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Published online by Cambridge University Press:  05 October 2015

Jordan J. Louviere
University of South Australia
Terry N. Flynn
University of Western Sydney
A. A. J. Marley
University of Victoria, British Columbia
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Best-Worst Scaling
Theory, Methods and Applications
, pp. 316 - 331
Publisher: Cambridge University Press
Print publication year: 2015

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Abernathy, A. P., Currow, D. C., Fazekas, B. S., Luszcz, M. A., Wheeler, J. L., and Kuchibhatla, M. (2008). Specialized palliative care services are associated with improved short- and long-term caregiver outcomes. Support Care Cancer, 16, 585–97.Google Scholar
Aczél, J., Roberts, F. S., and Rosenbaum, Z. (1986). On scientific laws without dimensional constants. Journal of Mathematical Analysis and Applications, 9, 389–416.Google Scholar
Al-Janabi, H., Coast, J., and Flynn, T. N. (2008). What do people value when they provide unpaid care for an older person? A meta-ethnography with interview follow-up. Social Science and Medicine, 67, 111–21.CrossRefGoogle Scholar
Al-Janabi, H., Flynn, T. N., and Coast, J. (2012). Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Quality of Life Research, 21, 167–76.CrossRefGoogle ScholarPubMed
Alston, J. M., and Chalfant, J. A. (1991). Unstable models from incorrect forms. American Journal of Agricultural Economics, 73, 1171–81.CrossRefGoogle Scholar
Amir, O., and Levav, J. (2008). Choice construction versus preference construction: the instability of preferences learned in context. Journal of Marketing Research, 45, 145–58.CrossRefGoogle Scholar
Amonini, C., Soutar, G. N., Sweeney, J. C., and McColl-Kennedy, J. R. (2007). Positional advantages and related marketing activities: an exploratory study of professional service firms, working paper. Perth: University of Western Australia.
Anderson, D. A., and Wiley, J. B. (1992). Efficient choice set designs for estimating availability cross-effect models. Marketing Letters, 3, 357–70.CrossRefGoogle Scholar
Anderson, N. H. (1970). Functional measurement and psychophysical judgement. Psychological Review, 77, 153–70.CrossRefGoogle Scholar
Anderson, N. H. (1982). Methods of Information Integration Theory. New York: Academic Press.Google Scholar
Angulo, A. M., Gil, J. M., Gracia, A., and Sanchez, M. (2000). Hedonic prices for Spanish red quality wine. British Food Journal, 102, 481–93.Google Scholar
Arnesen, T., and Trommald, M. (2005). Are QALYs based on time trade-off comparable? A systematic review of TTO methodologies. Health Economics, 14, 39–53.CrossRefGoogle ScholarPubMed
Arrow, K. (1963). Social Choice and Individual Values, edn. New Haven, CT: Yale University Press.Google Scholar
Auger, P., Devinney, T. M., and Louviere, J. J. (2007). Using best-worst scaling methodology to investigate consumer ethical beliefs across countries. Journal of Business Ethics, 70, 299–326.CrossRefGoogle Scholar
Aurifeille, J.-M., Quester, P., Lockshin, L., and Spawton, T. (2002). Global versus international involvement based segmentation: a cross-national exploratory study. International Marketing Review, 19, 369–86.CrossRefGoogle Scholar
Australian Trade Commission (2007). Business and Professional Services Capability Overview. Canberra: Australian Trade Commission.
Baker, D. W., Qaseem, A., Reynolds, P., Garnder, L. A., and Schneider, E. C. (2013). Design and use of performance measures to decrease low-value services and achieve cost-conscious care. Annals of Internal Medicine, 158, 55–9.CrossRefGoogle ScholarPubMed
Banerjee, S. B. (2002). Corporate environmentalism: the construct and its measurement. Journal of Business Research, 55, 177–91.CrossRefGoogle Scholar
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120.CrossRefGoogle Scholar
Barr, T. F., and McNeilly, K. M. (2003). Marketing: is it still just advertising? The experiences of accounting firms as a guide for other professional service firms. Journal of Services Marketing, 17, 713–29.Google Scholar
Batt, P. J., and Dean, A. (2000). Factors influencing the consumer's decision. Australia and New Zealand Wine Industry Journal, 15(marketing supp.), 34–41.Google Scholar
Baumgartner, H., and Steenkamp, J.-B. E. M. (2001). Response styles in marketing research: a cross-national investigation. Journal of Marketing Research, 38, 143–56.CrossRefGoogle Scholar
Beattie, J. M., Flynn, T. N., and Clark, A. M. (2013). Patient preferences for deactivation of implantable cardioverter-defibrillators: a response [letter]. JAMA Internal Medicine, 173, 1556–7.CrossRefGoogle Scholar
Becker, G. S. (1976). The Economic Approach to Human Behavior. University of Chicago Press.Google Scholar
Bednarz, A. (2006). Best-worst scaling and its relationship with multinomial logit, BAS thesis. Adelaide: University of South Australia.
Beggs, S., Cardell, S., and Hausman, J. (1981). Assessing the potential demand for electric cars. Journal of Econometrics, 17, 1–19.CrossRefGoogle Scholar
Bettman, J. R. (1979). An Information Processing Theory of Consumer Choice. Reading, MA: Addison-Wesley.Google Scholar
Bettman, J. R., Luce, M. F., and Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25, 187–217.CrossRefGoogle Scholar
Bettman, J. R., and Zins, M. A. (1977). Constructive processes in consumer choice. Journal of Consumer Research, 4, 75–85.CrossRefGoogle Scholar
Blankson, C., and Kalafatis, S. P. (1999a). Issues and challenges in the positioning of service brands: a review. Journal of Product and Brand Management, 8, 106–18.CrossRefGoogle Scholar
Blankson, C., and Kalafatis, S. P. (1999b). Issues of creative communication tactics and positioning strategies in the UK plastic card services industry. Journal of Marketing Communications, 5, 55–70.Google Scholar
Blankson, C., and Kalafatis, S. P. (2004). The development and validation of a scale measuring consumer/customer-derived generic typology of positioning strategies. Journal of Marketing Management, 20, 5–43.CrossRefGoogle Scholar
Brickley, M., Armstrong, R., Shepherd, J., and Kay, E. (1995). The relevance of health state utilities to lower third molar surgery. International Dental Journal, 45, 124–8.Google ScholarPubMed
Brouwer, W., Culyer, A., van Exel, J., and Rutten, F. F. H. (2008). Welfarism vs. extra-welfarism. Journal of Health Economics, 27, 325–38.CrossRefGoogle ScholarPubMed
Brown, S. D., and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology, 57, 153–78.CrossRefGoogle ScholarPubMed
Bruwer, J., and Li, E. (2007). Wine-related lifestyle (WRL) market segmentation: demographic and behavioural factors. Journal of Wine Research, 18, 19–34.CrossRefGoogle Scholar
Bruwer, J., Li, E., and Reid, M. (2002). Segmentation of the Australian wine market using a wine-related lifestyle approach. Journal of Wine Research, 13, 217–42.CrossRefGoogle Scholar
