A Fiscal Policy Foresight Tax Model, Shadow Economy Reduction, and E-Payment Institutionalization as a Result of Knowledge Management ()
1. Introduction
Foresight has recently become increasingly important for both businesses and governments worldwide (Suriyankietkaew & Petison, 2019). Not only are unpredictable economic and geopolitical developments making top executives of corporations and governments nervous about what will happen next (Oliva & Martinez-Sanchez, 2018). Epidemics (Monaghan, 2020), wars (Rodehau-Noack, 2021), climate change (Batisha, 2022), and food crises (Tétart, 2020) have all occurred in the past. Because of the now-established globalization, it is the interconnected influence of events that directly and simultaneously affect the economies and states of the world.
Today’s digital revolution economies and societies, with their mobile phone companionship, virtual reality, digital hope, dematerialization of processes, personal vanity, open innovation foresight networks and moral values crisis, are more concerned with the present than with the future (Calof et al., 2018). Despite this, global developments create a fluidity and reflection that transcends this mentality. After all, today’s society spends a large portion of its free time reproducing and consuming fictitious digital experiences (Folkvord et al., 2020), and when the “expensive electricity bill” arrives, it’s entirely the fault of governments and corporations (Oei et al., 2020).
Nevertheless, enterprises and governments are concerned and anxious for foresight. They are worried because they aim eternally at survival which is the reason for the existence not only of the human organism but also of any organized political, economic or social entity (Rutting et al., 2022). Adapting to changing economic, geopolitical, and social conditions is a time-honored survival strategy, but in today’s reality of globalization and the digital environment, doing so requires foresight and management expertise (Mofazali & Jahangiri, 2018).
Providence, insight, accurate and qualitative prediction, perspective, forward view, and vision are all examples of foresight (Fergusson, 2018; Magruk, 2021). Knowledge management is used to manage information, knowledge, innovation, learning sharing, integration, and improved performance, as well as to gain a competitive and strategic advantage (Zeng et al., 2019; da Silva Nascimento et al., 2021; Salamzadeh et al., 2022). It is critical that business and government leaders develop approaches that improve decision-making by integrating information (Alabugin et al., 2022), knowledge, innovation, key change agents to develop organizational learning, strategic visioning, proactive intelligence, knowledge management, and knowledge sharing that drive creating advantage strategies (Drew, 2006; Li & Sullivan, 2022).
No economic and, by extension, social stability can be achieved without fiscal stability. Knowledge management today leads us strategically into tomorrow, rather than pushing forward without anchoring in stagnant waters (Finesso & Van Ree, 2022). Nonetheless, fiscal foresight necessitates effective management of information, innovation, and social cohesion, which necessitates a knowledge economy free of distortions that cause social inequality (Schaillée et al., 2019; O’Donovan, 2020; Jewett et al., 2021; Choong & Leung, 2022). When a large percentage of businesses operate in the shadow economy, this causes distortion, social inequality, and the absence of law, while any foresight policies (Mohamed et al., 2020) resulting from proper knowledge management (Pellegrini et al., 2020) result in a game in favor of those operating in the shadow of the illegal economy (Héry & Malenfer, 2020).
Consumption is supported by the taxpayer’s personal disposable income from labor, capital rental, government transfers, and other sources of income (Becker, 1984). Assuming that all spending in an economy can be accomplished electronically, capital gains and losses are included in total disposable income (Keuschnigg & Nielsen, 2004). This research aims to contribute to a fiscal policy with foresight (Yang, 2005) that effectively manages knowledge and innovation, restoring social inequality under conditions of uncertainty, dynamics, and fluidity in a globalized setting (Mobayen & Baleanu, 2017). This study’s primary objective is to emphasize the significance of foresight and knowledge management for making strategic decisions under conditions of economic uncertainty (Varotsis & Katerelos, 2020). To rectify the distortions and social inequalities caused by the shadow economy, a linear model of electronic transactions is proposed (Meagher, 2005).
By effectively managing and integrating knowledge and innovation, it is possible to derive foresight policies that result in a digital financial transaction system that reduces the distortions caused by the shadow informal economy (Pathak et al., 2016; Setiawan et al., 2022), thereby reducing social inequality. This study demonstrates to business and government leaders how foresight through knowledge management creates a knowledge economy with strategic advantages under conditions of uncertainty (Bootz et al., 2019). In addition, e-payment behavior is a fundamental factor in the decline of the shadow economy (Panle & Okpara, 2021).
