The Moderating Role of Auditor Experience on Determinants of Computer-Assisted Auditing Tools and Techniques

Abstract

This study indicates that internal auditors need to fully adopt CAATs to improve the efficiency of auditing tasks. This paper investigates the determinants influencing CAAT adoption among internal auditors in Jordanian firms. This study investigates the roles of performance expectancy, effort expectancy, social influence, and facilitating conditions on the adoption of CAATs. Also, this study investigates the moderating variable of auditor experience. The data were collected using a survey that was sent to 420 internal auditors in auditing firms in Jordan. A total of 291 responses were collected, of which 279 proved to be valid for study. This study found that the adoption of CAATs is influenced by performance expectancy, facilitating conditions, social influence, and auditor experience. Conversely, effort expectancy has no influence. Furthermore, auditor experience moderates the relationship between performance expectancy and facilitating conditions for CAAT adoption. This study found that auditor experience does not moderate the relationship between effort expectancy or social influence and CAATs in auditing firms in Jordan.

1. Introduction

The technology landscape has progressed rapidly over the years, significantly influencing a broad spectrum of industries, including the auditing sector. One of the mechanisms used to improve effectiveness and efficiency in auditing is that of computer-assisted auditing techniques (CAATs) (Atta et al., 2024). CAATs are commonly utilized by an auditor during audit procedures to analyze audit-related information in an entity’s IT systems (Hendi & Kuntadi, 2024). According to Nasrah et al. (2023), there are several CAATs that auditors use and are interested in. These include electronic working papers, detection of fraud, general auditing software (GAS), auditing reports, audit history databases, computer-based training, security-of-network testing, ongoing monitoring, internet commerce, and security on the internet. Over the years, with technological advancements and accelerated digitalization brought about by the Fourth Industrial Revolution (IR 4.0), industries across the globe have undergone significant transformations in their functional areas (Yusoff et al., 2023), as the business landscape has transformed rapidly in parallel with technology (Shan et al., 2022). This has contributed to the metamorphosis of the auditing environment and supports for the auditing profession (Bouziane, 2025).

CAATs are beginning to have significance within the audit industry (Almagrashi et al., 2023; Atta et al., 2024). In broad terms, CAATs are described as the utilization of technology to support the accomplishment of auditing (Oktavianie & Teresa, 2023; Shan et al., 2022). The majority of auditing firms have come to understand the significance of incorporating auditing tools, analytics, and technology (Lutfi & Alqudah, 2023); nonetheless, the uses of CAATs by public auditing firms remain largely unclear (Atta et al., 2024; Khan, 2025). Thus, this study attempts to close the research gap recently described by previous studies (Atta et al., 2024; Khan, 2025) and to harmonize the contradicting results of earlier investigations for auditing firms. Furthermore, this study contributes by including the moderating variable of auditor experience (AE) to examine the effects of PE, EE, SI, and FCs on the adoption of CAATs in auditing firms, with particular attention paid to the moderating effect of AE. Moreover, this study extends the application of the UTAUT model. The individual’s behavioral intention (BI) is considered the primary driver of behavior and is believed to occur before actual use. In the UTAUT model, the intention to adopt and utilize CAATs acts as the dependent variable in the research, influenced by PE, EE, SI, and FCs (Venkatesh & Davis, 2000; Venkatesh et al., 2003). This provides the context for the original ‘intention to use’ concept within the CAAT setting. The purpose of this study is to investigate the determinants of PE, EE, SI, and FCs, which influence the adoption of CAATs among internal auditors in Jordanian firms. This study also aims to investigate the moderating role of AE.