Burgess, L., Street, D. J., Viney, R., and Louviere, J. J. (2006). Design of choice experiments in health economics. In Jones, A. M. (ed.), The Elgar Companion to Health Economics, 415–26. Cheltenham: Edward Elgar.Google Scholar
Busemeyer, J. R., and Rieskamp, J. (2014). Psychological research and theories on preferential choice. In Hess, S. and Daly, A. (eds.), Handbook of Choice Modelling: The State of the Art and the State of Practice, 49–72. Cheltenham: Edward Elgar.Google Scholar
Carnegie, D. (1948). How to Stop Worrying and Start Living. New York: Simon & Schuster.Google Scholar
Carson, R. T., Louviere, J. J., and Wei, E. (2010). Alternative Australian climate change plans: the public's views. Energy Policy, 38, 902–11.CrossRefGoogle Scholar
Casini, L., Corsi, A. M., and Goodman, S. (2009). Consumer preferences of wine in Italy: applying best-worst scaling. International Journal of Wine Business Research, 21, 64–78.CrossRefGoogle Scholar
Chaney, I. M. (2000). External search effort for wine. International Journal of Wine Marketing, 12, 5–21.CrossRefGoogle Scholar
Cheng, P. Y., and Chiou, W. B. (2010). Rejection or selection: influence of framing in investment decisions. Psychological Reports, 106, 247–54.CrossRefGoogle ScholarPubMed
Cliff, N. (1966). Orthogonal rotation to congruence. Psychometrika, 31, 33–42.CrossRefGoogle Scholar
Coast, J., Al-Janabi, H., Sutton, E. J., Horrocks, S. A., Vosper, J., Swancutt, D. R., and Flynn, T. N. (2012). Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Economics, 21, 730–41.CrossRefGoogle ScholarPubMed
Coast, J., Flynn, T. N., Natarajan, L., Sproston, K., Lewis, J., Louviere, J. J., and Peters, T. J. (2008). Valuing the ICECAP capability index for older people. Social Science and Medicine, 67, 874–82.CrossRefGoogle ScholarPubMed
Coast, J., Flynn, T. N., Salisbury, C., Louviere, J. J., and Peters, T. J. (2006). Maximising responses to discrete choice experiments. Applied Health Economics and Health Policy, 5, 249–60.CrossRefGoogle ScholarPubMed
Coast, J., Flynn, T. N., Sutton, E., Al-Janabi, H., Vosper, J., Lavender, S., Louviere, J. J., and Peters, T. J. (2008). Investigating choice experiments for preferences of older people (ICEPOP): evaluative spaces in health economics. Journal of Health Services Research and Policy, 13(supp. 3), 31–7.CrossRefGoogle ScholarPubMed
Coast, J, Peters, T, Natarajan, L, Sproston, K, and Flynn, T. N. (2008). An assessment of the construct validity of the descriptive system for the ICECAP capability measure for older people. Quality of Life Research, 17, 967–76.CrossRefGoogle ScholarPubMed
Coast, J., Salisbury, C., de Berker, D., Noble, A., Horrocks, S., Peters, T. J., and Flynn, T. N. (2006). Preferences for aspects of a dermatology consultation. British Journal of Dermatology, 155, 387–92.CrossRefGoogle ScholarPubMed
Cohen, E. (2009). Applying best-worst scaling to wine marketing. International Journal of Wine Business Research, 21, 8–23.CrossRefGoogle Scholar
Cohen, E., d'Hauteville, F., and Sirieix, L. (2009). A cross-cultural comparison of choice criteria for wine in restaurants. International Journal of Wine Business Research, 21, 50–63.CrossRefGoogle Scholar
Cohen, S. (2003). Maximum difference scaling: improved measures of importance and preference for segmentation. In Proceedings of the Sawtooth Software Conference: April 2003, 61–74. Sequim, WA: Sawtooth Software.Google Scholar
Cohen, S., and Markowitz, P. (2002). Renewing market segmentation: some new tools to correct old problems. In ESOMAR 2002 Congress Proceedings, 595–612. Amsterdam: ESOMAR.Google Scholar
Cohen, S., and Neira, L. (2003). Measuring preference for product benefits across countries: overcoming scale usage bias with maximum difference scaling. Paper presented at ESOMAR Latin America conference, Punta del Este, Uruguay, 5 May.
Cohen, S., and Orme, B. (2004). What's your preference?Marketing Research, 16, 32–7.Google Scholar
Coviello, N. E., Brodie, R. J., Danaher, P. J., and Johnston, W. J. (2002). How firms relate to their markets: an empirical examination of contemporary marketing practices. Journal of Marketing, 66, 33–46.CrossRefGoogle Scholar
Coviello, N. E., Brodie, R. J., and Munro, H. J. (1997). Understanding contemporary marketing: development of a classification scheme. Journal of Marketing Management, 13, 501–22.CrossRefGoogle Scholar
Craig, C. S., and Douglas, S. P. (2000). International Marketing Research. New York: John Wiley.Google Scholar
Crane, F. G. (1993). Professional services marketing in the future: challenges and solutions. Journal of Professional Services Marketing, 9, 3–12.CrossRefGoogle Scholar
Cutler, A., and Breiman, L. (1994). Archetypal analysis. Technometrics, 36, 338–47.CrossRefGoogle Scholar
David, H. A. (1988). The Method of Paired Comparisons, edn. London: Hodder Arnold.Google Scholar
Day, B., Bateman, I. J., Carson, R. T., Dupont, D., Louviere, J. J., Morimoto, S., Scarpa, R., and Wang, P. (2012). Ordering effects and choice set awareness in repeat-response stated preference studies. Journal of Environmental Economics and Management, 63, 73–91.CrossRefGoogle Scholar
Day, G. S., and Wensley, R. (1988). Assessing advantage: a framework for diagnosing competitive superiority. Journal of Marketing, 52, 1–20.CrossRefGoogle Scholar
De Jong, M. G., Steenkamp, J.-B. E. M., Fox, J.-P., and Baumgartner, H. (2008). Using item response theory to measure extreme response style in marketing research: a global investigation. Journal of Marketing Research, 45, 104–15.CrossRefGoogle Scholar
De Palma, A., Myers, G. M., and Papageorgiou, Y. Y. (1994). Rational choice under an imperfect ability to choose. American Economic Review, 84, 419–40.Google Scholar
Dean, R. (2002). The changing world of the international fine wine market. Australian and New Zealand Wine Industry Journal, 17, 84–8.Google Scholar
Diamantopoulos, A., Reynolds, N. L., and Simintiras, A. C. (2006). The impact of response styles on the stability of cross-national comparisons. Journal of Business Research, 59, 925–35.CrossRefGoogle Scholar
Dibb, S., and Simkin, L. (1993). The strength of branding and positioning in services. International Journal of Service Industry Management, 4, 25–35.CrossRefGoogle Scholar
Dimitriadou, E., Dolničar, S., and Weingessel, A. (2002). An examination of indexes for determining the number of clusters in binary data sets. Psychometrika, 67, 137–59.CrossRefGoogle Scholar
Dodson, J. A., Fried, T. R., Van Ness, P. H., Goldstein, N. E., and Lampert, R. (2013). Patient preferences for deactivation of implantable cardioverter-defibrillators. JAMA Internal Medicine, 173, 377–9.CrossRefGoogle ScholarPubMed
Dolan, P., Gudex, C., Kind, P., and Williams, A. (1995). A social tariff for EuroQol: results from a UK general population survey, Discussion Paper no. 138. University of York.
