Q

Quester, P.G. & Smart, J. (1998). The influence of consumption situation and product involvement over consumers‘ use of product attribute. Journal of Consumer Marketing, 15 (3), 220-238.

R

Rae, D.A. (1983). The value of visitors of improving visibility at Mesa Verde and Great Smoky Mountain National Parks. In R.D. Rowe & L.G. Chestnut (Eds.), Managing air quality and scenic resources at national parks and wilderness areas (pp. 217-234). Boulder, CO: Westview Press.

Raghavarao, D. & Wiley, J.B. (1987). Testing competing effects among soft drink brands. In C.E. McCulloch, S.J. Schwayer, G. Casella & S.R. Searle (eds.), Statistical design theory and practice (pp. 161-176). Ithaca, NY: Cornell University Press.

Raghavarao, D. & Wiley, J.B. (1994). Experimental designs for availability effects and cross effects with one attribute. Communications in Statistics, 23 (6), 1835-1846.

Raghavarao, D. & Wiley, J.B. (1998). Estimating main effects with pareto optimal subsets. Australian Journal of Statistics, 40 (4), 425-432.

Rahman, M. & Lorica, B.G. (1999). Attribute relative importance computation in conjoint analysis. Quality Control and Applied Statistics, 44 (6), 669-670. zugleich: Rahman, M. & Lorica, B.G. (1999). Attribute relative importance computation in conjoint analysis. Journal of Information and Optimization Sciences, 20 (1), 113-120.

Ramaswamy, R. (1997). Design and management of service processes: Keeping customers for life. Reading, MA: Addison-Wesley.

Ramaswamy, V. & Cohen, S.H. (2000). Latent class models for conjoint analysis. In A. Gustafsson, A. Herrmann & F. Huber (Eds.), Conjoint measurement - methods and applications (pp. 361-392). Berlin: Springer.

Rangaswamy, A. & Lilien, G.L. (1997). Software tools for new product development. Journal of Marketing Research, 34 (1), 177-184.

Rangaswamy, A., Burke, R. & Oliva, T.A. (1990). Brand equity and the extendibility of brand names (Working Paper No. 90-019). Pennsylvania: University, The Wharton School.

Rao, V.R. & Sattler, H. (1999). Measurement of price effects with conjoint analysis: Separating informational and allocative effects of price (Diskussionspapier Reihe A, Nr. 99/09). Jena: Universität, Wirtschaftswissenschaftliche Fakultät.
zugleich: In A. Gustafsson & A. Herrmann (Hrsg.), Conjoint measurement: Methods and applications (pp. 121-134). Berlin: Springer.

Rao, V.R. & Solgaard, H.S. (1977). An empirical evaluation of alternative multiattribute utility models. In S.C. Jain (Ed.), Research frontiers in marketing: Dialogues and directions, 1978 AMA Educators´ Proceedings.

Rao, V.R. & Soutar, G.N. (1975). Subjective evaluations for product design decisions. Decision Sciences, 6, 120-134.

Rao, V.R. & Winter, F.W. (1977). Application of the multivariate probit model for market segmentation and product design (Working Paper No. 338). Illinois: University of Illinois at Urbana, School of Business Administration.

Rao, V.R. (1977). Conjoint measurement in marketing analysis. In J.N. Sheth (Ed.), Multivariate methods for market and survey research (pp. 257-286). Chicago: American Marketing Association.

Rapoport, A. & Wallsten, T.S. (1972). Individual decision behavior. Annual Review of Psychology, 131-176.

Ratcliffe, J. & Buxton, M. (1999). Patients´ preferences regarding the process and outcomes of live-saving technology - An application of conjoint analysis to liver transplantation. International Journal of Technology Assessment in Health Care, 15 (2), 340-351.

Ratcliffe, J. (2000). The use of conjoint analysis to elicit willingness-to-pay values: Proceed with caution. International Journal of Technology Assessment in Health Care, 16 (1), 270-275.

Ratcliffe, J., Longworth, L. & Boulton, M. (1999). SD1: Using conjoint analysis to assess women’s preferences for maternity care services during intrapartum stage. Value in Health, 2 (5), 367.

Rathnow, P.J. (1993). Integriertes Variantenmanagement: Bestimmung, Realisierung und Sicherung der optimalen Produktvielfalt. Göttingen: Vandenhoeck & Ruprecht.

Reddy, V.S. (1994). The price sensitivity of industrial buyers to softwood lumber product and service quality: An investigation of the U.S. wood treating industry. Unpublished dissertation, Virginia Polytechnic Institute and State University, Blacksburg.

Reddy, V.S., Bush, R.J. & Roudik, R. (1996) A market-oriented approach to maximizing product benefits: Cases in U.S. forest products industries. In H. Juslin & M. Personen (Eds.), Environmental issues and market orientation: Current topics in forest products marketing (Publications No. 4, pp. 19-38). Department of Forest Economics, University of Helsinki.

Reibstein, D., Bateson, J.E. & Boulding, W. (1987). Conjoint analysis reliability: Empirical findings (Report No. 87-102). Cambridge, MA: Marketing Science Institute.

Reibstein, D., Bateson, J.E.G. & Boulding, W. (1988). Conjoint analysis reliability: Empirical findings. Marketing Science, 7 (3), 271-286.

Reineke, K.J. (1999). Marketingstrategien für Musikprogramme der Popmusik: Eine Untersuchung der marketingstrategischen Grundsatzentscheide der musikproduzierenden Unternehmungen im traditionellen und im virtuellen Musikmarkt. Dissertation, Universität Freiburg.

