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

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  • Jian Han

    (Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China)

  • Miaodan Fang

    (Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China)

  • Shenglu Ye

    (Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China)

  • Chuansheng Chen

    (Department of Psychological Science, University of California, Irvine, CA 92697, USA)

  • Qun Wan

    (Zhejiang Big Data Exchange Center, Jiaxing 314501, China)

  • Xiuying Qian

    (Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China)

Abstract

Response rate has long been a major concern in survey research commonly used in many fields such as marketing, psychology, sociology, and public policy. Based on 244 published survey studies on consumer satisfaction, loyalty, and trust, this study aimed to identify factors that were predictors of response rates. Results showed that response rates were associated with the mode of data collection (face-to-face > mail/telephone > online), type of survey sponsors (government agencies > universities/research institutions > commercial entities), confidentiality (confidential > non-confidential), direct invitation (yes > no), and cultural orientation (individualism > collectivism). A decision tree regression analysis (using classification and regression Tree (C&RT) algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained a linear correlation coefficient (0.578) between the predicted values and actual values, which was higher than the corresponding coefficient of the traditional linear regression model (0.423). A decision tree analysis (using C5.0 algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained an overall accuracy of 78.26% in predicting whether a survey had a high (>50%) or low (<50%) response rate. Direct invitation was the most important factor in all three models and had a consistent trend in predicting response rate.

Suggested Citation

  • Jian Han & Miaodan Fang & Shenglu Ye & Chuansheng Chen & Qun Wan & Xiuying Qian, 2019. "Using Decision Tree to Predict Response Rates of Consumer Satisfaction, Attitude, and Loyalty Surveys," Sustainability, MDPI, vol. 11(8), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2306-:d:223592
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    References listed on IDEAS

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    3. Gibson Kimutai & Alexander Ngenzi & Rutabayiro Ngoga Said & Ambrose Kiprop & Anna Förster, 2020. "An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks," Data, MDPI, vol. 5(2), pages 1-26, April.

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