Various techniques of multivariate analysis are applied to locating hotel chains' positions on perceptual maps based on consumers' views of those chains. Measuring and accounting for the many attributes used to develop positions is intensive and usually the analysis is segment-specific. Instead of complex methods that are hard for managers to apply, the use of self-organizing maps may be appropriate. The self-organizing map is a computer-based application of neural-network models that treat data as nodes with connections. Data are sorted to arbitrarily positioned nodes that represent a map of positions. In the simplest approach, the result is that the positions of hotels that fall on the same node are not different, while those that fall on adjacent nodes are different. In actuality, a hotel's position falls on one node sometimes and other nodes other times, depending on the market segment. The higher the frequency of a hotel's being located on a given node, the more focused its image. A hotel that finds itself widely distributed on many modes has a fuzzy image. An example of luxury hotels in Vienna demonstrates the hotels' positions for different guest segments.
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References
1.
Ahalt, S. C. , Krishnamurthy, A. K., Chen, P., Melton, D. E. (1990) Competitive learning algorithms for vector quantization. Neural >Networks, 3, 277-290.
2.
Albers, S. , Brockhoff, K. (1977) A procedure for new product positioning in an attribute space. European Journal of Operations Research, 1, 230-283.
3.
Aleksander, I. , Morton, H. (1990) An introduction into neural computing. London: Chapman and Hall.
4.
Arabie, P. , Carroll, J. D., DeSarbo, W. S. (1987) Three-way scaling and clustering. Newbury Park: Sage.
5.
Assael, H. (1985) Marketing management, strategy and action. Boston: Kent Publishing.
6.
Bagozzi, R. P. (1986) Principles of marketing management. Chicago: Science Research Associates.
7.
Blalock, H. M., Jr. (1971) The measurement problem: A gap between the languages of theory and research, in: Blalock, H. M., Blalock, A. B. (eds.), Methodology in social research. London: McGraw-Hill, pp. 5-27.
8.
Büyükkurt, B. K. , Büyükkurt, M. D. (1990) Robustness and smallsample properties of the estimators of probabilistic multidimensional scaling (PROSCAL). journal of Marketing Research, 27, 139-149.
9.
Carmichael, B. (1992) Using conjoint modelling to measure tourist image and analyse ski resort choice, in: Johnson, P., Thomas, B. (eds.), Choice and demand in tourism, London: Mansell, pp. 93-106.
10.
Carroll, J. D. , Chang, J. J. (1970) Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition, Psychometrika, 35, 283-319.
11.
Carroll, J. D. , Arabie, P. (1983) INDCLUS: An individual differences generalization of the ADCLUS model and the MAPCLUS algorithm. Psychoometrika, 48, 157-169.
12.
Caudill, M. (1993a) A little knowledge is a dangerous thing. AI Expert, 8, 16-22.
13.
Caudill, M. (1993b) Neural networks primer, 3rd. ed.San Francisco: Miller Freeman.
14.
Claxton, J. D. (1987) Conjoint analysis in travel research: A manager's guide, in: Brent Ritchie, J. R., Goeldner, C. R. (eds.), Travel, tourism, and hospitality research, New York: Wiley, pp. 459-469.
15.
Cooley, W. W. , Lohnes, P. R. (1971) Multivariate data analysis. New York: Wiley.
16.
Christoffersson, A. (1975) Factor analysis of dichotomized data. Psychometrika, 40, 5-32.
17.
Curry, B. , Moutinho, L. (1993) Neural networks in marketing: Modelling consumer responses to advertising. European Journal of Marketing, 27, 5-20.
18.
DeLeeuw, J. , Young, F. W., Takane, Y. (1976) Additive structure in qualitative data: An alternating least squares method with optimal scaling features. Psychometrika, 41, 471-503.
19.
DeLeeuw, J. (1982) Nonlinear principal components analysis, in: COMPSTAT proceedings in computational statistics, Vienna: Physica, pp. 77-89.
20.
DeSarbo, W. , Rao, V. R. (1986) A constrained unfolding methodology for product positioning. Marketing Science, 5, 1-19.
21.
DeSarbo, W. S. , Howard, D. J., Jedidi, K. (1991) Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis. Psychometrika, 56, 121-136.
22.