To date, the application of a fiscal policy model based on foresight has been limited to global economic turbulence, temporary political crises or pandemic outbreaks (Kimbell & Vesnić-Alujević, 2020; Nissen, 2020; Arauz, 2021; Gariboldi et al., 2021; Wood et al., 2021). However, the compulsion of electronic transactions is a potential consequence of an energy crisis, health emergencies, or capital controls enacted to control fiscal instability (Makki, 2012; Lin et al., 2015; Chang & Benson, 2022; Liu, 2022) as well as insecurity in daily family life due to a pandemic outbreak (Vally, 2016; Baker, 2021; Varotsis, 2022).
To advance fiscal knowledge management in the Greek government, this paper proposes a wealth-based income tax model with an obligatory electronic transaction legal framework. This research emphasizes the significance of planning ahead for taxes and coordinating data in order to improve fiscal policy. This study focuses on fiscal foresight as a result of knowledge management facilitated by a mandatory institutional framework of electronic transactions (Brousseau, 2000; Jacobides & Winter, 2005; Kato, 2019; Khan et al., 2021). Mandatory e-payments are a means of shrinking the shadow economy, and knowledge management is the key to achieving a model of fiscal foresight, according to two novel findings of the present study. Furthermore, this research emphasizes the importance of tax planning and information coordination in order to improve fiscal policy.
2. Theoretical Background
Assuming that foresight in politics and leadership is necessary in a hyper-informational age of complexity, then foresight in taxation is the means for an economy that aims to reduce social inequalities (Veghte, 2015). Particularly in an economy with a consistently high percentage of the shadow economy, high tax rates (Tafenau et al., 2010) that cause over-taxation of honest taxpayers (Fotiadis & Chatzoglou, 2021), ineffectiveness of tax audits (Awasthi & Engelschalk, 2018), and a widening of social inequality caused by unfair taxation (Huynh & Nguyen, 2020), tax planning becomes critical (Khuong et al., 2021).
Shadow economy distorts the economy by overtaxing honest taxpayers at the expense of addicted tax evaders, raising the cost of living, reducing employment, and causing economic inequality (Swanstrom et al., 2002). Waiting for voluntary emergence of shadow wealth (Gaspareniene et al., 2016a) through tax reductions does not appear to have produced comparable results in recent history (Dube & Casale, 2019; Andersen, 2020). The pain and lessons learned from the fiscal derailment caused by excessive public borrowing (Pagoulatos, 2020) to cover deficits caused by the impossibility of taxing the shadow economy preclude new experiments with selective regression of tax rates (Nickayin et al., 2022).
In a highly uncertain environment, foresight, on the other hand, translates into a new innovative perspective with beneficial and predictable outcomes (van der Duin, 2019). As a result, adapting to the new technological environment that prevails in the global economy, as well as the digitization of daily transactions, necessitates new foresight ideas (Vagnoni & Khoddami, 2016; Inkinen et al., 2021). When tax evasion becomes ingrained in the culture of social and professional groups, coercion and compulsion are the only ways to alleviate the social inequalities caused by over-taxation of honest taxpayers (Cohen, 1950; Keith, 1990; Bagus et al., 2011; Ozili, 2020). Adapting to the new digital age is critical when developing innovative scenarios in an uncertain environment (Buehring & Bishop, 2020). The institutionalization of the e-transaction obligation is a fiscal tool for the consolidation of economic distortions and the restoration of fiscal justice (Camerer, 1985). The latter is obvious when taxation foresight scenarios are based on mandatory transaction digitization to improve public finances.
2.1. Foresight
Foresight is a practical and accurate prediction that emerges from continuous activation to ensure future care (Crews, 2020). The ability to predict future social, economic, technological and environmental issues is only one aspect of foresight (Habegger, 2010). It contains elements of activation, impatience, and urgent provision for the future, without being burdened by some current events and developments (Rosa et al., 2021).
