Tax Compliance Challenge through Taxpayers’ Typology

: Tax compliance has become a very popular academic research topic. Understandably so, as all modern societies face the challenge of limiting tax evasion and the losses this phenomenon causes to government revenues. Given the increase in duties in the aftermath of the economic downturn affected by COVID-19, increasing taxpayer compliance is one of the main tasks for governments worldwide. This study aimed to identify critical gaps in understanding taxpayer heterogeneity. For this purpose, an exploratory factor analysis of taxpayer perceptual and attitudinal elements was carried out. Our analysis revealed six factors inﬂuencing taxpayer compliance with the tax system. Furthermore, the cluster analysis identiﬁed four groups of taxpayers, and signiﬁcant differences between the clusters and the descriptive proﬁle of each cluster were also found. The speciﬁcities of these clusters point to a conclusion that the time has come for policy makers to employ strategies that stimulate voluntary tax compliance with minimum cost to the tax system.


Introduction
Over the last forty years a significant number of studies have analysed taxpayers' motivation towards their tax liabilities.The literature often emphasises the complexities of tax compliance, suggesting it is influenced by a large number of determinants (Onu et al. 2018;Hashimzade et al. 2013).Tax compliance and related issues are as old as taxes themselves (Andreoni et al. 1998) and continue to be a hot topic, even today.
Despite the increase in research in this area, many studies continue to use the conventional economic model and its alterations to explain the taxpayers' reasoning.Allingham and Sandmo (1972) and similar models, derived from the theory of Nobel laureate Becker (1968), are based on the assumption that tax payments are made only because of the economic consequences that follow in the case of indiscipline.Although the traditional approach was under criticism (claiming it did not completely explain taxpayer compliance), its variations are still being developed and adapted further.Fischer et al. (1992) and Alm et al. (1995) argued that some non-economic factors also affect tax compliance.In the last two decades, many studies have discussed the division between economic and psychological factors that contribute to tax compliance (Bobek et al. 2007;Kirchler 2007;Lewis et al. 2009;Saad 2014;Alshira'h et al. 2020;Santoro et al. 2021).In this regard, Alm et al. (2012) and Marandu et al. (2015) claimed that tax compliance and its mechanisms could not be fully explained by purely economic considerations, just as enforcement is not the only determinant influencing it.In a recent large-scale experiment of 44 nations across five continents, Batrancea et al. (2019) suggested that both traditional and emerging, trust and power-based strategies were important in order to positively influence taxpayer compliance.Since there is still no consensus between researchers regarding the dominant tax compliance model, the conceptual objectives of this paper are to explore the existing theoretical knowledge of economic and psychological tax compliance concepts to define and describe the most important economic and psychological determinants of tax compliance.
The paradigm shift in the tax compliance concept has led to more research in this area, aimed at understanding the mechanisms of taxpayers' reasoning, intersecting different disciplines such as accounting, economics, political science, sociology and psychology.Over time, there has been significant sophistication in understanding the tax compliance concept, and interdisciplinary research has contributed significantly to this (Yong et al. 2019).Finally, two research approaches are now used in investigating tax compliance: economic and behavioural (Nguyen et al. 2020).
Based on the combination of these two approaches and the model proposed by Alm et al. (2012), this paper's empirical objectives are to examine the impact of established economic and psychological determinants on the taxpayers' tax compliance in the Republic of Croatia, to examine the impact of taxpayers' sociodemographic characteristics on their tax compliance in the Republic of Croatia and to compare the results with previous research.The data was collected through a survey and analysed using factor analysis to identify clusters of characteristics that lead to differences in tax compliance.The factor-cluster analysis can help achieve the research objective of better understanding the relationships and patterns between the variables within the identified clusters and factors.These patterns can be useful in understanding the underlying structure of the data and in making informed decisions based on the characteristics of the identified clusters.Finally, this work will hopefully contribute to a better understanding of taxpayer motivation and reasoning.The results of the study may shed light on possible improvements to the tax system that lead to more differentiated tax policies.In addition, the study aimed to identify not only the clusters of taxpayers, but also the economic and psychological factors that influence them.For the tax system to work and be satisfactory to the government and taxpayers, all factors that contribute to tax compliance must be defined and studied.The results provide tax policy makers with insight into taxpayer heterogeneity and may be useful for countries with similar economic and fiscal policies.
The remaining sections are Theoretical Perspective, Methodology, Results, and Discussion and Conclusions where the implications and limitations of the study are presented together with the directions for future research.

