The Moderating Role of Big Data and User Satisfaction in the

Predictors of Generalized Audit Software among Jordanian Auditing

Firms

AHMAD MAREI

Middle East University, Amman, JORDAN

Applied Science Research Centre, Applied Science Private University, Amman, JORDAN

Abstract: - Generalized Audit Software (GAS) is critical for auditing a firm's financial statements. However,

the usage of this software is widely limited to developed countries. The purpose of this study is to examine the

usage of GAS among auditing firms in Jordan. Based on the technology organization environment framework

(TOE), our study proposes organizational factors (technology cost benefits analysis (TCBA), technological

compatibility (TC) (technological factors (TF), top management support (TMS), organizational readiness (OR),

environmental factors (GAS complexity (GASC), and competitive pressure (CP)) to affect the GAS usage

(GASU). Furthermore, auditor satisfaction was proposed as a moderating variable. Moreover, the data was

collected from auditors using convenience sampling and analyzed using Smart PLS. The findings showed that

TCBA, TC, TMS, OR, and GASC are critical predictors of GASU. Additionally, CP has an insignificant effect

on GASU. Also, auditor satisfaction is not a moderating variable while big data moderated the effect of

Technological factors on GAS. Lastly, more studies are needed in GASU in developing countries to understand

the predictors of this technology among individuals. This means that decision-makers are advised to enhance

the knowledge of auditors regarding the usage of GAS and to spread the knowledge regarding the benefits of

GAS for auditors and auditing firms.

Key-Words: - Big Data, IS success, Complexity, generalized audit software, TOE, Compatibility

Received: January 15, 2023. Revised: May 28, 2023. Accepted: June 8, 2023. Published: June 23, 2023.

1 Introduction

Throughout the field of auditing, auditing

information systems (AIS) have emerged as the

golden standard. Comparing the AIS methods of the

small and medium-sized enterprises (SMEs) in

Jordan to those of other countries reveals that the

former is still in its infancy, [1], [2]. However, at the

present, IS auditors are already giving audit

assurance to businesses, [3]. Professional auditors

are usually called in to check up on IT systems to

make sure everything is running smoothly. As a

corollary, it is safe to say that different auditing

standards now apply due to the quick pace of

technological development, [4]. Thus, the utilization

of IT poses a threat to conventional auditing

practices, [5]. Meanwhile, auditors are hampered in

doing their duties by the constant evolution of

technology.

Due to the increasing prevalence of electronic

and paperless evidence, auditors have had to adapt

their methods to suit the advances in technology,

[6]. The audit should move its emphasis from

manual detection to technological prevention, [3].

Well-established tools have been created to aid

auditors in reaching audit goals. Auditors use GAS

to examine either real-time or obtained data from

various software, and CAATs have been developed

to assist auditors in conducting audits on

computerized accounting information (as stated by

[7], [8]). GAS features several tools for data

extraction and analysis, statistical analysis, and audit

expert systems, [9], [10].

Data extraction, querying, and sampling are all

examples of fundamental or broad uses of GAS.

Recently, big data has emerged as a new challenge

for the accounting profession. Big data plays a

significant role in accounting by allowing for more

accurate and efficient financial analysis and

reporting. With the ability to collect, store, and

process large amounts of data, organizations can

gain valuable insights into their financial

performance and make better-informed business

decisions. Additionally, big data can be used to

detect fraud and improve internal controls. Overall,

big data can help organizations to improve their

financial reporting, increase operational efficiency,

and make more informed decisions, [11]. The

hazards and threats to accounting IS data are

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constantly present. As an example, accounting and

financial publications often carry daily reports of

issues related to computer data, incorrect financial

information, violations of internal control, and

manipulation (as noted by [12], [13]). One

technique to ensure that the data generated for

accounting reports are devoid of mistakes and

misstatements is through auditing and the use of big

data analytical tools, [14], [15].

Studies noted that the use of technology is

costly and it might lead to a negative effect on

usage, [16]. Therefore, the cost-benefit analysis

might discourage auditors to use the technology of

GAS. Additionally, GASC might be also an

important factor for the user as shown in prior

literature, [17]. Nevertheless, auditing firms find

themselves in need to use the GAS due to the

external pressures from clients and rivals, [18], [19].