Frequently, foresight is associated with pessimism, which reinforces fear and insecurity and is linked to future events (Parton, 2020). Consequently, foresight frequently overshadows an optimistic outlook. However, foresight that does not motivate thoughts and actions regarding actual future events appears to be a self-fulfilling prophecy (Bottici & Challand, 2006). In light of the fact that one-sided unpleasant anticipation is positively associated with passive coping, foresight goes beyond mere prediction. It is about capturing, shaping, and creating the future through a method of organizing thoughts, actions, and perspectives in a dynamic environment (Fuerth, 2009)
The dynamic gives the future both realism and uncertainty. A dynamic that emerges from the interactions and interdependencies of agents, processes, impacts, inputs, and outputs in the larger socio-technical-economic-political environment that shapes the framework of the foresight system (Amanatidou & Guy, 2008). Nonetheless, dynamism is intertwined with nature, man, the environment, the universe itself, and the evolution of uncertainty. It is a dynamic system of foresight that also involves the production of the present (Haarhaus & Liening, 2020).
People, groups, businesses, and governments all work together in this ever-evolving arena of foresight to shape the future (Coates, 2010). A complex and uncertain future shaped by developments that interact with one another. Whether drastic or subtle, disruptions to the status quo have an impact on people’s and businesses’ day-to-day operations, ability to adapt, and long-term prospects. Strategic foresight through tools, techniques, and a methodological distinction offers a structured approach to scenario planning, creation, precognition, and change planning in a world that is constantly shifting within ontological and epistemological boundaries through a complex multifactorial prism of interrelated agents (Carlisle et al., 2016).
In intergovernmental management, foresight in a complex environment is an essential component of decision-making. Indeed, effective governance requires fundamental elements of prediction and foresight, whether at a low or high level of complexity (Solem, 2011). Furthermore, effective governance aimed at achieving harmonious order requires fundamental elements of analysis, prediction, and foresight at either a low or high level of complexity. Governance foresight strategies can be developed by bringing foresight activities closer to policy cycles through consultation and coordination (Janzwood & Piereder, 2019).
Tax foresight (Yang, 2007) is a fiscal equilibrium model based on a non-fundamental representation of the moving average of information aggregates of economic factors drawn simultaneously from different sources, as opposed to traditional econometric methods of fiscal policy (Leeper et al., 2011). Artificial intelligence in the big data process is used to manage fiscal knowledge. Tax foresight analyzes large volumes of tax data and provides multiple tax revenue and tax compliance scenarios using artificial intelligence and legislated machine learning based on predictive analytics (Bishop et al., 2020). This is accomplished through the use of social simulation tax planning models, on which foresight scenarios of fiscal knowledge management and knowledge management of tax compliance services can be based on various customer tax scenarios. Real-time fiscal knowledge management via open-access tax scenario management and registration software can be used as a tool for fiscal foresight, tax compliance, and taxpayer service delivery (Leeper et al., 2013). As a result, the following hypotheses were proposed:
H1. Fiscal policy reform is positively related to the foresight model.
H2. Tax revenue is positively related to the foresight model.
H3. The foresight model has a negative relationship with the shadow economy.
2.2. Knowledge Management and Fiscal Policymaking
Nowadays, information and knowledge management are critical factors in an organization’s and/or business’s competitive advantage. Knowledge management is an interdisciplinary approach that combines management and information (Adams & Lamont, 2003). Knowledge management is the transformation of information from collection, processing, creation, and use to the organization, filtering, and assimilation through human resources’ experiences, attitudes, and work context (Martensson, 2000). It is a strategic innovation management tool based on top management support, communication, creativity, culture, knowledge sharing, and an organization’s people (Wild et al., 2002).
As a result, knowledge management is a critical knowledge management process (Quintas et al., 1997), with creation followed by interpretation, dissemination, and refinement (Sadri McCampbell et al., 1999). Knowledge management is on the horizon in the public sector as a result of increased awareness of the need for operational rationalization (Luen & Al-Hawamdeh, 2001) and the need to focus on human resources as a result of the new knowledge economy (Seetharaman et al., 2002). The latter, in particular, is related to a fiscal policy that invests in information and communication technologies, with the goal of developing e-learning through knowledge management (Bhattacharya & Sharma, 2007).
Rapid adaptation to a digital macroeconomic environment is required for the new knowledge economy. Governments, the public sector, public organizations, and agencies that do not use a knowledge management technology framework face daily operational failures (Goldfinch, 2007). These failures in the public sector result in fiscal failures such as insufficient revenues, reliance on borrowing, over-taxation, weak social policy, and the presence of an uncontrolled shadow economy (Tran et al., 2022).