2. Review of the Literature and Development of Hypotheses

Theoretical framework regarding the motivation for CAAT adoption: Auditors employ CAATs to automate specific audit processes or procedures. Given that the majority of accounting processes are automated, the auditing of these accounting data thereafter adheres to these protocols. Various studies on technological acceptance have been published in the past two decades, accompanied by various recently suggested models. The UTAUT model serves as the foundation for this study. UTAUT offers a theoretical foundation for the adoption of technology. The UTAUT model consists of eight previous technological acceptance models (TAMs) and is the result of an extensive literature review. The authors included the key findings of eight theories in an effort to develop a coherent (not redundant) model that was empirically confirmed. The UTAUT model, developed by Venkatesh et al. (2003), is a thorough framework for individual IT acceptance that was developed by analyzing, contrasting, and assessing eight opposing ideas in order to pinpoint the main factors influencing user technology acceptance in diverse contexts. These models originated from psychological research and have been refined via several studies to guide IT adoption decisions.

2.1. Performance Expectancy (PE) and Adoption of CAATs

PE refers to the degree to which an individual believes that using technology will improve their performance in audit tasks (Meng et al., 2025; Rachael & Wijaya, 2025; Sengkalit & Abdullah, 2025; Venkatesh et al., 2003). Numerous recent research studies investigated the impact of performance expectation on CAATs (Almagrashi et al., 2023; Atta et al., 2024; Daoud, 2023; Fülöp et al., 2024; Handoko & Chu, 2021; Hendi & Kuntadi, 2024; JS et al., 2024; Mahzan & Lymer, 2014); in addition, the UTAUT model investigates the effect of PE on behavioral intention (Venkatesh et al., 2003). PE was found to positively influence the propensity to utilize CAATs in external audits (JS et al., 2024). The definition of performance is to carry out or complete an act, feat, or other activity (Oudat et al., 2024; Saleh et al., 2023). Furthermore, a recent study examined the relationship between CAATs on auditors’ performance and effort expectations in Jordan. The study revealed that EE and PE favorably affect the adoption of CAATs (Atta et al., 2024). Mahzan and Lymer (2014) suggested that PE and supportive conditions are significant elements affecting the effective adoption of general audit software in this field. Additionally, several research studies (Almagrashi et al., 2023; Daoud, 2023; Fülöp et al., 2024; Hendi & Kuntadi, 2024) identified a strong favorable relationship between PE and the use of CAATs. Conversely, a study indicated that PE and FCs do not significantly influence the intention of auditors to adopt CAATs among Indonesian external auditors (Handoko & Chu, 2021). As a result, the following hypothesis is proposed:

H1: 

PE positively influences the adoption of CAATs.

2.2. Effort Expectancy and Adoption of CAATs

EE indicates the amount of convenience that is related to using an instrument (Venkatesh et al., 2003; Nurlaela et al., 2025; Sari et al., 2024). The UTAUT model suggests that individuals are more inclined to adopt or use a technology if they perceive it to be user-friendly (Atta et al., 2024; Venkatesh et al., 2003). Recent studies (Almagrashi et al., 2023; Atta et al., 2024; Daoud, 2023; Handoko & Chu, 2021; JS et al., 2024) discovered that EE had a significant effect on auditors’ intentions to use CAATs during audits. On the contrary, other studies (Fülöp et al., 2024; Hendi & Kuntadi, 2024; Mahzan & Lymer, 2014; Oktavianie & Teresa, 2023) found that effort expectations did not impact the behavioral intention to utilize CAATs. Consequently, this research hypothesizes the following:

H2: 

EE positively influences the adoption of CAATs.

2.3. Social Influence and Adoption of CAATs

As explained by Venkatesh et al. (2003) and others (Flathmann et al., 2024; GC et al., 2024), the extent to which an individual believes that others of significance expect him or her to use a new tool is what SI involves, that is, how much auditors assume that their supervisors’ approval and acknowledgement of CAAT use will impact their choice to use them in an auditing setting. Recent literary studies (Almagrashi et al., 2023; Daoud, 2023; Handoko & Chu, 2021; Hendi & Kuntadi, 2024) discovered that auditors’ use of CAATs is significantly affected by SI. Nonetheless, one study (JS et al., 2024) indicated that SI unexpectedly had a detrimental impact on the intent to utilize CAATs, which was positively regulated by voluntariness; this is in contrast to other recent studies (Fülöp et al., 2024; Mahzan & Lymer, 2014; Oktavianie & Teresa, 2023) that found that effort expectancies and SI did not impact the behavioral intention to utilize CAATs. Consequently, this study posits the following:

H3: 

SI positively influences the adoption of CAATs.

