| Title |
Theoretical approach to taxpayer segmentation / |
| Authors |
Stankevicius, Evaldas ; Kundeliene, Kristina |
| DOI |
10.3846/cbme.2017.067 |
| ISBN |
9786094760129 |
| Full Text |
|
| Is Part of |
Contemporary issues in business, management and education’2017: 5th international scientific conference, 11-12 May, 2017, Vilnius, Lithuania: conference proceedings.. Vilnius : Technika, 2017. p. 127-135.. ISSN 2029-7963. ISBN 9786094760129 |
| Keywords [eng] |
taxpayers ; segmentation ; behaviour ; tax avoidance ; tax evasion |
| Abstract [eng] |
Current tax payers' segmentation is relatively limited and static. The existing segmentation problem, which is related to the tax payers‘ behaviour, requires modern segmentation-analysis methods and models, which would evaluate the change of economic and psychographic tax payers‘ indicators. It would allow the tax administrator to react to the shifting risks and recent circumstances, which predetermine the tax payment or evasion. The knowledge about the specific behaviour of the tax payers‘ segment groups (tax compliance/enforced tax obligations) would enable us to determine the main impacting factors. Accordingly, the most effective purposive administration tools may be applied to this group. Better comprehension of the tax payers‘ and their executives (decision-makers) behaviour would allow to enhance administrative institutions' abilities in analytics and to determine the yet unknown connections and phenomenons between separate tax payers in the context of effectual law basis. Clearer tax payers‘ identification by segment groups and behaviour risk factors, which are specific to separate groups, will enable faster determination of outliers as well as newly forming potential risks. Dynamic changes between separate groups or in them will form preconditions for timely implemention of tax payers‘ monitoring and control tools for reaching a positive change in the behaviour of taxpayers. |
| Published |
Vilnius : Technika, 2017 |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2017 |
| CC license |
|