Dolan, P., Gudex, C., Kind, P., and Williams, A. (1996). The time trade-off method: results from a general population study. Health Economics, 5, 141–54.3.0.CO;2-N>CrossRefGoogle ScholarPubMed
Dolničar, S., and Leisch, F. (2004). Segmenting markets by bagged clustering. Australasian Marketing Journal, 12, 51–65.CrossRefGoogle Scholar
Dolničar, S., and Leisch, F. (2010). Evaluation of structure and reproducibility of cluster solutions using the bootstrap. Marketing Letters, 21, 83–101.CrossRefGoogle Scholar
Doyle, P., and Wong, V. (1998). Marketing and competitive performance: an empirical study. European Journal of Marketing, 32, 514–35.CrossRefGoogle Scholar
Dröge, C., and Darmon, R. Y. (1987). Associative positioning strategies through comparative advertising: attribute versus overall similarity approaches. Journal of Marketing Research, 24, 377–88.CrossRefGoogle Scholar
Drolet, A. L., and Morrison, D. G. (2001). Do we really need multiple-term measures in service research?Journal of Service Research, 3, 196–204.CrossRefGoogle Scholar
Dyachenko, T., Walker Reczek, R., and Allenby, G. M. (2014). Models of sequential evaluation in best-worst choice tasks. Marketing Science, 33, 828–48.CrossRefGoogle Scholar
Ellis, B., and Mosher, J. S. (1993). Six Ps for four characteristics: a complete positioning strategy for the professional services firm – CPA's. Journal of Professional Services Marketing, 9, 129–45.CrossRefGoogle Scholar
Erdem, T., and Keane, M. (1996). Decision making under uncertainty: capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Science, 15, 1–20.CrossRefGoogle Scholar
Euromonitor International (2008). The World Market for Wine. London: Euromonitor International.
Fahy, J., Hooley, G., Cox, T., Beracs, J., Fonfara, K., and Snoj, B. (2000). The development and impact of marketing capabilities in central Europe. Journal of International Business Studies, 31, 63–81.CrossRefGoogle Scholar
Fiebig, D., Keane, M., Louviere, J., J., and Wasi, N. (2010). The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Marketing Science, 29, 393–421.CrossRefGoogle Scholar
Finn, A., and Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: the case of food safety. Journal of Public Policy and Marketing, 11, 12–25.Google Scholar
Fischhoff, B., Slovic, P., and Lichtenstein, S. (1980). Knowing what you want: measuring labile values. In Wallsten, T. S. (ed.), Cognitive Processes in Choice and Decision Behavior, 117–41. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.Google Scholar
Flynn, T. N. (2010a). Using conjoint analysis and choice experiments to estimate QALY values: issues to consider. Pharmacoeconomics, 28, 711–22.CrossRefGoogle Scholar
Flynn, T. N. (2010b). Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling. Expert Review of Pharmacoeconomics and Outcomes Research, 10, 259–67.CrossRefGoogle ScholarPubMed
Flynn, T. N., Huynh, E., Peters, T. J., Al-Janabi, H., Clemens, S., Moody, A., and Coast, J. (2015). Scoring the ICECAP-A capability instrument: estimation of a UK general population tariff. Health Economics, 24, 258–69.CrossRefGoogle ScholarPubMed
Flynn, T. N., Louviere, J. J., Marley, A. A. J., Coast, J., and Peters, T. J. (2008). Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Population Health Metrics, 6, 1–6.CrossRefGoogle ScholarPubMed
Flynn, T. N., Louviere, J. J., Peters, T. J., and Coast, J. (2007). Best-worst scaling: what it can do for health care research and how to do it. Journal of Health Economics, 26, 171–89.CrossRefGoogle Scholar
Flynn, T. N., Louviere, J. J., Peters, T. J., and Coast, J. (2008). Estimating preferences for a dermatology consultation using best-worst scaling: comparison of various methods of analysis. BMC Medical Research Methodology, 8: 76.CrossRefGoogle ScholarPubMed
Flynn, T. N., Louviere, J. J., Peters, T. J., and Coast, J. (2010). Using discrete choice experiments to understand preferences for quality of life: variance scale heterogeneity matters. Social Science and Medicine, 70, 1957–65.CrossRefGoogle ScholarPubMed
Flynn, T. N., and Marley, A. A. J. (2014). Best-worst scaling: theory and methods. In Hess, S. and Daly, A. (eds.), Handbook of Choice Modelling: The State of the Art and the State of Practice, 178–201. Cheltenham: Edward Elgar.Google Scholar
Flynn, T. N., Peters, T. J., and Coast, J. (2013). Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data. Journal of Choice Modelling, 6, 34–43.CrossRefGoogle Scholar
Fournier, S. (1998). Consumers and their brands: developing relationship theory in consumer research. Journal of Consumer Research, 24, 343–53.CrossRefGoogle Scholar
Freeman, A. M. (1993). The Measurement of Environmental and Resource Values. Washington, DC: Resources for the Future.Google Scholar
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.CrossRefGoogle Scholar
Friedman, M. (1962). Price Theory: A Provisional Text. Chicago: Aldine.Google Scholar
Frischknecht, B. D., Eckert, C., Geweke, J., and Louviere, J. J. (2014). A simple method for estimating preference parameters for individuals. International Journal of Research in Marketing, 31, 35–48.CrossRefGoogle Scholar
Ganzach, Y. (1995). Attribute scatter and decision outcome: judgment versus choice. Organizational Behavior and Human Decision Processes, 62, 113–22.CrossRefGoogle Scholar
Ganzach, Y., and Schul, Y. (1995). The influence of quantity of information and goal framing on decision. Acta Psychologica, 89, 23–36.CrossRefGoogle Scholar
Giergiczny, M., Chintakayala, P., Dekker, T., and Hess, S. (2013). Testing the consistency (or lack thereof) between choices in best-worst surveys. Paper presented at 3rd “International Choice Modelling” conference, Sydney, 5 July.
Gil, J. M., and Sanchez, M. (1997). Consumer preferences for wine attributes: a conjoint approach. British Food Journal, 99, 3–11.CrossRefGoogle Scholar
Gill, B., Griffin, B., and Hesketh, B. (2013). Changing expectations concerning life-extending treatment: the relevance of opportunity cost. Social Science and Medicine, 85, 66–73.CrossRefGoogle ScholarPubMed
Gluckman, R. L. (1990). A consumer approach to branded wines. European Journal of Wine Marketing, 2, 27–46.Google Scholar
Goodman, S. (2009). An international comparison of retail wine consumer choice. International Journal of Wine Business Research, 21, 41–9.CrossRefGoogle Scholar
Goodman, S., Lockshin, L., and Cohen, E. (2006). Using the best-worst method to examine market segments and identify different influences on consumer choice. Paper presented at 3rd “International Wine Business and Marketing Research” conference, Montpellier, July 6.