Fenton, M. , Pearce, P. (1988) Multidimensional scaling and tourism research. Annals of Tourism Research, 15, 236-254.
23.
Fodness, D. D. , Milner, L. M. (1992) A perceptual mapping approach to theme park visitor segmentation. Tourism Management, 13, 95-101.
24.
Freeman, J. A. , and Skapura, D. M. (1991) Neural networks, algorithms, applications, and programming techniques. Reading: Addison-Wesley.
25.
Gallant, S. I. (1993) Neural network learning and expert systems. Cambridge: MIT Press.
26.
Gartner, W. (1989) Tourism image: Attribute measurement of state tourism products using multidimensional scaling techniques. Journal of Travel Research, 28, 16-20.
27.
Grabler, K. (1993) Produktpositionierung für das Vienna Plaza: Empirische Methodenvergleiche anhand der Wiener Luxushotellerie. Unpublished diploma thesis at the Vienna University of Economics and Business Administration.
28.
Green, P. E. , Carmone, F. J. (1969) Multidimensional scaling: An introduction and comparison of nonmetric unfolding techniques. Journal of Marketing Research, 6, 330-341.
29.
Green, P. E. , Rao, V. R. (1971) Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355-363.
30.
Green, P. E. , Rao, V. R. (1972) Applied multidimensional scaling: A comparison of approaches and algorithms. New York: Holt, Rinehart & Winston.
31.
Green, P. E. (1975) Marketing applications of MDS: Assessment and outlook. Journal of Marketing, 39, 24-31.
32.
Green, P. E. , Carroll, J. D., Goldberg, S. M. (1981) A general approach to product design optimization via conjoint analysis. Journal of Marketing, 45, 17-37.
33.
Green, P. E. , Tull, D. S., Albaum, G. (1988) Research for marketing decisions, 5th ed.Englewood Cliffs, NJ: Prentice-Hall.
34.
Green, P. E. , Shifflet, D., Scarbrough, M. (1989) Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models. Interfaces, 19, 25-47.
35.
Green, P. E. , Krieger, A. M. (1993) Conjoint analysis with product-positioning applications, in: Eliashberg, J., Lilien, G. L. (eds.), Marketing. Amsterdam: North-Holland, pp. 467-515.
36.
Greenacre, M. (1984) Theory and applications of correspondence analysis. London: Academic Press.
37.
Hauser, J. R. , Clausing, D. (1988) The house of quality. Harvard Business Review, 3, 63-73.
38.
Haykin, S. (1994) Neural networks, A comprehensive foundation. New York: Macmillan.
39.
Hertz, J. , Krogh, A., Palmer, R. G. (1991) Introduction to the theory of neural computation. Redwood City, CA: Addison-Wesley.
40.
Hiotis, A. (1993) Inside a self-organizing map. AI Expert, 8, 38-43.
41.
Hoffman, D. L. , Perreault, W. D., Jr. (1987) Consumer preference and perception, in: Young, F. W., Hamer, R. M. (eds.), Multidimensional scaling, history, theory, and applications. Hillsdale, NJ: Lawrence Erlbaum, pp. 199-218.
42.
Howard, J. A. , Sheth, J. N. (1969) The theory of buyer behavior. New York: Wiley.
43.
Hruschka, H. , Natter, M. (1992) Using neural networks for clustering-based market segmentation. Research memorandum no. 307. Vienna: Institute for Advanced Studies.
44.
Hruschka, H. , Natter, M. (1993) Analyse von Marktsegmenten mit Hilfe konnexionistischer Modelle. Zeitschrift fuer Betriebs-wittschaft, 63, 425-442.
45.
Hruschka, H. (1993) Determining market response functions by neural network modelling: A comparison to econometric techniques, European Journal of Operations Research, 66, 27-35.
46.
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69; reprinted in: Andersen, J. C. A., Rosenfeld, E. (eds.) (1988). Neurocomputing: foundations of research. Cambridge: MIT Press, pp. 511-521.
47.
Kohonen, T. (1984; 3rd ed., 1989) Seff-organization and associative memory. New York: Springer.
48.
Kohonen, T. (1990) The self-organizing map, in: Proceedings of the IEEE, 78, 1464-1480; reprinted in: Mehra, P., Wah, B. W. (eds.) (1992), Artificial neural networks: Concepts and theory, Los Alamitos, CA: IEEE Computer Society Press, pp. 359-375.