A fiscal policy reform foresight model (Judd, 1985) reshapes a failure economy into a knowledge economy by integrating knowledge management into the public sector. Fiscal policy in the knowledge economy necessitates the complete digitization of financial transactions and their recording processes, as well as a digital orientation of tax control based on electronic intersections, knowledge management in tax authorities, artificial intelligence systems, and the digitization of the tax compliance legislative framework (Kamleitner et al., 2012).
A revised tax policy foresight (Ballard, 1987) focuses on personal income and expenses in order to achieve fair income redistribution and tax revenue security. Taxation on income acquisition and expenditure assumes the integration of tax behavior based on personal characteristics (Varotsis et al., 2017), as well as the elimination of the shadow economy, which can be accomplished by institutionalizing the obligation of all financial transactions through electronic means. Furthermore, the expansion towards a knowledge society, as reformed by a knowledge economy in which a revised fiscal policy of lower taxation intervenes, pushes the tax authority in the marginalization of the shadow economy by installing knowledge management through an artificial intelligent tax control tool.
In Greece, the tax authority is regarded as a self-contained entity with its own administration. Human resource management is characterized by extensive bureaucracy and a severe lack of motivation (Varotsis, 2019). An economy with persistently high percentages of the shadow economy, a fiscal policy with high tax rates that perpetuates social inequality, and a lack of resources for social policy. In particular, social inequalities are exacerbated when taxation is limited to recorded property according to tax returns, rather than focusing on personal property, income, and expenditure, an outdated procedure that tolerates the dishonesty of a large number of taxpayers over time. A mandatory electronic payment system, in which the recorded tax material is identical to the records of the financial transaction information systems, can completely reform an economy with such fiscal peculiarities (Tsindeliani et al., 2021). It is about a challenge that will result in tax equity and increased social policy.
H4. The relationship between the foresight model and fiscal policymaking is strengthened by knowledge management.
H5. The growth of the knowledge economy is positively related to the foresight model.
H6. The foresight model is positively related to the expansion of social policy.
2.3. E-Payment Behavior and Shadow Economy
An e-payment is the electronic and/or digital payment of a financial transaction. It refers to an intangible transaction that occurs without the use of paper money and can take various forms such as online payment, e-banking, mobile payment, card payment, near field communication (NFC) payment, telematics payment, or/and any other intangible means of payment (Markoska & Ivanochko, 2018; Karjaluoto et al., 2020).
Despite the fact that there are studies relating e-payment to consumer behavior and e-commerce (Junadi & Sfenrianto, 2015; Liu et al., 2021), from a fiscal planning perspective, tax behavior exhibits specific differences and characteristics that go beyond intentions and are delimited by the motivation on tax cheating (Welch et al., 2005) and the fear of compliance (Scholz & Pinney, 1995) in the tax regulatory framework, with frequently unforeseen outcomes (Katerelos & Varotsis, 2017). However, e-payment behavior approaches tax behavior (Treiblmaier et al., 2006) when all payments in an economy are made electronically (Night & Bananuka, 2020). In an economy where an electronic payment tax system is applied to all economic transactions, e-payment behavior appears in every citizen-producer-consumer transaction, thereby digitally shaping the GDP of the economy according to electronic transactions.
Security (Lin & Nguyen, 2011), usability (Hsieh et al., 2013; Schuh & Stavins, 2016), ease of use (Jose Liebana-Cabanillas et al., 2014), and the perceived risk of using e-payment methods are crucial for the adoption of immaterial monetary transactions (Ho et al., 2020). Enhancing e-payments is advantageous for economic development, resulting in a favorable increase in public revenues (Wisniewski et al., 2020). In addition, the widespread use of mobile phones has the effect of increasing the perceived utility of online mobile e-payments among users (Garrett et al., 2014; Bailey et al., 2022). Specifically, the availability of e-payments through multiple channels (bank cards, POS, online payments, mobile payments, NFC) complements the consumer public by eliminating cash transactions (Trutsch, 2016). This conclusion is also supported by the characteristics of tourists’ e-payment intentions (Sun et al., 2022). In a knowledge economy, the universal use of intangible payments in transactions after legislative regulation may have a peer-to-peer social influence effect (van der Cruijsen & Knoben, 2021). A fiscal policy informed by knowledge management that digitizes financial transactions reduces the shadow economy (Zhanabekov, 2022) and has a positive impact on tax revenues.
