EXTRAORDINARY CRIMES OF CORRUPTION VERSUS EXTRAORDINARY EVENTS OF COVID-19 IN INDONESIA

Indra Kusuma¹, Reskino²

Universitas Trisakti, Indonesia

UIN Syarif Hidayatullah Jakarta, Indonesia

ï¿½ Corresponding Author: Reskino

Abstract

corruption, fraud triangle, inflation (CPI), crime rate, government spending

The COVID-19 pandemic has presented new challenges in governance in Indonesia, including increasing opportunities for corruption in the management of public funds. This study aims to examine the influence of internal factors, such as the weakness of the internal control system, and external factors, such as inflation, on corruption during the COVID-19 pandemic. The research method used panel data analysis with Partial Least Square (PLS) software in 32 provinces in Indonesia during the 2018-2021 period. The findings show that inflationary pressures significantly affect corruption actions, while weaknesses in the internal control system do not show a significant effect. These results indicate that the pandemic, in addition to being a health crisis, also provides opportunities for corrupt practices due to weak supervision. The implications of this study emphasize the need to improve internal control systems and holistic anti-corruption strategies, including increased transparency and accountability in the management of public funds during emergencies.

ï¿½ 2024 by the authors. Submitted for possible open access publication

under the terms and conditions of the Creative Commons Attribution (CC BY SA) license (https://creativecommons.org/licenses/by-sa/4.0/).

1 Introduction

Corruption has long been a global challenge that haunts various countries, including Indonesia. Corruption is considered an extraordinary crime because of its significant impact on the economy, governance, and public trust (Abdullah, 2019). In a global context, corruption is often considered a major obstacle to economic and social development (Wei, 1999). According to a report by Transparency International, corruption has a damaging effect not only on government institutions but also on the private sector, which can hinder investment and economic growth (Collier, 2002).

The COVID-19 pandemic has brought new challenges to the world, including in the context of governance. In Indonesia, the pandemic has opened up new opportunities for corrupt practices, especially in the management of social assistance funds (Setiawan & Jesaja, 2022). For example, the case of corruption of social assistance funds by government officials during the pandemic has been in the spotlight (Tempo, 2020). The World Health Organization (WHO) even mentioned that the pandemic could exacerbate corruption by creating loopholes in the surveillance system (World Health Organization, 2020).

The corruption that occurred during the COVID-19 pandemic shows how emergencies can be used for personal gain. This condition underscores the importance of strengthening the supervision and accountability system, especially in crises (Lemhannas RI, 2021). The global outbreak of COVID-19 has affected many sectors, ranging from the health sector to the economic sector. Countries in Asia, the Americas, Africa, Europe, and Australia/Oceania experienced a sharp economic contraction (contraction) due to the global COVID-19 outbreak. The economic growth of affected countries is influenced by pandemic cases, exposure time, population, regional differences, and differences in country status (Junaedi & Salistia, 2020).

In addition to having a negative impact on the Indonesian economy, namely a slowdown in economic growth, the outbreak of the COVID-19 pandemic has had a positive impact on crime incidents and a decrease in criminal cases. The government's efforts to limit community activities (PPKM) limit the space for criminals to commit crimes. The number of crime incidents (total crime) in 2019 was 269,324; This figure continued to decline in 2020 and 2021, to 247,218 incidents and 239,481 incidents, respectively. Even in cases of corruption that are considered extraordinary crimes, corruption has declined. The number of cases recorded by the National Police Headquarters of the Republic of Indonesia (Polri) shows that corruption cases have decreased from 2019, 2020, and 2021 to 488, 376, and 364 corruption crimes, respectively (Directorate of Social Resilience Statistics, 2022).

Similar to data from the National Police Headquarters, the Corruption Eradication Commission (KPK) also released data on corruption crimes (TPK), showing a decrease in 2020 from the previous year, from 147 to 91 cases, but in 2021 it rose again to 108 cases (Corruption Eradication Commission, 2023). In 2021, based on the results of Indonesian Corruption Watch (ICW) monitoring, the APBD sector is the place where the most corruption cases occur. Between 2020-2021, when the government made efforts to handle the global Covid-19 outbreak, several parties took advantage of the government's efforts to commit acts of corruption in the procurement of goods/services in the form of medical devices (known as medical devices) and also social assistance (known as Bansos) (Anandya et al., 2022). The difference in data on the number of cases handled by the National Police and the Corruption Eradication Commission is related to the authority according to the Corruption Eradication Commission law. Corruption cases worth more than one billion rupiah are handled by the KPK, and under it are handled by the National Police of the Republic of Indonesia and the Attorney General of the Republic of Indonesia.

Corruption that occurred during the Covid 19 global outbreak is one of the types in the form of corruption in social assistance, whose perpetrators are members of the community and government officials who occupy low to high positions, ranging from minor corruption to major corruption. People involved in social assistance corruption cases act as social assistance to several villages in Tigaraksa District, Tangerang Regency (CNN Indonesia, 2021a). Village officials who can be said to be government officials who occupy low positions are perpetrators of corruption cases. Research conducted by Indonesia Corruption Watch (ICW) states that the most vulnerable funds to corruption in its budget are village funds, and village governments are institutional actors (CNN Indonesia, 2021). Government officials who occupy high positions are not spared from social assistance corruption; The Corruption Eradication Committee (KPK) has appointed JB as Minister of Social Affairs, two Commitment Officials (PPK), and two private bribers as suspects in the Covid-19 social assistance corruption at the Ministry of Social Affairs (Tempo, 2020).

The events mentioned above show that fraud, both in the form of corruption and fraudulent financial reporting, also occurred during the COVID-19 pandemic in an entity/organization, both in the private sector and in the government sector. The perpetrators come from the community, the private sector, and government officials. Corruption is a concrete form of "white-collar crime" (Muhammad, 1994). Corrupt behavior is motivated to behave in a certain way by the need for achievement, affiliation, and power (Setiawan & Jesaja, 2022). A 1953 study conducted by Donald R. Cressey found that conditions that tend to trigger fraud, pressures/motives, opportunities, and attitudes/rationalization are known as the fraud triangle (Cressey, 1971).