49.
Kruskal, J. B. (1964) Nonmetric multidimensional scaling, Psychometrika, 29, 1-27; 115-199.
50.
Kruskal, J. B. , Carroll, J. D. (1969) Geometrical models and badness-of-fit functions, in: Krishnaiah, P. R. (ed.), Multivariate analysis, Vol. II, New York: Academic Press, pp. 639-671.
51.
Kruskal, J. B. , Shepard, R. N. (1974) A nonmetric variety of linear factor analysis. Psychometrika, 39, 123-157.
52.
Lewis, R. C. (1984) Isolating differences in hotel attributes. Cornell H.R.A. Quarterly, 25, 64-77.
53.
Lewis, R. C. (1985a) Predicting hotel choice: The factors underlying perception. Cornell H.R.A. Quarterly, 25, 82-96.
54.
Lewis, R. C. (1985b) The market position: Mapping guests' perceptions of hotel operations. Cornell H.R.A. Quarterly, 26, 86-99.
55.
MacKay, D. B. , Zinnes, J. L. (1986) A probabilistic model for the multidimensional scaling of proximity and preference data. Marketing Science, 5, 325-344.
56.
MacKay, D. B. (1989) Probabilistic multidimensional scaling: An anisotropic model for distance judgments. Journal of Mathematical Psychology, 33, 187-205.
57.
Martinetz, T. , Schulten, K. (1994) Topology representing networks. Neural Networks, 7, 507-522.
58.
Mazanec, J. (1978) Strukturmodelle des Konsumverhaltens. Wien: Orac.
59.
Mazanec, J. (1989) Consumer behavior in tourism, in: Witt, S. F., Moutinho, L. (eds.), Tourism marketing and management handbook. New York: Prentice Hall, pp. 63-82.
60.
Mazanec, J. (1990) Market segmentation once again: Exploring neural network models, in: Association Internationale d'Experts Scientifiques du Tourisme (eds.), Tourist research as a commitment. St. Gall: AIEST, pp. 36-53.
61.
Mazanec, J. (1992) Classifying tourists into market segments: A neural network approach. Journal of Travel and Tourism Marketing, 1, 39-59.
62.
Mazanec, J. (1993a) Apriori and aposteriori segmentation: Heading for unification with neural network modeling. in: Proceedings of the 22nd EMAC Conference, European Marketing Academy, Vol. 1, Barcelona: ESADE, pp. 889-917.
63.
Mazanec, J. (1993b) Exporting EUROSTYLES to the USA. International Journal of Contemporary Hospitality Management, 5, 3-9.
64.
Muthén, B. (1978) Contributions to factor analysis of dichotomous data. Psychometrika, 43, 551-560.
65.
Muthén, B. 0. (1989) Dichotomous factor analysis of symptom data. Sociological Methods and Research, 18, 19-65.
66.
Muthén, B. O. , Hofacker, C. (1988) Testing the assumptions underlying tetrachoric correlations. Psychometrika, 53, 563-578.
67.
Myers, J. H. , Tauber, E. (1977) Market structure analysis. Chicago: AMA.
68.
NeuralWare, Inc. (1991a) Neural computing, NeuralWorks Professional II/Plus. Pittsburgh: Technical Publications Group.
69.
NeuralWare, Inc. (1991b) Reference guide, NeuralWorks Professional II/Plus. Pittsburgh: Technical Publications Group.
70.
Norusis, M. J. (1990) SPSS Base System User's Guide. Chicago: SPSS, Inc.
71.
Oja, E. , Lampinen, J. (1994) Unsupervised learning for feature extraction, in: Zurada, J. M., Marks, R. J., Robinson, C. J. (eds.), Computational intellignce imitating life. New York: IEEE Press, pp. 13-22.
72.
Pal, N. R. , Bezdek, J. C., Tsao, E. C.-K. (1993) Generalized clustering networks and Kohonen's self-organizing scheme. IEEE Transactions on Neural Networks, 4, 549-557.
Parasuraman, A. , Zeithame, V., Berry, W. (1988) SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. journal of Retailing, 64, 12-40.
75.
