Factors influencing artificial intelligence adoption in the accounting profession: the case of public sector in Kuwait Free

Artificial intelligence (AI) is a rapidly evolving technology that can potentially transform various fields and sectors, including accounting (Ghura and Harraf, 2021). In this regard, AI can be defined as “the capability of a machine to imitate intelligent human behavior” (Aghion et al., 2017).

While there is an ongoing debate about whether AI will alleviate accountants’ duties and improve accounting efficiency (Albawwat and Frijat, 2009), recent research suggests that AI can enhance accountants’ productivity (Fast and Horvitz, 2017; Solaimani et al., 2020). For example, AI can automate repetitive tasks, classify items, upload documents automatically, provide more accurate information by processing the collected data and recommend the track to find the best possible findings (Maione and Leoni, 2021). Moreover, AI can provide value-added services to accounting clients, such as predictive analytics, risk assessment, fraud detection and business insights (Seethamraju and Hecimovic, 2022). As a result, the automation of accounting processes is fast becoming necessary for accounting firms that want to remain competitive in the fast-paced business environment (Chukwuani and Egiyi, 2020).

However, adopting AI in accounting has challenges and barriers, especially in the public sector, where specific organizational, environmental and regulatory factors may influence the diffusion and acceptance of AI innovations. Accordingly, Agostino and Arnaboldi (2016) found that the public sector is distinguished by a high level of complexity, diversity and accountability, which may provide limitations and challenges when deploying AI systems in accounting. In addition, public sector accounting is subject to various standards and regulations that may affect the compatibility and compliance of AI tools with the existing rules and norms (Bakarich and O'Brien, 2021). On the other hand, public sector accounting involves stakeholders with different interests and expectations, which may affect the trust and acceptance of AI systems among public sector accountants and their clients (Bracci and Vagnoni, 2006).

This paper aims to examine the factors influencing AI adoption in the accounting profession in the public sector context, focusing on the case of Kuwait. Kuwait is an interesting case to study because it faces several obstacles and risks in diversifying its economy and implementing Vision 2035. These include a heavy reliance on oil revenues, a lack of private sector investment, an overreliance on foreign labor and bureaucratic inefficiencies (Olver-Ellis, 2020). Moreover, the public sector plays a significant role in Kuwait’s economy, with 76% of Kuwaiti nationals working in the public sector (Ghura et al., 2021). Adopting AI can be particularly difficult because AI systems might replace many automated tasks in the public sector, and Kuwaiti labor law mandates that national citizens have the legal right to employment in the public sector (Olver-Ellis, 2020). Hence, this may create opportunities and challenges for adopting AI in public-sector accounting (Alenezi et al., 2017).

Using a developed framework and collated data from a survey questionnaire with public sector accountants in Kuwait, this paper identifies the organizational and individual factors that affect the adoption of AI tools in public-sector accounting. The developed model explains how these two dimensions influence the adoption and diffusion of AI for the accounting profession in public organizations (Lai, 2017). The paper also discusses the implications of AI adoption for public sector accounting practice, quality and accountability. Finally, the paper contributes to the literature on AI and accounting by providing an empirical investigation of AI adoption in a specific public sector setting that has yet to be widely studied.

This study is divided into different sections, with the first section being the introduction. Section 2 discusses the literature review. Section 3 presents the research model, followed by the research methodology. Section 5 presents the analysis and results. The final section presents the study’s discussion, conclusions and future insights.

The convergence of AI and accounting is a challenging and potentially rewarding area of research for social scientists, business owners and managers (Ghura and Harraf, 2021). Furthermore, the link between AI and accounting has recently sparked an increased interest (Nayak and Sahoo, 2021; Stancu and Dutescu, 2021). This is due to enhanced data availability and increased usage of technologies such as AI. Therefore, considering the recent events in the new global economy, it is increasingly difficult to overlook AI’s contribution to the accounting industry’s progress (Mohmmad et al., 2020).

AI is changing the way professionals work globally. Like other industries, AI has a significant impact on accounting and auditing. Aside from saving accountants time and providing accurate information, AI-enabled systems help accountants stay competitive in the market. It helps accountants and finance officers be more accurate and productive (Jin, 2022). These technologies can save time and money while providing critical decision-making insights (Mancini, 2021). Moreover, firms generate significant revenue while lowering operating costs (Alsheiabni et al., 2019).