The phenomenon of fraud, for example, in the form of corruption in social assistance, is not in accordance with stakeholders' expectations. Social assistance is supposed to be received by the people who need it, but it is reduced/cut. The phenomenon of fraud during the COVID-19 global outbreak has become an interesting topic, so much research has been carried out on social, economic, and legal aspects.

Several previous studies examining fraud in the form of false financial statements have been conducted (Azizah & Reskino, 2023; Khamainy et al., 2022; Kurniawan & Reskino, 2023; Md Nasir & Hashim, 2021; Puteri & Reskino, 2023; Reskino, Mohamed, et al., 2023; Reskino & Darma, 2023a; Seifzadeh et al., 2022). Research conducted by (Azizah and Reskino, 2023) detects false financial statements using a new theory, namely the Heptagon fraud theory developed by (Reskino, 2022). (Khamainy et al., 2022) tested fake financial statements using the fraud diamond model (Kurniawan & Reskino, 2023) conducted tests on ministries and government agencies using the Pentagon fraud analysis model, (Md Nasir & Hashim, 2020) conducted a test in Malaysia that tested GCG related to fraudulent financial statements and tested fake financial statements using a hexagonal fraud analysis model. The ethics test of Islamic work affects fraudulent financial statements moderated by fraud prevention carried out by (Reskino, Salwani Mohamed, et al., 2023) on Islamic financial institution companies in Indonesia. Furthermore, the testing of false financial reporting was researched by (Reskino and Darma, 2023), which tested financial hardship as an intervention variable in testing false financial reporting. Finally, a study conducted by (Seifzadeh et al., 2022) assessed the relationship between managerial confirmation and possible fraud in financial statements. From the literature above, there is not much research on false financial statements related to COVID-19 conditions. Therefore, this study fills in the gaps in the literature that previous researchers have not studied.

In addition to research that examines fraud in the form of fraudulent financial statements during the Covid 19 pandemic, many parties are also interested in studying corruption as a form of fraud. Research on the phenomenon of corruption during the Covid-19 pandemic, especially from a legal, economic, social, and political perspective and its implications in preventing the enforcement and prosecution of corruption cases (Disantara et al., 2022) uses a qualitative approach, not many studies use this quantitative approach. Corruption research using quantitative methods has been widely studied but with samples from before the COVID-19 pandemic.

Research on corruption using a quantitative approach was carried out (Saputra & Setiawan, 2021) by using proxy values for non-compliance with laws and regulations in the Audit Report (LHP) of the Audit Board of the Republic of Indonesia (Erlando, 2019) (Abdullah, 2019) and (Akbar, 2012) using proxies for the Corruption Perception Index (GPI), in contrast to (Rahmasari & Setiawan, 2021a) using proxy for state/regional loss values which has committed to measuring corruption as a dependent variable. However, it is still rare to use proxies for the number of small and large corruption cases.

The discussion of corruption is not spared from external and internal factors on the part of actors or entities/organizations that affect an act of corruption. Internal factors include government revenue-expenditure assets, weaknesses in the internal control system (SPI), non-compliance with laws and regulations, and accountability. External factors include inflation, growth, and the human development index. The results of research on the influence of government spending on corruption show that the operating expenditure ratio has no effect, and the capital expenditure ratio has a positive effect (Abdullah, 2019), while other studies show that the value of spending has a negative effect (Erlando, 2019). Economic growth has a negative impact on corruption (Erlando, 2019), while other studies show that economic growth does not (Abdullah, 2019; Saputra & Setiawan, 2021). The Human Development Index (HDI) has a positive effect on the Corruption Perception Index (GPI). The higher the HDI, the lower the level of corruption (Erlando, 2019), while other studies show that HDI does not affect corruption (Abdullah, 2019; Saputra & Setiawan, 2021). This study shows the inconsistency of the research results obtained regarding the influence of internal and external factors of actors or entities on corruption.

Research on corruption mainly focuses on external factors from the economic side, while the social influence of the surrounding environment has not been widely studied. One of the social influences behind criminal acts in the form of corruption is the level of crime in the surrounding environment. Individuals are much more likely to commit crimes if they consider that criminal activity is widespread and tend to conclude that the risk of being caught for crime is low (Kahan, 1997). This is in line with research by Joanna Golden, which found that a local and positive crime environment is associated with the likelihood that companies are involved in financial reporting fraud and that companies headquartered in high-crime areas are associated with more significant financial reporting fraud (Golden, 2021). For research on corruption, the relationship and influence of the criminal environment have not been widely studied.

Based on the facts and background above, corrupt practices are still rampant, even looking for opportunities during the COVID-19 pandemic. In addition, there are still some inconsistencies between the results of various previous studies, so it is interesting to test and obtain empirical evidence on the influence of internal and external factors on the perpetrator side or the entity/organization side from the perspective of the fraud triangle theory as a factor that can cause corruption crimes during the global pandemic. COVID-19 at the provincial level in Indonesia.

This research is expected to contribute by increasing understanding of the scientific field of fraud in general and the field of corruption in particular, answering problems that may exist in indications of corruption and integrating fraud triangle theory and interdisciplinary theory (political, economic, and cultural) to find the dominant factors in the emergence of corruption. In addition, stakeholders can lead to the formulation of strategies to reduce corruption in prevention and prosecution.

2 Materials and Method

This research is in the form of causality research, quantitative studies and hypothesis testing research. This study intends to examine the influence of one variable that causes the effect of change on other variables (Sekaran & Bougie, 2016). The method applied in this study is quantitative descriptive analysis. The data analysis method in this study uses the Partial Least Square (PLS) analysis method with the SmartPLS tool. Because when using regression, many data must be discarded to meet the assumption of normality. Thus, PLS is used as an analytical tool because of the benefits of being free from the assumption of normality (Ghozali & Latan, 2020). The data analyzed in this study is secondary data obtained based on information from the Central Statistics Agency (BPS) report, Provincial Government Financial Statements (Audited), and BPK Audit Report.

The population in this study is corruption cases in all provinces in Indonesia. In the selection of samples, this study applies a purposive sampling technique with the aim that the data obtained is more representative and in line with the needs of this research. Of the total corruption cases in 34 provinces, corruption cases were taken in 32 provinces that met research needs within two years before and two years after the Covid 19 pandemic (2018-2021). The data collection technique is carried out through the following methods:

Secondary Data

Data is collected from official sources such as Statistical Reports from the Central Statistics Agency (BPS). Provincial Government Financial Statements (audited). Report on the Results of the Audit of the Financial Audit Agency (BPK). Data related to crime rates and other socio-economic indicators.

