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2014 | OriginalPaper | Chapter

1. Problem Setting

Author : Vithala R. Rao

Published in: Applied Conjoint Analysis

Publisher: Springer Berlin Heidelberg

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Abstract

Several interdependent decisions are involved in the formulation of a marketing strategy for a brand (of a product or service). These include not only decisions about the product’s characteristics but also its positioning, communication, distribution, and pricing to chosen sets of targeted customers. The decisions will need to be made in the wake of uncertain competitive reactions and a changing (and often unpredictable) environment. For a business to be successful, the decision process must include a clear understanding of how customers will choose among (and react to) various competing alternatives. It is well accepted in marketing that choice alternatives can be described as profiles on multiple attributes and that individuals consider various attributes while making a choice. While choosing, consumers typically make trade-offs among the attributes of a product or service. Conjoint analysis is a set of techniques ideally suited to studying customers’ choice processes and determining tradeoffs.

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Appendix
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Footnotes
1
See Corstjens and Gautschi (1983) for detailed methods for testing these axioms.
 
2
This method is quite similar to preference analysis in multidimensional scaling which focuses on estimating the ideal points for or weights on perceptual dimensions. These functions will be described in Chap. 2.
 
3
A computer software called Policy-PC offered by the Executive Decision Services, Albany, NY allows for a menu of utility functions.
 
4
See Wilkie and Pessemier (1973) for a comprehensive review.
 
5
Hybrid models involve a combination of several tasks aimed to increase the “efficiency” of data collection in conjoint studies usually for products with a large number of attributes. We will discuss these in Chaps. 2 and 3.
 
6
Three surveys were conducted among firms that provide marketing research services by Wittink and his colleagues on the commercial use of conjoint analysis in Europe (1986–91) and USA (1981–85 and 1971–80). While the estimates of actual numbers of projects varied greatly, the authors documented that 698 projects were conducted by 17 firms in the US during the 5 years 1976–80 as compared to 1,062 projects by 66 firms in the US during the 5 years 1981–85. In Europe, 956 projects were conducted by 59 firms during the 5 years, 1986–91. These numbers show extensive diffusion of the methodology on both sides of the Atlantic. Table 1.11 summarizes the results on the utilization of various methods of data collection, analysis, and specific purpose of the conjoint studies in the three surveys
 
7
While we are describing an example of product design here, the method of conjoint analysis is versatile in tackling various managerial problems such as product line decisions, competitive decisions, product/service pricing and the like. Several chapters in the book are devoted to these applications.
 
8
This high value of R-square is due to the hypothetical data. In general, the fits of the model to data at the individual level will not be this high (and average around 0.7 or so). The fits for some individuals will be poor for a variety of reasons such as unreliable responses and complexity of the task involved. We will discuss later incentive-compatible methods of data collection which ensure more reliable responses.
 
9
Other measures such as partial R-squared exist for this purpose; we will discuss them in Chap. 2
 
10
The SAS Optex Code for the Transportation Example is as follows:
data ab; n = 1; do time = 10 to 30 by 10; do fare = 55 to 115 by 30; output; n = n + 1; end; end; run;
proc optex data = ab seed = 73462 coding = orth; class time fare; model time fare; blocks structure = (42)3; run;
output out = try number = 1 blockname = blk; proc print data = try; run.
 
11
The number of observations was 126 (=3 × 42). The likelihood ratio for the model was 144.5 with 4 degrees of freedom.
 
12
Readers may also be interested in the classic paper, Green and Wind (1975), for a comprehensive application of the ratings-based conjoint method.
 