Green, P. E., and Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–63.CrossRefGoogle Scholar
Green, P. E., and Rao, V. R. (1972). Applied Multidimensional Scaling: A Comparison of Approaches and Algorithms. New York: Holt, Rinehart & Winston.Google Scholar
Green, P. J. (1984). Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. Journal of the Royal Statistical Society B, 46, 149–92.Google Scholar
Greene, W. H. (2003). Econometric Analysis, edn. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Grewal, I., Lewis, J., Flynn, T. N., Brown, J., Bond, J., and Coast, J. (2006). Developing attributes for a generic quality of life measure for older people: preferences or capabilities?Social Science and Medicine, 62, 1891–901.CrossRefGoogle ScholarPubMed
Gupta, A. K., and Govindarajan, V. (1984). Business unit strategy, managerial characteristics and business unit effectiveness at strategy implementation. Academy of Management Journal, 27, 25–41.Google Scholar
Gupta, S. (2005). Youden squares and row–column designs. In Encyclopedia of Biostatistics, 8. New York: John Wiley.Google Scholar
Hall, J., and Lockshin, L. (2000). Using means–end chains for analysing occasions – not buyers. Australasian Marketing Journal, 8, 45–54.CrossRefGoogle Scholar
Hawkins, G. E., Marley, A. A. J., Heathcote, A., Flynn, T. N., Louviere, J. J., and Brown, S. D. (2014a). Integrating cognitive process and descriptive models of attitudes and preferences. Cognitive Science, 38, 701–35.CrossRefGoogle ScholarPubMed
Hawkins, G. E., Marley, A. A. J., Heathcote, A., Flynn, T. N., Louviere, J. J., and Brown, S. D. (2014b). The best of times and the worst of times are interchangeable. Decision, 1, 192–214.CrossRefGoogle Scholar
Heady, R. B., and Lucas, J. L. (1997). PERMAP: an interactive program for making perceptual maps. Behavioral Research Methods, Instruments and Computers, 29, 450–5.CrossRefGoogle Scholar
Heathcote, A., and Love, J. (2012). Linear deterministic accumulator models of simple choice. Frontiers in Psychology, 3, 1–19.CrossRefGoogle ScholarPubMed
Heckman, J. J., and Snyder, J. M. (1997). Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators. RAND Journal of Economics, 28, S142–S189.CrossRefGoogle Scholar
Hensher, D. A., Louviere, J. J., and Swait, J. (1998). Combining sources of preference data. Journal of Econometrics, 89, 197–221.CrossRefGoogle Scholar
Hensher, D. A., Rose, J. M., and Greene, W. H. (2005). Applied Choice Analysis: A Primer. Cambridge University Press.CrossRefGoogle Scholar
Herbig, P. A., and Milewicz, J. C. (1993). Marketing signaling in the professional services. Journal of Professional Services Marketing, 8, 65–80.CrossRefGoogle Scholar
Herche, J., and Engelland, B. (1996). Reversed-polarity items and scale unidimensionality. Journal of the Academy of Marketing Science, 24, 366–74.CrossRefGoogle Scholar
Hill, C. J., and Neeley, S. E. (1988). Differences in the consumer decision process for professional versus generic services. Journal of Services Marketing, 2, 17–23.Google Scholar
Hodge, T. G., Brown, M. H., and Lumpkin, J. R. (1990). The use of market plans and advertising among accounting firms: is this profession a viable candidate for marketing?Journal of Professional Services Marketing, 6, 43–52.Google Scholar
Hooley, G., Broderick, A., and Moller, K. (1998). Competitive positioning and the resource-based view of the firm. Journal of Strategic Marketing, 6, 97–116.CrossRefGoogle Scholar
Hooley, G., and Greenley, G. (2005). The resource underpinnings of competitive positions. Journal of Strategic Marketing, 13, 93–116.CrossRefGoogle Scholar
Hooley, G., Saunders, J., and Piercy, N. (2004). Marketing Strategy and Competitive Positioning. London: Prentice Hall.Google Scholar
Horsky, D., and Rao, M. R. (1984). Estimation of attribute weights from preference comparisons. Management Science, 30, 801–22.CrossRefGoogle Scholar
Huber, P. J. (1963). Pairwise comparison and ranking: optimum properties of the row sum procedure. Annals of Mathematical Statistics, 34, 511–20.CrossRefGoogle Scholar
Huber, V. L., Neale, M. A., and Northcraft, G. B. (1987). Decision bias and personnel selection strategies. Organizational Behavior and Human Decision Processes, 40, 136–47.CrossRefGoogle Scholar
Hume, D. (1889 [1757]). The Natural History of Religion. London: A. & H. Bradlaugh Bonner.Google Scholar
Hutchinson, J. W., Zauberman, G., and Meyer, R. (2010). On the interpretation of temporal inflation parameters in stochastic models of judgment and choice. Marketing Science, 29, 133–9.CrossRefGoogle Scholar
Jacoby, J., and Olson, J. C. (1977). Consumer response to price: an attitudinal, information processing perspective. In Wind, Y. and Greenberg, M. (eds.), Moving Ahead with Attitude Research, 73–86. Chicago: American Marketing Association.Google Scholar
Jenster, P., and Jenster, L. (1993). The European wine industry. International Journal of Wine Marketing, 5, 30–74.CrossRefGoogle Scholar
Jones, M., Mothersbaugh, D. L., and Beatty, S. E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55, 441–50.CrossRefGoogle Scholar
Juliano, L., and Wilcox, K. (2011). Choice, rejection, and elaboration on preference-inconsistent alternatives. Journal of Consumer Research, 38, 229–41.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus & Giroux.Google Scholar
Kalafatis, S. P., Tsogas, M., and Blankson, C. (2000). Positioning strategies in business markets. Journal of Business and Industrial Marketing, 15, 416–37.CrossRefGoogle Scholar
Kaldjian, L. C., Curtis, A. E., Shinkunas, L. A., and Cannon, K. T. (2009). Goals of care toward the end of life: a structured literature review [review article]. American Journal of Hospice and Palliative Medicine, 25, 501–11.CrossRefGoogle Scholar
Kass-Bartelmes, B. L., Hughes, R., and Rutherford, M. K. (2003). Advance care planning: preferences for care at the end of life. Research in Action, 12.Google Scholar
Keeney, R. C., and Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: John Wiley.Google Scholar
Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J. J., Kahn, B. E., and Townsend, C. (2013). Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40, 3803–12.CrossRefGoogle Scholar