Even though GAS has always been marketed as a

tool to support the auditor’s effective and efficient

job, few studies have examined the adoption and

usage of this technology among auditing firms in

particular in developing countries such as Jordan,

[6], [20].

Adoption theories such as TAM and UTAUT

were designed specifically for individual usage.

However, this study deals with the organizational

usage of technology such as GAS, [21], [22]. For

this reason, the TOE is deployed in this study

because it accounts for the organizational aspects as

well as the technological and environmental aspects.

Therefore, this study utilizes TOE. This is because

the use of GAS in audit companies is influenced by

several factors, according to reviews of the

literature. These factors can be contained by the

TOE, and it includes the TF (TCBA and TC), OF

(TMS and readiness), and EF (GASC and CP). In

addition, information system success (ISS) is

designed to assess the satisfaction of using new

technology. User satisfaction is critical and has been

used as a moderator by a few studies, [23], [6], [24],

[25]. Therefore, this study deployed user satisfaction

as a moderating variable.

The research looks at the adoption of GAS, and

the terms used here apply to any computer-assisted

audit application tool used by audit firms to conduct

audit tasks. As a result, the objectives of this study

are to investigate the factors that influence the use

of GAS. It is also to examine the moderating effect

of auditors' satisfaction with GAS as well as the

moderating role of BG. The literature review,

methods, results, analysis, and discussion are

covered in the parts that follow, along with the

conclusion.

2 Literature Review

The literature on GAS is reviewed in this part, along

with the theoretical and conceptual frameworks, and

the hypotheses are presented.

2.1 Generalized Audit Software (GAS)

GAS has the potential to assist auditors in detecting

errors in financial statements by verifying data from

accounting software's quality, completeness,

ownership, value, accuracy, categorization, and

disclosure (as reported by Bradford et al., 2020 and

Normahazan et al., 2020). Auditing guidelines favor

GAS use due to its benefits. These software

programs allow auditors to access accounting

systems and analyze client financial data, [17].

Several researchers believe that the use of GAS has

been more frequent by companies in the UK and the

US while its usage by companies in developing

countries is still limited, [1], [2], [8], [26].

The use of GAS by UK external auditors is

researched by [27], and the findings showed that

audit businesses in the UK have an abnormally low

rate of GAS adoption. For auditing small customers,

the perceived value of employing GAS is low. As a

result, about 73 percent of external auditing firms do

not utilize GAS. Some of the individuals who

participated in the survey were aware of the benefits

of GAS, but they were put off by what they

regarded to be its high cost of installation, steep

learning curve, convoluted adoption process, and

difficult usage. As a result, they opted for more

conventional methods of manual auditing.

Other studies that have examined the adoption

of GAS noted that other factors might impact the

adoption of GAS, and these include ease of use,

usefulness, facilitating conditions, and external

factors. Nevertheless, recent studies noted that the

usage of GAS is still limited among auditors, [28].

In addition, researchers found differences in the

perception of using GAS among countries.

Competence in IT is crucial for the professional

accountant, as stated by [29], who surveyed people

in the United States and Germany to gauge their

perceptions of their experience with GAS. There are

noticeable distinctions between the auditing

communities in the two nations. Therefore, this

study is focusing on the predictors of using GAS

among Jordanian auditors.

2.2 Theoretical Framework

This study utilizes the TOE which was initially

created by [30], on the premise that an efficient

business model should be tailored to its specific

circumstances in terms of both its internal

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operations and its external environment. TOE is a

comprehensive model that includes technology-

related factors as well as OF and EF. Many studies

have shown that the TOE framework is consistent

with other adoption theories like the Diffusion of

Innovations (DOI) and institutional theory, [23],

[31].

Variables of DOI in most of the studies are

included in TOE. In addition, the internal and

external factors are in line with the institutional

theory, [32]. Despite the aforementioned drawback,

the TOE framework provides a useful jumping-off

place for investigating several elements that would

aid in comprehending the innovation adoption

behavior, as shown by its constant empirical

support, [33], [34], [35]. The TOE framework helps

analyze the acceptance and performance of

technological breakthroughs, [36]. This study is

utilizing the TOE to examine the predictors of

adopting GAS by auditing companies in Jordan. In

addition, the ISS can explain user satisfaction, [37],

[38]. Therefore, this study uses this theory to

explain the moderating role of auditor satisfaction.