Literature
go back to reference Allenby, G. M., Arora, N., & Gonter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, 34, 152–162.CrossRef Allenby, G. M., Arora, N., & Gonter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, 34, 152–162.CrossRef
go back to reference Anderson, N. H. (1970). Functional measurement and psychological judgment. Psychological Review, 77, 153–170.CrossRef Anderson, N. H. (1970). Functional measurement and psychological judgment. Psychological Review, 77, 153–170.CrossRef
go back to reference Ben-Akiva, M., & Lerman, S. R. (1991). Discrete choice analysis. Cambridge, MA: MIT Press. Ben-Akiva, M., & Lerman, S. R. (1991). Discrete choice analysis. Cambridge, MA: MIT Press.
go back to reference Carroll, J. D., & Green, P. E. (1995). Psychometric methods in marketing research: Part I, conjoint analysis. Journal of Marketing Research, 32, 385–391.CrossRef Carroll, J. D., & Green, P. E. (1995). Psychometric methods in marketing research: Part I, conjoint analysis. Journal of Marketing Research, 32, 385–391.CrossRef
go back to reference Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of Marketing, 46, 44–53.CrossRef Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of Marketing, 46, 44–53.CrossRef
go back to reference Cattin, P., Gelfand, A., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29–35.CrossRef Cattin, P., Gelfand, A., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29–35.CrossRef
go back to reference Corstjens, M. L., & Gautschi, D. A. (1983). A comparative analysis of specification tests for the utility function. Management Science, 29(12), 1393–1413.CrossRef Corstjens, M. L., & Gautschi, D. A. (1983). A comparative analysis of specification tests for the utility function. Management Science, 29(12), 1393–1413.CrossRef
go back to reference Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95–106.CrossRef Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95–106.CrossRef
go back to reference Fishbein, M. (Ed.). (1967). Readings in attitude theory and measurement. New York: Wiley. Fishbein, M. (Ed.). (1967). Readings in attitude theory and measurement. New York: Wiley.
go back to reference Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–363.CrossRef Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–363.CrossRef
go back to reference Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103–123.CrossRef Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103–123.CrossRef
go back to reference Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54, 3–19.CrossRef Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54, 3–19.CrossRef
go back to reference Green, P. E., & Wind, Y. (1975). New way to measure consumers’ judgments. Harvard Business Review, 53, 107–117. Green, P. E., & Wind, Y. (1975). New way to measure consumers’ judgments. Harvard Business Review, 53, 107–117.
go back to reference Green, P. E., Krieger, A. M., & Vavra, T. G. (1997). Evaluating new products. Marketing Research, 9, 12–19. Green, P. E., Krieger, A. M., & Vavra, T. G. (1997). Evaluating new products. Marketing Research, 9, 12–19.
go back to reference Huber, J. Conjoint analysis: How we got here and where we are. Sawtooth Conference, 2004. Huber, J. Conjoint analysis: How we got here and where we are. Sawtooth Conference, 2004.
go back to reference Johnson, R. M. (1974). Trade-off analysis of consumer values. Journal of Marketing Research, 11, 121–127.CrossRef Johnson, R. M. (1974). Trade-off analysis of consumer values. Journal of Marketing Research, 11, 121–127.CrossRef
go back to reference Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley. Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley.
go back to reference Krantz, D. H., David, H., Duncan Luce, R., Tversky, A., & Suppes, P. (1971). Foundations of measurement volume I: Additive and polynomial representations. New York/London: Academic. Krantz, D. H., David, H., Duncan Luce, R., Tversky, A., & Suppes, P. (1971). Foundations of measurement volume I: Additive and polynomial representations. New York/London: Academic.
go back to reference Louviere, J. J., Hensher, D. A., & Swait, J. (2000). Stated choice methods: analysis and applications. Cambridge, UK: Cambridge University Press.CrossRef Louviere, J. J., Hensher, D. A., & Swait, J. (2000). Stated choice methods: analysis and applications. Cambridge, UK: Cambridge University Press.CrossRef
go back to reference Luce, D., & Tukey, J. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.CrossRef Luce, D., & Tukey, J. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.CrossRef
go back to reference McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic Press. McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic Press.
