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Status Quo Bias in Configuration Systems

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Modern Approaches in Applied Intelligence (IEA/AIE 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6703))

Abstract

Product configuration systems are an important instrument to implement mass customization, a production paradigm that supports the manufacturing of highly-variant products under pricing conditions similar to mass production. A side-effect of the high diversity of products offered by a configurator is that the complexity of the alternatives may outstrip a user’s capability to explore them and make a buying decision. A personalization of such systems through the calculation of feature recommendations (defaults) can support customers (users) in the specification of their requirements and thus can lead to a higher customer satisfaction. A major risk of defaults is that they can cause a status quo bias and therefore make users choose options that are, for example, not really needed to fulfill their requirements. In this paper we present the results of an empirical study that aimed to explore whether there exist status quo effects in product configuration scenarios.

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References

  1. Barker, V., O’Connor, D., Bachant, J., Soloway, E.: Expert systems for configuration at Digital: XCON and beyond. Communications of the ACM 32(3), 298–318 (1989)

    Article  Google Scholar 

  2. Fleischanderl, G., Friedrich, G., Haselboeck, A., Schreiner, H., Stumptner, M.: Configuring Large Systems Using Generative Constraint Satisfaction. IEEE Intelligent Systems 13(4), 59–68 (1998)

    Article  Google Scholar 

  3. Mittal, S., Frayman, F.: Towards a Generic Model of Configuration Tasks. In: 11th International Joint Conference on Artificial Intelligence, Detroit, MI, pp. 1395–1401 (1990)

    Google Scholar 

  4. Sabin, D., Weigel, R.: Product Configuration Frameworks - A Survey. IEEE Intelligent Systems 13(4), 42–49 (1998)

    Article  Google Scholar 

  5. Stumptner, M.: An overview of knowledge-based configuration. AI Communications (AICOM) 10(2), 111–126 (1997)

    Google Scholar 

  6. Cöster, R., Gustavsson, A., Olsson, T., Rudström, A.: Enhancing web-based configuration with recommendations and cluster-based help. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, Springer, Heidelberg (2002)

    Google Scholar 

  7. Felfernig, A., Mandl, M., Tiihonen, J., Schubert, M., Leitner, G.: Personalized User Interfaces for Product Configuration. In: International Conference on Intelligent User Interfaces (IUI 2010), pp. 317–320 (2010)

    Google Scholar 

  8. Mandl, M., Felfernig, A., Teppan, E., Schubert, M.: Consumer Decision Making in Knowledge-based Recommendation. Journal of Intelligent Information Systems (2010) (to appear)

    Google Scholar 

  9. Huffman, C., Kahn, B.: Variety for Sale: Mass Customization or Mass Confusion. Journal of Retailing 74, 491–513 (1998)

    Article  Google Scholar 

  10. Bostrom, N., Ord, T.: The Reversal Test: Eliminating Status Quo Bias in Applied Ethics. Ethics (University of Chicago Press) 116(4), 656–679 (2006)

    Google Scholar 

  11. Kahneman, D., Knetsch, J., Thaler, R.: Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. The Journal of Economic Perspectives 5(1), 193–206 (1991)

    Article  Google Scholar 

  12. Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47(2), 263–291 (1979)

    Article  MATH  Google Scholar 

  13. Ritov, I., Baron, J.: Status-quo and omission biases. Journal of Risk and Uncertainty 5, 49–61 (1992)

    Article  Google Scholar 

  14. Samuelson, W., Zeckhauser, R.: Status quo bias in decision making. Journal of Risk and Uncertainty 1(1), 7–59 (1988)

    Article  Google Scholar 

  15. Mandl, M., Felfernig, A., Teppan, E., Schubert, M.: Consumer Decision Making in Knowledge-based Recommendation. Journal of Intelligent Information Systems (to appear)

    Google Scholar 

  16. Kurniawan, S.H., So, R., Tseng, M.: Consumer Decision Quality in Mass Customization. International Journal of Mass Customisation 1(2-3), 176–194 (2006)

    Article  Google Scholar 

  17. Kamali, N., Loker, S.: Mass customization: On-line consumer involvement in product design. Journal of Computer-Mediated Communication 7(4) (2002)

    Google Scholar 

  18. Huber, J., Payne, W., Puto, C.: Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis. Journal of Consumer Research 9(1), 90–98 (1982)

    Article  Google Scholar 

  19. Simonson, I., Tversky, A.: Choice in context: Tradeoff contrast and extremeness aversion. Journal of Marketing Research 29(3), 281–295 (1992)

    Article  Google Scholar 

  20. Yoon, S., Simonson, I.: Choice set configuration as a determinant of preference attribution and strength. Journal of Consumer Research 35(2), 324–336 (2008)

    Article  Google Scholar 

  21. Teppan, E.C., Felfernig, A.: Calculating Decoy Items in Utility-Based Recommendation. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 183–192. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  22. Teppan, E., Felfernig, A.: Impacts of Decoy Elements on Result Set Evaluation in Knowledge-Based Recommendation. International Journal of Advanced Intelligence Paradigms 1(3), 358–373 (2009)

    Article  Google Scholar 

  23. Felfernig, A., Gula, B., Leitner, G., Maier, M., Melcher, R., Schippel, S., Teppan, E.: A Dominance Model for the Calculation of Decoy Products in Recommendation Environments. In: AISB 2008 Symposium on Persuasive Technology, pp. 43–50 (2008)

    Google Scholar 

  24. Tversky, A., Kahneman, D.: The Framing of Decisions and the Psychology of Choice. Science, New Series 211, 453–458 (1981)

    MathSciNet  MATH  Google Scholar 

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Mandl, M., Felfernig, A., Tiihonen, J., Isak, K. (2011). Status Quo Bias in Configuration Systems. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-21822-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21821-7

  • Online ISBN: 978-3-642-21822-4

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