Kind, P., Dolan, P., Gudex, C., and Williams, A. (1998). Variations in population health status: results from a United Kingdom national questionnaire survey. British Medical Journal, 316, 736–41.CrossRefGoogle ScholarPubMed
Kivetz, R., Netzer, O., and Srinivasan, V. S. (2004). Alternative models for capturing the compromise effect. Journal of Marketing Research, 41, 237–57.CrossRefGoogle Scholar
Keown, C., and Casey, M. (1995). Purchasing behaviour in the Northern Ireland wine market. British Food Journal, 97, 17–20.CrossRefGoogle Scholar
Kotler, P., Hayes, T., and Bloom, P. N. (2002). Marketing Professional Services. Paramus, NJ: Prentice Hall.Google Scholar
Landon, S., and Smith, C. E. (1998). Quality expectations, reputation and price. Southern Economic Journal, 64, 628–47.CrossRefGoogle Scholar
Lee, J. A., Soutar, G., and Louviere, J. J. (2007). Measuring values using best-worst scaling: the LOV example. Psychology and Marketing, 24, 1043–58.CrossRefGoogle Scholar
Lee, J. A., Soutar, G., and Louviere, J. J. (2008). The best-worst scaling approach: an alternative to Schwartz's values survey. Journal of Personality Assessment, 90, 335–47.CrossRefGoogle ScholarPubMed
Levin, I. P., Jasper, J. D., and Forbes, W. S. (1998). Choosing versus rejecting options at different stages of decision making. Journal of Behavioral Decision Making, 11, 193–210.3.0.CO;2-G>CrossRefGoogle Scholar
Levin, I. P., Prosansky, C. M., Heller, D., and Brunick, B. M. (2001). Prescreening of choice options in “positive” and “negative” decision-making tasks. Journal of Behavioral Decision Making, 14, 279–93.CrossRefGoogle Scholar
Lipovetsky, S., and Conklin, M. W. (2014). Best-worst scaling in analytical closed-form solution compared with other methods. Journal of Choice Modelling, 10, 60–8.CrossRefGoogle Scholar
Llewellyn-Thomas, H. A., Sutherland, H. J., and Thiel, E. C. (1993). Do patients’ evaluations of a future health state change when they actually enter that state?Medical Care, 31, 1002–12.CrossRefGoogle ScholarPubMed
Lockshin, L., and Cohen, E. (2011). Using product and retail choice attributes for cross-national segmentation. European Journal of Marketing, 45, 1236–52.CrossRefGoogle Scholar
Lockshin, L., Jarvis, W., d'Hauteville, F., and Perrouty, J. P. (2006). Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Quality and Preference, 17, 166–78.CrossRefGoogle Scholar
Lockshin, L., Rasmussen, M., and Cleary, F. (2000). The nature and roles of a wine brand. Australia and New Zealand Wine Industry Journal, 15, 17–24.Google Scholar
Lockshin, L., Spawton, T., and Macintosh, G. (1997). Using product, brand and purchasing involvement for retail segmentation. Journal of Retailing and Consumer Services, 4, 171–83.CrossRefGoogle Scholar
Loftus, G. R. (1978). On interpretation of interactions. Memory and Cognition, 6, 312–19.CrossRefGoogle Scholar
Louviere, J. J. (1988a). Analyzing Decision Making: Metric Conjoint Analysis. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Louviere, J. J. (1988b). Conjoint analysis modelling of stated preferences: a review of theory, methods, recent developments and external validity. Journal of Transport Economics and Policy, 22, 93–119.Google Scholar
Louviere, J. J. (1994). Conjoint analysis. In Bagozzi, R. P. (ed.), Advanced Methods of Marketing Research, 223–59. Cambridge, MA: Basil Blackwell.Google Scholar
Louviere, J. J. (2001). Choice experiments: an overview of concepts and issues. In Bennett, J. and Blamey, R. (eds.), The Choice Modelling Approach to Environmental Valuation, 13–36. Cheltenham: Edward Elgar.Google Scholar
Louviere, J. J. (2013). Modeling single individuals: the journey from psych lab to the app store. In Hess, S. and Daly, A. (eds.), Choice Modelling: The State of the Art and the State of Practice, 1–48. Cheltenham: Edward Elgar.Google Scholar
Louviere, J. J., Carson, R. T., Burgess, L., Street, D. J., and Marley, A. A. J. (2011). Sequential preference questions: factors influencing completion rates using an online panel, working paper. University of Technology, Sydney.
Louviere, J. J., Hensher, D. A., and Swait, J. (2000). Stated Choice Methods: Analysis and Application. Cambridge University Press.CrossRefGoogle Scholar
Louviere, J. J., and Lancsar, E. (2009). Discrete choice experiments in health: the good, the bad, the ugly and toward a brighter future. Health Economics, Policy and Law, 4, 527–46.CrossRefGoogle Scholar
Louviere, J. J., and Meyer, R. J. (2008). Formal choice models of informal choices: what choice modeling research can (and can't) learn from behavioral theory. In Malhotra, N. K. (ed.), Review of Marketing Research, vol. IV, 3–32. Bingley, UK: Emerald.Google Scholar
Louviere, J. J., and Street, D. J. (2000). Stated preference methods. In Hensher, D. A. and Button, K. (eds.), Handbook in Transport, vol. I, Transport Modelling, 131–44. Amsterdam: Pergamon.Google Scholar
Louviere, J. J., Street, D. J., and Burgess, L. (2003). A 20+ years’ retrospective on choice experiments. In Wind, Y. and Green, P. E. (eds.), Marketing Research and Modeling: Progress and Prospects: A Tribute to Paul E. Green, 201–14. New York: Kluwer Academic.Google Scholar
Louviere, J. J., Street, D. J., Burgess, L., Wasi, N., Islam, T., and Marley, A. A. J. (2008). Modelling the choices of single individuals by combining efficient choice experiment designs with extra preference information. Journal of Choice Modelling, 1, 128–63.CrossRefGoogle Scholar
Louviere, J. J., and Swait, J. (2010). Discussion of “Alleviating the constant variance assumption in decision research: theory, measurement, and experimental test” [commentary]. Marketing Science, 29, 18–22.CrossRefGoogle Scholar
Louviere, J. J., Swait, J., and Anderson, D. (1995). Best-worst conjoint: a new preference elicitation method to simultaneously identify overall attribute importance and attribute level partworths, working paper. Gainesville: University of Florida.
Louviere, J. J., and Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. Journal of Marketing Research, 20, 350–67.CrossRefGoogle Scholar
Louviere, J. J., and Woodworth, G. (1990). Best-worst scaling: a model for largest difference judgments, working paper. Edmonton: University of Alberta.
