Abstract
The essay by the psychologist Luce and the statistician Tukey (1964) can be viewed as the origin of conjoint analysis (Green and Srinivasan 1978; Carroll and Green 1995). Since its introduction into marketing literature by Green and Rao (1971) as well as by Johnson (1974) in the beginning of the 1970s, conjoint analysis has developed into a method of preference studies that receives much attention from both theoreticians and those who carry out field studies. For example, Cattin and Wittink (1982) report 698 conjoint projects that were carried out by 17 companies in their survey of the period from 1971 to 1980. For the period from 1981 to 1985, Wittink and Cattin (1989) found 66 companies in the United States that were in charge of a total of 1062 conjoint projects. Wittink, Vriens, and Burhenne counted a total of 956 projects in Europe carried out by 59 companies in the period from 1986 to 1991 (Wittink, Vriens, and Burhenne 1994; Baier and Gaul 1999). Based on a 2004 Sawtooth Software customer survey, the leading company in Conjoint Software, between 5,000 and 8,000 conjoint analysis projects were conducted by Sawtooth Software users during 2003. The validation of the conjoint method can be measured not only by the companies today that utilize conjoint methods for decision-making, but also by the 989,000 hits on www.google.com. The increasing acceptance of conjoint applications in market research relates to the many possible uses of this method in various fields of application such as the following:
-
new product planning for determining the preference effect of innovations (for example Bauer, Huber, and Keller 1997; DeSarbo, Huff, Rolandelli, and Choi 1994; Green and Krieger 1987; 1992; 1993; Herrmann, Huber, and Braunstein 1997; Johnson, Herrmann, and Huber 1998; Kohli and Sukumar 1990; Page and Rosenbaum 1987; Sands and Warwick 1981; Yoo and Ohta 1995; Zufryden 1988) or to
-
improve existing achievements (Green and Wind 1975; Green and Srinivasan 1978; Dellaert et al., 1995), the method can also be applied in the field of
-
pricing policies (Bauer, Huber, and Adam 1998; Currim, Weinberg, and Wittink 1981; DeSarbo, Ramaswamy, and Cohen 1995; Goldberg, Green, and Wind 1984; Green and Krieger 1990; Kohli and Mahajan 1991; Mahajan, Green, and Goldberg 1982; Moore, Gray-Lee, and Louviere 1994; Pinnell 1994; Simon 1992; Wuebker and Mahajan 1998; Wyner, Benedetti, and Trapp 1984),
-
advertising (Bekmeier 1989; Levy, Webster, and Kerin 1983; Darmon 1979; Louviere 1984; Perreault and Russ 1977; Stanton and Reese 1983; Neale and Bath 1997; Tscheulin and Helmig 1998; Huber and Fischer 1999), and
-
distribution (Green and Savitz 1994; Herrmann and Huber 1997; Oppewal and Timmermans 1991; Oppewal 1995; Verhallen and DeNooij 1982).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Acito, F. (1977), An Investigation of some Data Collection Issues in Conjoint Measurement, American Marketing Association Educators’ Proceedings, 82–85.
Acito, F. (1979), Industrial Product Concept Testing, Industrial Marketing Management, 10, 157–164.
Agarwal, M. (1988), Comparison of Conjoint Methods, Proceedings of the Sawtooth Software Conference on Perceptual Mapping, Sun Valley, 51–57.
Akaah, I. P. (1988), Cluster Analysis versus Q-Type Factor Analysis as a Disaggregation Method in Hybrid Conjoint Modeling: An empirical Investigation, Journal of the Academy of Marketing Science, 19, 309–314.
Akaah, I. P. and P. K. Korgaonkar (1983), An Empirical Comparison of the Predictive Validity of Self-Explicated, Huber-Hybrid, Traditional Conjoint and Hybrid-conjoint Models, Journal of Marketing Research, 20, 187–197.
Allenby, G. M., N. Arora, and J. L. Ginter (1995), Incorporating prior Knowledge into the Analysis of Conjoint Studies, Journal of Marketing Research, 32, 152–162.
Alpert, M. I., J. F. Betak, and L. L. Golden (1978), Data gathering Issues in Conjoint Measurement, Working paper, Graduate School of Business, The University of Texas at Austin.