Whether e-payments bypass the shadow economy (Haruna & Alhassan, 2022), the question arises as to whether the digital shadow economy is simultaneously being strengthened (Gaspareniene & Remeikiene, 2015). Previous research (Gaspareniene et al., 2016b) has demonstrated that although the profile characteristics of those who participate in the digital shadow economy are not significantly different from those who participate in the traditional shadow economy, there are specific factors that may stimulate the shadow digital economy (Mroz, 2016). These factors can be summed up as the desire for a lower price, the level of social influence for illegal online transactions, and the lack of fear of punishment. However, by strengthening the legal sanctions framework, the incentive for tax evasion can be significantly reduced (Hasseldine et al., 2007).
H7. The relationship between the foresight model and e-payment behavior is positive.
H8. The shadow economy has a negative relationship with e-payment behavior.
3. Research Methodology
To test the eight hypotheses, an empirical survey was conducted. Based on previous empirical studies, the anonymous questionnaire was created (Sondakh, 2017; Varotsis & Katerelos, 2019). The survey was carried out by creating and distributing an anonymous questionnaire to a sample of the population via the snowball method who responded positively to an invitation to participate. The questionnaire examines the relationship between implementing a fiscal policy foresight tax model and tax revenues, improving tax revenues, adjusting tax rates, reforming fiscal policy, and government knowledge management and sharing. There were two sections to the anonymous questionnaire. The first contained demographic information, while the second was a 20-item list to be rated on a five-point Likert scale. The questionnaire was created with the intention of developing a fiscal policy foresight tax model that aims to reduce the shadow economy by institutionalizing the obligation of electronic payments in a knowledge economy. After checking the scales’ reliability and construct comprehensibility, data collection began.
An anonymous questionnaire was used to conduct the survey, which had 320 participants. Using snowball sampling, 6.170 anonymous questionnaires were mailed, yielding 320 respondents (response rate: 5.2%). The sample included 159 men (49.7%) and 161 women (50.3%) from the entire Greek territory. Email and social networks were used to contact participants. The demographic characteristics of the sample are shown in Table 1. Through an online survey, participants voluntarily completed an anonymous questionnaire about a model of fiscal policy foresight.
The sample consists of 161 women and 159 men, as shown in Table 1. Random samples are distributed across Greece. 45% of the sample is productive age (36 - 50), 37.50% is young, and 15.50% is over 51. 46.30% are single, 43.40% are married, 8.40% are divorced, and 1.90% are widowed. The education level is 36.6% high school, 44.00% bachelor’s, and 20.00% postgraduate. In terms of income, 26.6% of the sample earn less than 5000? 48.10% earn between 6000 and 20,000? 21.9% earn between 20,000 and 50,000? and 3.4% earn more than 50,000? The private sector employs 57.20% of the sample, the public sector employs 19.70%, and the rest are unemployed or work in another occupation. Table 1 displays the sample’s characteristics.
The main reason for using an online survey was the ease with which the random sample could be reached via social media and electronic mail. The online survey achieved wide sample dispersion, an easy-to-use questionnaire filling process, and confidentiality of the participant’s personal information.
Data Methods, Measure and Data Analysis
To test the study’s hypotheses, an empirical survey was conducted with Greek taxpayers. The questionnaire was based on an empirical e-payment study (Ming-Yen Teoh et al., 2013) and a tax evasion study (Hashimzade et al., 2013) that looked at the relationship between e-payment behavior, tax revenues, the shadow economy, knowledge management, and the Foresight e-Tax Model Readjustment. Greece was the site of the study’s research. The Greek economy has been plagued by persistent fiscal issues, a sizable shadow economy, excessive taxation, and a lack of effective tax collection mechanisms (Berdiev et al., 2018; Kotios et al., 2018; Kottaridi & Thomakos, 2018; Yfantopoulos & Chantzaras, 2018; Hazakis, 2022). According to the findings of the aforementioned studies, e-payment behavior is related to increased tax revenues and a reduction in the shadow economy.