Pawlik, K. (1968) Dimensionen des Verhaltens, Bern/Stuttgart: Huber.
76.
Renaghan, L. M. , Kay, M. Z. (1987) What meeting planners want: The conjoint-analysis approach, Cornell/I H.R.A. Quarterly, 28, 67-76.
77.
Ritter, H. , Schulten, K. (1988) Convergence properties of Kohonen's topology conserving maps: Fluctuations, stability, and dimension selection, Biological Cybernetics, 60, 59-71.
78.
Rittinghaus-Mayer, D. (1993) Die Anwendung, von Neuronalen Netzen in der marketingforschung. Muenchen: Akademischer Verlag.
79.
Roberts, J. H. , Lilieii, G. L. (1993) Explanatory and predictive models of consunser behavior, in: Eliashberg, J., Lilien, C. L. (eds.), Marketing. Amsterdam: North-Holland, pp. 27-82.
80.
Rumelhart, D. E. , Hinton, G. E., Williams, R. J. (1986) Learning internal representation by error propagation; in: Rumelhart, D. E., McClelland, J. L. (eds.). Parallel distributed processing, Vol. I: Foundations. Cambiidge: MIT Press, pp. 318-362.
81.
Shepard, R. N. (1972) A taxonomy of some principal types of data and of multidimeinsional methods for their analysis, in: Shepard, R. N., Romney, A. K., Nerlove, S. B. (eds.), Multidimensional scaling,theory and applications in the behavioral sciences, Vol. 1. New York: Seminar Press, pp. 21-47.
82.
SPSSCategories (1990) Chicago: SPSS, Inc.
83.
Stegmüller, W. (1973) Logische Analyse der Struktur ausgereifter physikalischer Theorien,"Non-statement View" von Theorien, Heidelberg: Springer.
84.
Takane, Y. , Young, F. W., deLeeuw, J. (1977) Nonmetric individual differences multidimensional scaling: An alternating least squrces method with optimal scaling features. Psychometrika, 42, 7-67.
85.
Thiran, P. , Peiris, V., Heim, P., Hochet, B. (1994) Quantization effects in digitally behaving circuit implementations of Kohonen networks. IEEE transactions on Neural Networks, 5, 450-458.
86.
Überla, K. (1977) Faktorenanalyse, 2. Aufl, Berlin: Springer.
87.
Urban, G. L. , Hauser,J.R.(1993) Design and marketing of new products, 2nd ed.Englewood Cliffs, NJ: Prentice-Hall.
88.
Ward Systems Group (1993) Neuro Windows, Neural Network Dynamic Link Library3rd.ed.Frederick: Ward Systems Group, Inc.
89.
Wasserman, P. D. (1989) Neural computing, theory and practice. New York: Van Nostrand Reinhold.
90.
Widrow, B. , Hoff, M. E. (1960; reprinted 1988) Adaptive switching circuits, in: Andersen, J. A., Rosenfeld, E. (eds.), Neurocomputing,, foundations of resrarch. Cambridge: MIT Press, pp. 126-134.
91.
Young,, F. W. , Torgerson, W. S. (1967) TORSCA: A FORTRAN IV program for Shepard-Kruskal multidimensional scaling analysis. Behavioral Science, 8, 71-72.
92.
Young, F. W. , Takane, Y., DeLeeuw, J. (1978) The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features. Psychometrika, 43, 279-281.
93.
Young, F. W. (1987) Theory, in: Young, F. W., Hamer, R. M. (eds.), multidimentional Scaling,: History, theory, and applications, Hillsdale, NJ: Lawrence Erlbaum, pp. 42-158.
94.
Zins, A. (1991) multidimensionale Scaling von Freizeiteinrichtungen,Ein Methodenvergleich unterschiedlicher Datengewinnungsverfahren. Vienna: Scrvice Fachverlag.
95.
Zins, A. (1994) On the derivation of a common space of competing leisure attraction types. Journal of Marketing Management, 10, 179-190; and in: Hooley, G. J., Hussey, M. K. (eds.), Quantitative methods in marketing. New York: Academic Press, pp. 179-190.
96.
Zurmühl, R., Falk, S. (1984) Matrizen und ihre Anwendungen, Vol. 1, 5th ed. Berlin: Springer.