Luce, R. D. (1959). Individual Choice Behavior: A Theoretical Analysis. New York: John Wiley.Google Scholar
Luce, R. D., and Suppes, P. (1965). Preference, utility, and subjective probability. In Luce, R. D., Bush, R. R. and Galanter, E. (eds.), Handbook of Mathematical Psychology, vol. III, 249–410. New York: John Wiley.Google Scholar
Luo, X., Rindfleisch, A., and Tse, D. K. (2007). Working with rivals: the impact of competitor alliances on financial performance. Journal of Marketing Research, 44, 73–83.CrossRefGoogle Scholar
Lussier, D. A., and Olshavsky, R. W. (1979). Task complexity and contingent processing in brand choice. Journal of Consumer Research, 6, 154–65.CrossRefGoogle Scholar
Lynch, J. G. (1985). Uniqueness issues in the decompositional modeling of multiattribute overall evaluations: an information integration perspective. Journal of Marketing Research, 22, 1–19.CrossRefGoogle Scholar
MacDonald, E., and Uncles, M. (2007). Consumer savvy: conceptualisation and measurement. Journal of Marketing Management, 23, 497–517.CrossRefGoogle Scholar
Mack, J. W., Weeks, J. C., Wright, A. A., Block, S. D., and Prigerson, H. G. (2010). End-of-life discussions, goal attainment, and distress at the end of life: predictors and outcomes of receipt of care consistent with preferences. Journal of Clinical Oncology, 28, 1203–8.CrossRefGoogle ScholarPubMed
Magidson, J., and Vermunt, J. K. (2007) Removing the scale factor confound in multinomial logit choice models to obtain better estimates of preference. In Proceedings of the Sawtooth Software Conference: October 2007, 139–54. Sequim, WA: Sawtooth Software.Google Scholar
Marley, A. A. J. (1968). Some probabilistic models of simple choice and ranking. Journal of Mathematical Psychology, 5, 311–32.CrossRefGoogle Scholar
Marley, A. A. J. (1989). A random utility family that includes many of the “classical” models and has closed form choice probabilities and choice reaction times. British Journal of Mathematical and Statistical Psychology, 42, 13–36.CrossRefGoogle Scholar
Marley, A. A. J., and Flynn, T. N. (2015). Best and worst scaling: theory and application. In Wright, J. D. (ed), International Encyclopedia of the Social and Behavioral Sciences, edn., vol 2. Oxford: Elsevier Science, pp. 548–52.CrossRef
Marley, A. A. J., Flynn, T. N., and Louviere, J. J. (2008). Probabilistic models of set-dependent and attribute-level best-worst choice. Journal of Mathematical Psychology, 52, 281–96.CrossRefGoogle Scholar
Marley, A. A. J., and Islam, T. (2012). Conceptual relations between expanded rank data and models of the unexpanded rank data. Journal of Choice Modelling, 5, 38–80.CrossRefGoogle Scholar
Marley, A. A. J., and Louviere, J. J. (2005). Some probabilistic models of best, worst, and best-worst choices. Journal of Mathematical Psychology, 49, 464–80.CrossRefGoogle Scholar
Marley, A. A. J., and Pihlens, D. (2012). Models of best-worst choice and ranking among multiattribute options (profiles). Journal of Mathematical Psychology, 56, 24–34.CrossRefGoogle Scholar
Marley, A. A. J., and Regenwetter, M. (in press). Choice, preference, and utility: probabilistic and deterministic representations. In Batchelder, W., Colonius, H., Dzhafarov, E. and Myung, J. (eds.), New Handbook of Mathematical Psychology. Cambridge University Press.
McAlexander, J. H., Schouten, J. W., and Scammon, D. L. (1991). Positioning professional services: segmenting the financial services market. Journal of Professional Services Marketing, 7, 149–66.CrossRefGoogle Scholar
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In Zarembka, P. (ed.), Frontiers in Econometrics, 105–42. New York: Academic Press.Google Scholar
McFadden, D. (1999). Rationality for economists?Journal of Risk and Uncertainty, 19, 73–105.CrossRefGoogle Scholar
McFadden, D., and Reid, F. (1975). Aggregate travel demand forecasting from disaggregated behavioral models. Transportation Research Record: Travel Behavior and Value, 534, 24–37.Google Scholar
McFadden, D., and Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15, 447–70.3.0.CO;2-1>CrossRefGoogle Scholar
McFadden, D., Train, K., and Tye, W. (1978). An application of diagnostic tests for the independence from irrelevant alternatives property of the multinomial logit model. Transportation Research Record: Forecasting Passenger and Freight Travel, 637, 39–46.Google Scholar
McIntosh, E. (2003). Using discrete choice experiments to value the benefits of health care, PhD thesis. University of Aberdeen.
McIntosh, E., Clarke, P., Frew, E. J., and Louviere, J. J. (2010). Applied Methods of Cost–Benefit Analysis in Health Care. Oxford University Press.Google Scholar
McIntosh, E., and Louviere, J. J. (2002). Separating weight and scale value: an exploration of best-attribute scaling in health economics. Paper presented at Health Economists’ Study Group, Brunel University London, 6 July.
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefGoogle ScholarPubMed
Mitchell, V. W., and Greatorex, M. (1988). Consumer risk perception in the UK wine market. European Journal of Marketing, 22, 5–15.CrossRefGoogle Scholar
Monroe, K. B. (1990). Pricing: Making Profitable Decisions. New York: McGraw-Hill.Google Scholar
Monroe, K. B., and Krishnan, R. (1985). The effect of price on subjective product evaluations. In Jacoby, J. and Olson, J. C. (eds.), Perceived Quality, 209–23. Lexington, MA: Lexington Books.Google Scholar
Moskowitz, H. R., and Rabino, S. (1994). Sensory segmentation: an organizing principle for international product concept generation. Journal of Global Marketing, 8, 73–93.CrossRefGoogle Scholar
Mueller, S., Lockshin, L., and Louviere, J. J. (2010). What you see may not be what you get: asking consumers what matters may not reflect what they choose. Marketing Letters, 21, 335–50.CrossRefGoogle Scholar
Mueller, S., Lockshin, L., Saltman, Y., and Blanford, J. (2010). Message on a bottle: the relative influence of wine back label information on wine choice. Food Quality and Preference, 21, 22–32.CrossRefGoogle Scholar
Mueller, S., and Rungie, C. (2009). Is there more information in best-worst choice data? Using the attitude heterogeneity structure to identify consumer segments. International Journal of Wine Business Research, 21, 24–40.CrossRefGoogle Scholar
NIHR (2006). Health technology assessment (HTA) programme. NIHR,–96.pdf.
O'Connor, A., Boyd, N. F., Warde, P., Stolbach, L., and Till, J. E. (1987). Eliciting preferences for alternative drug therapies in oncology: influence of treatment outcome description, elicitation technique and treatment experience on preferences. Journal of Chronic Disease, 40, 811–18.CrossRefGoogle ScholarPubMed
Orme, B. (2009). Anchored scaling in maxdiff using dual response, research paper. Sequim, WA: Sawtooth Software.