Arora, Neeraj and Joel Huber (2001), “Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments,” Journal of Consumer Research, 28, (September), 273–283.
Assmus, E. F. and J. K. Key (1992), Designs and their Codes, Cambridge.
Baier, D., and W. Gaul (1996), Analyzing Paired Comparisons Data Using Probabilistic Ideal Point and Vector Models, in: Bock, H. H., Polasek, P., eds., Data Analysis and Information Systems, Berlin, 163–174.
Baier, D., and W. Gaul (1999), Optimal Product Positioning Based on Paired Comparison Data, Journal of Econometrics, 89, 365–392.
Baier, D. and W. Gaul (1995), Classification and Representation using Conjoint Data, in W. Gaul and D. Pfeifer eds., From data to knowledge: Theoretical and Practical Aspects of Classification, Data Analysis, and Knowledge Organization, Berlin, 298–307.
Bateson, J. E., D. Reibstein, and W. Boulding (1987), Conjoint Analysis Reliability and Validity: a Framework for future Research, in: American Marketing Association, ed., Review of Marketing, Chicago, 451–481.
Bauer, H. H., F. Huber, and R. Adam (1998), Utility oriented design of service bundles in the hotel industry based on the conjoint measurement method, in: Fuerderer, R., Herrmann, A. and Wuebker, G., eds., Optimal Bundling-Marketing Strategies for Improving economic performance, Wiesbaden, 269–297.
Bauer, H. H., F. Huber, and T. Keller (1997), Design of Lines as a product-policy Variant to retain Customers in the Automotive Industry, in Johnson, M., Herrmann, A., Huber, F. and Gustafsson, A., Customer Retention in the Automotive Industry-Quality, Satisfaction and Retention, Wiesbaden, 67–92.
Carmone, F. J., P. E. Green, and A. K. Jain (1978), Robustness of Conjoint Analysis: Some Monté Carlo Results, Journal of Marketing Research, 15, 300–303.
Carroll, J. D. (1972), Individual Differences and Multidimensional Scaling, in: Shepard, R. N., Romney, A. K., Nerlove, S. B., eds., Multidimensional Scaling-Theory and applications in behavioral sciences, Vol. 1, New York.
Cattin, P. and F, Bliemel (1978), Metric vs. Nonmetric Procedures for Multiattribute Modeling: Some Simulation Results, Decision Sciences, 9, 1978, 472–480.
Cattin, P. and M. Weinberger (1980), Some Validity and Reliability Issues in the Measurement of Attribute Utilities, in: Olsen, Jerry C., ed., Advances in Consumer Research, 7, 780–783.
Cattin, P. and D. R. Wittink (1977), Further knowledge beyond Conjoint Measurement: Toward a comparison of methods, Advances in Consumer Research, 4, 41–45.
Cattin, P. and D. R. Wittink (1982), Commercial Use of Conjoint Analysis: A Survey, Journal of Marketing, 46, 44–53.
Cerro, D. (1988), Conjoint Analysis by Mail, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 139–143.
Cochran, W. G. and G. M. Cox (1957), Experimental Designs, New York.
Colberg, T. (1977), Validation of Conjoint Measurement Methods: a Simulation and empirical Investigation, Dissertation, University of Washington.
Currim, I. S., C. B. Weinberg, and D. R. Wittink (1981), Design of Subscription Programs for a Performing Arts Series, Journal of Consumer Research, 8, 67–75.
Darmon, R. Y. (1979), Setting Sales Quotas with Conjoint Analysis, Journal of Marketing Research, 16, 133–140.
Davey, K. S. and T. Elrod (1991), Predicting Shares from Preferences for Multiattribute Alternatives, working paper, University of Alberta.
De Soete, G., J. D. Carroll (1983), A Maximum Likelihood Method for Fitting the Wandering Vector Model, Psychometrika, 48, 553–566.
De Soete, G. and W. DeSarbo (1991), A latent Class Probit Model for Analyzing pick Any/N data, Journal of Classification, 8, 45–63.
De Soete, G. and S. Winsberg (1994) A latent Class Vector Model for Preference Ratings, Journal of Classification, 8, 195–218.