Theoretical Perspective
2.1.Tax Audits Allingham and Sandmo's (1972) economic deterrence theory of tax compliance and the findings of many other authors have emphasised audit probability and rates as crucial factors in their tax compliance models.For example, Alm (1991) and Alm et al. (1995), argued that the taxpayers' judgments were affected significantly by the level of audit rate and audit strategy.Slemrod et al. (2001) reported that taxpayers who were provided with feedback about the certainty of being audited more closely scrutinised their tax report in the subsequent period than those who were not given such information.In line with Slemrod et al. (2001), other studies showed higher compliance in taxpayers who were more likely to be audited (Alm et al. 2012;Blackwell 2007;Dubin et al. 1990;Webley et al. 1991;Nguyen 2022).Kleven et al. (2011) conducted a large-scale field experiment in Denmark to test the predictions of an augmented classical tax compliance model.They showed that previous audits and threat-of-audit letters had a substantial positive impact on self-reported income.Nguyen et al. (2020) showed that voluntary tax compliance was directly affected by three factors: audit probability, corporate reputation and business ownership.It has been suggested that audits have not only a large impact but a persistent one as well (Advani et al. 2017).This effect reaches approximately 26 per cent (on average) in the fourth year after the tax year to which the audit applies.
However, a number of studies reported different and mixed findings, claiming that audit has no significant impact on tax compliance (Graetz et al. 1986;Cowell 1990;Erard and Feinstein 1994), while others (e.g., Ghosh and Crain 1996) claimed the opposite.Mendoza et al. (2017) suggested that the association between auditing level and tax compliance was non-linear.Their findings showed that that there was a level of auditing after which compliance declined, suggesting that the enforcement strategies might have a certain limit regarding the taxpayers' behaviour.
In their laboratory experiments, Guala and Mittone (2005) presented another interesting phenomenon that should be included in further research of audit probability and economic determinants of tax compliance.They named it the "bomb crater effect" and defined it as a considerable diminution of taxpayer compliance directly after an audit.Later on, the "bomb crater effect" was examined and its significance confirmed by numerous studies (Mittone et al. 2017;Kastlunger et al. 2009;DeBacker et al. 2015;Maciejovsky et al. 2007).
The broad scope of the literature on audit level and probability surely serves as a verification of its importance.However, the problem with audits is that they represent a significant cost to public finance and yet eliminate only a part of tax evasion.In other words, audits should be adequately combined with other tax compliance factors.In addition to this, and more recently, authors proposed that "nudge" mechanisms can be useful during auditing.Some authors propose the self-funding reward system in combination with the traditional auditing process to improve compliance rates (see Fatas et al. 2021).

Tax Rate
Almost every fundamental theory of tax compliance includes tax rate as one of the most influential factors.Similar to Allingham and Sandmo (1972), Srinivasan (1973) and Fischer et al. (1992) highlighted the role of tax rate in obtaining tax compliance.However, the standard economic model and its expansions do not offer a clear explanation of the relationship between tax rate and tax compliance (or tax evasion).
Generally, studies report mixed findings on the relationship between tax rate and tax compliance.One group of authors argues that higher tax rates reduce effective income and, consequently, make tax evasion lucrative.In other words, they suggest that compliance is lower at high tax rates (Ali et al. 2001;Boylan and Sprinkle 2001;Christian and Gupta 1993;Lang et al. 1997;Collins and Plumlee 1991).Blackwell's (2007) meta-analysis of twenty laboratory experiments, carried out between 1987 and 2006, showed that higher tax rates might lead to less compliance.Similar findings were found in a study by Alm et al. (2012).There are many studies with opposite findings, suggesting that a reduction in effective income leads to an increase in absolute risk aversion, and therefore, tax evasion declines (Alm et al. 1995;Feinstein 1991).A small number of studies show that tax rates have zero impact on tax compliance (Baldry 1987;Porcano 1988;Modugu et al. 2012).