The literature review discusses accounting AI adoption. The technology-organization-environment (TOE) framework, established by Tornatzky and Fleischer (1990), illustrates how the technical, organizational and environmental contexts impact how firms accept and execute technological advancements. Considering technical, organizational and environmental aspects, the TOE framework helps explain accounting technology adoption. This approach allows researchers to discover crucial elements affecting adoption behavior and propose ways to increase new technology adoption (Oliveira and Martins, 2011).

Another common theory used in the AI adoption and accounting field is the technology acceptance model (TAM). TAM is a theoretical framework that explains individuals' acceptance of information systems (Ma and Liu, 2004). According to TAM, users’ behavioral intentions indicate whether they would embrace technology. Behavioral intentions are, in turn, influenced by users’ perceptions of the technology's utility in carrying out the task and perceived simplicity (Ma and Liu, 2004). Researchers in accounting have used TAM to examine factors influencing the adoption of accounting systems (Souza et al., 2017). Using this framework, researchers can identify key factors that influence adoption behavior and develop strategies to promote the successful adoption of new technologies in accounting.

Recent research used the unified theory of acceptance and use of technology (UTAUT) framework to examine technology acceptance, determined by the effects of performance expectancy, effort expectancy, social influence and facilitating conditions (Chao, 2019). In addition, in accounting, UTAUT can be used to explain the adoption behavior of technological innovations such as cloud-based accounting systems (Dwivedi et al., 2019).

AI technology has the potential to revolutionize the accounting profession by increasing the accuracy of financial reporting, reducing costs and improving efficiency. Despite these potential benefits, adopting AI technology in the public sector has been slower than in the private sector. Various factors have been identified in the literature that influence adopting AI technology in the accounting profession in public sector settings. This section explores these factors in detail.

The lack of resources and expertise is a significant barrier to adopting AI technology in public-sector accounting. Implementing AI technology requires significant infrastructure, software and training investment, which can be a challenge for many public sector organizations. AI technology requires high computing power and significant storage capacity, which may be too expensive for smaller organizations. In addition, there may be a shortage of AI experts in the public sector, making it difficult for organizations to implement AI technology without the necessary expertise.

Research has shown that small and medium-sized accounting firms are more likely to face difficulties in adopting AI technology due to a lack of resources (Miah and Hasan, 2019). In a study conducted in Vietnam, Dang et al. (2019) found that the lack of resources and expertise was a significant barrier to adopting AI in accounting. Similarly, Chen et al. (2019) noted that the lack of AI expertise and the cost of implementation were the primary barriers to AI adoption in the accounting profession.

Implementing AI technology in the accounting profession can lead to job displacement, as AI technology can perform many of the tasks humans previously performed. This can create resistance to change among employees, as they fear that AI technology will replace their jobs. Concerns about the impact of AI on the workforce can lead to a lack of support for adopting AI technology among employees and stakeholders.

Several studies have highlighted the potential impact of AI on the workforce in the accounting profession. For example, El-Kassar and Abbas (2020) found that employees' fear of job displacement was a significant barrier to adopting AI technology in accounting. Similarly, Lobo and Varghese (2019) noted that employees' concerns about job displacement were a significant barrier to adopting AI in accounting.

Implementing AI technology requires significant organizational processes and culture changes, which can be challenging for some organizations to implement. In addition, there may be resistance from employees and stakeholders who are hesitant to adopt new technology. Resistance to change can be a significant barrier to adopting AI technology in accounting.

Research has shown that resistance to change is a significant barrier to adopting AI technology in accounting. For example, Uyar and Kılıç (2021) found resistance to change was a significant barrier to adopting AI technology in accounting in Turkey. Mahmoud and Al-Khouri (2021) also noted that resistance to change was a significant barrier to adopting AI technology in the accounting profession in the United Arab Emirates.

A lack of understanding of AI technology is also a significant barrier to its adoption in public accounting. Many public sector organizations lack knowledge of AI technology's potential benefits and limitations, which can prevent them from making informed decisions about its adoption. In addition, there may be a lack of understanding of the technical requirements to implement AI technology.

Research has highlighted the lack of understanding of AI technology as a significant barrier to its adoption in accounting. For example, Islam et al. (2020) found that the need for more understanding of AI technology was a significant barrier to its adoption in accounting in Bangladesh. Hoque et al. (2018) also noted that the lack of understanding of AI technology was a significant barrier to its adoption in accounting.

Regulatory and ethical concerns can also influence the adoption of AI technology in the accounting profession. AI technology can raise ethical concerns, such as bias in decision-making or violation of data privacy laws. In addition, regulatory requirements may need to be modified to accommodate the use of AI technology in accounting practices.