go back to reference Rao, V. R. (1977). Conjoint measurement in marketing analysis. In J. N. Sheth (Ed.), Multivariate methods in market and survey research. Chicago: American Marketing Association. Rao, V. R. (1977). Conjoint measurement in marketing analysis. In J. N. Sheth (Ed.), Multivariate methods in market and survey research. Chicago: American Marketing Association.
go back to reference Rao, V. R. (2009). Developments in conjoint analysis. In B. Weiringa (Ed.), Handbook of marketing decision models. New York: Springer. Rao, V. R. (2009). Developments in conjoint analysis. In B. Weiringa (Ed.), Handbook of marketing decision models. New York: Springer.
go back to reference Rao, V. R., & Soutar, G. N. (1975). Subjective evaluations for product design decisions. Decision Sciences, 6(1), 120–134.CrossRef Rao, V. R., & Soutar, G. N. (1975). Subjective evaluations for product design decisions. Decision Sciences, 6(1), 120–134.CrossRef
go back to reference Rosenberg, M. J. (1956). Cognitive structure and attitudinal affect. Journal of Abnormal and Social Psychology, 53, 367–372.CrossRef Rosenberg, M. J. (1956). Cognitive structure and attitudinal affect. Journal of Abnormal and Social Psychology, 53, 367–372.CrossRef
go back to reference Srinivasan, V., & Park, C. S. (1997). Surprising robustness of self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34(2), 286–291.CrossRef Srinivasan, V., & Park, C. S. (1997). Surprising robustness of self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34(2), 286–291.CrossRef
go back to reference Srinivasan, V., & Wyner, G. A. (1989). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91–111). Lexington: Lexington Books. Srinivasan, V., & Wyner, G. A. (1989). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91–111). Lexington: Lexington Books.
go back to reference Thurstone, L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286.CrossRef Thurstone, L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286.CrossRef
go back to reference Wilkie, W. L., & Pessemier, E. A. (1973). Issues in marketing’s use of multi-attribute-models. Journal of Marketing Research, 10, 428–441.CrossRef Wilkie, W. L., & Pessemier, E. A. (1973). Issues in marketing’s use of multi-attribute-models. Journal of Marketing Research, 10, 428–441.CrossRef
go back to reference Wind, Y., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: designing a hotel with consumer-based marketing. Interfaces, 19(January–February), 25–47. Wind, Y., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: designing a hotel with consumer-based marketing. Interfaces, 19(January–February), 25–47.
go back to reference Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53, 91–96.CrossRef Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53, 91–96.CrossRef
go back to reference Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11, 41–52.CrossRef Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11, 41–52.CrossRef
go back to reference Carroll, J. D. (1969). Categorical conjoint measurement. Paper presented at Meeting of Mathematical Psychology, Ann Arbor, MI. Carroll, J. D. (1969). Categorical conjoint measurement. Paper presented at Meeting of Mathematical Psychology, Ann Arbor, MI.
go back to reference Cattin, P, & Dick R. W. (1976). A Monte Carlo study of metric and nonmetric estimation methods for multiattribute models. Research Paper No. 341, Graduate School of Business, Stanford University. Cattin, P, & Dick R. W. (1976). A Monte Carlo study of metric and nonmetric estimation methods for multiattribute models. Research Paper No. 341, Graduate School of Business, Stanford University.
go back to reference DeSarbo, W. S., Gelfand, A. E., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29–35.CrossRef DeSarbo, W. S., Gelfand, A. E., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29–35.CrossRef
go back to reference Green, P. E. (1984). Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research, 21, 155–169.CrossRef Green, P. E. (1984). Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research, 21, 155–169.CrossRef
go back to reference Green, P. E., & DeSarbo, W. S. (1979). Componential segmentation in the analysis of consumer tradeoffs. Journal of Marketing, 43, 83–91.CrossRef Green, P. E., & DeSarbo, W. S. (1979). Componential segmentation in the analysis of consumer tradeoffs. Journal of Marketing, 43, 83–91.CrossRef
go back to reference Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45, 33–41.CrossRef Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45, 33–41.CrossRef
go back to reference Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22, 168–184.CrossRef Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22, 168–184.CrossRef
go back to reference Herman, S. (1988). Software for full-profile conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 117–130). Ketchum, ID: Sawtooth Software. Herman, S. (1988). Software for full-profile conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 117–130). Ketchum, ID: Sawtooth Software.