2.3 Conceptual Framework and Hypotheses

Developing

Based on TOE and the IS success, this study

proposed that TF (TCBA, TC), OF (TMS, OR), and

EF (GASC, CP) will have a direct significant effect

on the usage of GAS by auditing companies in

Jordan. In addition, based on ISS, auditor

satisfaction with GAS is predicted to be a

moderating variable between the OF, TF, EF, and

the GASU. Based on these assumptions, the next

sections discuss the hypotheses of this study.

2.3.1 Technology Cost-Benefit Analysis and

GASU

According to economists, the current technologies

used by businesses are the result of embracing

innovations that bring forth novel outcomes and

substantial gains, [14]. Therefore, many theories

that seek to explain "adoption behavior" rest heavily

on the assumption that the potential advantages of

innovations are significant factors in the final choice

to embrace them. In this research, technology cost-

benefit means an audit firm's perceived advantages

from audit technology surpass its cost. Auditors

must consider TCBA when choosing audit software

tools to use in performing their test of controls, [39],

[40]. Several researchers, [41], [42], have

demonstrated that relative advantage is a major

predictor in making the GAS adoption choice. This

study predicts that audit companies will be more

inclined to utilize GAS if they see its usage as likely

to deliver higher benefits.

H1: TCBA affects positively the GASU.

2.3.2 Technological Compatibility and GASU

The term "compatibility" is used to describe how

well prospective adopters believe an invention will

meet their values, beliefs, experiences, and

requirements, [43]. One's confidence in learning and

using a new piece of technology and the satisfaction

one derives from doing so improves in proportion to

the degree to which that technology is seen as

compatible with one's current repertoire of skills and

knowledge, [44], [45]. Compatibility was found to

be a significant factor in the usage of new

technology, [46], [47] and is a major factor in using

GAS, but the mixed findings on its impact deserve

further research, [1]. Therefore, this study proposes

the following:

H2: TC affects positively the GASU.

2.3.3 Top Management Support and GASU

Utilizing ICT in the company would be improved

with the participation of senior management, [48].

Top management is in charge of the majority of the

company's resources (i.e., technical, financial, and

human resources), and their support is crucial for

the successful deployment of a new IT innovation,

[49]. TMS is crucial when deciding whether or not

to implement audit technology in an organization,

[1], [2]. In this study, the following is proposed:

H3: TMS affects positively the GASU.

2.3.4 Organizational Readiness and GASU

Alignment between the nature of the technological

change and the capacities of businesses is a well-

established idea in the literature on organization and

information technology that is crucial to the

effective adoption of GAS technology, [50], [51].

OR refers to an organization's potential to take

advantage of new information technologies and

apply them to its advantage, [52]. The adoption of

computer-assisted auditing techniques has

previously been studied, with findings indicating

that the availability of appropriate physical and

technology resources inside an organization is a

major motivator for using auditing technology, [53].

Studies found that OR affected the usage of auditing

technology such as GAATs, [1], [54], [55].

Therefore, the following is proposed:

H4: OR affects positively the GASU.

2.3.5 GAS Complexity and GASU

The perceived complexity of new technology is a

significant consideration when assessing its value.

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The degree to which a newly developed piece of

technology is difficult to both grasp and apply is

referred to as its complexity (Rogers, 2003). Many

auditors previously believed that using audit

software required extensive training and experience

due to technical issues like the complexity of the

audit software itself and difficulties in accessing

client data, [2], [54], [55]. More complexity leads

companies to not use GAS, [56], [57]. Accordingly,

GASC is expected to harm its users.

H5: GASC negatively affects the GASU.

2.3.6 Competitive Pressure and GASU

In a competitive business climate, those that

implement information systems stand out from those

who do not, [58]. Even though CP was not a major

driver of ERP adoption according to [59], research

in other IT adoption contexts, such as e-business,

found the reverse to be true, [60], [61], [62]. The use

of GAATs was favorably impacted by CP, [39],

however, the research on TOEs provides conflicting

findings on the topic of CP. Nonetheless, the

researchers here are assuming a beneficial outcome.

H6: CP affects positively the GASU.

2.3.7 Satisfaction as a Moderator

Satisfaction is defined as the match between the

expected benefits with the obtained benefits from

using specific services or products, [23]. High

auditor satisfaction can enhance their perception of

the organizational, technological, and EF, [1].