Tax System Complexity
With the increasing sophistication of tax legislation, the complexity of the tax system has become an ongoing and developing issue (Richardson and Sawyer 2001).Depending on its form, the term "tax complexity" has been differently explained in the literature.Cuccia and Carnes (2001) focus on the complexity of tax rules, while Cox and Eger (2006) emphasise procedural complexity.In another set of studies, researchers are focused on the taxpayers' view, and specifically on the low degree of legibility (Barney et al. 2012;Pau et al. 2007;Saw and Sawyer 2010) as a determinant of tax complexity.
Evans and Tran-Nam (2014) define tax complexity as a multidimensional concept viewed differently by different categories of people in the tax system.They categorised three separate definitions of tax complexity-from the tax accountants', tax advocates' and taxpayers' points of view.Tax preparers undoubtedly play a role in tax system simplification, and in that way, they can often facilitate tax noncompliance.Erard (1993) emphasised the need for a joint analysis of tax preparation and tax compliance levels.Although the author established the connection between certified tax preparation and a higher level of noncompliance, he suggested that some beneficial social outcomes (especially educational) were significant.In addition to this, the motivation to use tax preparers is to reduce incertitude and fill in the data correctly-which suggests that tax system complexity is an important issue (Hite et al. 1992;Niemirowski and Wearing 2003).
Furthermore, numerous other studies link tax system complexity to tax compliance issues (Chau and Leung 2009;Cox and Eger 2006).After examining different determinants of noncompliance across 45 countries, Richardson (2006) concluded that complexity is the most important determinant for a taxpayer's compliance.Kirchler et al. (2006) claimed that when tax laws are perceived as less complex, taxpayers' intention to comply is higher.In their analysis, Saad (2014) recommended that future research should examine the impact of tax complexity on taxpayers' noncompliance.Finally, today there is a tendency to use technology that can both simplify the tax system and help increase its integrity by reducing corruption options (Bird and Zolt 2008).

Social Norms and Tax Morale
Insights from behavioural economics have been embraced and implemented in two extensive (but partially overlapping) areas of research.One sequel keeps its focus on the individual factor (personal norms), while the other extends to the analysis of group considerations, mainly social norms (Alm 2019).
The concept of "social norms" as a tax compliance factor has been studied by many researchers in this field (Alm et al. 1999;Slemrod 2016;Scholz and Pinney 1995;Wenzel 2004aWenzel , 2004b)).However, Cialdini and Trost (1998) were the first authors to define the term as "rules and standards that are understood by members of a group, and that guide and/or constrain social behaviour without the force of law" (p.152).Alm et al. (1995) conducted a study regarding unrecognised inter-country diversities in compliance rates, due to social norms discrepancies.Many other authors followed this example, referring to different societal norms in connection with the taxpaying culture (i.e., Cummings et al. 2006;Torgler 2002).Their results generally suggest that social norms play a role in tax compliance, but also emphasise the need for the precise differentiation of the term.In a large-scale natural field experiment carried out in the United Kingdom on more than 200,000 individuals, Hallsworth et al. (2017) revealed that social norm messages persistently affect tax compliance behaviour.In the research of the determinants of active filing behaviour, the results from Santoro et al. (2021) suggest that social norms could significantly encourage filing.Torgler (2011, p. 5) concludes, by linking social norms and tax morale, that "an increase in social norms increases the moral costs of behaving illegally and, therefore, reduces the incentives to evade taxes".
In analysing tax compliance, numerous studies were found that incorporated and examined the role of attitudes, although they were not always labelled as such.In their analysis of factors beyond the purely economic, Alm et al. (2012) found that attitudes were repeatedly identified as the source of tax morale.This is consistent with the study by Schmölders (1959), which showed that tax morale is reflected in attitudes towards tax compliance and tax evasion.Torgler (2006) defined tax morale as "intrinsic motivation" to meet tax obligations.However, some of the researchers consider tax morale as an umbrella term encompassing all observed tax compliance (Luttmer and Singhal 2014).Andreoni et al. (1998), Kirchler (2007) and Torgler (2011) empirically demonstrated that incorporating tax morale into tax compliance models was effective.Many other studies showed that taxpayers' attitudes are significantly positively correlated to tax compliance (Ali et al. 2014;Cummings et al. 2009;Kornhauser 2007;Nguyen 2022).They also argue that tax morale should complement, not substitute, other determinants of tax compliance.The OECD (2019) points to complex interactions between tax morale and other drivers of tax compliance while the recent increasing application of behavioural economics in the field of tax compliance shows that tax administrations are seeking to use tax morale knowledge to improve compliance (OECD 2017).