Dellaert, B., A. Borgers and H. Timmermans (1995), A Day in the City: Using Conjoint Experiments to urband Tourists’Choice of Activity Packages, Tourism Management, 16, 347–353.
DeSarbo, W. S., J. D. Carroll, D. R. Lehmann, and J. O’Shaughness (1982), Three-way Multivariate Conjoint Analysis, Marketing Science, 1, 323–350.
DeSarbo, W. S., R. L. Oliver, and A. Rangaswamy (1989), A simulated annealing Methodology for Clusterwise Linear Regression, Psychometrika, 54, 707–736.
DeSarbo, W. S., A. Ramaswamy, and K. Chaterjee (1992), Latent Class Multivariate Conjoint Analysis with Constant Sum Ratings Data, working paper, University of Michigan.
DeSarbo, W. S., V. Ramaswamy, and S. H. Cohen, (1995), Market Segmentation with Choice-based Conjoint Analysis, Marketing Letters, 6, 137–147.
DeSarbo, W. S., M. Wedel, M. Vriens, and V. Ramaswamy (1992), Latent Class Metric Conjoint Analysis, Marketing Letters, 3, 273–288.
DeSarbo, W., L. Huff, M. M. Rolandelli, and J. Choi (1994), On the Measurement of Perceived Service Quality, in: R. T. Rust, and R. L. Oliver (ed.), Service Quality: New directions in theory and practice, London, 201–222.
Diamantopoulos, A., B. Schlegelmilch, and J. P. DePreez (1995), Lessons for Pan-European Marketing? The Role of Consumer Preferences in fine-tuning the Product Market Fit, International Marketing Review, 12, 38–52.
Finkbeiner, C. T. (1988), Comparison of Conjoint Choice Simulators, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 75–105.
Finkbeiner, C. T. and P. J. Platz (1986), Computerized versus Paper and Pencil Methods: a Comparison Study, paper presented at the Association of Consumer Research Conference, Toronto.
Gaul, W. (1989), Probabilistic Choice Behavior Models and their Combination With Additional Tools Needed for Applications to Marketing, in: De Soete, G., Feger, H., Klauer, K.-H., eds., New Developments in Psychological Choice Modeling, Amsterdam, 317–337.
Gaul, W. and E. Aust (1994), Latent Class Inequality Constrained Least Square Regression, working paper, University of Karlsruhe.
Gaul, W., U. Lutz, and E. Aust (1994), Goodwill towards domestic Products as Segmentation Criterion: An empirical Study within the Scope of Research on country-of-origin effects, in: Bock H. H., Lenski, W. and Richter, M., eds., Information systems and Data Analysis, Studies in Classification and data analysis, and knowledge organization, 4, 415–424.
Goldberg, S. M., P. Green, and Y. Wind (1984), Conjoint Analysis of Price Premiums for Hotel Amenities, Journal of Business, 57, 111–147.
Green, P. E. and V. R. Rao (1971), Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research, 8, 355–363.
Green, P. E. and V. Srinivasan (1978), Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5, 103–123.
Green, P. E. and V. Srinivasan (1990), Conjoint Analysis in Marketing: New Developments With Implications for Research and Practice, Journal of Marketing, 54, 3–19.
Green, P. E. and D. S. Tull (1982), Methoden und Techniken der Marketingforschung, Stuttgart.
Green, P. E. and Y. Wind (1975), New Way to Measure Consumers’ Judgments, Harvard Business Review, 53, 107–117.
Green, P. E. and A. M. Krieger (1990), A hybrid Conjoint Model for price-demand Estimation, European Journal of Operations Research, 44, 28–38.
Green, P. E. and K. Helsen (1989), Cross-validation Assessment of Alternatives to individual-level Conjoint Analysis: a case study, Journal of Marketing Research, 26, 346–350.
Green, P. E., K. Helsen, and B. Shandler (1988), Conjoint Internal Validity under alternative Profile Presentations, Journal of Consumer Research, 15, 392–397.
Green, P. E. and A. M. Krieger (1987), A simple Heuristic for Selecting ‘good’ Products in Conjoint Analysis, Application of Management Science, 5, 131–153.