Auditor satisfaction as predicted in this research

moderates the impact of variables such as TF, EF,

and OF on GASU. This is because the high

satisfaction of auditors with GAS will lead them to a

better perception of the variables and vice versa. In

line with this proposition, prior literature indicated

that satisfaction with the auditing technology is

critical for its usage, [2], [4], [14]. Accordingly, the

following is proposed:

H7: Auditor satisfaction with GAS moderates the

effect of TF on GASU.

H8: Auditor satisfaction with GAS moderates the

effect of OF on GASU.

H9: Auditor satisfaction with GAS moderates the

effect of EF on GASU.

2.3.8 Moderating Role of Big Data

Big data is the term for massive datasets that may be

processed computationally to uncover patterns,

trends, and relationships, often pertaining to human

behavior and interactions, [63]. In the field of

accounting, big data can be used to analyze large

amounts of financial data, such as transactions, tax

records, and financial statements, to identify trends

and patterns that may not be immediately apparent.

There are several ways that big data can be used in

accounting research.

Big data can play a moderating role in the effect

of TF on accounting by providing a large and

diverse set of financial information that can be

analyzed to identify patterns and trends. This can

help organizations make more accurate and

informed decisions, such as identifying areas for

cost savings, forecasting revenue, and detecting

fraud. Additionally, big data can be used to

automate and streamline accounting processes, such

as invoice processing and financial reporting, which

can improve efficiency and reduce the potential for

human error, [64], [65], [66], [67]. Big data in this

study is proposed as a moderating variable between

TF and GAS. Accordingly, the following is

hypothesized:

H10: Big data moderates the effect of TF on GASU.

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Table 1. Data Screening

Variable

Response

Missing value

Normality

Multicollinearity

Skewness

Kurtosis

Tolerance*

VIF

TCBA

148

-.564

-.313

.615

1.412

148

-.409

-.491

.623

1.343

TMS

148

-.283

-.412

.643

1.577

148

-.316

-.531

.688

1.431

GASC

148

-.540

-.643

.761

1.286

148

-.411

-.518

.645

1.745

148

-.419

-.522

.649

1.752

148

-.274

-.438

.766

1.288

GASU

148

-.301

-.597

3 Methodology

This study uses a quantitative approach and employs

a survey questionnaire to gather data. The target

population for the study is auditing companies

located in Jordan. Due to the lack of information

regarding these auditors, the convivence sampling

technique is deployed. A snowballing and network

referral technique is used to reach the auditor. As a

role of thumb, [68], pointed out that several 100-150

responses are sufficient for the use of structural

equation modelling (SEM). Using the G*power

technique, the minimum sample size for six

predictors with a confidence level of 0.95 and

margin error of 0.05, is 89 responses.

Data was gathered in Jordan through an online

survey questionnaire. The variables were measured

using methods from previous studies, [23], [69],

[43], [70]. To accommodate the official language in

Jordan, the questionnaire was translated into Arabic.

A validation process was conducted by inviting

experts who can understand Arabic and English to

validate the measurement. Pilot testing was initiated

to evaluate the measurement reliability, after which

the data collection process began. The questionnaire

was distributed to respondents, and they are asked to

forward it to those who are fit to answer the

questionnaire. This has resulted in the collection of

157 questionnaires. Five questionnaires were

removed due to the notion that they are empty. No

missing value is recorded due to the use of the

“required” function where respondents cannot

proceed without filling out all the questions. The

outliers were checked using a boxplot and four

responses were deleted. The data was found to have

a normal distribution as the skewness and kurtosis

values were less than one (1). This is in line with the

suggestion of [68]. The presence of multicollinearity

was assessed by checking the tolerance value

(>0.20) and the variation inflation factor (VIF) (<5),

which is in accordance with the recommendations of

[68]. The values of VIF and tolerance are within the

accepted range and this has led to the conclusion

that there is no multicollinearity among the

variables. The results of missing values, normality,

and multicollinearity are presented in Table 1.

Using Smart PLS version 4, the data are processed,

and both the measurement model (MM) and the

structural model (SM) are evaluated.

4 Findings

The outputs of this research look at both the MM

and SM models and analyze the descriptive

information that the respondents provided.