The confidential survey was divided into five sections. Part A collected demographic information from participants; Part B elicited their views on the tax system and e-payments through thirty questions; Part C assessed their perspectives on fiscal policy through forty questions, and Part D elicited their views on the foresight model through fourteen questions. Section E discusses twenty-two possible motivators for the shadow economy. The survey’s goal was to provide a general overview of how taxes are handled in Greece. Another goal was to investigate how a linear model could shed light on the fiscal dynamics of Greece’s forecast.
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Table 1. Characteristics of the sample.
SPSS 20.0 was used for all regression analyses (IBM Corp., Armonk, NY, USA). Table 2 lists the variables that were measured. Participants were asked to complete a sensitivity scale with the values “totally disagree”, “disagree”, “neutral”, “agree” and “disagree” to assess the dependent and independent variables. The relationships between the dependent and independent variables, as well as the eight hypotheses, were investigated using standard multiple regression analysis. To investigate the relationship between the explanatory variables, a multicollinearity test was performed. Outlier analysis was used to eliminate cases with extreme values on one variable or an unusual combination of scores on two or more variables, resulting in a 100% success rate.
4. Results
The variables used in regression analysis are listed in Table 2. Measurement scales based on tax revenue, tax saving, adjusted tax revenue, fiscal policy reform, shadow economy, knowledge management and knowledge economy were used in this study. The validity and reliability of these measures have been established in recent research (Ganguly et al., 2019; Soneka & Phiri, 2019; Ode & Ayavoo, 2020; Schniederjans et al., 2020). The Durbin-Watson statistical test for all three models was rejected at the p < .05 level owing to the possibility of autocorrelation.
The personal wealth multiple determination coefficient R2 = .269 is slightly higher than the linear model of personal characteristics (Varotsis & Katerelos, 2018). The linear expenditure model continues to have an upwardly positive effect on tax collection. The main distinction of this model is that it examines tax collectability in terms of the daily realization of financial transactions through electronic means, thereby reducing tax evasion, which is a constant phenomenon in the Greek economy. As a result, when taxation is calculated not only on declared incomes but also on the taxpayer’s real property and is recorded by the obligation to realize personal expenses at 100% by electronic means, the tax collection result is positive. Table 2 contains descriptions of the regression variables.
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Table 2. Descriptions of the regression variables.
The developed model explains 12.6% of the total variance at the end of the first phase, while the final model explains 26.9% of the total variance, as indicated by the value of the coefficient of multiple determination R2 = .269. The correlation coefficient between the predicted values of the dependent value and the values of the independent values is 24.9%. Fiscally, this result is interpreted as a 100% conversion of the obligation to carry out financial transactions, resulting in a 24.9% increase in tax material. Pearson’s correlations are shown in Table 3.
Table 4 and Table 5 show the results of the standard multiple regression analysis and the summary results of the model, respectively. Table 4 shows the tax savings calculation for the years 2022 to 2025 following fiscal policy reformation based on the e-payments foresight tax model (e-FTM). Table 5 summarizes the multiple regression model for personal wealth. The regression analysis revealed a positive relationship between foresight e-tax model and knowledge management, with a standardized coefficient β of .217; this result was significant at the .001 level (Table 6). Furthermore, the β scores for tax saving, adjusted tax revenue, shadow economy and knowledge economy were .026, .137, .106 and −.125, respectively. The foresight e-tax multiple regression model is summarized in Table 6 (adjusted R2 = .303). The model was statistically significant.
5. Discussion
This empirical study demonstrated that a fiscal model based on foresight and the reduction of the shadow economy as a result of knowledge and economy management can significantly improve public finances. Knowledge management and knowledge economy in fiscal policy is concerned with identifying factors that improve public finances (Al Ahbabi et al., 2019). Foresight in public finance identifies tools to influence fiscal policy in the medium to long term.
Note. TR = tax revenue; TS = tax saving; AR = adjusted tax revenue; FP = fiscal policy reform; SE = shadow economy; KM = knowledge management; KE = knowledge economy; PM = e-payment behavior; FM = foresight e-tax model.
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Table 4. Tax savings calculation for the years 2022 to 2025 after reformation fiscal policymaking based on the e-payments foresight tax model (e-FTM). FOUR e-FTM CASE SCENARIOS (30%, 25%, 20%, 15%) of tax revenue from 2021 to 2025 (amounts in millions of ?.