Documentation Techniques

Documents relevant to the study, such as crime statistics reports, human development index (HDI) data, and regional expenditure realization reports, support the analysis.

Data Panel Approach

This study uses panel data with regression analysis techniques through Smart PLS (Partial Least Square) software. The data covers 32 provinces in Indonesia during the two years before and two years after the COVID-19 pandemic (2018ï¿½2021). The collected data is then processed to test the hypothesis using the PLS data analysis model, which does not require the assumption of normality.

3 Results and Discussion

Descriptive Statistical Test

The descriptive statistical testing in this study is intended to provide an overview of the characteristics of the research variables, including the number of observations (N), the mean value, the highest value (maximum), the lowest value (minimum), and the standard deviation value that describes the data distribution.

Table 1. Descriptive Statistics

\	N	Mean	Median	Min	Max	Standard Deviation
CORRUPT	128	22.648	15	0	171	25.649
CRT	128	143.703	133	15	416	77.757
IPM	128	71.131	71.29	60.06	81.11	3.906
PLANS	128	29.106	28.93	27.98	30.75	0.664
BO	128	29.166	29.03	27.37	31.62	0.813
BM	128	27.67	27.66	25.9	30.28	0.693
BRANDS	128	27.698	27.63	25.59	30.23	1.12
LOK	128	0.5	1	0	1	0.5
TP	128	21.703	19	6	71	11.503
IKK	128	107.151	101.21	88.67	227.9	22.11
CPI	128	120.524	126.529	102.937	148.13	15.524

Table 1 shows that the corruption variable has a standard deviation value that is more significant than the average value, which indicates that the variable data is heterogeneous. For other variables, the standard deviation is less than the average value of each variable. The data is homogeneous, thus showing that this study has good data quality.

Model Evaluation

Evaluation of the Outer Model (Measurement Model)

This external model evaluation determines the relationship between the latent variable and its indicator. Alternatively, the outer model can be defined as the relationship between each indicator and its latent variable. Evaluation of measurement models uses standard evaluation criteria that support measurement parameters, reliability, and measurement validity.ï¿½ï¿½ ï¿½ï¿½ï¿½(Hair et al., 2019; Sarstedt et al., 2017).ï¿½

Table 2. Building Reliability and Validity

	Alpha Cronbach	rho_A	Composite Reliability	Extracted Average Variance (AVE)
BM	1	1	1	1
BO	1	1	1	1
BRANDS	1	1	1	1
CRP	1	1	1	1
CRT	1	1	1	1
CPI	1	1	1	1
IKK	1	1	1	1
IPM	1	1	1	1
LOK	1	1	1	1
PLANS	1	1	1	1
TP	1	1	1	1

Table 2 above shows that the model has good reliability, with a Cronbach Composite Alpha, rho_A and a Reliability value of 1 (above 0.7). The table also shows that the model has good validity and accuracy, with an Average Variance Extracted (AVE) value of 1 (above 0.5) so that the model can represent the actual phenomenon.

Table 3. Statistics of Collinearity (VIF)

	BM	BO	BRANDS	CORRUPT	CRT	CPI	IKK	IPM	LOK	PLANS	TP
VIF	1	1	1	1	1	1	1	1	1	1	1

Table 3 shows that there is no multicollinearity problem with a Collinearity Statistics (VIF) value of 1 (below 3).

Evaluation of the Internal Model (Structural Model)

In-model evaluation/structural model evaluation is carried out to ensure that the structural model built is robust and accurate. Structural model evaluation uses standard evaluation criteria to support structural model measurement parameters (Hair et al., 2019; Sarstedt et al., 2017).ï¿½

Table 4. Structural Model Test (Internal Model)

Variable	R square	R Square Customized
CORRUPT	0.354	0.299

In Table 4 above, this study has an Adjusted R-square value of 0.299. This means that 29.9% of the independent variable can only explain the dependent variable, while 70.1% is influenced by other variables outside this study, such as social and political variables. According to the criteria, the R-square value of 0.354 is more significant than 0.33 but lower than 0.67, indicating an explanation of the medium/medium research model (Ghozali & Latan, 2020).ï¿½

Table 5. Compatibility Test Results

	Saturated models	Forecast Models
SRMR	0	0
d_ULS	0	0
d_G	0	0
Squares	0	0
NFI	1	1

The PLS model fit test can be seen from the Standardized Root Mean Square (SRMR) value in the model and can be used to avoid specification errors. The PLS model is declared to meet the match model criteria if it meets the SRMR limiter criteria of <0.10, and the model is declared a perfect match if the SRMR value is <0.08. From Table 3 above, the test results show that the SRMR value of the Saturated Model is 0.000, and the SRMR value of the Estimated Model has an SRMR of 0.000. Because the value of the SRMR saturation model and the estimation model is less than 0.10, the PLS research model is declared fit and meets the goodness of fit criteria, so this model has feasibility in testing the research hypothesis.

Hypothesis Testing With Bootstrapping

Table 6. Summary of Path Coefficient Results and Original Hypothesis Testing (Bootstrapping)

Variable	Original Sample (O)	T Statistics (\|O/STDEV\|)	P Value	Conclusion
BM -> CORUP	0.008	0.07	0.944	Rejected
BO-> CORRUPT	0.044	0.246	0.806	Rejected
BTRANS -> CORRUPT	0.241	2.299	0.023	Accepted
CRT -> CORRUPT	-0.202	2.038	0.044	Accepted
CPI -> CORRUPT	0.513	6.589	0.000	Accepted
IKK -> CORRUPT	0.002	0.02	0.984	Rejected
IPM -> CORRUPT	-0.029	0.344	0.732	Rejected
LOK -> CORRUPT	0.099	0.943	0.348	Rejected
PTRANS -> CORRUPT	0.014	0.07	0.944	Rejected
TP -> CORRUPT	0.037	0.367	0.714	Rejected

Table 6 shows that bootstrapping can be used to evaluate the statistical significance of different path analyses and process outcomes, including path coefficients. The results showed that three variables (BTRANS, CRT and CPI) affected the CORRUPTUP variable; the other variables did not.