4.1 Profile of Respondents

This research contains a total of 148 replies that are

reliable and comprehensive. Seventy-six percent of

the people who responded were males, whereas just

24 percent were females. The majority of

respondents have at least a bachelor's degree (71%)

and have worked in auditing for more than ten years

on average (63%). The majority of respondents are

also older than 35 years old (63%).

4.2 Measurement Model

To assess the MM, we first looked at its factor

loading (FL), then at its validity, and finally at its

reliability, [68]. All of the objects' FLs were

analyzed, and those with the lowest FL were

removed from consideration. The elements OR1

from OR, GASC5 from GASC, and GASU3 from

GASU were all removed from the analysis since

they had a low FL. Both Cronbach's alpha (CA) and

the composite reliability (CR) increased after the

items were eliminated from the study. Because the

average variance extracted (AVE) is larger than 0.50

for all variables, it has been determined that the

research satisfies the criteria for having good

convergent validity. As a result of the fact that the

square root of AVE (which is shown in bold in

Table 2) is more than the cross-loading of the

variables, the discriminant validity was also

considered to be acceptable. Table 2 presents the

findings that were obtained using the measurement

model.

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4.2 Structural Model

The R-square, F-square, and path coefficient are

used to evaluate SM. According to the model's R-

square, which is 0.509, 50.9% of the variance in

GASU can be accounted for. The allowable value

for the f-square (f2) is 0.02. Except for the

CPGASU, all the pathways' f-squares (effect sizes)

are adequate. These numbers are, however, low

since the corresponding assumptions are not

accepted. The results of the SM evaluation are

presented in Table 3.

4.3 Hypotheses Testing

This study developed direct hypotheses and

moderating hypotheses. The hypotheses were tested

as shown in Table 3 and Figure 1. The figure shows

the path and the path coefficient as well as the p-

value.

Fig. 1: Results of Structural Model

For the first hypothesis, the H1 is supported

because the effect of TCBA on GASU is positive

(B=0.301, P<0.05). Thus, H1 is supported as shown

in Table 3. For H2, the effect of TC on GASU is

significant (B=0.382, P>0.05). Therefore, H2 is

supported. H3 is supported because the effect of

TMS on GASU is significant (B=0.101, P>0.05).

Similarly, the effect of OR on GASU is positive and

significant at a path coefficient of 0.223 and a p-

value less than 0.05. Thus, H4 is supported. For H5,

the GASC negatively affected the GASU at B=-

0.214 and the P-value is less than 0.05, indicating

that H5 is supported. For H6, the p-value of the

effect of CP on GASU is insignificant because it is

Table 2. Result of the Measurement Model

Variable

Response

Missing value

Normality

Multicollinearity

Skewness

Kurtosis

Tolerance*

VIF

TCBA

148

-.564

-.313

.615

1.412

148

-.409

-.491

.623

1.343

TMS

148

-.283

-.412

.643

1.577

148

-.316

-.531

.688

1.431

GASC

148

-.540

-.643

.761

1.286

148

-.411

-.518

.645

1.745

148

-.419

-.522

.649

1.752

148

-.274

-.438

.766

1.288

GASU

148

-.301

-.597

Table 3. Result of Structural Model

Path

Std.

Conclusion

TCBAGASU

0.301

0.11

2.69

0.01

Supported

0.06

0.509

TCGASU

0.382

0.08

4.57

0.00

Supported

0.00

TMSGASU

0.101

0.03

3.16

0.00

Supported

0.01

ORGASU

0.223

0.08

2.84

0.00

Supported

0.05

GASCGASU

-0.214

0.09

-2.24

0.03

Supported

0.04

CPGASU

0.011

0.09

0.12

0.90

Rejected

0.09

TF*ASGASU

-0.004

0.13

-0.81

0.94

Rejected

0.00

OF*ASGASU

-0.041

0.11

-0.41

0.74

Rejected

0.00

EF*ASGASU

-0.022

0.10

-0.18

0.42

Rejected

0.00

H10

TF*BDGASU

0.118

0.03

3.26

0.00

Supported

0.04

0.541

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greater than 0.05. Thus, H6 is rejected. Overall, H1-

H5 is supported while H6 is rejected.