Fairness
Although defined in the 1980s, as one of Jackson and Milliron's (1986) key variables of compliance behaviour, only later did fairness become the focus of other researchers.As Richardson and Sawyer (2001) pointed out, studies regarding perceptions of fairness and their linkage to compliance behaviour were on the rise, but their number was still inefficient to lead to conclusive results.The same authors went on to conclude that the importance of the perception of fairness should not be neglected in future research.
It is generally accepted that fairness is a multidimensional determinant that comprises vertical, horizontal, procedural, distributive and retributive dimensions.In this research, the term "general fairness" will be used (see Richardson 2006).Research in the tax compliance field most often deals with distributive fairness and procedural fairness.Researchers agree that if citizens perceive the allocation of tax burdens and benefits as fair, and if they are satisfied with the quality of public services, they consequently show more willingness to obey tax laws and regulations voluntarily (Bosco and Mittone 1997;Braithwaite 2003;Falkinger 1995;Hartner et al. 2008;Kim 2002;Richardson 2005;Verboon and Goslinga 2009;Kirchler et al. 2008;Guzel et al. 2019;Gobena 2021).In their research, which aimed to identify when and why procedural fairness positively influenced voluntary tax compliance, van Dijke and Verboon (2010) found that trust in authorities may be the core prerequisite for this phenomenon.Koumpias et al. (2021) made a significant contribution in examining trust in government organizations and its effects.In their paper, they established differences in the levels of trust based on citizens interactions with organizations.In other words, trust in output government organizations (those ensuring public goods and services) has a stronger and more positive association with tax morale than trust in an input government organization (Koumpias et al. 2021, p. 4).

Methodology
Researching and understanding taxpayer compliance has never been straightforward, primarily because those taxpayers who evade or avoid taxes are strongly motivated to cover up that behaviour (Alm and McKee 2006).Research methods in this area can be classified into one of the following groups-historical data, surveys, and experiments (Slemrod 1992).Kirchler and Wahl (2010) suggested combining methods with the aim of broadening the understanding of taxpayers' reasoning.Alm (2019) argued that, regardless of the specificity of the data source or methodology, one should keep in mind that there are disadvantages in using any of the available methods in this field of study.Nevertheless, it must be acknowledged that such methods have provided many important insights.Moreover, one should be aware of the fact that attitudes do not necessarily anticipate behaviour.However, Onu (2016) argues that there are theoretical arguments backing the attitudes and behaviour relationship.Moreover, Ajzen (1991) suggests that by measuring attitudes specifically related to the behaviour (such as tax compliance attitudes) raises the chances of valid results.
Based on the existing literature and established research gaps, the empirical objectives of this paper are to examine the impact of the chosen economic and psychological determinants on the taxpayers' compliance, to examine the impact of the taxpayers' sociodemographic characteristics on tax compliance and to compare the results with previous research.

Research Design
A questionnaire was chosen as the main method and instrument for data collection in the current study.To better understand taxpayers' attitudes and motivations, a selfadministered questionnaire was developed.It consisted of three parts: (1) the economic determinants of tax compliance, (2) the psychological determinants of tax compliance, and (3) the socio-demographic data (gender, education level, employment status, monthly income, seniority).The economic determinants of tax compliance (tax audits and tax rates) were measured with 6 items, as proposed by Tenidou et al. (2015) and van Dijke et al. (2019).Psychological determinants of tax compliance (tax morale, social norms, tax system complexity and fairness perceptions) were assessed with 19 items as suggested by Onu et al. (2018), Kirchler et al. (2006), and Hauptman et al. (2015).The two aforementioned scales were slightly modified due to the specificities of the Croatian tax system.To express their opinion, respondents were presented with a 5-point Likert scale ranging from "1-Strongly Disagree" to "5-Strongly Agree".