2.4. Facilitating Conditions and Adoption of CAATs

An FC refers to the extent to which an individual recognizes the presence of organizational and technological infrastructure that facilitates system usage (Venkatesh et al., 2003; An et al., 2023; Mohammed et al., 2023). Studies indicated that the external auditor’s actual use of CAATs was positively influenced by FCs and the intention to use CAATs (Fülöp et al., 2024; JS et al., 2024). Likewise, research identified that FCs, trust, contentment, and behavioral intention are the primary determinants of CAAT use (Almagrashi et al., 2023; Daoud, 2023). This is contrary to other recent studies that indicated that FCs have no substantial impact on auditors’ intention to adopt CAATs (Handoko & Chu, 2021; Mahzan & Lymer, 2014; Oktavianie & Teresa, 2023). Consequently, this study posits the following:

H4: 

FCs positively influence the adoption of CAATs.

2.5. Moderating Effect of Auditor Experience

The major goal of this research is to assess the validity of the theory’s components and perform a thorough assessment of the influence of moderating variables presented in the UTAUT model. Furthermore, the moderator is believed to be measurable according to the research methodology employed in this study. A previous study (Venkatesh et al., 2003) discovered that the concept of SI could be relevant when the moderating factors of age, gender, experience, and voluntariness are included. For example, during the testing phase, it was discovered that SI had a considerable effect on the required adoption of UTAUT.

As a result, in this study, we incorporate an assessment of the effect of AE into our analysis of this specific construct. However, this study does not distinguish the moderators of age, voluntary usage, and gender. Experience in the creation and testing of UTAUT has also been shown to have a moderating influence on EE and FCs, along with the moderating effect on SI. EE is not a significant factor of adoption for an experienced adopter, but FCs become increasingly relevant as competence develops (Venkatesh et al., 2003). Auditors verify financial statements’ correctness and transparency to ensure financial reporting integrity (Hezabr et al., 2023; Oudat et al., 2021; Qeshtaa & Ali, 2020). As a result, this study suggests the following hypotheses:

H5: 

AE positively influences the adoption of CAATs.

H6: 

AE moderates the relationship between PE and the adoption of CAATs.

H7: 

AE moderates the relationship between EE and the adoption of CAATs.

H8: 

AE moderates the relationship between SI and the adoption of CAATs.

H9: 

AE moderates the relationship between FCs and the adoption of CAATs.

As a result, our theoretical model takes an analogous approach by including AE as a moderator in our study. We believe that this component might be studied by utilizing survey-based methodologies, as indicated in our work.

3. Methods

3.1. Research Design

A research design outlines the approaches to be employed in the collection and analysis of study data. This study employed SPSS 26 and SmartPLS 4.1 for statistical analysis and model estimation. This study employed a deductive methodology grounded in the UTAUT model. Data were gathered by a structured questionnaire survey, with the 420 selected respondents being auditors from firms in Jordan. The questionnaire had seven components. Section 1 addressed the demographic profile of the auditor, whereas Sections 2 through 7 focused on the constructs examined in this study: PE, EE, SI, FCs, AE, adoption of CAATs, and AE.

The questionnaire items were derived from previous works (Al-Hiyari et al., 2019; Almagrashi et al., 2023; Atta et al., 2024; Mahzan & Lymer, 2008; Shan et al., 2022; Venkatesh et al., 2003) but were modified to align with this study’s aims. To assist respondents in submitting answers and to enhance concept evaluation, each item in Sections 2 through 7 was accompanied by a seven-point Likert scale with 27 items. Reliability, average variance extracted, discriminant validity, and regressions were conducted to establish the validity of the data. Consistency was assessed using item correlation via the calculation of Cronbach’s alpha, where an alpha value of 0.7–0.9 indicates consistency as indicated by a recent study (Hair et al., 2021b; Hair & Alamer, 2022). However, as noted by the literature, Cronbach’s alpha is not a test; rather, it confirms that the questions accurately measure a common construct.

