Using Decision Tree to Predict Response Rates of Consumer Satisfaction, Attitude, and Loyalty Surveys

Abstract

Response rate has long been a major concern in survey research. Based on 244 published studies on consumer satisfaction, attitude and loyalty that are predictors of customer retention and behavior, this study aimed to identify predictors of response rates. A decision tree analysis (using the C5.0 algorithm on 70% of the studies as the training set and 30% as the test set) revealed that a model with seven attributes of the surveys attained an accuracy of 80.52% in predicting whether surveys had high (> 50%) or low ( no), followed by mode of data collection (face-to-face or mail > telephone or online). If it was telephone or online survey, 20 items was the crucial cutoff point for length of survey. The accuracy of the decision tree model was higher than that of the traditional logistic regression.



Author Information
Jian Han, Zhejiang University, China

Paper Information
Conference: ACP2018
Stream: Qualitative/Quantitative Research in any other area of Psychology

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