Research has highlighted the importance of addressing regulatory and ethical concerns for successfully adopting AI technology in accounting. Yang and Lin (2018) noted that regulatory requirements might be modified to accommodate the use of AI technology in accounting practices. Similarly, Mahmoud and Al-Khouri (2021) noted the importance of successfully addressing ethical and regulatory concerns to adopt AI technology in accounting.

The framework of AI adoption was divided into organizational and individual levels. At the organizational level, certain variables help to embrace AI models. On the other hand, the individual level demonstrates the variables that support employees to adopt AI applications. The developed framework in Figure 1 is built on assumptions derived from the abovementioned literature.

Creativity is usually fostered through workplace behavior. Senior management's attitudes, behaviors and work ethics shape the culture that subordinates emulate (Aboelmaged, 2014). Management influences innovation culture. Thus, the leadership team must communicate and demonstrate attention to AI in the workplace. Leadership reluctance to adopt novel administrative practices might prevent AI adoption in the workplace. Management that embraces new technology fosters excitement, especially with financial incentives (Van Noordt and Misuraca, 2020). To this end, this study hypothesizes:

A top-down communication mechanism is critical in the implementation of new ideas. Top management must support a sound communication system since their commitment to engaging subordinate personnel can impact the firm’s readiness to accept innovation. According to the resource-based theory, a lack of support from senior management might result in diminished competitiveness and an inability to adopt new technologies (Buzko et al., 2016). Clement and Eketu (2019) assert that organizational leadership that minds the welfare of their employees can easily retain and engage with them due to the high levels of motivation and feelings of job satisfaction that they arouse in them. The management can collect employee views on the need to update technology, increasing AI acceptance. As a result, this study proposes the following hypothesis:

Companies often use competitive pressure to deploy new technologies, responding to current market dynamics and demands (Yang et al., 2015). This competitiveness relates to the market danger. Such pressure increases the danger of losing market share. This can motivate to embrace; consequently, AI technology is one of the leading techniques firms use. According to Fast and Horvitz (2017), implementing AI can foster innovation that benefits individuals and firms, improving managerial decision-making and consumer experiences. Thus:

Government policies also influence an organization's readiness to use AI. Tax policies, organizational obligations, accountability and ethics in decision-making processes are all explored in this study (Wang and Siau, 2018). The adoption of AI differs by country due to various policies. For example, numerous companies in China have adopted AI due to lenient regulations and technological competence. However, negative perceptions regarding its complexity discourage further adoption (Chen et al., 2020). Some policies are changed to accommodate AI-enabled products and services. These efforts promote innovation. Therefore:

An organization's technological and financial resources must be considered while adopting and integrating new technologies. The issues of a company determine its ability to adopt new technology. Therefore, organizations must have technological know-how and resources in software, networking and computer hardware to foster innovation. According to a 2016 Narrative Science survey, over 59% of firms with good data management abilities use AI technologies (Zhang et al., 2020).

Human employees and system technology are two types of organizational resources. An organization's human capabilities should be enough to comprehend the approaches required to incorporate intelligent robots. Unfortunately, firms lacking human skills frequently fail to integrate new technology and adapt to change (Alsheibani et al., 2018). However, research reveals that firms with sufficient human infrastructure and technology can deploy AI. Hence, this study proposes the following hypothesis:

Perceived usefulness refers to a corporation's perceived benefits from implementing AI. Based on the research topic, usefulness shows how businesses use AI beyond the original system's efficiency, and adaptability describes how it may be infused into existing systems (Kumar et al., 2016). The perceived benefits of adopting new technology can considerably affect a company's decision to accept it or not. Other research has found a link between innovation adaptability and organizational readiness to adopt new technology (Aboelmaged, 2014). A company’s prospects of adopting AI increase when deep learning and machine learning (ML) are used in daily accounting procedures. Thus:

Perceived ease of use is one of the critical factors in the acceptance of new technologies that would be described as the user’s simplicity and convenience and influences customers’ interest (Abdallah et al., 2023). Usability is required for acceptance. Organizations use AI applications that are suitable for their operations. Simple organizational processes call for straightforward AI technologies, whereas complex ones demand the reverse. Wang and Siau (2018) state that easy-to-use and reliable AI applications develop trust among users. Trust-building is a two-step process that starts from scratch to continuous trust. It is dynamic; hence, maintaining trust requires relevant and capable AI technologies that elucidate work-related actions or conclusions, secure personal data and bond, socialize, interface and collaborate appropriately with humans. These benefits need to outweigh fears attached to AI, such as replacement and displacement from work, to ensure the trust remains. A bad user experience and interface increase rejection. Chi et al. (2020) reveal that AI devices with human-like features, such as anthropomorphism and technological knowledge, shape attitudes toward AI.