Data Collection
The study was conducted in Croatia from April to May 2021.It was carried out online and invitations were sent to a random sample of Croatian individual taxpayers (income tax).The sampling was based on the willingness and availability of participants to complete the questionnaires.In order to ensure the content validity of the questionnaire and to test the respondents' understanding of the questions, the questionnaire was piloted with a sample of 40 participants.Some minor issues were identified during the pilot.As a result, wording of some of the questions was changed to ensure that the participants could clearly articulate and answer the questions.
To optimise the scope in this online survey, two sampling strategies were used.Regarding the first subsample, the data was collected by random sms invitations to mobile phones.In a second subsample, social media invitations via paid Facebook and Instagram advertisements were created, targeting specific sociodemographic groups (inspired by Rinken et al. 2020).Only individuals older than 18 years were asked to participate in the survey.A total of 299 were valid and accepted for this study, representing a response rate of 69.5%.

Data Analysis
The data were analysed in five steps using IBM SPSS Statistics 26.First, a descriptive analysis was carried out to examine the socio-demographic profile of respondents.This was followed by an exploratory factor analysis (EFA) to reduce the number of determinants of tax compliance (31 in total) to a smaller number of factors.The original multi-item constructs were reduced in the first step and also adjusted to the specificities of the tax system.Therefore, the theoretical framework was significantly modified through the EFA.
Principal axis factoring with direct oblimin rotation was used as the factor extraction method.Prior to factor analysis, the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) and Bartlett's test for sphericity were applied.In addition, only factors with eigenvalues greater than 1 were retained, while items with factor loadings and communalities greater than 0.3 were retained in the final factor matrix.Reliability alphas within each dimension were calculated to best determine the internal consistency of a factor.Third, participants were then divided into segments through cluster analysis using the tax compliance factor scores.In this study, a non-hierarchical clustering method was used, more specifically the K-means clustering method.Fourth, possible statistically significant differences between taxpayer segments in terms of tax compliance factors were explored through ANOVA.This was supported by a subsequent post hoc analysis using the Hochberg GT2 post hoc test in the case of homogeneous/nearly equal variances and the Games-Howell post hoc test in the case of non-homogeneous variances.Finally, possible significant sociodemographic differences between taxpayer segments were tested.