The effect of cost-of-living-inflationary pressures (consumer price index) on corruption.

The results of the significance test of individual parameters in Table 4 showed a positive β coefficient value of 0.513, with a P value of significance value of 0.000. A significance value of less than 0.05 means that H1a is accepted, so it can be said that the inflation variable (CPI) has a positive effect on corruption. The results of this study support the results of research conducted, which explains that inflation has a positive relationship/influence on corruption (Akï¿½a and Ata, 2012; Braun and Tella, 2004),ï¿½

This research illustrates how inflation can create opportunities for corruption. As the cost of living increases, so do the needs of individuals and institutions. The pressure to meet the needs in the midst of limited legal resources can encourage certain individuals to commit acts of corruption. While the study shows a positive correlation, it is important to remember that inflation is not the only driver of corruption. Other factors, such as weak law enforcement, lack of transparency, and permissive culture, can also contribute to the prevalence of corruption.

Therefore, efforts to eradicate corruption must be comprehensive, not just about controlling inflation. Strengthening institutions, increasing transparency, and fostering an anti-corruption culture are important steps to effectively eradicate corruption.

This study opens the door for further research to understand the mechanism behind the relationship between inflation and corruption. Additionally, identifying the right strategies to combat corruption, especially in the context of high inflation, is important.

The effect of cost-of-living-inflationary pressures (expensive construction index) on corruption.

Analysis of the significance of individual parameters revealed a positive beta coefficient (β) of 0.002. It shows a positive correlation between cost-of-living inflationary pressures (as measured by the construction cost index) and corruption. In other words, when construction inflation increases, there may also be an increase in corruption rates.

However, the P-value associated with significance is 0.984. A P value greater than 0.05 (a generally accepted threshold) indicates that this finding is not statistically significant. This implies that the inflation variable (CPI) does not have a significant influence on corruption. Although there is a positive relationship between the construction cost index and corruption, it does not mean that the index directly causes an increase in prices (markup) in procurement. The construction cost index is just one of the factors considered when setting the Unit Price Estimate (HPS). Many other elements, such as project efficiency, material quality, and unethical business practices, can also affect markup.

The Influence of Attitudes/Rationalization on Corruption Actions

The influence of the level of rationalization-crime on corruption.

The study delves into the complex relationship between crime rationalization and corruption, offering an interesting analysis with statistically significant results. The significance test of individual parameters revealed a negative beta coefficient (β) of -0.202, with a P value with a significance value of 0.044, indicating a positive relationship between crime and corruption rates. These findings are in line with previous research by (Golden, 2021 and Kahan, 1997), suggesting that higher crime rates can lead to a phenomenon known as fraud rationalization. In a society with normal acceptance of criminal activity, the boundaries between acceptable and unacceptable behavior can become blurred, potentially leading to an increase in corruption.

While this study points towards a positive association, it does not necessarily establish causation. Other factors, such as weak law enforcement or a culture of impunity, can encourage crime and corruption. In addition, the study could benefit from further investigation into the specific mechanisms by which crime rates rationalize corruption.

The effect of work site rationalization on corruption.

This study explores the influence of workplace rationalization on corruption, offering interesting, significant test results. While Table 4 shows a positive beta coefficient (β) of 0.099, indicating a relationship, the P-significance value is 0.348 (greater than the generally accepted threshold of 0.05). This requires a rejection of the H2a2 hypothesis, which indicates that the worksite variable has no significant effect on corruption. In simpler terms, placing an office in a high-crime neighborhood does not necessarily lead to a higher likelihood of corruption.

These findings are in line with previous research that emphasized the importance of culture and good governance as a bulwark against corrupt behavior. While the location of the office can exert some influence, factors such as values and norms embedded in the organization, along with a strong system of accountability and transparency, play a more important role in preventing corruption.

It is important to acknowledge that this study establishes a correlation, not a causation. Office location and corruption may be related, but one does not necessarily lead to the other. Other factors, such as organizational culture or incentive structure, maybe more influential. Additionally, the impact of office locations may depend on the context, varying based on the type of industry, local culture, and the severity of crime in the surrounding area.

While crime-prone job sites can create pressure for individuals to commit fraud, the opportunity for such actions is not always location-bound. Regardless of the location of the office, strong internal controls and systems within an organization can minimize these opportunities.

Similarly, an individual's rationalization for committing fraud may not always come from the job site. Organizational culture, social norms, and personal values can play a greater role in influencing an individual's rationalization. Likewise, the ability to commit fraud is not inherently related to the job site. The skills and knowledge needed can be obtained anywhere.

Finally, individuals' arrogance that leads to fraud is not solely related to location. Factors such as an individual's perception of the effectiveness of law enforcement or a culture of impunity within an organization can significantly affect their level of vanity.

The analysis, informed by the Pentagon's fraud theory, highlights that workplace rationalization is only one of the factors contributing to corruption. Organizational culture, accountability systems, and social norms play a more significant role in preventing fraud. Further research is needed to explore how elements of the Pentagon's fraud theory interact with workplace rationalization and corruption. Understanding these complex interactions can help us formulate more effective and targeted strategies to combat corruption by addressing all contributing factors.

The influence of rationalization of quality of life on corruption.

In this study, the value of the negative coefficient β shows that, theoretically, there is a negative relationship between quality of life and corruption intentions. This means that the higher a person's quality of life, the lower the likelihood that they will have the intention to engage in corrupt behavior. However, these findings obtained a significant P value (0.732), suggesting that this relationship has no statistical significance. When the P-value is greater than the specified significance level (usually 0.05), as in this case, we do not have enough evidence to reject the null hypothesis. In this case, the null hypothesis states that there is no relationship between quality of life and corruption intentions. Therefore, the results of the analysis show that there is no significant relationship between quality of life and corruption intentions during the COVID-19 pandemic.

Further interpretations suggest that while the quality of life may have declined during the pandemic and government-imposed restrictions on movement, this has not significantly affected individuals' corruption intentions. This highlights the complexity of other factors that can influence corrupt intentions, such as internal controls, ethical values, or other psychological and social factors. As such, these findings emphasize the importance of understanding the factors that influence corrupt behavior in the context of crises such as pandemics, as well as the need for a more holistic approach to designing corruption prevention policies.