Anthropomorphism evokes mixed feelings; for instance, human-like AI devices that offer hotel services arouse negative attitudes toward robots in customers. Alternatively, people who possess IT skills significantly appreciate AI. Lastly, the innovation's facilities are vital to use. These features may simplify activities, save time and money or increase usage. Therefore, this study proposes the following hypothesis:

By BAT, accountants always favor practices that profit them. So, the technology's usefulness is assessed by whether it generates accounting profit for the firm. López-Cabarcos et al. (2020) state that knowledge is essential to every organization; however, its uses differ. It can be acquired and exploited as proposed by the knowledge-based theory. In addition, it can be presented in two forms, tacit and explicit. Tacit denotes perceptible knowledge only when applied and communicated through personal interaction, while explicit refers to easily communicable and systematic knowledge. As put forward by knowledge-based theory, the uniqueness of tacit knowledge can individualize products and services, providing firms with competitive advantages. AI, ML and emerging digital technologies codify and elucidate that form of knowledge constitute organizational data. Therefore, firms ensure profitability. Similarly, Kaya et al. (2019) assert that the European banking sector has benefited significantly from integrating AI into banking services. It boosts banks' return on assets and labor productivity, reducing costs and giving them a competitive edge. These advantages have resulted in increased investments in AI, amounting to US$24bn in 2018, and experimentation and implementation of the emerging technology in Europe, particularly in the United Kingdom (UK), France and Germany. Accounting profit is thus a requirement for AI deployment. Therefore:

AI adoption requires a behavioral intention to use the technology in this situation. Furthermore, an easy-to-use experience is needed to sustain this practice. Many theories highlighted the previous interrelationships. For example, the transtheoretical model or stages of change suggests that change occurs in phases and involves numerous actions. The stages are pre-contemplation, contemplation, preparation, action and maintenance. Actions include considering change, planning for it, adopting new habits and constantly practicing new behaviors (Quartuch et al., 2021). Employees hold negative perceptions about AI and fear integrating it into their tasks. Besides significantly altering their professions, employees fear it might replace them (Mirbabaie et al., 2022). These fears could hinder its acceptance in the workplace. Nonetheless, demonstrating its ease to use and assurance of employees' job security can facilitate its acceptance. Behavior follows personal and environmental factors proposed by the social cognitive theory. Actions, one's own or others, and their outcomes are great motivators of learning (Beauchamp et al., 2019). Reinforcements induce learning as well. Reinforcement, in this case, is the simplicity of using AI in the workplace to encourage its adoption. Thus:

As indicated earlier, there is a strong argument in the literature on the opportunities created for an organization that adopts AI in its operations and the associated challenges of applying these emerging technologies in accounting. The conceptual framework assumes several potential factors influencing public organizations to adopt AI in their accounting activities. It involves the underlying theories of AI and accounting to form its deductive argument for using AI in accounting. The conceptual framework shown in Figure 1 depicts the interactions and associations among the variables.

The questionnaire of this study was developed using multiple sources to measure the dependent, independent and mediation variables (Yang et al., 2015; Buzko et al., 2016 Wang and Siau,2018; Li et al., 2019). The developed questionnaire consists of 39 questions, as presented in  Appendix, and is split into two sections to consider the logical order of questions. The first section includes five questions related to demographic characteristics. The second section comprises 34 questions for the dependent, independent and mediator variables covering AI adoption (five questions), organizational level factors (15 questions), individual-level factors (six questions), accounting profit and behavioral intention (six questions) and additional questions (two questions) to find some insights like the type of the organization and to what extent the participant familiar to AI. Following a logical order of listing the questions keeps the respondents’ focused. All participants were given a confidentiality form to show the study’s purpose and ensure their names were kept anonymous. Also, they were allowed to opt out or leave any unanswered questions at any point.

The judgmental sampling technique is an approach in which the researcher trusts their judgment when selecting population samples to contribute to the study. The logic behind a judgmental sampling technique is to get a representative sample using sound judgment, saving time and money (Taherdoost, 2016). Therefore, a judgment sampling approach was used to select the targeted sample from Kuwaiti public organizations. This study's sample consisted of accounting professionals experienced in Kuwait’s public organizations. Considering the extensive size of the public firms, which constitute a vast sector employing numerous accounting professionals. Data were collected through questionnaires distributed among the selected public organizations. This study adopted a cross-sectional time frame with acceptable accuracy and cost-effectiveness (Souza et al., 2017). The research data were collected over two months, November−December 2021.