3.2. Framework of This Study

A theoretical framework (Figure 1) was used in this study to test the hypotheses developed for the investigation. This framework’s stated goal was to examine the moderating role of AE between performance expectation, EE, social impact, and FCs and the adoption of CAATs.

3.3. Study Instrument Variables

The questionnaire consisted of twenty-seven (27) statements utilizing seven-point Likert scales. Respondents indicated their degree of agreement for each statement using a scale from one to five. The questionnaire items displayed in

Table 1. Questionnaire items.

Variables of the Study	Items
PE	Using CAATs enhances the effectiveness of audits. Utilizing CAATs can save time spent on unproductive processes. Computer-assisted auditing techniques might be valuable in my profession. Using CAATs helps me complete duties more rapidly. Using CAATs boosts my productivity. Using CAATs will boost my chances of receiving a raise.
EE	I find CAATs simple to employ. Learning CAATs is simple for me. I can ensure clear and intelligible communication with computer-assisted auditing tools. I can easily learn CAATs.
SI	My co-workers that use computer-assisted auditing are more acknowledged. Numerous individuals who are in the field believe that I should implement computer-assisted auditing methodologies. This business’s senior management has been instrumental in the implementation of computer-assisted auditing methodologies. The utilization of computer-assisted auditing methodologies has been generally endorsed by the organization. My behavior is influenced by individuals who believe that I should employ computer-assisted auditing techniques.
FC	The system is incompatible with other CAATs that I employ. I possess the requisite knowledge to implement computer-assisted auditing methodologies. I possess the requisite resources to implement computer-assisted auditing methods. For those experiencing difficulties with computer-assisted audit techniques, a designated individual or group is available to provide support.
Adoption of CAATs	I anticipate adopting CAATs in the near future. I plan to implement computer-assisted auditing methodologies during this year’s engagement. I anticipate that I will fully use CAATs in the near future. I anticipate that I will implement CAATs in the near future. I intend to implement CAATs during this year’s engagement.
AE	My auditing knowledge includes using computer-assisted techniques. My previous auditing experience has instilled in me a sense of enthusiasm for the benefits of utilizing CAATs more effectively. This allows me to employ CAATs effectively during audit engagements. With extensive auditing expertise, I am well versed in the usage of CAATs. Recognizing traditional audit techniques helps me better comprehend CAATs. Having previous experience, I am comfortable using CAATs in various inspections.

were designed to assess the links between EE, PE, SI, and FCs and the adoption of CAATs, with AE serving as a moderator.

3.4. Pilot Study

Following the construction of the questionnaire, a pilot test was carried out with fifty auditors who had a minimum of one year of experience using computer-assisted auditing tools and techniques. The purpose of this test was to further investigate the validity and reliability of the instruments. As a result of the fact that the Cronbach’s alpha coefficient, the kurtosis coefficient, and the curvature coefficient were all within the intended range for all variables of this study, covering EE, PE, SI, FCs, and AE, which serves as a moderator, the pilot study determined that the questionnaire was suitable for further research. Therefore, this study distributed 420 questionnaires to internal auditors.

4. Results and Empirical Analysis

This investigation was conducted on two consistent models with SmartPLS. The findings provide significant insight into the relationship among the researched variables.

