CHAPTER 5 THE IMPACT OF PUNISHMENT ON THE RATE OF EFD USE
5.3 Fear of the punishment and rate of EFD uses
5.3.1 Awareness of non-compliance punishments
It is unarguable that the fear of punishment is subject to the knowledge of the consequences of not using Electronic Fiscal Devices (Hastuti, 2014). Knowing the importance of this knowledge, the study analysed the feedback from respondents to know their awareness of punitive measures due to non-compliance. Table 5-10 presents the results.
Table 5-10: Awareness of punishment due to non-compliance
Frequency Percent Valid Percent Cumulative Percent
Highly aware 43 15.4 15.4 15.4
Aware 68 24.4 24.4 39.8
Moderately Aware 102 36.6 36.6 76.3
Unware 45 16.1 16.1 92.5
Highly unaware 21 7.5 7.5 100.0
Total 279 100.0 100.0
Source: (Author’s Design, 2020)
Based on Table 5-10, only 39.8% of respondents are aware of the consequences of non- compliance, in terms of punishment. In this case, if the fear of punishment were to determine the level of compliance, only this percentage of respondents is likely to have a positive response to compliance. With this general information, it is apparent that more knowledge is required to raise the awareness of users on the consequence of non- compliance. Different methods may be adopted in training taxpayers; such methods include traditional trainings, use of posters, use of radio and television sessions, and the use of electronic media (Olowookere & Fasina, 2013). Further to this, the study determined whether the knowledge of the consequences relates to the fear of punishment. In the first part of relationship testing, the study adopted the One Way ANOVA model for determining the significance of the categorical relationship. Table 5-11 presents the results.
Table 5-11: Awareness of punishment and the fear of punishment – ANOVA test Sum of Squares df Mean Square F Sig.
Between Groups 12.071 4 3.018 2.183 .071
Within Groups 377.329 273 1.382
Total 389.399 277
Source: (Author’s Design, 2020)
According to Table 5-11, there is no significant categorical relationship between the level of awareness of punishments and the fear of punishment. This suggests that the fear of respondents cannot be categorised based on their awareness of punitive measures.
Additionally, the study determined the impact of the awareness of punitive measures towards the level of fear generated among respondents by analysing Ordinal Regression model. The results in Table 5-12 suggest an insignificant impact of the awareness of punishment due to evasion on the fear of punishment. This relationship acknowledges 2.6% of the impact between the two variables. In addition, the lowest p-value in parameters of the independent variable (that is the awareness of punitive measures) was 0.216, which is greater than the threshold. Based on this information, the awareness of punitive measures does not significantly influence the level of fear; therefore, it cannot be used for prediction.
Studies by Ajaz and Ahmad (2010) and Chariye (2016) suggested that one reason which caused the awareness of punishment not to have a significant relationship with the fear of punishment is the room for corruption. Where loopholes for corruption are evident, taxpayers are likely not to fear the punishment because they can easily navigate through the administrative system and avoid the punishment (Ng‟eni, 2016). Another reason is the lenience of the government towards the full enforcement of the law. In a society where the government does not take full responsibility for enforcing the low, the level of fear will be show no difference among all members of the society, regardless of the level of knowledge (Modugu, Eragbhe, & Izedonmi, 2012; Deyganto, 2018).
Table 5-12: Awareness of punishment and fear of punishment – ordinal regression extracts
Model output Observed value
Model fitting information p-value 0.147 Nagelkerke r-square value 0.026
Parameter estimates The lowest p-value is 0.216 Source: (Author’s Design, 2020)
Knowing the nature of the relationship expressed in Table 5-12, the study determined whether the level of awareness of punishment could offer a better influence on the rate of EFD use. The study transformed parameters of the independent variable (that is, the awareness of punishment) to a three-level Likert scale (High, Moderate and Low). In this case, the study combined inputs which were closely related: High and very high formed a new parameter known as high, while low and very low formed a new parameter called low.
Table 5-13 presents the results of the analysis upon using the ordinal regression model.
Table 5-13: The awareness of punishment and the rate of EFD use – Ordinal regression extracts
Element of measurement Value
Model fitting information 0.000
Nagelkerke Pseudo r-square 0.080
Parameter estimates for the input variable (awareness of punishment)
High 0.002
Moderate 0.000
Low Reference value Source: (Author’s Design, 2020)
Based on the information presented in Table 5-13, the analysis suggests a significant impact of the perceived level of awareness of punishment on the rate of EFD use. First, the relationship between the awareness of punishment due to non-compliance and the rate of EFD use fits the ordinal equation, because the model fitting p-value is 0.000. Furthermore, a significant difference is identified across parameters of the input variable, when the referenced parameter is set as “low”; all p-values are less than 0.05. In this case, the awareness of respondents on the perceived level of punishment (exercised by the revenue authority) determines the rate of using EFD. When the perception is low, the use is equally low, and vice versa. This element of findings is supported by authors such as Armborst (2017) and Nkwe (2013), where both suggested the size of the punishment to determine compliance with tax laws.