From the perspective of the Pentagon Fraud theory, the findings show that despite the decline in quality of life during the COVID-19 pandemic and the restrictions on movement imposed by the government, there is no significant influence on individuals' corrupt intentions. This suggests that factors in the Pentagon Fraud theory, such as pressure, opportunity, rationalization, capacity, and motives, may not significantly influence corrupt intentions in such crises (Albrecht et al., 2019; Cressey, 1953; Johnston, 2005; Rokeach, 1973; Sutherland, 1983).

Although external pressures such as declining quality of life and movement restrictions can trigger corrupt intentions, the findings suggest that these are not significant triggers. This suggests that in the context of a pandemic, other factors, such as internal control and individual ethical values, may be more influential in determining corrupt behavior. As such, these findings provide a deeper understanding of the complexity of corrupt behavior in crises and demonstrate the importance of engaging broader factors in understanding corruption dynamics.

The Effect of Opportunity on Corruption Actions

The effect of income transfer opportunities on corruption.

The results of the significance test of individual parameters in Table 4 show a positive β coefficient value of 0.014, with a P value of significance value of 0.944. A significance value greater than 0.05 means that H3a is rejected, so it can be said that the transfer income variable does not affect corruption. Income transfer is an opportunity for certain individuals to misuse funds, but during the COVID-19 pandemic, the movement space for these individuals was limited, so this opportunity was not taken.

The study's findings revealed that income transfer opportunities did not significantly influence corruption during the COVID-19 pandemic. The analysis yielded a positive beta coefficient of 0.014, which theoretically shows a positive correlation between income transfer opportunities and corruption behavior (Albrecht et al., 2019).

However, the analysis also yielded a significant P-value (0.944), which is substantially higher than the standard significance level (typically 0.05). This leads to the rejection of the alternative hypothesis (H3a), which argues that the variable income transfer has a significant impact on corruption. In simpler terms, there is not enough statistical evidence to establish a link between income transfer opportunities and corrupt behavior.

Further interpretations suggest that while income transfers can provide opportunities for individuals to misuse funds, the COVID-19 pandemic and the measures put in place limit the individual's ability to act. As a result, existing opportunities are not significantly utilized to commit acts of corruption.

These findings highlight the importance of considering unique social and economic factors, such as the pandemic, to understand the dynamics of corruption. While income transfer opportunities can theoretically affect corruption, other factors, such as movement control, can mitigate their impact. Therefore, these results offer a deeper understanding of the complexity of corrupt behavior during crises such as the pandemic, emphasizing the need to consider the broader context in corruption analysis.

The effect of opportunity spending from operations on corruption.

The results of the significance test of individual parameters in Table 4 show a positive β coefficient value of 0.044, with a P value of 0.806. A significance value greater than 0.05 means that H3b1 is rejected, so it can be said that the variable of operational expenditure does not affect corruption. Operational spending is one of the government's efforts to run good governance, but tightening and reallocating operational spending and restricting movement reduces a person's intention to commit corruption.

The results of this study show that there is no significant relationship between operational expenditure and the level of corruption. However, this does not negate the potential role of operational expenditure in efforts to prevent corruption. This is because corruption is a complex phenomenon that is influenced by various social, political, and economic factors. Factors such as a country's culture, political values, and political system can have a significant influence. Countries with a culture that values transparency and accountability tend to have lower levels of corruption. In addition, economic and social inequality can also strengthen corruption. High inequality often leads to dissatisfaction and injustice, prompting individuals to seek unethical ways for personal gain.

Similarly, the quality of government institutions plays an important role. Institutions that are weak or vulnerable to manipulation and collusion tend to have higher levels of corruption. When the process of spending and using public funds is not transparent, the opportunity to commit acts of corruption increases. Therefore, increasing transparency and accountability in public financial management must be part of the corruption prevention strategy.

Given the Pentagon's fraud theory, corruption often results from the opportunities, pressures, and rationalizations faced by individuals or organizations. Although there is no significant direct link between operational spending and corruption, factors such as opportunity and pressure are still relevant. For example, research suggests that tightening oversight and controlling the use of public funds may be more effective in reducing corruption incentives, even though operational spending is important. This is in line with the concept of chance in the Pentagon's fraud theory, where opportunities are reduced through increased oversight and control. The pressure faced by individuals or organizations also affects the level of corruption. Widespread economic and social inequality, for example, can create pressure to seek unethical ways for personal gain. Therefore, it is necessary to design an effective corruption prevention policy.

By linking the research findings to the Pentagon's fraud theory, we can understand that although the variable of operational expenditure is not directly related to the level of corruption, other factors in the theory are still important for prevention efforts. Policy recommendations should include strategies that reduce opportunities and pressures while strengthening rationalizations that emphasize integrity and ethics in the management of public budgets.

The effect of opportunity capital expenditure on corruption.

The results of the significance test of individual parameters in Table 4 show a positive β coefficient value of 0.008, with a P value with a significance value of 0.944. A significance value greater than 0.05 means that H3b2 is rejected, so it can be said that the capital expenditure variable does not affect corruption. Operational expenditure is one of the government's efforts to make development evenly distributed, but tightening and reallocating capital expenditure reduces the number and value of projects, thereby reducing corruption.

The results show that there is no significant direct relationship between capital expenditure opportunities and corruption levels. However, these opportunities can still have an indirect impact through complex mechanisms. Large capital expenditures often create high incentives for corrupt practices, as these projects can be a source of huge profits for corrupt actors. However, if capital expenditure management is carried out effectively, including through enhanced supervision and increased transparency in the project procurement process, the potential for corruption can be significantly reduced.

In addition, reallocating and tightening capital expenditures can also affect the dynamics of corruption in the context of equitable development. By allocating resources to more equitable and strategically important projects, governments can reduce the opportunities for corruption associated with large, expensive projects that are more vulnerable to abuse.

However, focusing on capital expenditure alone is not enough to tackle corruption. Broader structural reforms in government systems and institutions are also needed. These reforms include improving the legal system and law enforcement, strengthening independent supervisory institutions, and increasing transparency and accountability in the management of public funds. With a holistic and comprehensive approach, governments and other stakeholders can develop more effective strategies for reducing corruption levels and ensuring more efficient and transparent use of public budgets.