4.1. The Outer Model for Measuring

In PLS, the initial stage is to evaluate the measurement or outer model using four criteria that Hair et al. (2021a) presented. A loading of 0.70 validates indicator reliability, and a composite reliability (CR) of 0.70 or above confirms internal consistency. Fornel and Larcker (1981) stated that if the average variance extracted (AVE) is 0.50 or greater, it indicates convergent validity. To establish discriminant validity, the AVE of each latent component must be higher than the greatest squared correlation of that factor with any other latent factor. The degree to which the latent variable is different from other variables may be captured by discriminant validity (Hair et al., 2021a, 2021b). This research employs two methods—the Fornell–Larcker criterion and cross-loadings. The Fornell–Larcker requirement is met if each latent variable’s square root of the AVE is larger than its correlations with other variables (Fornel & Larcker, 1981). All variables exhibit satisfactory discriminant validity, as shown in Tables 4 and 5, as they show a larger amount of variation when considered alone than when compared to other latent variables.

Table 2. Assessment of reflected measurement model reliability and validity.

	Cronbach’s Alpha	Composite Reliability (rho_a)	Composite Reliability (rho_c)	Average Variance Extracted (AVE)
Adoption of CAATs	0.840	0.844	0.883	0.558
AE	0.865	0.873	0.903	0.654
EE	0.815	0.821	0.890	0.729
FC	0.797	0.802	0.868	0.622
PE	0.878	0.881	0.909	0.624
SI	0.823	0.825	0.876	0.587

,

Table 3. Outer loads of items.

	Adoption of CAATs	IE	EE	FC	PE	SI
AE1		0.699
AE2		0.890
AE3		0.847
AE4		0.841
AE5		0.749
AOC1	0.782
AOC2	0.825
AOC3	0.707
AOC4	0.713
AOC5	0.710
AOC6	0.735
EE1			0.849
EE2			0.878
EE3			0.834
FC1				0.800
FC2				0.730
FC3				0.792
FC4				0.831
PE1					0.774
PE2					0.844
PE3					0.804
PE4					0.850
PE5					0.731
PE6					0.728
SI1						0.771
SI2						0.829
SI3						0.800
SI4						0.705
SI5						0.720

,

Table 4. HTMT matrix.

	Adoption of CAATs	IE	EE	FC	PE	SI
Adoption of CAATs
AE	1.016
EE	0.855	0.758
FC	0.953	0.868	0.934
PE	0.997	0.983	0.858	0.949
SI	0.888	0.799	0.743	0.769	0.861

and

Table 5. The Fornell–Larcker criterion.

	Adoption of CAATs	IE	EE	FC	PE	SI
Adoption of CAATs	0.747
AE	0.869	0.808
EE	0.71	0.642	0.854
FC	0.785	0.727	0.747	0.789
PE	0.858	0.858	0.725	0.794	0.79
SI	0.743	0.68	0.609	0.628	0.737	0.766

, as well as Figure 2 and Figure 3, show that the findings are all within the acceptable range. This allows for a risk-free evaluation of the structural model, which is the second model researched in this study.

4.2. Analysis of Structural Models

The proposed relationships among PE, EE, SI, and FCs and the adoption of CAATs were investigated in this study using PLS bootstrapping. Auditors’ experience served as a moderator. This analysis was performed during the concluding phase of the PLS SEM model evaluation. PLS analysis typically evaluates path coefficients. Sekaran & Bougie (2016) examined standardized regression coefficients, which are essential for understanding the relationship between this study’s variables. Path coefficients in path analysis quantify the intensity and direction of interactions among variables. To guarantee that the numbers of cases and observations in the sample are comparable, path coefficient evaluations should employ a bootstrap method with a minimum sample size of 5000 (Hair & Alamer, 2022). The initial sample size was 500, and this investigation employed 5000 resampling iterations using bootstrap cases. The objective was to introduce moderate errors and to generate t-values. The results and values of standardized path coefficients for hypothesis testing are utilized in this study to analyze structural paths, as illustrated in Figure 3 and

Table 6. Results of the hypotheses.