Note: Y = Year, GDP = Gross Domestic Product, TAX = Tax Revenue, e-FTM = Foresight e-Tax Model Readjustment, TS = Tax Saving, ATR = Adjusted Tax Revenue. GDP Source: Hellenic Fiscal Council; * Source: ELSAT; ** Source: OECD.
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Table 5. Summary of personal wealth multiple regression model.
a. Predictors: (Constant), REGR factor score 5 fa 2; b. Predictors: (Constant), REGR factor score 5 fa 2, 2 fa 2; c. Predictors: (Constant), REGR factor score 5 fa 2, 2 fa 2, 3 fa 2; d. Predictors: (Constant), REGR factor score 5 fa 2, 2 fa 2, 3 fa 2, 1 fa 2; e. Predictors: (Constant), REGR factor score 5 fa 2, 2 fa 2, 3 fa 2, 1 fa 2, 4 fa 2; f. Dependent Variable: Tax.
The revision of the fiscal function appears to be an important factor in reducing the shadow economy (Huynh, 2020). GDP growth is required for the elimination of the shadow economy. Direct digital recording of monetary transactions is involved in electronic payments. As a result, e-payment behavior has a negative relationship with GDP and the shadow economy (Mansour & Zaki, 2020). The hypotheses of the current empirical study examine the impact of a fiscal policy foresight model variation following the mandatory institutionalization of electronic payments on GDP, the shadow economy, knowledge management, and the fiscal function’s economy. Some useful conclusions were reached using the basic hypothesis as a foundation, as discussed further below.
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Table 6. Regression of the foresight e-tax model.
Note: N = 101; standardised coefficients are shown; *** significant at p < .0001, ** at p < .01, * at p < .05. CI=confidence interval; LL=lower limit; UL=upper limit.
According to H1, the foresight model is positively related to fiscal policy reform. The negative coefficient for fiscal policy reform (β = −.430) rejected this hypothesis. Taking into account the foresight e-tax model readjustment based on the personal wealth model, the linear regression results highlight the opposite course of fiscal policy in relation to the full digitization of financial transactions. As a result, fiscal policy decisions in recent years, such as the continuation of overdue debts through multi-installment settlement programs or out-of-court settlement, have resulted in taxpayers’ addiction to the extension of debt payment. To summarize, a tax foresight model based on the obligation of electronic transactions necessitates fiscal policy in the opposite direction, that is, toward timely e-payment of taxes, full implementation of accounting e-transactions, and digitization of all tax procedures via technological augmented reality applications (Roussou et al., 2019).
H2 hypothesizes that the foresight e-tax model is positively related to tax revenue based on the fiscal discipline foresight model (Agenor & Yilmaz, 2011). Despite the fact that it was not statistically significant, the regression analysis did not confirm the positive relationship (β = .003). Given that the tax system as it was formed in the previous decade is based on the capture of incomes rather than actual transactions, excluding shadow economy transactions, this result emphasizes that a model of tax foresight presupposes the elimination of tax evasion. According to the personal wealth model, the current tax system results in a tax loss ranging from 44.266 billion ?to 88.452 billion ? The implementation of a tax foresight model necessitates a redefinition of taxable material in relation to unrecorded real incomes as a result of Greece’s high rates of tax evasion (Vasardani, 2011).
H3 indicates a negative relationship between the foresight model and the shadow economy. However, the regression analysis revealed that the shadow economy had a statistically significant and positive relationship (β = .106; sig. = .059) with the foresight model, thereby refuting H3. However, the highly positive coefficient (β = .217, sig. = .001) confirmed H4 that the foresight model is positively related to fiscal policymaking due to knowledge management. These findings decouple tax planning foresight from the shadow economy, allowing tax collectability to be achieved through fiscal management of knowledge. Furthermore, it confirms that the failure of a tax system to reduce the shadow economy is clearly due to knowledge of fiscal management rather than a flaw in the existing tax system (Zagler & Dürneckerd, 2003).
H5 suggests that the foresight model is related to knowledge economy growth, whereas H6 suggests that the foresight model is related to social policy expansion. However, the regression analysis revealed that the knowledge economy had a statistically significant negative relationship with the foresight model (β = −.125; sig. = .054); thus, H5 was rejected. This finding suggests that in a tax foresight model, more knowledge management rather than a knowledge economy has a positive impact. As a result, tax performance foresight is disconnected from the development of the knowledge economy as well as social characteristics that resist tax revenue growth (Riege & Lindsay, 2006). According to the findings of this study, the expansion of social policy is more a result of knowledge management than knowledge economy, so H6 was rejected.