The Pentagon's fraud theory emphasizes chance as a key factor. Although the study did not find a direct link between capital expenditure opportunities and corruption rates, the opportunities created by large and expensive projects remain an important factor. These projects are often a potential source for corrupt practices due to the high value of contracts and the lack of transparency in budget management. Tightening and reallocating capital expenditure is an effort to control society to reduce opportunities and incentives for corruption. By changing norms and practices that can support or justify corrupt behavior, this social control can help reduce the level of corruption in the management of public funds.

Transfer spending opportunities fight corruption.

The results of the significance test of individual parameters in Table 4 show a positive β coefficient value of 0.241, with a P value with a significance value of 0.023. A significance value of less than 0.05 means that H3b3 is accepted, so it can be said that the transfer expenditure variable has a positive effect on corruption. The results of this study support the results of research conducted by those who state that government spending tends to affect the level of corruption. Transfer spending for the City/Regency government has the potential to be diverted by certain individuals because City/Regency government revenues have decreased due to the COVID-19 pandemic. The results of the study show that transfer spending opportunities have a significant positive influence on the level of corruption. These findings provide valuable insights into the dynamics of corruption in the context of government spending, especially related to transfer spending (Erlando, 2019).ï¿½

Transfer spending is one of the government's main instruments in distributing funds to the regions to support economic development and equity. However, these findings highlight that the opportunities associated with transfer spending also carry a high risk of corrupt practices. Factors such as lack of oversight, low transparency, and weaknesses in the transfer fund management system can be exploited by certain individuals for corruption purposes. The decline in City/Regency government revenues due to the COVID-19 pandemic has further complicated the context of transfer spending. In difficult economic conditions, the pressure to obtain additional resources through corrupt practices can increase. Individuals who have access to and control over transfer spending may see opportunities to enrich themselves or their group in an unethical way.

The results of this study also support previous findings that show that government spending, in general, tends to affect corruption levels. This underscores the importance of good and efficient public budget management in preventing and reducing corruption. Strict supervision, high transparency, and active participation of the public in monitoring the use of transfer funds can be important strategies for overcoming corruption risks.

From the perspective of the Pentagon's fraud theory, the opportunities created by transfer spending can be exploited by certain individuals for corruption purposes. Lack of supervision, low transparency, and weaknesses in the transfer fund management system can provide opportunities for corrupt practices such as abuse of authority, collusion, and nepotism. External contexts, such as the decline in government revenues due to the COVID-19 pandemic, also complicate the dynamics of corruption related to transfer spending. In situations where resources are limited, and the pressure to obtain additional funds is increasing, individuals with access to and control over transfer expenditures may see opportunities to enrich themselves or their group in an unethical way.

Thus, these findings highlight the importance of improving public financial governance, especially in terms of managing transfer spending, to reduce the risk of corruption. Broader structural reforms, including strengthening oversight and law enforcement institutions and increased public participation in public budget oversight, also need to be considered as part of efforts to create a cleaner and more transparent environment for the management of public funds.

BPK-Opportunity Findings on Corruption.

The results of the significance test of individual parameters in Table 4 show a positive β coefficient value of 0.037, with a P value with a significance value of 0.714. A significance value greater than 0.05 means that H3c is rejected, so it can be said that the BPK's findings do not affect corruption. The weaknesses of the internal control system are reflected in the findings of the BPK during the COVID-19 pandemic did not affect corruption because most of the perpetrators of corruption were carried by the general public involved in the distribution of social assistance (Bansos). The internal control system cannot control this. The results of this study encourage in-depth reflection on the role and effectiveness of internal control systems in the prevention and reduction of corruption. This is particularly relevant given the findings of the BPK, which revealed weaknesses in the system.

BPK has an important role in examining and supervising the management of public finances at all levels of government. The findings of the BPK are often considered a key indicator related to the effectiveness of public financial governance and the level of corruption in a country. However, the results of this study show that the findings of the BPK do not have a significant direct impact on the level of corruption. This shows that the weaknesses identified in the internal control system, as reflected in the BPK's findings, have not been able to effectively prevent corrupt practices. In the context of the COVID-19 pandemic, the weaknesses identified by the CPC in the management of public funds may not directly translate to a reduction in corrupt practices.

Furthermore, a more in-depth analysis revealed that the perpetrators of corruption during the COVID-19 pandemic were mostly members of the general public involved in the distribution of social assistance (Bansos). This highlights the importance of improving the internal control system at the government level and strengthening oversight and transparency in the distribution and management of social assistance funds.

Inefficient and vulnerable management of social assistance funds shows that internal control systems alone cannot overcome complex corruption challenges, especially in the midst of emergencies such as pandemics. Therefore, there needs to be a fundamental change in the approach to corruption prevention, including increased transparency, accountability, and public participation in the supervision and management of public funds.

From the perspective of the Pentagon's fraud theory, chance plays an important role. Although the BPK's findings can identify weaknesses in the internal control system, if the opportunity for corruption remains, then the findings will not have a significant impact on reducing corrupt practices. Corrupt perpetrators can still find loopholes to commit corruption despite the findings of the BPK. In addition, pressure can also be a factor that affects corrupt practices. In the COVID-19 pandemic situation, the pressure to obtain additional resources or to meet urgent needs can increase, thus strengthening the motivation to commit acts of corruption. Rationalization, or the mental process by which individuals formulate reasons or justifications for committing acts of corruption, also plays an important role. The weakness of the internal control system revealed by the BPK's findings can be a reason for corrupt actors to violate the rules and commit corruption crimes. They may feel that the risk of committing corruption is low or that the benefits they obtain outweigh the possible consequences.

Thus, these findings highlight the importance of continuing to improve the internal control system and strengthening monitoring and control mechanisms in the management of public funds, especially in emergencies such as the COVID-19 pandemic. Only with a holistic approach based on the principles of good governance can we address the challenges of corruption and ensure a more efficient and transparent use of public funds.

