–291

    Google Scholar 

  • Russell J. A. (1980) A circumplex model of affect. Journal of Personality and Social Psychology 39(6): 1161–1178

    Article  Google Scholar 

  • Sackmann S., Strüker J., Accorsi R. (2006) Personalization in privacy-aware highly dynamic systems. Communications of the ACM 49(9): 32–38

    Article  Google Scholar 

  • 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.

  • Schütte S., Eklund J., Alxelsson J., Nagamachi M. (2004) Concepts, methods and tools in kansei engineering. Theoretical Issues in Ergonomics Science 5(3): 214–231

    Article  Google Scholar 

  • Scheiberg S. L. (1990) Emotions on display: The personal decoration of workspace. American behavioral Scientist 33(3): 330–338

    Article  Google Scholar 

  • 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): 93–118

    Article  Google Scholar 

  • Schraagen J. M., Chipman S. F., Shalin V. L. (2000) Cognitive task analysis. Lawrence Erlbaum Associates, London

    Google Scholar 

  • 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.

  • 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): 65–75

    Article  Google Scholar 

  • Shepherd A. (2000) Hierarchical task analysis. Taylor & Francis, New York

    Google Scholar 

  • Simpson T. W., Siddique Z., Jiao J. (2005) Product platform and product family design: Methods and applications. Springer, New York

    Google Scholar 

  • Stanton N. A. (2006) Hierarchical task analysis: Developments, applications, and extensions. Applied Ergonomics 37(1): 55–79

    Article  Google Scholar 

  • Storbeck J., Clore G. L. (2007) On the interdependence of cognition and emotion. Cognition Emotion 21(6): 1212–1237

    Article  Google Scholar 

  • Suh N. (2001) Axiomatic design: Advances and applications. Oxford University Press, New York

    Google Scholar 

  • Sul C., Lee K., Wohn K. (1998) Virtual stage: A location-based karaoke system. Multimedia 5(2): 42–52

    Article  Google Scholar 

  • 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): 56–75

    Article  Google Scholar 

  • Tague N. R. (2004) The quality toolbox. ASQ Quality Press, Milwaukee, WI

    Google Scholar 

  • 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): 428–429

    Google Scholar 

  • 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 201–211

    Chapter  Google Scholar 

  • Tseng M. M., Jiao J. (1996) Design for mass customization. CIRP Annals-Manufacturing Technology 45(1): 153–156

    Article  Google Scholar 

  • Tseng M. M., Jiao J. (2001) Mass customization. In: Salvendy G. (eds) Handbook of industrial engineering, technology and operation management. Wiley, New York

    Google Scholar 

  • Tseng M. M., Piller F. (2003) The customer centric enterprise: Advances in mass customization and personalization. Springer, New York/Berlin

    Book  Google Scholar 

  • Tseng M. M., Jiao R. J., Wang C. (2010) Design for mass personalization. CIRP Annals—Manufacturing Technology 59(1): 175–178

    Article  Google Scholar 

  • 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): 135–145

    Article  Google Scholar 

  • Venasen J. (2007) What is personalization. A conceptual framework. European Journal of Marketing 41(5–6): 409–418

    Google Scholar 

  • Ververidis D., Kotropoulos C. (2006) Emotional speech recognition: Resources, features, and methods. Speech Communication 48(9): 1162–1181

    Article  Google Scholar 

  • Wansink B. (2003) Using laddering to understand and leverage a brand’s equity. Qualitative Market Research 6(2): 111–118

    Article  Google Scholar 

  • 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): 1553–1567

    Article  Google Scholar 

  • Wickens C. D., Hollands J. G. (1999) Engineering psychology and human performance. Prentice Hall, New Jersey

    Google Scholar 

  • 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): 783–809

    Article  Google Scholar 

  • 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 199–213

    Chapter  Google Scholar 

  • Woods D. D., Roth E. M. (2006) Joint cognitive systems: Patterns in cognitive systems engineering. Taylor & Francis, Boca Raton, FL

    Book  Google Scholar 

  • Xirogiannis G., Stefanou J., Glykas M. (2004) A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26(2): 257–268

    Article  Google Scholar 

  • 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): 4080–4093

    Article  Google Scholar 

  • 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): 273–285

    Article  Google Scholar 

  • Yeung C. W. M., Wyer R. S. (2004) Affect, appraisal and consumer judgment. Journal of Consumer Research 31(2): 412–424

    Article  Google Scholar 

  • 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.

  • Zajonc R. B. (1980) Feeling and thinking: Preferences need no inferences. American Psychologist 35: 151–175

    Article  Google Scholar 

  • 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): 393–402

    Article  Google Scholar 

  • 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): 222–228

    Article  Google Scholar 

  • 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): 613–624

    Article  Google Scholar 

  • 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): 794–805

    Article  Google Scholar 

  • Zhou F., Xu Q., Jiao R. (2011) Fundamentals of product ecosystem design for user experience. Research in Engineering Design 22(1): 43–61

    Article  Google Scholar 

  • 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): 179–189

    Article  Google Scholar 

  • 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): 0310101–0310109

    Article  Google Scholar 

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Zhou, F., Ji, Y. & Jiao, R.J. Affective and cognitive design for mass personalization: status and prospect. J Intell Manuf 24, 1047–1069 (2013). https://doi.org/10.1007/s10845-012-0673-2

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