Green, P. E. and A. M. Krieger (1992), An Application to Optimal Product Positioning Model to Pharmaceutical Products, Marketing Science, 11, 117–132.
Green, P. E. and A. M. Krieger (1993), A simple Approach to Target Market Advertising Strategy, Journal of the Market Research Society, 35, 161–170.
Green, P. E. and A. M. Krieger (1993), Conjoint Analysis with product-positioning Applications, J. Eliashberg, G. J. Lilien eds., Marketing, Handbooks in OR&MS, 5, 467–515.
Green, P. E. and J. Savitz (1994), Applying Conjoint Analysis to Product Assortment and Pricing in Retailing Research, Pricing Strategy and Practice, 4–19.
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.
Hagerty, M. R. (1986), The cost of simplifying Preference Models, Marketing Science, 5, 298–324.
Herrmann, A., B. Franken, F. Huber, M. Ohlwein, and R. Schellhase (1999), The Conjoint Analysis as an Instrument for Marketing Controlling taking a public Theatre as an Example, International Journal of Arts Management, forthcoming.
Herrmann, A. and F. Huber (1997), Utility orientated Product Distribution, The International Review of Retail, Distribution and Consumer Research, 8, 369–382.
Herrmann, A., F. Huber, and C. Braunstein (1997), Standardization and Differentiation of Services: a cross-cultural study based on Semiotics, Means End Chains and Conjoint Analysis, Academy of Marketing/American Marketing Association Proceedings of 31st Annual Conference 7th July 1997, Manchester Metropolitan University.
Hruschka, H. (1986), Market definition and Segmentation Using Fuzzy Clustering Methods, International Journal of Research in Marketing, 3, 117–134.
Huber, F. and M. Fischer (1999), Measurement of Advertising Response-Results of a conjointanalytical Study, Proceedings of the Academy of Marketing Science World Conference, Malta.
Huber, G. P. (1974), Multiattribute Utility Models: a Review of filed and field-like Studies, Management Science, 20, 1393–1402.
Huber, J. (1997), What we have learned from 20 Years of Conjoint Research: When to use self-explicated, graded pairs, full profiles or choice experiments, Sawtooth Software Conference Proceedings, Seattle, 243–256.
Huber, J., D. Ariely, and G. Fischer (1997), The Ability of People to express Values with Choices, Matching and Ratings, working paper, Fuqua School of Business, Duke University.
Jain, A. R., F. Acito, N. Malhorta, and V. Mahajan (1979), A Comparison of internal Validity of alternative Parameter Estimation Methods in decompositional Multiattribute Preference Models, Journal of Marketing Research, 16, 313–322.
Jain, A. R., N. Malhorta, and C. Pinson (1980), Stability and Reliability of part-worth utility in Conjoint Analysis: a longitudinal Investigation, working paper, European Institute of Business Administration, Brüssel.
Johnson, M., A. Herrmann, and F. Huber (1998), Growth through Product Sharing Services, Journal of Service Research, 1, 167–177.
Johnson, R. M. (1974), Trade-Off Analysis of Consumer Values, Journal of Marketing Research, 11, 121–127.
Kahneman, D. and A. Tversky (1979), Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263–291.
Kamakura, W. A. (1988), A least squares Procedure for Benefit Segmentation with Conjoint Experiments, Journal of Marketing Research, 25, 157–167.
Kamakura, W. A. and R. K. Srivastava (1986), An ideal-point probabilistic Choice Model for heterogeneous Preferences, Marketing Science, 5, 199–218.
Kohli, R. and R. Sukumar (1990), Heuristics for Product-Line-Design using Conjoint Analysis, Management Science, 36, 1464–1478.
Kohli, R. and V. Mahajan (1991), A reservation-price Model for optimal Pricing of Mulitattribute Products in Conjoint Analysis, Journal of Marketing Research, 28, 347–354.
Krishnamurthi, L. (1988), Conjoint Models of Family Decision Making, International Journal of Research in Marketing, 5, 185–198.
Krishnamurthi, L. and D. R. Wittink (1991), The Value of Idiosyncratic Functional Forms in Conjoint Analysis, International Journal of Research in Marketing, 8, 301–313.