4 Conclusion

Crime is a common phenomenon that often occurs in society. However, crime cannot be separated from the role of the surrounding environment, which shapes the personality of the perpetrator of crime. The surrounding environment is formed from economic, social, and cultural factors. Uneven government policies, especially economic policies, also trigger criminal behavior. Crime occurs not only in society but also in the corporate and government sectors.

The financial policies and performance of an entity/organization, both in the private sector and the government sector, are influenced by the cultural environment in which the entity is located and also the cultural environment of the organization. The moral quality of an employee is influenced by the outlook on life and the influence of the surrounding cultural environment. If both cultural environments make sense, they encourage improved employee performance. On the other hand, an inadequate cultural environment tends to influence the way employees work in the wrong direction. In the end, unscrupulous employees do something that has the potential to harm the organization, one of which is fraud.

5. References

Abdullah, F. (2019). Analysis of Determinants of Corruption in Local Governments in Indonesia (Case Study on 11 Cities in Indonesia in 2008-2017) [Thesis]. Uin Syarif Hidayatullah Jakarta.

Akbar, M. (2012). Analysis of Economic Determinants of Corruption in the Era of Decentralization in 12 Indonesian Provincial Capitals. Student Scientific Journal of the Faculty of Economics and Business, Vol 1 No 2.

Akï¿½a, H., & Ata, AY (2012). The Relationship between Inflation and Corruption: Evidence from Panel Data in Developed and Developing Countries. International Journal of Economics and Financial Issues, 2(3), 281ï¿½295. www.econjournals.com

Anandya, D., Ramadhana, K., & Easter, L. (2022). Monitoring Report on Trends in Corruption Case Enforcement in 2021.

Association of Certified Fraud Examiners. (2023). Scam 101: What is a scam? https://www.acfe.com/fraud-resources/fraud-101-what-is-fraud

Azizah, S., & Reskino. (2023a). Financial Statements for Fraud Detection: Testing Fraud Heptagon Theory. Journal of Accounting and Governance, 4 (2022).

Azizah, S., & Reskino, R. (2023b). Financial Statements Fraud Detection: Testing Fraud Heptagon Theory. Journal of Accounting and Governance, 4(1), 17. https://doi.org/10.24853/jago.4.1.17-37

Financial Audit Agency of the Republic of Indonesia. (2010). Examination Guide Series "Recognizing Fraud & Its Symptoms. Directorate of Research and Development of BPK RI.

Boyd, J., & Edwards, S. (1995). Introduction to Fraud, Corruption, and Ethics (WORKING PAPER NO. 57).

Braun, M., & Di Tella, R. (2004). Inflation, inflation variability, and corruption. Economics and Politics, 16(1), 77ï¿½100. https://doi.org/10.1111/j.1468-0343.2004.00132.x

CNN Indonesia. (2021a, August 3). The Tangerang District Attorney's Office Named a Suspect for Social Assistance Corruption of IDR 800 Million. Https://Www.Cnnindonesia.Com/Nasional/20210803100143-12-675643/Kejari-Tangerang-Tetapkan-Tersangka-Korupsi-Bansos-Rp800-Juta.

CNN Indonesia. (2021b, September 13). ICW: In 2021, the most corrupt village officials in Indonesia. Https://Www.Cnnindonesia.Com/Nasional/20210912162748-12-693206/Icw-Tahun-2021-Aparat-Desa-Paling-Korup-Di-Indonesia.

Collier, M. (2002). Explaining corruption: An institutional choice approach. In Crime, Law & Social Change (Vol. 38).

Cressey, D.R. (1971). Money of Others: A study in the social psychology of embezzlement (Reprint 1953 Ed). Wadsworth Publishing Company.

Directorate of Social Resilience Statistics. (2022). Crime Statistics 2022.

Disantara, FP, Naftali, SA, & Putra, YA (2022). Enigma Enigma Corruption Eradication during the Covid-19 Pandemic. Journal of USM Law Review, 5 No 1, 61ï¿½79. https://doi.org/10.21787/jbp.13.2021.29-40

Erlando, A. (2019). Economic Study of Corruption in Several Cities in Indonesia. Journal of EcceS, 6 Number 2, 130ï¿½151.

Ghozali, I., & Latan, H. (2020). Partial Least Squares Concepts, Techniques and Applications Using the SmartPLS 3.0 Program for Empirical Research. Undip Publishing Board.

Emas, J. (2021). Local Crime Environment and Corporate Financial Violations Using Benford's Law. Journal of Forensic Accounting Research, 6(1), 436ï¿½460. https://doi.org/10.2308/jfar-2021-003

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. In European Business Review (Vol. 31, Edition 1, pp. 2ï¿½24). Emerald Group Publishing Ltd. https://doi.org/10.1108/EBR-11-2018-0203

Hakim, W.Q., McNatt, D.B., & Xu, W. (2011). Antecedents and the effects of national corruption: A meta-analysis. World Business Journal, 46(1), 93ï¿½103. https://doi.org/10.1016/j.jwb.2010.05.021

Junaedi, D., & Salistia, F. (2020). THE IMPACT OF THE COVID-19 PANDEMIC ON THE ECONOMIC GROWTH OF AFFECTED COUNTRIES. Proceedings of the 2020 National Symposium on State Finance, 2 No 1, 995ï¿½1115.

Kahan, DM (1997). Social Influence, Social Meaning, and Prevention. Virginia Law Review, 83(2), 349ï¿½395. URL:http://www.jstor.org/stable/1073780http://www.jstor.org/stable/1073780?seq=1&cid=pdf-reference#references_tab_contents

Khamainy, A.H., Ali, M., & Setiawan, M.A. (2022a). Detecting financial statement fraud through a new fraud diamond model: the case of Indonesia. Journal of Financial Crime, 29(3), 925ï¿½941.