Results
Exploratory factor analysis yielded a KMO measure of sampling adequacy of 0.785, a significant Bartlett's test of sphericity with chi-square = 2537.515(df = 300) and p = 0.000, indicating that the covariance matrix was appropriate for conducting factor analysis.Six items with low communalities (less than 0.30) were excluded from further analysis.Finally, EFA with direct oblimin rotation from the 25 perception items yielded six factors with eigenvalues greater than one, which explained 60.23% of the total variance (see Table 1).According to Hair et al.'s (2013) rule of thumb, all standardised factor loadings (except one) were greater than 0.50, which suggests that the sample size of 299 participants was large enough to increase the significance level of the findings.Additionally, three or more items for each factor and the level of communalities indicated moderately good conditions and sufficient sample size (Leandre et al. 2012).Furthermore, all Cronbach's alpha values were at the acceptable reliability level, i.e., higher than the recommended standard of 0.70 (Cortina 1993).Table 1 shows the results of the exploratory factor analysis, indicating standardised factor loadings, mean values, and standard deviations of all items examined.The first factor, "tax morale", comprised eight items related to personal attitudes towards tax liabilities in order to better understand the decision-making process in tax manipulation, particularly in relation to the justification of tax compliance.With an eigenvalue of 4.90, this factor explained 17.54% of the total variance.The second factor, "tax system complexity", is characterised by five items relating to the understanding of tax legislation and a clear understanding of the tax regulation.This factor had an eigenvalue of 3.22 and explained 10.81% of the total variance.The third factor, "tax rates", included three items focusing on attitudes towards the level of tax rates and their impact on the shadow economy and the labour market.This factor had an eigenvalue of 2.27 and explained 7.2% of the total variance.Like the first factor, the third component had a higher mean (4.20) and a reliability alpha of 0.83.The fourth factor, "tax audits", was derived from three items relating to the efficiency of tax audits conducted by the authorities.It yielded an eigenvalue of 2.20 and explained 6.8% of the total variance.The fifth factor, "social norms", was characterised by three items relating to the influence of social groups, namely family, friends and people from the neighbourhood, on the respondents' tax compliance.This factor yielded an eigenvalue of 1.32 and explained 3.62% of the total variance.The final factor, "perception of fairness", comprised three items reflecting the respondents' perceptions of fairness in tax audits, the level of taxes, and the decision-making processes and public services executed and provided by tax authorities.This factor had an eigenvalue of 1.16 and accounted for about 2.88% of the variance in the data.
Having uncovered the dimensions underlying the tax compliance determinants, the next step in the analysis was to cluster the respondents.The cluster analysis identified different groups of respondents based on their perceptions of the six factors described in the previous analysis.A non-hierarchical K-means clustering method was used as this method is more efficient with larger data sets (n > 200) and is more suitable for grouping cases rather than variables compared to the hierarchical technique (Johnson and Wichern 1998).After checking cluster membership, distance information, and final cluster centres, a four-cluster solution was found to be most appropriate.Furthermore, the mean values of each factor were calculated for the members of each cluster (see Table 2).The resulting ANOVA tests showed that all six factors contributed to the differences between the four clusters (Sig., p < 0.001) (Table 2).In addition, post hoc analyses using Hochberg GT2 or Games-Howell tests examined the differences between the clusters on all six tax compliance factors.Comparison of the means showed that the taxpayer segments differ from each other, confirming the statistically significant differences in the means.Finally, four segments were labelled based on the importance of the factors for tax compliance.The largest number of taxpayers fell into the second (N = 94) and third (N = 72) clusters, while the first (N = 67) and fourth (N = 64) clusters were almost equal according to the number of respondents.Participants gathered in the first cluster perceived the combination of economic and psychological determinants as crucial for their tax compliance, but the dominant ones were tax rates and tax audits (tax rates = 4.38; tax audits = 3.26).Since the taxpayers in this cluster were significantly under the influence of the financial and deterrence factors, it is obvious that external (system) changes could stimulate their tax compliance.The type of taxpayer elicited in this cluster might be defined as "Extrinsically motivated" taxpayers.