Hypothesis Results	Original Sample (O)	Standard Deviation (STDEV)	T Value	p Value	Supported or Rejected
H1: PE positively influences the adoption of CAATs.	0.176	0.079	2.213	0.027	Supported
H2: EE positively influences the adoption of CAATs.	0.087	0.050	1.725	0.085	Rejected
H3: SI positively influences the adoption of CAATs.	0.179	0.046	3.858	0.000	Supported
H4: FCs positively influence the adoption of CAATs.	0.148	0.059	2.485	0.013	Supported
H5: AE positively influences the adoption of CAATs.	0.466	0.079	5.898	0.000	Supported
H6: AE moderates the relationship between PE and the adoption of CAATs.	0.124	0.057	2.170	0.030	Supported
H7: AE moderates the relationship between EE and the adoption of CAATs.	0.067	0.039	1.725	0.085	Rejected
H8: AE moderates the relationship between SI and the adoption of CAATs.	−0.044	0.050	0.894	0.371	Rejected
H9: AE moderates the relationship between FCs and the adoption of CAATs.	−0.145	0.064	2.270	0.023	Supported

.

Table 7. Squared values.

	R Square	Adjusted R Square
Adoption of CAATs	0.847	0.839

presents the results of the R-squared value. The resulting ratio was 0.847, suggesting that 84.7% of the variation in the adoption of CAATs is explained by the variables included in the research model. This signifies a substantial input from various informants, customized to align with our study variables. Table 7 shows that the adjusted R value was 0.839, marginally lower than the R value. The adjusted R value accounts for the prediction error arising from the inclusion of additional predictors relative to the variables. Consequently, the model accounts for some of the variability in the adoption of CAATs among auditing firms.

5. Conclusions

5.1. Concluding Remarks

The Smart PLS analysis results demonstrated interrelationships among the four independent variables (PE, EE, SI, FCs) affecting the adoption of CAATs, with AE serving as a moderator, in Jordanian auditing companies. The coefficient findings for hypothesis testing are shown in Table 7.

H1: 

PE positively influences the adoption of CAATs. This hypothesis, which links PE to the intention to use CAATs, is significantly supported (T = 2.213, p = 0.027). The adoption and usage of CAATs are correlated with a heightened level of PE among Jordanian external auditors, leading to an assumption of a strong intention to utilize these tools in auditing. This aligns with other recent studies (Almagrashi et al., 2023; Daoud, 2023; Fülöp et al., 2024; Hendi & Kuntadi, 2024), while it disagrees with another study (Handoko & Chu, 2021).

H2: 

EE positively influences the adoption of CAATs. The findings suggest that the impact of EE on CAAT adoption is not significant (T = 1.725, p = 0.085). The ease of use of a tool does not guarantee its adoption by auditors. Other indicators, such as perceived value and external pressures, may hold greater significance. This is consistent with the findings of Ali (Fülöp et al., 2024; Hendi & Kuntadi, 2024; Mahzan & Lymer, 2014; Oktavianie & Teresa, 2023).

H3: 

SI positively influences the adoption of CAATs. This study demonstrated a significant relationship between SI and CAAT adoption (T = 3.858, p = 0.000). Auditors are influenced by supervisors, managers, and the leadership team in their decisions regarding the adoption of CAATs. When workplace culture incentivizes the use of these tools, auditors are more inclined to implement them. This outcome is aligned with other studies (Almagrashi et al., 2023; Daoud, 2023; Handoko & Chu, 2021; Hendi & Kuntadi, 2024).

H4. 

FCs significantly influence the adoption of CAATs, as evidenced by a substantial impact on internal auditors’ usage (T = 2.485, p = 0.013). If internal auditors are offered training, workshops, and robust advice on educational possibilities, they are more inclined to utilize these resources. These findings are consistent with other studies (Almagrashi et al., 2023; Daoud, 2023).

H5: 

AE positively influences the adoption of CAATs. The findings indicate a positive and statistically significant correlation between the adoption of CAATs and AE (T = 5.898, p = 0.000). The utilization of CAATs correlates positively with auditors’ experience, as they recognize the benefits of these systems.