Khamainy, A.H., Ali, M., & Setiawan, M.A. (2022b). Detecting financial statement fraud through a new fraud diamond model: the case of Indonesia. Journal of Financial Crime, 29(3), 925ï¿½941. https://doi.org/10.1108/JFC-06-2021-0118

Corruption Eradication Commission. (2023, January 17). TPK Statistics by Region. https://www.kpk.go.id/id/statistik/penindakan/tpk-berdasarkan-wilayah

Kurniawan, D., & Reskino. (2023a). The Role of Good Corporate Governance on Financial Statement Fraud: A Pentagon Fraud Perspective on Ministries and Government Agencies. In March 2023 (Vol. 21, Edition 1). http://jurnalnasional.ump.ac.id/index.php/kompartemen/

Kurniawan, D., & Reskino, R. (2023b). The Role of Good Corporate Governance on Financial Statement Fraud: A Pentagon Fraud Perspective on Ministries and Government Agencies. Compartment: Scientific Journal of Accounting, 21(1), 111. https://doi.org/10.30595/kompartemen.v21i1.16531

Kusuma, I., Perdana, H. D., & Suranta, S. (2017). False Financial Reporting by Regency/City Governments in Indonesia1. Asia Pacific Journal of Fraud, 2(1), 27. https://doi.org/10.21532/apfj.001.17.02.01.03

Lemhannas RI. (2021, November 29). Governor of Lemhannas RI: Corruption prevention should not just be a slogan. Lemhannas RI. https://www.lemhannas.go.id/index.php/berita/berita-utama/1342-gubernur-lemhannas-ri-pencegahan-korupsi-tidak-boleh-hanya-menjadi-slogan

Maria, E., Halim, A., Suwardi, E., & Miharjo, S. (2019). Exploration Opportunities To Commit Fraud In Local Governments, Indonesia. Indonesian Journal of Accounting and Finance, 16(1), 1ï¿½16. https://doi.org/10.21002/jaki.2019.01

Md Nasir, N.A., & Hashim, H.A. (2020). Corporate governance performance and financial statement fraud: evidence from Malaysia. Journal of Financial Crime, 28(3), 797ï¿½809. https://doi.org/10.1108/JFC-09-2020-0182

Muhammad, R. (1994). Corruption as a form of white-collar crime. Jumal Law No. 2, I, 33ï¿½43.

Nafisah, S., & Triyanto, DN (2019). Transparency, Accountability, And Audit Findings At Bpk Ri To Minimize The Level Of Corruption. Research in Management and Accounting, 2(2), 67ï¿½75. https://doi.org/10.33508/rima.v2i2.2603

ï¿½zşahin, Ş., & ï¿½ï¿½ler, G. (2017). Consequences of corruption on inflation in developing countries: Evidence from panel cointegration and causality tests. Economics, 5(4). https://doi.org/10.3390/economies5040049

Patty, A. (2021). Redefinition of Corruption as "Extra Ordinary Crime." Satuharapan.Com. https://www.satuharapan.com/read-detail/read/redefinisi-korupsi-sebagai-extra-ordinary-crime

Puteri, N. N., & Reskino. (2023). Analysis of False Financial Statements Using Hexagon's Fraud Approach with Audit Committee as a Moderating Variable. International Journal of Business and Management Discovery (IJBMI) ISSN, 12(1), 35ï¿½48. https://doi.org/10.35629/8028-12013548

Rahmasari, A., & Setiawan, D. (2021). Determinants of Fraud in Local Government. JDA Journal of Accounting Dynamics, 13(1), 37ï¿½50. https://doi.org/10.15294/jda.v13i1.29137

Reskino, & Darma, A. (2023). The role of financial hardship and fraudulent financial reporting: Mediation effect testing. Journal of Accounting and Investment, 24(3), 779ï¿½804. https://doi.org/10.18196/jai.v24i3.18397

Reskino, M., I. S., M., N., & Sulistyowati, E. (2023). Islamic Work Ethics, Good Corporate Governance Practices and False Financial Statements. Asia-Pacific Journal of Management Accounting, 18(2), 79ï¿½112.

Reskino, R. (2022). Mara Technology University Fraud Prevention Mechanism And Its Influence On The Performance Of Reskino Islamic Financial Institutions.

Reskino, R., & Darma, A. (2023b). The role of financial distress and false financial reporting: Mediation effect testing. Journal of Accounting and Investment, 24(3), 779ï¿½804. https://doi.org/10.18196/jai.v24i3.18397

Saputra, N. A. A., & Setiawan, D. (2021). Fiscal Decentralization, Accountability and Indications of Corruption: Evidence from Indonesia. Bina Praja Journal, 29ï¿½40. https://doi.org/10.21787/jbp.13.2021.29-40

Sarstedt, M., Ringle, C.M., & Hair, J. F. (2017). Modeling of Partial Least Square Structural Equations. In the Market Research Handbook (pp. 1ï¿½40). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-1

Search Group. (1981). Dictionary of Criminal Justice Data Terminology (Second Edition). U.S. Department of Justice, Law Enforcement Assistance Administration, National Criminal Justice Information Service and Statistics.

Seifzadeh, M., Rajaeei, R., & Allahbakhsh, A. (2022). The relationship between entrenched management and financial statement fraud. Journal of Facility Management, 20(1), 102ï¿½119. https://doi.org/10.1108/JFM-02-2021-0026

Sekaran, U., & Bougie, R. J. (2016). Research Methods for Business: A Skill-Building Approach (Seventh). John Wiley & Sons.

Setiawan, I., & Jesaja, CP (2022). Analysis of Corrupt Behavior of Government Apparatus in Indonesia (Study on the Management of Social Assistance in the Era of the Covid-19 Pandemic). Journal of Bureaucratic Media, 33ï¿½50. https://doi.org/10.33701/jmb.v4i2.2744

Time. (2020, December 6). KPK Arrests Minister of Social Affairs Juliari Batubara. Https://Nasional.Tempo.Co/Read/1411940/Kpk-Tangkap-Menteri-Sosial-Juliari-Batubara.

Law of the Republic of Indonesia Number 1 of 2023 concerning the Criminal Code (2023).

United Nations. (2020, October 15). Statement on Corruption in the Context of COVID-19. United Nations. https://www.unodc.org/unodc/en/corruption/covid19.html

U.S. Department of Justice. (2015). Research and Evaluation of the Feasibility of White-Collar Crime and Public Corruption. www.nij.gov/funding/Pages/faqs.aspx.

Wei, S.J. (1999). Corruption in Economic Development: Useful Oil, Minor Disturbances, or Major Obstacles? www.worldbank.org/html/dec/Publications/Workpapers/home.html.

World Health Organization. (2020, March 11). Opening remarks by the WHO Director-General at a media briefing on COVID-19 - March 11, 2020. World Health Organization. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020