Kuhfeld, W. D. (1997), Efficient Experimental Designs using Computerized Searches, Sawtooth Software Conference Proceedings, Seattle, 71–86.
Levy, M., J. Webster, and R. A. Kerin (1983), Formulating Push Marketing Strategies: a Method and Application, Journal of Marketing, 47, 25–34.
Louviere, J. (1984), Using discrete Choice Experiments and mulitnominal Logit Models to forecast Trial in a competitive Retail Environment: a fast food Restaurant Illustration, Journal of Retailing, 60, 81–107.
Luce, R. D. and J. W. Tukey (1964), Simultaneous Conjoint Measurement-A New Type of Fundamental Measurement, Journal of Mathematical Psychology, 1, 1–27.
Mahajan, V., P. E. Green, and S. M. Goldberg (1982), A Conjoint Model for Measuring Self and Cross-Price/Demand Relationships, Journal of Marketing Research, 19, 334–342.
McCullough, J. and R. Best (1979), Conjoint Measurement: Temporal Stability and Structural Reliability, Journal of Marketing Research, 16, 26–31.
Mishra, S., U. N. Umesh, and D. E. Stem (1989), Attribute Importance weights in Conjoint Analysis: Bias and Precision, Advances in Consumer Research, 16, 605–611.
Mohn, N. C. (1989), Simulated purchase ‘Chip’ testing versus trade-off (conjoint) analysis, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 53–63.
Montgomery, D. B. and D. R. Wittink (1980), The predictive Validity of Conjoint Analysis for alternative Aggregation Schemes, Market Science Institute, ed., Market Measurement and Analysis, Cambridge, 298–309.
Montgomery, D. B., D. R. Wittink, and T. Glaze (1977), A predictive Test of individual level Concept Evaluation and trade-off Analysis, Research paper No. 415, Graduate School of Business, Stanford University.
Moore, W. L., J. Gray-Lee, and J. J. Louviere (1994), A cross-validity Comparison of Conjoint Analysis and Choice Models at different levels of Aggregation, working paper, University of Utah, Salt Lake City.
Moore, W. L. and M. B, Holbrook (1990), Conjoint Analysis on objects with environmentally correlated Attributes: The questionable Importance of representative Design, Journal of Consumer Research, 6, 490–497.
Neal, W. D. and S. Bathe (1997), Using the Value Equation to evaluate Campaign Effectiveness, Journal of Advertising Research, 37, 80–85.
Ogawa, K. (1987), An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis, Marketing Science, 6, 66–81.
Oppedijk van veen, W. M. and D. Beazley (1977), An Investigation of alternative Methods of Applying the trade-off Model, Journal of Market Research Society, 19, 2–9.
Oppewal, H. (1995), Conjoint experiments and retail planning: Modeling consumer choice of shopping centre and retailer reactive behavior, thesis, Eindhoven.
Orme, B. K., M. I. Alpert, and E. Chistensen (1997), Assessing the validity of Conjoint Analysis-continued, Sawtooth Software Conference Proceedings, Seattle, 209–226.
Page, A. and H. F. Rosenbaum (1987), Redesigning Product Lines with Conjoint Analysis: how Sunbeam does it, Journal of Product Innovation Management, 4, 120–137.
Page, A. and H. F. Rosenbaum (1989), Redesigning Product Lines with Conjoint Analysis: a reply to Wittink, Journal of Product Innovation Management, 6, 293–296.
Parker, B. R. and V. Srinivasan (1976), A consumer Preference Approach to the Planning of rural primary health-care facilities, Operations Research, 24, 991–1025.
Pearson, R. W. and R. F. Boruch (1986), Survey Research designs: Towards a better Understanding of their Cost and Benefits, Berlin.
Pekelman, D. and S. K. Senk (1979): Improving prediction in conjoint analysis, Journal of Marketing Research, 16, 211–220.
Perreault, W. D. and F. A. Russ (1977), Improving Physical Distribution Service Decisions with trade-off Analysis, International Journal of physical Distribution and Materials Management, 7, 3–19.
Pinnell, J. (1994), Multi-Stage Conjoint Methods to Measure Price Sensitivity, in: Weiss, S., ed., Sawtooth News, 10, 5–6.
