Data Analysis Machine Learning and Applications Episode 2 Part 10 docx

Conjoint Analysis for Complex Services Using Clusterwise HB Procedures 437

Table 3. Validity values for the total sample and for the clusters for HB estimation (“in to￾tal sample”: HB estimation at the individual total sample level; “in segment”: separate HB

estimation at the individual cluster 1 resp. 2 level)

Cluster 1 Cluster 2

Total sample (n=79)* (n=82)

(n=161)* In Total In In Total In

Sample Segment Sample Segment

First-choice-hit-rate

(using draws, n=10,000) 62.57 % 72.38 % 72.39 % 53.12 % 53.14 %

Mean Spearman

(using draws, n=10,000) 0.727 0.780 0.778 0.677 0.671

First-choice-hit-rate

(using mean draws) 65.22 % 75.95 % 74.68 % 54.88 % 57.32 %

Mean Spearman

(using mean draws) 0.748 0.802 0.797 0.696 0.700

* . . . one respondent had missing holdout data and could not be considered

considered. Furthermore we were interested whether clusterwise estimation can op￾timize the “results” of HB estimation. A clear answer is not possible up to now. In

our empirical investigation in some cases we had improvements with respect to the

validity values (cluster 2) and in some cases not (cluster 1).

This means that our proposition in the paper can help to reduce the problems that

occur when service preference measurement via conjoint analysis is the research

focus. HB estimation seems to improve validity even in case of complex services

with immaterial attributes and levels that cause perceptual uncertainty and preference

heterogeneity. However, going further with the more complicated way of performing

clusterwise HB estimation doesn’t provide automatically better results.

Nevertheless, further comparisons with larger sample sizes and other research ob￾jects are necessary. Furthermore, the possibilities of other validity criteria for clearer

statements could be used.

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