H7 contends that the foresight model is related to e-payment behavior, whereas H8 contends that the shadow economy is related to e-payment behavior negatively. While H7 was not confirmed by the regression model’s non-statistically significant results, H8 is supported by both the personal wealth model and the negative relationship between the shadow economy and e-payment behavior (−.504). After all, it is e-payment behavior that is the primary reforming factor in a tax foresight model based on the mandatory institutionalization of e-transactions, resulting in real-time e-clearance of tax (Azmi et al., 2016). This finding confirms that reforming the tax system is only possible by institutionalizing the full obligation of e-transactions, without the possibility of cash transactions that feed the shadow economy to increase tax revenues. However, the findings suggest that in order to increase tax revenues, e-payment behavior cannot be based on a predictive model. It could instead be based on knowledge management. However, e-payment behavior is the primary factor in shrinking a shadow economy that has grown over time.
The present research has yielded some intriguing conclusions, which are deduced from the aforementioned. In order to improve the tax system and tax collection, a model of tax foresight should be based on fiscal knowledge management, as suggested by both the post-fiscal policy reform readjustment model based on the e-payments prospective tax model and the linear regression model of tax foresight. In addition, the key factor in the development of the shadow economy is e-payment behavior. Whether due to e-clearance of tax or because e-payment of transactions is not related to tax evasion due to e-recording of the transaction, the transition from a conventional economy to an e-economy indicates the possibility of a model of tax foresight that reduces the shadow economy, thereby increasing taxable material and taxes collected.
6. Implications, Limitations and Future Research
This study highlights the significance of planning ahead for taxes and coordinating data in order to improve fiscal policy. On the basis of the e-payments taxation perspective model, four distinct fiscal policy reform scenarios that result in increased tax revenues were examined. Both the personal wealth model and the predictive e-tax model bolstered this study’s fundamental theoretical framework. These findings support the notion that a model of tax foresight based on the fiscal management of knowledge and the institutionalization of the mandatory nature of e-transactions improves fiscal resources thematically.
Despite this, the current study is based on statistical data regarding the factors that influence a tax foresight model. Despite the intrinsic validity of the study, the methodology employed, and the five-point Likert scale, the study has two significant limitations (Rasinger, 2010). First, the impossibility of documenting the truthful responses of the participants, as the confidentiality of personal financial information necessitated the preparation of an anonymous questionnaire, as well as the tendency of respondents to avoid the choice of low- and high-threshold responses, particularly when the topics concerning tax and e-payment behavior.
Furthermore, because tax reforms are frequently implemented in the Greek economy, the implementation of a tax foresight model is hampered by a dynamic legislative framework. Because tax reform trends are likely to change in the future, their impact on the tax foresight model must be investigated separately. Furthermore, the rapid expansion of the use of electronic devices influences general e-information behavior and will most likely influence e-payment behavior in the near future. A study that examines the relationship between tax and e-payment behavior and incorporates new changes in the dynamic frameworks of tax policy and e-information behavior may yield differentiated results in the near future.
7. Conclusion
This study examined the factors associated with the implementation of a model of tax foresight based on the legal requirement to conduct electronic transactions. The relationship between the foresight taxation model and seven different factors was investigated, with the conclusion that knowledge management has a positive effect on increasing tax revenues. In addition, the inverse relationship between e-payment behavior and the shadow economy was confirmed, reinforcing the belief that the requirement of e-transactions dramatically increases taxable material and, by extension, tax revenues, thereby reducing tax evasion.
In conclusion, this research places tax foresight and knowledge management at the forefront of fiscal policy reform. Scenarios in which e-transactions are institutionalized and taxes are collected using artificial intelligence software significantly increase tax revenues. E-payment adoption is correlated with a decline in the shadow economy and an increase in GDP. Knowledge management improves social policy as a result of mandating e-payments. A model of tax foresight based on the institutionalization of the compulsion of e-transactions in the economy can achieve social justice through equitable taxation and the mitigation of distortions resulting from tax evasion.
Data Availability Statement
Due to the nature of this research, the participants did not agree for their data to be shared publicly, so supporting data are not available.