The taxpayers in the second cluster were the ones under the influence of the financial factor (tax rates = 4.48) but characterised by strong tax morale (tax morale = 3.53) as well.Respondents from this cluster can be described as the ones who possess an intrinsic motivation in complying with their tax liabilities.Their voluntary tax compliance could be stimulated, with minimum cost to the tax system, by empowering a psychological contract between taxpayers and government.This cluster might be recognizable as "Morally committed" taxpayers.
In the third cluster, taxpayers were highly focused on tax rates, questioning the level of income tax rates and the overall tax burden (rates = 4.62).This group of taxpayers is slightly frustrated with the current tax system (tax system complexity = 2.24) and rates the fairness of the system as very low (perception of fairness = 1.60), while they do not perceive that their family or friends have any expectations regarding their tax compliance (social norms = 2.30).Since psychological factors could not stimulate their compliance, the type of taxpayer described in this cluster could be named "Financially motivated" taxpayers.
Taxpayers in the fourth cluster considered that the opportunity to pay a smaller amount of tax should not be taken and they agreed that people in their surroundings have some expectations regarding their tax compliance (social norms = 3.41).This suggests that they perceived paying taxes as their personal duty, and they might cooperate for the common good.This cluster is named "Socially committed" taxpayers.
The results of the ANOVA post hoc comparisons for the cohorts of taxpayers (Table 2) showed some statistically significant differences between the four groups.Extrinsically motivated taxpayers differ significantly from morally committed taxpayers on perceptions of tax system complexity, tax audits, social norms and perceptions of fairness.Extrinsically motivated taxpayers differ significantly from financially motivated taxpayers regarding tax morality, the complexity of the tax system, tax audits and perceptions of fairness.The comparison between extrinsically motivated taxpayers and the last group of taxpayers, socially committed taxpayers, shows some statistically significant differences in terms of tax rates and tax audits.Morally committed taxpayers differ significantly from extrinsically motivated and socially committed taxpayers in terms of tax morality.Moreover, the same group of taxpayers differs significantly from socially committed taxpayers in terms of tax rates, while social norms distinguish them from financially motivated taxpayers.The third group of taxpayers, financially motivated taxpayers, differs from extrinsically motivated and socially committed taxpayers in terms of tax rates.The last group of taxpayers, namely the socially committed taxpayers, differs significantly from the financially motivated taxpayers in terms of tax morale, complexity of the tax system, social norms and perceptions of fairness.Furthermore, the same cohort's perceptions of tax audits and perceptions of fairness differ significantly from those of morally committed taxpayers.
Table 3 shows the socio-demographic characteristics of the four clusters.All identified homogeneous case clusters averaged between 41 and 44 years of age (with a standard deviation of approximately ±12.3) and were predominantly female.Respondents in all four groups predominantly held master's degrees and worked in the private sector with more than 11 years of professional experience.Possible statistically significant socio-demographic differences between the segments of taxpayers were tested, pointing out one differencemonthly income.It was evident that the majority of the financially motivated cohort came from the highest income range, which was clearly different from the other groups, especially the extrinsically motivated and socially committed cohorts.

