Research NoteUsing dummy regression to explore asymmetric effects in tourist satisfaction: A cautionary note
Introduction
Multiple regression analysis with dummy variables is a popular technique used for exploring asymmetries in the relationship between the performance of particular product/service attributes and overall customer satisfaction (OCS). As revealed by Table 1, half of available peer-reviewed studies using this technique were published in journals related to travel and tourism (i.e. seven out of 14), and even 11 out of 14 studies in journals with a primary focus on service industries.
A review of these studies reveals, however, a widespread misuse of dummy regression which is very likely to result in misleading implications in theory and model building and in guiding managerial action—i.e. the use of standardized weights as measures of effect instead of unstandardized ones. Five out of 14 studies do not mention which type of weight was used, whereas only Alegre and Garau (2011) and Mikulić and Prebežac (2011) explicitly report use of the unproblematic (unstandardized) type. This brief note thus aims to raise awareness about this problem and to explain why standardized weights should generally be avoided when analyzing variable effect sizes with dummy regression. In elaborating the problem, the methodological foundations of dummy regressions in asymmetry assessments are first briefly discussed, followed by an illustrative case example that empirically demonstrates the problem and its implications.
Section snippets
Asymmetry assessments with dummy regression
When using dummy regression to explore asymmetric effects in customer satisfaction (CS), data about attribute-level performance (AP) and OCS are needed. Typically, these are collected via CS surveys using direct rating or Likert-type scales. The data are then used to perform a multiple regression analysis with binary-coded ratings of AP as predictors and ratings of OCS as the dependent variable:where b0 is the constant, pj the incremental change in OCS as a
The problem of standardized weights in dummy regression
Although dummy regressions are methodologically appropriate for assessing asymmetric effects as described in the previous section, interpretations of results as previously described are reliable (in fact valid), only if analyses are based on unstandardized regression weights. This is because binary dummy variables do not have any true numerical meaning like variables in regular regressions, why there is no meaningful straightforward interpretation of standardized regression weights in dummy
Case study
To empirically underpin the points put forward, data from a survey on CS with an airline ticket purchase service are used (n = 786; the used data are part of a larger survey). AP and OCS were measured with 7-point Likert scales. In coding the data for the dummy regression only lowest and highest AP-levels were binary transformed into dummy variables (i.e. AP-scores of 1 and 7). Table 2 provides an overview of distributional characteristics of unstandardized binary dummy variables (i.e. number of
Conclusion
Although the statistics literature explicitly argues against the use of standardized dummy regression weights as measures of effect, there appears to be a lack of awareness about this issue in customer satisfaction studies that apply the technique. Neither is there a sound reason for preferring standardized to unstandardized weights in regular dummy regressions, nor can standardized weights be meaningfully interpreted. In fact, their use as measures of effect carries only the risk of providing
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