H6: 

AE moderates the connection between PE and the adoption of CAATs. The research results indicate that audit experience moderates the correlation between PE and the use of CAATs (T = 2.170, p = 0.030). Internal auditors will utilize CAATs when they recognize that their application will enhance performance, owing to heightened sensitivity about performance consequences. The auditors possess the expertise to assess the capabilities of the systems and technologies.

H7: 

AE moderates the relationship between EE and the adoption of CAATs. This study revealed that AE did not substantially alter the link between effort expectation and CAATs (T = 1.725, p = 0.085). This indicates that the internal auditor’s job may or may not be effective.

H8: 

AE moderates the relationship between SI and the adoption of CAATs. This study indicated that auditors’ experience does not significantly moderate the relationship between SI and CAATs (T = 0.894, p = 0.371). The internal experience does not influence the adoption of CAATs by managers, supervisors, and team members.

H9: 

AE moderates the relationship between FCs and the adoption of CAATs. The research revealed a notable correlation between facilitation and CAATs (T = 2.270, p = 0.023). Experienced auditors subsequently secure enhanced managerial backing, resources, and instruction for the integration of CAATs. Their format enables the incorporation of these tools into their tasks and responsibilities.

5.2. This Study’s Theoretical and Practical Implications

This study enhances existing knowledge by offering insights relevant to auditing firms, stakeholders, and policymakers globally and particularly in Jordan. This study enhances the technology acceptance literature by highlighting the moderating role of AE on the acceptance of CAATs, a factor that has been largely overlooked in existing research. The findings indicate that an auditor’s experience influences their readiness to fully embrace CAATs provided they perceive CAATs as beneficial and have access to the requisite support. This study theoretically enhances audit knowledge by introducing novel concepts and results for auditors in Jordan and globally, providing a clear depiction of auditors’ roles. The research emphasizes the critical role of policymakers and regulatory bodies in fostering the comprehensive adoption of technology-driven audit practices. Implementing technological audits and systems requires laws, regulations, policies, and instructions. To improve information exchange in industries, particularly in auditing firms, it is important to promote CAAT certificates and hold workshops and training to improve the efficiency and effectiveness of their use. The importance of this work consists of providing critical insights into PE, EE, SI, and FCs on the adoption of CAATs and using the moderating influence of AE in auditing firms. This issue is significant for auditing firms in Jordan, the area, and globally, particularly as nations strive to modernize audit functions and integrate modern technologies such as IT, Artificial Intelligence (AI), Internet of Things (IoT), etc. This paper provides insights into AE with technology and the full adoption CAATs and the impact of organizational support and social dynamics on CAAT use. These insights can help auditing firms and regulators develop strategies to maintain CAATs as a critical component in an increasingly digital environment despite the risk of obsolescence.

5.3. Limitations and Further Research

The research relies on a technology adoption model (TAM) to investigate the adoption of CAATs among internal auditors in Jordan. The findings enhance the understanding of the drivers of CAAT adoption by internal auditors in Jordan, and this research highlights the significance of the moderating variable of auditors’ experience in auditing businesses. This study indicates that performance expectation and SI are critical elements influencing the adoption of CAATs among auditing companies, whereas EE was deemed irrelevant. Moreover, AE affects the correlation between performance expectation and CAATs in auditing organizations, although SI and EE do not experience this moderating effect.

These findings underscore the need for extensive training, workshops, and seminars; robust support from firms; and suitable regulatory frameworks, public policy, and regulations that could support the implementation and use of CAATs in the auditing field. This study has the following restrictions. The research concentrates on internal auditors in Jordanian enterprises, which may restrict the generalizability of the conclusions to external auditors or other industries. Moreover, a possible constraint is the choice of the survey, which may affect the study outcomes or be prone to bias. This study recommends that future research use a qualitative methodology. Future research may include more variables, such as technological elements like IT expertise or IT infrastructure, or focus on a different sector.