Discussion and Conclusions
This paper focused on identifying taxpayer clusters and examining the factors that shape these clusters using a sample of 299 individual taxpayers.The results followed the well-established idea that taxpayers are not a homogeneous group and that they are stimulated and motivated to meet their tax obligations by very different mechanisms.A factor analysis was conducted that identified the following significant factors: tax audits, tax rates, complexity of the tax system, tax morale, social norms, and perceptions of fairness.
The cluster analysis identified four distinct groups of respondents based on their perceptions of the six factors significant to tax compliance.The clusters were named as follows: "Extrinsically motivated", "Morally committed", "Financially motivated", and "Socially committed" taxpayers.The first and third clusters seemed to be mostly influenced by the economic factors, while second and fourth were significantly under the influence of psychological determinants, which is somewhat similar to the Torgler's (2003) results of the typology of taxpayers.Torgler's (2003) "Intrinsic taxpayer" can be compared to a "Morally committed" taxpayer in this case, since they are driven by their individual emotions and responsibilities.The same author also identified the type of taxpayers who were socially committed (as our fourth cluster) and named them "Social taxpayers".The specificities of these clusters offer a practical solution for tailoring taxation strategies towards them.It is obvious that there are still many taxpayers who fulfil their tax liabilities because of the existence of deterrent factors such as tax audit, or the crucial role for their decision is played by a financial determinant (tax rate).But it should be noted there are also morally and socially committed taxpayers who make their decisions according to their own moral standards or taking into account the social norms in their environment.This is consistent with Braithwaite's (2003) statement about how tax compliance can be improved by persuading and encouraging taxpayers to cooperate.Thus, the time has come for policy makers to employ such strategies and stimulate voluntary tax compliance.In addition, it needs to be emphasised that such strategies do not necessarily imply a high level of expense to the tax system.After all, researchers have lately offered significant evidence about the fact that enforcement strategies do not always bring efficiency (Mendoza et al. 2017;Kirchler et al. 2008).
This might be an opportunity to analyse and introduce a reward mechanism which, for example, redistributes the collected fines from noncompliant to compliant taxpayers in a form of a symbolic rewards (especially given its positive effects, see Fatas et al. 2021).Combatting the tax evasion problem by incorporating this new evidence about taxpayers would surely be a step forward towards service-based and trust-based climates (Kaplanoglou and Rapanos 2015;Gangl et al. 2020) beyond purely enforced interventions.Jackson and Milliron (1986) were some of the first authors to argue that age, gender and education should always be taken into account when examining tax compliance.According to Fischer et al. (1992), sociodemographic variables have no direct influence on taxpayer compliance.However, they do show that there is a significant indirect influence that is evident in the possibility of tax evasion and attitude.Hofmann et al. (2017) made an important contribution with their meta-analysis of survey studies in 111 countries, demonstrating the importance of sociodemographic factors and arguing that they should not be neglected in future studies.In their study, they focused on age, gender, education, and income in order to estimate the impact on compliance while taking geographical regions into account.A more thorough and nuanced study of the tax compliance by the wealthy taxpayers is of utmost importance for public efficiency (Gangl and Torgler 2020).The results of this study suggest that the clustering procedure statistically differentiates the groups regarding their monthly income.Moreover, it sheds light on a better understanding of the determinants of the tax compliance of wealthy citizens and implies the need for practical solutions that would lead to an optimised and fair tax system for the middle, lower and upper class citizens.The results also point out that although it is very important to rely on an economic framework, evidence strongly suggests that taxpayers are motivated by other factors, many of them beyond purely financial.In the last 20 years of research, a whole range of potential determinants was identified; they should be acknowledged and used to incentivise tax compliance.Therefore, a major challenge for upcoming researchers is to investigate how these emerging determinants shape tax compliance.Understanding and improving tax morale and the fairness of the system as well as analysing the norms surely hold the potential to increase revenue with minimal enforcement mechanisms.This is the reason why regulatory institutions should recognise that there is no one-size-fitsall solution for taxpayers.Above all, it is an important task to initiate a change in the discussion on taxation and replace the traditional frameworks with different strategies that combine economic and socio-psychological factors (Batrancea et al. 2019).In other words, the traditional tax system infrastructure calls for measures that encourage taxpayers' willingness to pay-with or without the tax authority watching over their shoulders (Braithwaite 2003).
The results of this study raise some questions for policy makers and may help them to understand and promote voluntary tax compliance in Croatia, but they may also be a helpful starting point for research in countries with similar economic and fiscal policies.After identifying clusters and the factors behind taxpayer attitudes, these findings can hopefully stimulate future research on taxpayer heterogeneity and optimal strategies to promote tax compliance.Although this work contributes new insights to existing research, it has certain limitations that should be kept in mind.First, it focuses exclusively on income tax.However, it is well known that, in reality, taxpayers often suffer from the overall tax burden.As a result, there may be differences in taxpayer compliance.Second, despite the fact that questionnaires are widely used in the field of tax compliance research, these instruments have their own disadvantages (Alm and Torgler 2011), such as unconscientious and dishonest responses, lack of personalization, difficulties in conveying feelings and emotions, etc.In this research, the term "general fairness," is used but it is undeniable that this is a multidimensional concept.Therefore, future research calls for more detailed elaboration and differentiation between distributive (horizontal, vertical, exchange), procedural and retributive justice.It would also be quite important to continue the discussion regarding the influence of the presence of third-party reporting (an external, economic factor) in voluntary tax compliance.In this context, in line with present studies such as that by Kleven et al. (2011), taxpayers' employment status information should indicate whether individuals are self-employed or salaried.Finally, the findings are not necessarily generalizable to the context of countries beyond Croatia (and similar countries).Future research could consider other analysis techniques, such as regression analysis or causal inference methods, including instrumental and control variables that can be used to address the potential presence of endogeneity and its impact on the interpretation of results.Moreover, future research should focus on developing this research framework and expanding it to a larger international comparative study.Conducting surveys of taxpayers from other countries could shed light on whether there are differences and similarities that could lead to the assumption that there are common characteristics in taxpayer compliance.In addition, taxpayers' perceptions and attitudes should be observed over time to better understand how fiscal changes affect taxpayer tax compliance.Finally, future studies could use a more comprehensive list of the determinants of tax compliance and extend the findings of the current study.

Table 1 .
Results of exploratory factor analysis.

Table 2 .
Clusters and post hoc analysis.