go back to reference Johnson, R. M. (1987). Adaptive conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 253–265). Ketchum, ID: Sawtooth Software. Johnson, R. M. (1987). Adaptive conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 253–265). Ketchum, ID: Sawtooth Software.
go back to reference Kamakura, W. (1988). A least squares procedure for benefit segmentation with conjoint experiments. Journal of Marketing Research, 25, 157–167.CrossRef Kamakura, W. (1988). A least squares procedure for benefit segmentation with conjoint experiments. Journal of Marketing Research, 25, 157–167.CrossRef
go back to reference Krishnamurthi, L, & Wittink, D. R. (1989). The partworth model and its applicability in conjoint analysis. Working Paper, College of Business Administration, University of Illinois. Krishnamurthi, L, & Wittink, D. R. (1989). The partworth model and its applicability in conjoint analysis. Working Paper, College of Business Administration, University of Illinois.
go back to reference Kruskal, J. B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal of the Royal Statistical Society, 27, 251–263. Series B. Kruskal, J. B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal of the Royal Statistical Society, 27, 251–263. Series B.
go back to reference Pekelman, D., & Sen, S. L. (1979). Improving prediction in conjoint analysis. Journal of the American Statistical Association, 75, 801–816. Pekelman, D., & Sen, S. L. (1979). Improving prediction in conjoint analysis. Journal of the American Statistical Association, 75, 801–816.
go back to reference Shocker, A. D., & Srinivasan, V. (1977). LINMAP (Version II): A FORTRAN IV computer program for analyzing ordinal preference (Dominance) judgments via linear programming techniques for conjoint measurement. Journal of Marketing Research, 14, 101–103. Shocker, A. D., & Srinivasan, V. (1977). LINMAP (Version II): A FORTRAN IV computer program for analyzing ordinal preference (Dominance) judgments via linear programming techniques for conjoint measurement. Journal of Marketing Research, 14, 101–103.
go back to reference Srinivasan, V. (1988). A conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19, 295–305.CrossRef Srinivasan, V. (1988). A conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19, 295–305.CrossRef
go back to reference Srinivasan, V., Jain, A. K., & Malhotra, N. K. (1983). Improving the predictive power of conjoint analysis by constrained parameter estimation. Journal of Marketing Research, 20, 433–438.CrossRef Srinivasan, V., Jain, A. K., & Malhotra, N. K. (1983). Improving the predictive power of conjoint analysis by constrained parameter estimation. Journal of Marketing Research, 20, 433–438.CrossRef
go back to reference Srinivasan, V., & Wyner, G. A. (1989b). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91–111). Lexington, MA: Lexington Books. Srinivasan, V., & Wyner, G. A. (1989b). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91–111). Lexington, MA: Lexington Books.
go back to reference van der Lans, I. A., & Heiser, W. H. (1992). Constrained part-worth estimation in conjoint analysis using the self-explicated utility model. International Journal of Research in Marketing, 9, 325–344.CrossRef van der Lans, I. A., & Heiser, W. H. (1992). Constrained part-worth estimation in conjoint analysis using the self-explicated utility model. International Journal of Research in Marketing, 9, 325–344.CrossRef
go back to reference Adamowicz, W., Swait, J., Boxal, P., Louviere, J., & Williams, M. (1997). Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. Journal of Environmental Economics and Management, 32, 65–84.CrossRef Adamowicz, W., Swait, J., Boxal, P., Louviere, J., & Williams, M. (1997). Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. Journal of Environmental Economics and Management, 32, 65–84.CrossRef
go back to reference Baarsma, B. (2003). The valuation of the IJmeer nature reserve using conjoint analysis. Environmental and Resource Economics, 25(3), 343–356.CrossRef Baarsma, B. (2003). The valuation of the IJmeer nature reserve using conjoint analysis. Environmental and Resource Economics, 25(3), 343–356.CrossRef
go back to reference Beenstock, M., Goldin, E., & Haitovsky, Y. (1998). Response bias in a conjoint analysis of power outages. Energy Economics, 20, 135–156.CrossRef Beenstock, M., Goldin, E., & Haitovsky, Y. (1998). Response bias in a conjoint analysis of power outages. Energy Economics, 20, 135–156.CrossRef
go back to reference Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy & Marketing, 11, 2–25. Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy & Marketing, 11, 2–25.
go back to reference Gerardz, K., Shanahanz, M., & Louviere, J. J. (2003). Using stated preference discrete choice modelling to inform healthcare decision–making: A pilot study of breast screening participation. Applied Economics, 35, 1073–1085.CrossRef Gerardz, K., Shanahanz, M., & Louviere, J. J. (2003). Using stated preference discrete choice modelling to inform healthcare decision–making: A pilot study of breast screening participation. Applied Economics, 35, 1073–1085.CrossRef