For the moderating effect of AS between TF

and GASU, the findings in Figure 2 showed that AS

did not moderate the effect of TF on GASU. The P-

value of the path is -0.004, indicating that the effect

is not significant. Thus, H7 is rejected. For H8, the

effect is not significant, and this indicates that AS

did not moderate the effect between OF and GASU.

Thus, H8 is rejected. For H9, the moderating effect

AS between EF and GASU is also insignificant

leading to the rejection of H9.

For H10, BG moderated positively the effect of TF

on GASU. As shown in Figure 2. Thus, H10 is

supported.

Fig. 2: Moderating Role of Big Data

5 Discussion and Implications

This research effort was conducted to identify the

predictors of using GAS by auditing firms in Jordan.

The findings showed that TCBA, TC, TMS, OR,

and GASC are important predictors for auditing

firms in Jordan to use GAS. The benefits of using

GAS as well as its compatibility with the existing

systems might be encouraging factors to use the

system. In addition, the support of the management

of the use of GAS is critical in this process. The OR

played an essential role. The existing hardware,

software, and network to support the usage as well

as the knowledge of auditors in using the GAS can

be behind the significant effect. The high

complexity of the GAS might discourage auditors

from using the system. Interestingly, the results of

this study support the previous research that found

that TCBA, TC, TMS, OR, and GASC are critical

for the usage of the GAS and other auditing

systems, [1], [2], [54], [55].

For the effect of CP, it was found that this

variable is not an important predictor of GASU.

This could be due to the notion that using GAS is

still relatively new in the context of Jordan and

rivalry is still limited. This finding of CP is in line

with the findings of researchers who indicated that

the effect of CP on using new auditing technology is

not significant, [59]. This study also found that

auditor satisfaction did not moderate the effect of

TF, OF, and EF on GASU. This could be due to the

notion that those who used the GAS are still limited

and their satisfaction level is similar at a moderate

level, which has not caused any statistical

differences. BG is a moderating variable, and this

suggests that auditing companies in developing

countries should benefit from the technology to

improve the efficiency of auditing.

This study contributed to the usage of GAS

especially in developing countries by examining the

predictors and deploying a combination of theories

that help in explaining almost half of the variation in

GASU. The study validated the theory of TOE as

well as ISS in the context of Jordan. The study also

contributed by identifying the role of auditor

satisfaction. For decision-makers, they are

recommended to increase the benefits that can be

obtained from the GAS. Decision makers are also

advised to conduct training courses for auditors to

master the knowledge regarding the use of GAS.

Decision makers are advised to assess the usage of

GAS among the auditing firms in Jordan and make

action to increase this usage by administrating

seminars and workshops for auditors and auditing

firms to teach them how to use the GAS technology.

6 Conclusion

This study was conducted on auditing firms in

Jordan to understand the predictors of using GAS.

The data was collected from auditors and analyzed

using Smart PLS. The validities and reliabilities of

the data were confirmed, and the hypotheses were

tested. The findings showed that TCBA, TC, TMS,

OR, and GASC are critical predictors of GASU.

Auditor satisfaction did not moderate the effect of

TF, OF, and EF on GASU while BG moderated the

effect of TF on GASU. The findings are limited to

the context of auditors in Jordan. The use of

the convivence sampling technique was due to the

lack of information about the auditor. For future

work, it is suggested to duplicate this study using

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DOI: 10.37394/23207.2023.20.121

Ahmad Marei

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different sampling techniques such as random

sampling. Future work is also recommended to

include more variables such as trust and IT

knowledge. These variables could help in explaining

the variation in GASU. Also, more studies are

needed in GASU in developing countries to

understand the predictors of this technology among

individuals. Decision makers are advised to enhance

the knowledge of auditors regarding the usage of

GAS and to spread the knowledge regarding the

benefits of GAS for auditors and auditing firms.

“

Acknowledgments:

The author would like to express their gratitude to

Middle East University in Amman, Jordan for the

financial support that was provided to cover the

publication fee of this article.”

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Ahmad Marei carried out the theoretical framework,

prepared study hypotheses after the gap appeared in

previous studies, prepared methodology, statistics

and write the conclusion of the study

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

The financial support from Middle East University

in Amman, Jordan that will provide to cover the

publication fee of this article.

Conflict of Interest

There is no conflict related to this study

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(Attribution 4.0 International, CC BY 4.0)

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