This article focuses on discrete choice analysis (DCA), which offers an effective approach for incorporating customer preferences into operating decisions in hospitality businesses. First the theoretical background of DCA is presented, including a discussion of how DCA compares to conjoint analysis. The authors then present a guide to designing and conducting a DCA study. Conducting a discrete choice study involves identifying the attributes relevant to customers'choices and the appropriate levels of these attributes, designing an experiment, collecting data and estimating parameters using a multinomial logit model. Finally, the strategic implications of DCA in hospitality management research are discussed.
Get full access to this article
View all access options for this article.
References
1.
AndersonN. H. (1981).Foundations of information integration theory.New York: Academic Press
2.
AndersonN. H. (1982).Methods of information integration theory,New York: Academic Press
3.
Ben-AkivaM.LermanS. R. (1991).Discrete choice analysis,Massachusetts: The MIT Press
4.
BishopY. M.MFienbergS. E.HollandP. W. (1991).Discrete multivariate analysis: Theory and practice.Massachusetts: The MIT Press
5.
BowenD. E.CummingsT. G. (1990).Suppose we took service seriously.Service management effectiveness, Jossey-Bass Publishers, 1–4
6.
CarmoneF. J.GreenP. E. (1987).Model misspecification in multiattribute parameter estimation.Journal of Marketing Research,18(2), 87–93
7.
CurrimI. S. (1981).Using segmentation approaches for better prediction and understanding from consumer mode choice models.Journal of Marketing Research,18, 301–309
8.
DeSarboW. S.WedelM.VriensM.RamaswamyV. (1992).Latent class metric conjoint analysis.Marketing Letters,3, 273–289
9.
DillonW. R.KumarA.Smith de BorreroM. (1993, Feb.).Capturing individual differences in paired comparisons: An extended btl model incorporating descriptor variables.Journal of Marketing Research, 42–51
10.
Econometric Software.LIMDEP: Program and user guide, New York, 1996
11.
ElrodT.LouviereJ. J.DaveyK. S. (1992).An empirical comparison of rating-based and choice-based conjoint models.Journal of Marketing Research,29, 368–377
12.
FinnA.LouviereJ. J. (1992).Determining the appropriate response to evidence of public concern: The case of food safety.Journal of Public Policy and Marketing, 11(1), 12–25
13.
GenschD. H. (1985).Empirically testing a disaggregate choice model for segments.Journal of Marketing Research,22, 462–467
14.
GenschD. H.ReckerW. W. (1979).The multinomial, multiattribute logit choice model.Journal of Marketing Research,16(2), 124–132
15.
GreenP. E.SrinivasanV. (1990, Oct.).Conjoint analysis in marketing: New developments with implications for research and practice.Journal of Marketing, 3–19
16.
GreenP. E.KriegerA. M. (1991, Oct.).Segmenting markets with conjoint analysis.Journal of Marketing, 20–31
17.
GuadagniP. M.LittleJ. D. (1983).A logit model of brand choice calibrated on scanner data.Marketing Science,2(3), 203–237
18.
HagertyM. R. (1986).The cost of simplifying preference models.Marketing Sciences,5(4), 298–319
19.
HahnG. J.ShapiroS. S. (1966).A catalog and computer program for the design and analysis of orthogonal symmetric and asymmetric fractional factorial experiments.New York: General Electric Research and Development Center
HorowitzJ. L.LouviereJ. J. (1993).Testing predicted choices against observations in probabilistic discrete-choice models.Marketing Science,12(3), 270–279
22.
Intelligent Marketing Systems,CONSERV, Software program and user's manual, Edmonton, Canada, 1992
23.
Intelligent Marketing Systems,NTELOGIT, Software program and user's manual, Edmonton, Canada, 1992
24.
LouviereJ. J. (1988).Analyzing decision making: Metric conjoint analysis.Newbury Park, California: SAGE Publications
25.
LouviereJ. J. (1984).Using discrete choice experiments and multinomial logit choice models to forecast trail in a competitive retail environment: A fast food restaurant illustration.Journal of Retailing,60(4), 81–107
26.
LouviereJ. J. (1988, January).Conjoint analysis modeling of stated preferences.Journal of Transport Economics and Policy, 93–119
27.
LouviereJ. J.WoodworthG. G. (1983).Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data.Journal of Marketing Research,20, 350–367
28.
LouviereJ. J.TimmermansH. (1990).Stated preference and choice models applied to recreation research: A review.Leisure Sciences,12, 9–32
29.
LouviereJ. J.TimmermansH. (1990).A review of recent advances in decompositional preference and choice models.Journal of Economic and Social Geography,81(3), 214–224
30.
LouviereJ. J.TimmermansH. (1992).Testing the external validity of hierarchical conjoint analysis models of recreational destination choice.Leisure Sciences,14, 179–194
31.
LouviereJ. J.TimmermansH. (1990).Using hierarchical information integration to model consumer responses to possible planning actions: Recreation, destination choice illustration.Environment and Planning,22, 291–308
32.
LouviereJ. J.OppewalH.TimmermansH.ThomasT. (1994).Handling large number of attributes in conjoint analysis: Who says existing techniques can't be applied? But if you want an alternative, how about hierarchical choice experiments?Working Paper.
33.
LouviereJ. J.TimmermansH. (1990).Stated preferences and choice models applied to recreation research: A review.Leisure Sciences,12, 9–32
34.
LovelockC. H. (1992).A basic toolkit for service management.Managing services: Marketing, operations, and human resources, (Second Edition), Englewood Cliffs, New Jersey: Prentice Hall
35.
McFaddenD. (1986).The choice theory approach to marketing research.Marketing Science,5(4), 275–297
36.
MooreW. L.Gray-LeeJ.LouviereJ. J. (1996).A cross-validity comparison of conjoint analysis and choice models at different levels of aggregation. Working Paper, David Eccles School of Business, University of Utah, May
37.
McLeanR.AndersonV. (1984).Applied factorial and fractional designs.New York: Marcel Deckker
38.
OppewalH.LouviereJ. J.TimmermansH. J. P. (1994, February).Modeling hierarchical conjoint processes with integrated choice experiments.Journal of Marketing Research,31, 92–105
39.
PullmanM. E. (1996).Optimal service design: Integrating marketing and operations elements for capacity decisions. Working paper, Colorado State University
40.
PunjG. N.StaelinR. (1978, November).The choice process for graduate business schools.Journal of Marketing Research, 15, 588–598
41.
SullivanR. S. (1981).The service sector: Challenges and imperatives for research in operations management.Journal of Operations Management, 2(4), 211–214
42.
ShoemakerS. (1994).Understanding the marketing research process: A guide to using an outside research supplier.International Journal of Hospitality Management,13(1), 39–56
43.
SwaitJ.LouviereJ. J. (1993, August).The role of the scale parameter in the estimation and comparison of multinomial logit models.Journal of Marketing Research,30, 305–314
44.
TimmermansH. (1981).Consumer choice of shopping center: An information integration approach.Regional Studies,16(3), 171–182
45.
VermaR. (1996).A model for effective operations management integrating constrained-optimization theory and customer choice patterns, Ph.D. dissertation, University of Utah
46.
VermaR.ThompsonG. M. (1996).Basing service management on customer determinants: The importance of hot pizza,Cornell Hotel and Restaurant Administration Quarterly,37(3), 18–23
47.
WoodworthG. G.GilbertC.FoxM. F. (1990).Documentation for the LOGIT program to estimate multinomial logit models
48.
ZenorM. J.SrivastavaR. K. (1993).Inferring market structure with aggregate data: A latent segment approach.Journal of Marketing Research,30(3), 369–379