Pullman, M. E., K. J. Dodson, and W. L. Moore (1999),. A comparison of conjoint methods when there are many attributes, Marketing Letters, 10(2), 125–138.
Punj, G. and D. W. Stewart (1983), Cluster Analysis in Marketing Research: Review and Suggestions for Application, Journal of Marketing Research, 20, 134–148.
Robinson, P. J. (1980), Applications of Conjoint Analysis to Pricing Problems, in: D. B. Montgomery and D. R. Wittink eds., Proceedings of the first ORSA/TMS Special interest conference on market measurement and analysis, Report 80-103, Cambridge, 183–205.
Rosko, M. D. and W. F. McKenna (1983), Modelling consumer choices of health plans: A comparison of two techniques, Social Sciences and Medicine, 17, 421–429.
Safizadeh, M. H. (1989), The internal Validity of the trade-off Method of Conjoint Analysis, Decision Science, 20, 451–461.
Sands, S. and K. Warwick (1981), What product Benefits to offer to whom: an Application of Conjoint Segmentation, California Management Review, 24, 69–74.
Segal, M. N. (1982), Reliability of Conjoint Analysis: contrasting Data Collection Procedures, Journal of Marketing Research, 13, 211–224.
Shah, K. R. and B. K. Sinha (1989), Theory of Optimal Designs, Berlin.
Simon, H. (1992b), Pricing Opportunities-And How to Exploit Them, Sloan Management Review, 34, 55–65.
Slovic, P., D. Fleissner, and S. Bauman (1972), Analyzing the use of Information in Investment Decision Making: a methodological proposal, Journal of Business, 45, 283–301.
Srinivasan, V., A. K. Jain, and N. K. Malhotra (1983), Improving predictive Power of Conjoint Analysis by constrained Parameter Estimation, Journal of Marketing Research, 20, 433–438.
Srinivasan, V. and C. S. Park (1997), Surprising Robustness of the self-explicated Approach to Customer Preference Structure Measurement, Journal of Marketing Research, 34, 286–291.
Srinivasan, V. and A. D. Shocker (1973), Linear Programming Techniques for Multidimensional Analysis of Preferences, Psychometrika, 38, 337–369.
Srinivasan, V., A. D. Shocker, and A. G. Weinstein (1973), Measurement of a Composite Criterion of Managerial Success, Organizational Behavior and Human Performance, 9, 147–167.
Stahl, B. (1988), Conjoint Analysis by Telephone, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 131–138.
Stanton, W. W. and R. M. Reese (1983), Three Conjoint Segmentation Approaches to the Evaluation of Advertising Theme Creation, Journal of Business Research, 11, 201–216.
Steckel, J. H., W. DeSarbo, and V. Mahjan (1990), On the Creation of acceptable Conjoint Analysis Experimental Designs, Decision Sciences, 22, 435–442.
Steenkamp, J. B. and M. Wedel (1991), Segmenting Retail Markets on Store Image using a consumer-based Methodology, Journal of Retailing, 7, 300–320.
Steenkamp, J. B. and M. Wedel (1993), Fuzzy clusterwise Regression in Benefit Segmentation Application and Investigation into its Validity, Journal of Business Research, 26, 237–249.
Tscheulin, D. K. and B, Helmig. (1998), The optimal Design of Hospital Advertising by Means of Conjoint Measurement, Journal of Advertising Research, 38, 35–46.
Tscheulin, D. K. and C. Blaimont (1993), Die Abhängigkeit der Prognosegüte von Conjoint-Studien von demographischen Probanden-Charakteristika, Zeitschrift für Betriebswirtschaftslehre, 63, 839–847.
Tversky, A. and D. Kahneman (1991), Loss Aversion and Riskless Choice: A Reference Dependent Model, Quarterly Journal of Economics, 6, 1039–1061.
Van der Lans, I. A., P. W. Verlegh, and H. N. Schifferstein (1999), An Empirical Comparison of various individual-level Hybrid Conjoint Analysis Models, in: Hildebrandt, L., Annacker, D. and Klapper, D., eds., Proceedings of the 28th EMAC Conference, Berlin.
