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Machine leaching approaches to assess public attitude towards value-added tax in Saudi Arabia

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  • Nahid Sultana

    (Imam Abdulrahman Bin Faisal University)

Abstract

Value added tax (VAT) is one of the highly powerful government revenue resources. The public perception of VAT is an important area of empirical research in business, economics, and social sciences. Saudi Arabia implemented VAT on January 1, 2018. This study explores Saudi residents' awareness, attitude, and acceptance towards VAT. A total of 405 responses were collected via an online random survey. The Chi-square test was conducted to assess the association of the respondent's views on the positive impact of the VAT system with relevant attributes. About 62% of the respondents think that VAT cannot reduce their social obligations, while 57% of consumers believe that it has no positive impact on Saudi Arabia, indicating that most consumers do not have a very high level of positive attitude towards VAT implementation. Two competing machine learning algorithms, viz., logistic regression analysis and C5.0 decision tree, were applied in developing the empirical models to predict residents' perceptions concerning the positive impact of VAT. The predictor importance charts for the developed models were also analyzed. The results indicated that both models' performances are comparable; however, decision tree model predictions are closer to the actual responses. The decision tree showed high accuracy values (97.78%) and a higher area under the receiver operating characteristic (ROC) curve (0.991), suggesting that the results are highly feasible. The decision-makers could consider the significant factors determined in this study to make an efficient strategy for motivating the consumers towards the VAT system.

Suggested Citation

  • Nahid Sultana, 2022. "Machine leaching approaches to assess public attitude towards value-added tax in Saudi Arabia," SN Business & Economics, Springer, vol. 2(9), pages 1-18, September.
    • Handle: RePEc:spr:snbeco:v:2:y:2022:i:9:d:10.1007_s43546-022-00319-x
    DOI: 10.1007/s43546-022-00319-x
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