Orth, U., and Malkewitz, K. (2008). Holistic packaging design and consumer brand impression. Journal of Marketing, 72, 64–81.CrossRefGoogle Scholar
Padgett, D., and Mulvey, M. S. (2007). Differentiation via technology: strategic positioning of services following the introduction of disruptive technology. Journal of Retailing, 83, 375–91.CrossRefGoogle Scholar
Paine, T. (1776). Common Sense. Philadelphia: Robert Bell.Google Scholar
Paulhus, D. L. (1991). Measurement and control of response bias. In Robinson, J. P., Shaver, P. R. and Wright, L. D. (eds.), Measures of Personality and Social Psychological Attitudes, 17–59. San Diego: Academic Press.Google Scholar
Payne, J. W., Bettman, J. R., and Johnson, E. J. (1992). Behavioral decision research: a constructive processing perspective. Annual Review of Psychology, 43, 87–131.CrossRefGoogle Scholar
Payne, J. W., Bettman, J. R., and Schkade, D. A. (1999). Measuring constructed preferences: towards a building code. Journal of Risk and Uncertainty, 19, 243–70.CrossRefGoogle Scholar
Perrouty, J.-P., d'Hauteville, F., and Lockshin, L. (2006). The influence of wine attributes on region of origin equity: an analysis of the moderating effect of consumers’ perceived expertise. Agribusiness, 22, 323–41.CrossRefGoogle Scholar
Podsakoff, P. M., MacKenzie, S. B., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.CrossRefGoogle ScholarPubMed
Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free Press.Google Scholar
Porter, M. E. (1996). What is strategy?Harvard Business Review, 74, 61–78.Google Scholar
Potoglou, D., Burge, P., Flynn, T. N., Netten, A., Malley, J., Forder, J., and Brazier, J. E. (2011). Best-worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Social Science and Medicine, 72, 1717–27.CrossRefGoogle ScholarPubMed
Quester, P., and Smart, J. (1998). The influence of consumption situation and product involvement over consumers’ use of product attribute. Journal of Consumer Marketing, 15, 220–38.CrossRefGoogle Scholar
Rabin, M. (1998). Psychology and economics. Journal of Economic Literature, 3, 11–46.Google Scholar
Ratcliffe, J., Couzner, L., Flynn, T. N., Sawyer, M., Stevens, K., Brazier, J., and Burgess, L. (2011). Valuing child health utility 9D health states with a young adolescent sample: a feasibility study to compare best-worst discrete choice experiment, standard gamble and time trade-off methods. Applied Health Economics and Health Policy, 9, 15–27.CrossRefGoogle ScholarPubMed
Reeder, R. R., Brierty, E. G., and Reeder, B. H. (1987). Industrial Marketing: Analysis, Planning and Control. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Richardson, J. (1994). Cost utility analysis: what should be measured?Social Science and Medicine, 39, 7–21.CrossRefGoogle ScholarPubMed
Roberts, K., Varki, S., and Brodie, R. (2003). Measuring the quality of relationships in consumer services: an empirical study. European Journal of Marketing, 37, 169–96.CrossRefGoogle Scholar
Rose, J. M. (2013). Interpreting discrete choice models based on best-worst data: a matter of framing, ITLS Working Paper no. 13-22. University of Sydney.
Rose, J. M., and Bliemer, M. C. J. (2009). Constructing efficient stated choice experimental designs. Transport Reviews, 29, 587–617.CrossRefGoogle Scholar
Royal College of Surgeons of England (1997). Current Clinical Practice and Parameters of Care: The Management of Patients with Third Molar (syn: Wisdom) Teeth. London: Royal College of Surgeons of England.
Rungie, C. M., Coote, L. V., and Louviere, J. J. (2011). Structural choice modelling: theory and applications to combining choice experiments. Journal of Choice Modelling, 4, 1–29.CrossRefGoogle Scholar
Rungie, C. M., Coote, L. V., and Louviere, J. J. (2012). Latent variables in discrete choice experiments. Journal of Choice Modelling, 5, 145–56.CrossRefGoogle Scholar
Ruta, E., Garrod, G., and Scarpa, R. (2008). Valuing animal genetic resources: a choice modelling application to indigenous cattle in Kenya. Agricultural Economics, 38, 89–98.Google Scholar
Ryan, M., Netten, A., Skatun, D., and Smith, P. (2006). Using discrete choice experiments to estimate a preference-based measure of outcome: an application to social care for older people. Journal of Health Economics, 25, 927–44.CrossRefGoogle ScholarPubMed
Salciuviene, L., Auruskeviciene, V., and Lydeka, Z. (2005). An assessment of various approaches for cross-cultural consumer research. Problems and Perspectives in Management, 3, 147–59.Google Scholar
Salisbury, L. C., and Feinberg, F. M. (2010a). Alleviating the constant stochastic variance assumption in decision research: theory, measurement and experimental test. Marketing Science, 29, 1–17.CrossRefGoogle Scholar
Salisbury, L. C., and Feinberg, F. M. (2010b). Temporal stochastic inflation in choice-based research. Marketing Science, 29, 32–9.CrossRefGoogle Scholar
Salkeld, G., Solomon, M., Butrow, P., and Short, L. (2005). Discrete-choice experiment to measure patient preferences for the surgical management of colorectal cancer. British Journal of Surgery, 92, 742–7.CrossRefGoogle ScholarPubMed
Salomon, J. A. (2003). Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data. Population Health Metrics, 1, 12–14.CrossRefGoogle ScholarPubMed
San Miguel, F., Ryan, M., and Scott, A. (2002). Are preferences stable? The case of health care. Journal of Economic Behavior and Organization, 48, 1–14.CrossRefGoogle Scholar
Saxe, R., and Weitz, B. (1982). The SOCO scale: a measure of the customer orientation of salespeople. Journal of Marketing Research, 19, 343–51.CrossRefGoogle Scholar
Scarpa, R., Notaro, S., Louviere, J. J., and Raffaelli, R. (2011). Exploring scale effects of best/worst rank ordered choice data to estimate benefits of tourism in alpine grazing commons. American Journal of Agricultural Economics, 93, 813–28.CrossRefGoogle Scholar
Schwappach, D. L. B., and Strasmann, T. J. (2006). Quick and dirty numbers? The reliability of a stated-preference technique for the measurement of preferences for resource allocation. Journal of Health Economics, 25, 432–48.Google ScholarPubMed
Sen, A. (1982). Choice, Welfare and Measurement. Cambridge, MA: Harvard University Press.Google Scholar
Shafir, E. (1993). Choosing versus rejecting: why some options are both better and worse than others. Memory and Cognition, 21, 546–56.CrossRefGoogle ScholarPubMed
Shanteau, J. (1980). The concept of weight in judgment and decision making: a review and some unifying proposals, Center for Research on Judgment and Policy Report no. 228. Colorado: University of Colorado.
Shaw, M., Keeghan, P., and Hall, J. (1999). Consumers judge wine by its label, study shows. Wine Industry Journal, 14, 84–7.Google Scholar
Simonson, I. (2008). Will I like a “medium” pillow? Another look at constructed and inherent preferences. Journal of Consumer Psychology, 18, 155–69.CrossRefGoogle Scholar
Singer, P. A., Martin, D. K., and Kelner, M. (1999). Quality end-of-life care: patients’ perspectives. Journal of the American Medical Association, 281, 163–8.Google ScholarPubMed
Skuras, D., and Vakrou, A. (2002). Consumer's willingness to pay for origin labelled wine: a Greek case study. British Food Journal, 104, 898–912.CrossRefGoogle Scholar
Slater, S. F., and Narver, J. C. (1994). Market orientation, customer value, and superior performance. Business Horizons, 37, 22–8.CrossRefGoogle Scholar
Slovic, P. (1995). The construction of preference. American Psychologist, 50, 364–71.CrossRefGoogle Scholar
Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 20, 3–8.Google Scholar
Speed, R. (1998). Choosing between line extensions and second brands: the case of the Australian and New Zealand wine industries. Journal of Product and Brand Management, 7, 519–36.CrossRefGoogle Scholar
Srinivasan, R., Rangaswamy, A., and Lilien, G. L. (2005). Turning adversity into advantage: does proactive marketing during a recession pay off?International Journal of Research in Marketing, 22, 109–25.CrossRefGoogle Scholar
Steenkamp, J.-B. E. M., and Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–107.CrossRefGoogle Scholar
Steenkamp, J.-B. E. M., and Ter Hofstede, F. (2002). International market segmentation: issues and perspectives. International Journal of Research in Marketing, 19, 185–213.CrossRefGoogle Scholar
Steinhauser, K. E., Clipp, E., McNeilly, M., Christakis, N. A., McIntyre, L. M., and Tulsky, J. A. (2000). In search of a good death: observations of patients, families, and providers. Annals of Internal Medicine, 132, 825–32.CrossRefGoogle ScholarPubMed
Street, D. J., and Burgess, L. (2007). The Construction of Optimal Stated Choice Experiments: Theory and Methods: Hoboken, NJ: John Wiley.CrossRefGoogle Scholar
Street, D. J., Burgess, L., and Louviere, J. J. (2005). Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments. International Journal of Research in Marketing, 22, 459–70.CrossRefGoogle Scholar
Street, D. J., Burgess, L., Viney, R., and Louviere, J. J. (2008). Designing discrete choice experiments for health care. In Ryan, M., Gerard, K. and Amaya-Amaya, M. (eds.), Using Discrete Choice Experiments to Value Health and Health Care, 47–72. Dordrecht: Springer.Google Scholar
Street, D. J., and Street, A. P. (1987). Combinatorics of Experimental Design. Oxford: Clarendon Press.Google Scholar
SUPPORT investigators (1995). A controlled trial to improve care for seriously ill hospitalized patients: the study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). Journal of the American Medical Association, 274, 1591–8.