Verhallen, T. and G. J. DeNooij (1982), Retail Attributes and shopping Patronage, Journal of Economic Psychology, 2, 439–455.
Vriens, M. (1995), Conjoint analysis in Marketing, Ph. D thesis, Capelle.
Vriens, M., H. Oppewal, and M. Wedel (1998), Ratings-based versus choice-based Latent Class Conjoint Models-an empirical comparison, Journal of the Market Research Society, 40, 237–248.
Vriens, M., H. R. van der Scheer, J. C. Hoekstra, and J. P. Bult (1998), Conjoint Experiments for direct mail Response Optimization, European Journal of Marketing, 32, 323–339.
Vriens, M., M. Wedel, and T. Wilms (1996), Metric Conjoint Segmentation Methods: a Monte Carlo comparison, Journal of Marketing Research, 33, 73–85.
Vriens, M. and D. Wittink (1992), Data Collection in Conjoint Analysis, unpublished manuscript.
Wedel, M. and C. Kistemaker (1989), Consumer Benefit Segmentation using clusterwise Linear Regression, International Journal of Research in Marketing, 6, 45–59.
Wedel, M. and J. B. Steenkamp (1989), Fuzzy clusterwise Regression Approach to Benefit Segmentation, International Journal of Research in Marketing, 6, 241–258.
Wedel, M. and J. B. Steenkamp (1991), A clusterwise Regression Method for simultaneous fuzzy market structuring and Benefit Segmentation, Journal of Research in Marketing, 28, 385–396.
Winer, B. J. (1973), Statistical Principles in Experimental Design, New York.
Witt, K. J. (1997), Best Practice in Interviewing via the Internet, Sawtooth Software Conference Proceedings, Seattle, 15–34.
Wittink, D. R. and P. Cattin (1981), Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study, Journal of Marketing Research, 18, 101–106.
Wittink, D. R. and P. Cattin (1989), Commercial Use of Conjoint Analysis: An Update, Journal of Marketing, 53, 91–96.
Wittink, D. R. and D. Montgomery (1979), Predicting validity of trade-off analysis for alternative Segmentation Schemes, American Marketing Association Educator’s Conference, Chicago, 69–73.
Wittink, D. R., M. Vriens, and W. Burhenne (1994), Commercial Use of Conjoint Analysis in Europe: Results and Critical Reflections, International Journal of Research in Marketing, 11, 41–52.
Wright, P. and M. A. Kriewall (1980), State-of-mind Effects on the Accuracy with which Utility Functions predict marketplace Choice, Journal of Marketing Research, 17, 277–293.
Wuebker, G. and V. Mahajan (1998), A conjoint analysis-based Procedure to measure Reservation Price and to optimally Price Product Bundles, in: Fuerderer, R., Herrmann, A. and Wuebker, G., eds., Optimal Bundling-Marketing Strategies for Improving economic performance, Wiesbaden, 157–176.
Wyner, G. A., L. H. Benedetti, and B. M. Trapp (1984), Measuring the quantity and mix of Product Demand, Journal of Marketing, 48, 101–109.
Yoo, D. I, and H. Ohta (1995), Optimal Pricing and Product Planning for new Mulitattribute Products based on Conjoint Analysis, International Journal of Production Economics, 38, 245–254.
Young, F. W. (1972), A model for polynomial Conjoint Analysis algorithms, in: Shepard, R., Romney, A. K. and Nerlove, S. B., eds., Multidimensional Scaling-Theory and Applications in Behavioral Sciences, New York, 69–104.
Zandan, P. and L. Frost (1989), Customer Satisfaction Research using disks-by-mail, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 5–17.
Zufryden, F. (1988), Using Conjoint Analysis to predict trial and repeat-purchase Patterns of new frequently purchased Products, Decision Sciences, 19, 55–71.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Gustafsson, A., Herrmann, A., Huber, F. (2007). Conjoint Analysis as an Instrument of Market Research Practice. In: Gustafsson, A., Herrmann, A., Huber, F. (eds) Conjoint Measurement. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71404-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-71404-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71403-3
Online ISBN: 978-3-540-71404-0
eBook Packages: Business and EconomicsBusiness and Management (R0)