Reiners, W. (1996). Multiattributive Präferenzstrukturmodellierung durch die Conjoint Analyse: Diskussion der Verfahrensmöglichkeiten und Optimierung von Paarvergleichsaufgaben bei der adaptiven Conjoint Analyse. Münster: Lit.

Reiners, W., Jütting, A., Melles, T. & Holling, H. (1996). Optimierung von Paarvergleichsaufgaben der adaptiven Conjoint Analyse. Forschungsreferat zum 40. Kongreß der Deutschen Gesellschaft für Psychologie.

Renaghan, L.M. & Kay, M.Z. (1987). What meeting planners want: The conjoint-analysis approach. Cornell Hotel & Restaurant Administration Quarterly, 28, 67-76.

Reutterer, T. & Kotzab, H.W. (2000). The use of conjoint-analysis for measuring preferences in supply chain design. Industrial Marketing Management, 29 (1), 27-35.

Riedesel, P.L. (1985, September 13). Conjoint analysis is a worthwhile tool, but be sure data are valid. Marketing News, 19, 36-43.

Rink, D.R. (1987). An improved preference data collection method: Balanced incomplete block designs. Journal of the Academy of Marketing Science, 15 (1), 54-61.

Robinson, I. (1983). An investigation into the reliability of conjoint analysis experiments: A test of an alternative data collection method for analysing consumer preferences in the hi-fi market. University of Bradford.

Robinson, P.J. (1980). Application of conjoint analysis to pricing problems. In D.B. Montgomery & D.R. Wittink (Eds.), Proceedings of the first ORSA / TIMS special interest conference on market measurement and analysis (pp. 183-205). Cambridge, MA: Marketing Science Institute.

Robinson, S.N. (1994). Research needs in the intercity bus and rail transportation industry. In Ritchie & Goeldner (Eds.), Travel tourism, hospitality research: Handbook for managers and researchers (2^nd ed., pp. 327-334).

Roe, B., Boyle, K.J. & Teisl, M.F. (1996). Deriving estimates of compensating variation from conjoint data. Journal of Environmental Economics and Management, 31, 145-159.

Rolfes, B. & Benna, R. (1998). Beratung: Die Präferenzen der Kunden. Die Bank, 9, 543-547.

Ronning, G. (2001). Estimation of discrete choice models with minimal variation of alternative-specific variables. Tübingen: Wirtschaftswissenschaftliche Fakultät der Eberhard-Karls-Universität.

Roozen, I.T.M. & De Pelsmacker, P. (1998). Attributes of environmentally friendly consumer behavior. Journal of International Consumer Marketing, 10 (3), 21-41.

Rosenbaum, P.R. (1999). Blocking in compound dispersion experiments. Technometrics, 41 (2), 125-134.

Rosko, M.D. & McKenna, W.F. (1983). Modeling consumer choices of health plans: A comparison of two techniques. Social Sciences and Medicine, 17, 421-429.

Rosko, M.D., DeVita, M., McKenna, W.F. & Walker, L.R. (1985). Strategic marketing applications of conjoint analysis: Am HMO perspective. Journal of Health Care Marketing, 5 (4), 27-38.

Rosko, M.D., Walker, L.R., McKenna, W. & DeVita, M. (1983). Measuring consumer preferences for ambulatory medical care arrangements. Journal of Medical Systems, 7 (6), 545-554.

Ross, R.B. & Gulledge, L.G. (1989). The manager versus the customer: A comparison of values. In Proceedings of the Sawtooth Software Conference. Gaining a competitive advantage through PC-Based interviewing and analysis (Vol. 1, pp. 183-195). Ketchum, ID: Sawtooth Software.

Ryan, M. & Farrar, S. (1994). A pilot study using conjoint analysis to establish the views of users in the provision of orthodontic services in Grampian (Discussion paper No. 07/94). Aberdeen: University, Health Economics Research Unit.

Ryan, M. & Farrar, S. (2000). Using conjoint analysis to elicit preferences for health care. British Medical Journal, 320 (7248), 1530-1532.

Ryan, M. & Hughes, J. (1997). Using conjoint analysis to assess womens preferences for miscarriage management. Health Economics, 6 (3), 261-273.

Ryan, M. (1993). Valuing the benefits of health care: Conjoint analysis or contingent valuation. Working paper, University of Aberdeen, Health Economics Research Unit.

Ryan, M. (1994). A pilot study using conjoint analysis to establish the views of users in the provision of orthodontic services in Grampian. Working paper, University of Aberdeen, Health Economics Research Unit.

Ryan, M. (1995). Establishing arguments in the infertile person´s utility function using the economic instrument of conjoint analysis. Working paper, University of Aberdeen, Health Economics Research Unit.

Ryan, M. (1995). Using conjoint analysis to value surgical versus medical management of miscarriage. Working paper, University of Aberdeen, Health Economics Research Unit.

Ryan, M. (1996). Using conjoint analysis in health care: Unresolved methodological issues. Working paper, University of Aberdeen, Health Economics Research Unit.

Ryan, M. (1996). Using consumer preferences in health care decision making: The application of conjoint analysis. London: Office of Health Economics.

Ryan, M. (1999). A role for conjoint analysis in technology assessment in health care? International Journal of Technology Assessment in Health Care, 15 (3), 443-457.

Ryan, M. (1999). Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Social Science & Medicine, 48, 535-546.

Ryan, M., McIntosh, E. & Shackley, P. (1998). Methodological issues in the application of conjoint analysis in health care. Health Economics, 7 (4), 373-378.