Sutton, E. J., and Coast, J. (2014). Development of a supportive care measure for economic evaluation of end-of-life care using qualitative methods. Palliative Medicine, 28, 151–7.CrossRefGoogle ScholarPubMed
Swait, J. (1994). A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data. Journal of Retailing and Consumer Services, 1, 77–89.CrossRefGoogle Scholar
Swait, J., and Andrews, R. L. (2003). Enriching scanner panel models with choice experiments. Marketing Science, 22, 442–60.CrossRefGoogle Scholar
Swait, J., and Louviere, J. J. (1993). The role of the scale parameter in the estimation and comparison of multinomial logit models. Journal of Marketing Research, 30, 305–14.CrossRefGoogle Scholar
Sweeney, J. C., and Soutar, G. N. (2001). Consumer perceived value: the development of a multiple item scale. Journal of Retailing, 77, 203–20.CrossRefGoogle Scholar
Szeinbach, S. L., Barnes, J. H., McGhan, W. F., Murawski, M. M., and Corey, R. (1999). Using conjoint analysis to evaluate health state preferences. Drug Information Journal, 33, 849–58.CrossRefGoogle Scholar
Tan, J., and Peng, M. W. (2003). Organizational slack and firm performance during economic transitions: two studies from an emerging economy. Strategic Management Journal, 24, 1249–63.CrossRefGoogle Scholar
Thompson, K. E., and Vourvachis, A. (1995). Social and attitudinal influences on the intention to drink wine. International Journal of Wine Marketing, 7, 35–45.CrossRefGoogle Scholar
Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–86.CrossRefGoogle Scholar
Thurstone, L. L. (1928). Attitudes can be measured. American Journal of Sociology, 33, 529–54.CrossRefGoogle Scholar
Tsetsos, K., Chater, N., and Usher, M. (2012). Salience driven value integration explains decision biases and preference reversal. Proceedings of the National Academy of Sciences, 109, 9659–64.CrossRefGoogle ScholarPubMed
Tsevat, J., Goldman, L., Soukup, J. R., Lamas, G. A., Connors, K. F., Chapin, C. C., and Lee, T. H. (1993). Stability of time-tradeoff utilities in survivors of myocardial infarction. Medical Decision Making, 13, 161–5.CrossRefGoogle ScholarPubMed
Tustin, M., and Lockshin, L. (2001). Region of origin: does it really count?Australia and New Zealand Wine Industry Journal, 16, 139–43.Google Scholar
Tversky, A. (1972). Elimination by aspects: a theory of choice. Psychological Review, 79, 281–99.CrossRefGoogle Scholar
Tversky, A. (1996). Contrasting rational and psychological principles in choice. In Zeckhauser, R. J., Keeney, R. L. and Sebenius, J. K. (eds.), Wise Choices: Decisions, Games, and Negotiations, 5–21. Boston: Harvard Business School Press.Google Scholar
United Nations Conference on Trade and Development (2004). Trade and Development Aspects of Professional Services and Regulatory Frameworks. New York: United Nations Conference on Trade and Development.
Van Buiten, M., and Keren, G. (2009). Speakers’ choice of frame in binary choice: effects of recommendation mode and option attractiveness. Judgment and Decision Making, 4, 51–63.Google Scholar
Verhage, B. J., Yavas, R., and Green, R. T. (1990). Perceived risk: a cross-cultural phenomenon?International Journal of Research in Marketing, 7, 297–303.CrossRefGoogle Scholar
Vermunt, J. K., and Magidson, J. (2005). Latent GOLD 4.0: User's Guide. Belmont, MA: Statistical Innovations.Google Scholar
Wagenmakers, E. J., Kryptos, A. M., Criss, A. H., and Iverson, G. (2012). On the interpretation of removable interactions: a survey of the field 33 years after Loftus. Memory and Cognition, 40, 145–60.CrossRefGoogle ScholarPubMed
Waller, A., Currow, D., and Lecathelinais, C. (2008). Development of the Palliative Care Needs Assessment Tool (PC-NAT) for use by multi-disciplinary health professionals. Palliative Medicine, 22, 956–64.CrossRefGoogle ScholarPubMed
Ward, S., and Lewandowska, A. (2005). Shelter in the storm: marketing strategy as moderated by the hostile environment. Marketing Intelligence and Planning, 23, 670–87.CrossRefGoogle Scholar
Wetzels, M., de Ruyter, D., and Birgelen, M. V. (1998). Marketing service relationships: the role of commitment. Journal of Business and Industrial Marketing, 13, 406–23.CrossRefGoogle Scholar
Whitty, J. A., Ratcliffe, J., Chen, G., and Scuffham, P. A. (2014). Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best-worst scaling methods. Medical Decision Making, 34, 638–54.CrossRefGoogle ScholarPubMed
Winkler, J. D., Kanouse, D. E., and Ware, J. E. (1982). Controlling for acquiescence response set in scale development. Journal of Applied Psychology, 67, 555–62.CrossRefGoogle Scholar
Wong, N., Rindfleisch, A., and Burroughs, J. E. (2003). Do reverse-worded items confound measures in cross-cultural consumer research? The case of the material values scale. Journal of Consumer Research, 30, 72–91.CrossRefGoogle Scholar
Wyner, G. A. (2006). Truth or consequences. Marketing Research, 18, 6–7.Google Scholar
Yavas, R., and Riecken, G. (2001). A comparison of medical professionals with favorable and unfavorable attitudes toward advertising: an empirical study. Health Marketing Quarterly, 18, 13–26.CrossRefGoogle Scholar
Yellott, J. I. (1977). The relationship between Luce's choice axiom, Thurstone's theory of comparative judgment, and the double exponential distribution. Journal of Mathematical Psychology, 15, 109–44.CrossRefGoogle Scholar
Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence. Journal of Marketing, 52, 2–22.CrossRefGoogle Scholar