go back to reference Haefele, M., & Loomis, J. B. (2001). Improving statistical efficiency and testing robustness of conjoint marginal valuations. American Journal of Agricultural Economics, 83(5), 1321–1327.CrossRef Haefele, M., & Loomis, J. B. (2001). Improving statistical efficiency and testing robustness of conjoint marginal valuations. American Journal of Agricultural Economics, 83(5), 1321–1327.CrossRef
go back to reference Hensher, D. A., Louviere, J. J., & Swait, J. (1999). Combining sources of preference data. Journal of Econometrics, 89, 197–221.CrossRef Hensher, D. A., Louviere, J. J., & Swait, J. (1999). Combining sources of preference data. Journal of Econometrics, 89, 197–221.CrossRef
go back to reference Kienast, P., MacLachlan, D., McAlister, L., & Sampson, D. (1983). Employing conjoint analysis in making compensation decisions. Personnel Psychology, 36(2), 301–313.CrossRef Kienast, P., MacLachlan, D., McAlister, L., & Sampson, D. (1983). Employing conjoint analysis in making compensation decisions. Personnel Psychology, 36(2), 301–313.CrossRef
go back to reference Maddala, T., Phillips, K. A., & Johnson, F. R. (2003). An experiment on simplifying conjoint analysis designs for measuring preferences. Health Economics, 12, 1035–1047.CrossRef Maddala, T., Phillips, K. A., & Johnson, F. R. (2003). An experiment on simplifying conjoint analysis designs for measuring preferences. Health Economics, 12, 1035–1047.CrossRef
go back to reference Miguel, S. F., Ryan, M., & McIntosh, E. (2000). Applying conjoint analysis in economic evaluations: An application to menorrhagia. Applied Economics, 32, 823–833.CrossRef Miguel, S. F., Ryan, M., & McIntosh, E. (2000). Applying conjoint analysis in economic evaluations: An application to menorrhagia. Applied Economics, 32, 823–833.CrossRef
go back to reference Morrison, M., Bennett, J., Blamey, R., & Louviere, J. J. (2002). Choice modeling and tests of benefit transfer. American Journal of Agricultural Economics, 84, 161–170.CrossRef Morrison, M., Bennett, J., Blamey, R., & Louviere, J. J. (2002). Choice modeling and tests of benefit transfer. American Journal of Agricultural Economics, 84, 161–170.CrossRef
go back to reference Poortinga, W., Steg, L., Vlek, C., & Wiersma, G. (2003). Household preferences for energy saving measures: A conjoint analysis. Journal of Economic Psychology, 24(1), 49–64.CrossRef Poortinga, W., Steg, L., Vlek, C., & Wiersma, G. (2003). Household preferences for energy saving measures: A conjoint analysis. Journal of Economic Psychology, 24(1), 49–64.CrossRef
go back to reference Roe, B., Boyle, K. J., & Teisel, M. F. (1996). Using conjoint analysis to derive estimates of compensating variation. Journal of Environmental Economics and Management, 31, 145–159.CrossRef Roe, B., Boyle, K. J., & Teisel, M. F. (1996). Using conjoint analysis to derive estimates of compensating variation. Journal of Environmental Economics and Management, 31, 145–159.CrossRef
go back to reference Schroeder, H. W., & Louviere, J. (1999). Stated choice models for predicting the impact of user fees at public recreation sites. Journal of Leisure Research, 31(3), 300–324. Schroeder, H. W., & Louviere, J. (1999). Stated choice models for predicting the impact of user fees at public recreation sites. Journal of Leisure Research, 31(3), 300–324.
go back to reference Skjoldborg, U. S., & Gyrd-Hansen, D. (2003). Conjoint analysis. The cost variable: An Achilles’ heel? Health Economics, 12, 479–491.CrossRef Skjoldborg, U. S., & Gyrd-Hansen, D. (2003). Conjoint analysis. The cost variable: An Achilles’ heel? Health Economics, 12, 479–491.CrossRef
go back to reference Telser, H., & Zweifel, P. (2002). Measuring willingness to pay for risk reduction: An application of conjoint analysis. Health Economics, 11, 129–139.CrossRef Telser, H., & Zweifel, P. (2002). Measuring willingness to pay for risk reduction: An application of conjoint analysis. Health Economics, 11, 129–139.CrossRef
go back to reference Viney, R., Savage, E., & Louviere, J. (2005). Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Economics, 14, 349–362.CrossRef Viney, R., Savage, E., & Louviere, J. (2005). Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Economics, 14, 349–362.CrossRef
go back to reference Zinkham, F. C., & Zinkham, G. M. (1994). An application of conjoint analysis to capital budgeting: The case of innovative land management systems. Managerial Finance, 20(7), 37–50.CrossRef Zinkham, F. C., & Zinkham, G. M. (1994). An application of conjoint analysis to capital budgeting: The case of innovative land management systems. Managerial Finance, 20(7), 37–50.CrossRef
Metadata
Title
Problem Setting
Author
Vithala R. Rao
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-540-87753-0_1