The demographic background of participants was analyzed using Excel 2013. The study used the partial least square structural equation modeling technique to examine the descriptive data, evaluate the measurement's validity and reliability and verify the hypotheses. According to Dwivedi et al. (2019), the approach provides a remarkably adaptable design for the concurrent evaluation of complex models, management of reflective and formative measurement models, and constructs consisting of a single item. The path-weighting scheme setting was used with SmartPLS 3 software to conduct calculations.

The research model was evaluated through a two-step procedure. The use of the measurement model facilitated the assessment of the dependability and legitimacy of the measurements. In contrast, the structural model was used to evaluate the structural interconnection between the constructs. The initial phase involved using the measurement model, which comprised two levels, namely, the first-order and second-order levels. The subsequent step involves the structural model. The initial factors are reflective constructs. The study analyzed reflective measurement models through the use of various criteria. These criteria included indicator loading, which was required to be greater than 0.7; composite reliability, which was needed to be greater than 0.6; average variance extracted (AVE), which was required to be greater than 0.5; and discriminant validity, which refers to the degree to which a construct is genuinely distinct from other constructs (Schroeder et al., 2019). Consequently, the Fornell–Larcker criterion was used to achieve this objective.

At the second-order level of analysis, it is observed that all factors are indicative of formative measurements. The study used three criteria: outer weight with a minimum value of zero, p-value with a maximum threshold of 0.05 and variance inflation factor (VIF) with a maximum limit of 5. In the second phase, the structural model was used to examine the research hypotheses and assess the correlation between variables using the path coefficient, p-value and R². Subsequently, the standardized root mean square residual (SRMR) was evaluated to determine the model's overall fit. When the SRMR value equals 0, it indicates an optimal fit, whereas 0.085 indicates a satisfactory fit. The structural model calculations were conducted using the bootstrapping procedure with 5,000 bootstraps to evaluate its efficacy.

The study involved the distribution of 450 surveys to experienced accounting professionals working in public organizations in Kuwait. A total of 393 surveys were completed and returned, resulting in a response rate of 78.3%. Table 1 shows the characteristics of the respondents that confirm males' higher participation than females. Also, it indicates that participants holding bachelors are more than 50% of the sample. Regarding ages, the results show that age groups are computed consistently.

In addition, the survey has also collected data on the job category of the participants. Table 2 provides the results that indicate higher participation from accountants (70%), followed by those who are working in auditing (17%), and lastly, the bookkeeper category, which indicates the lowest participants (13%). It indicates that most participants were lay in the group 5–10 years, food by juniors less than five years, contributed by 109 responses.

Five Likert-scale questions evaluate the factors. One strongly disagrees, and five strongly agree. Table 2 shows the factor mean, standard deviation, skewness and kurtosis. All variables' means are 3–4 scores, indicating that the respondents have a positive perception of all variables. Regulatory support has the highest mean response of 4.057, while behavioral intention scores the lowest mean of 3.2911. The standard deviation indicating data variations indicates that the perceived usefulness has the highest variation of 1.190. In contrast, regulatory support shows the lowest standard deviation of 0.821, indicating that ideas are stable on the regulatory support. Respondents tend to be semi-consistent in their beliefs, showing around one standard deviation. All variables have negative skewness, indicating left-side tail distribution. Kurtosis is normal because all values are between −3 and 3.

Table 3 provides a summary of the measurements for the first-order construct. In terms of outer loading, all items are above 0.7 except AA1 and CP3. As AA1 scores a shallow value of 0.299, it is eliminated from the model. However, CP3 scores 0.619, which is acceptable as it is more than 0.4, and the variable score values of Cronbach's alpha, composite reliability and average variance were extracted. In addition, regulatory support scores Cronbach's alpha of 0.692, which is acceptable because it scores 0.831 for composite reliability and 0.622 for AVE.

Table 4 shows the findings of the Fornell–Larcker criteria. The square root of each variable’s AVE was greater than its highest correlation with any other variable, indicating that discriminant validity is established (Afthanorhan et al., 2021). Therefore, the variable shares more variance with its correlated indicators than any other variable.

The formative construct measurements (second-order) results are presented in Table 5. All weights were significant > 0, p-value < 0.05, and VIF values were below 5.

The structural model was used to test the significance of the nine hypotheses of this study. Table 6 shows the path coefficient and p-value of the organizational and individual factors. The results of organizational factors indicate that organizational culture and regulatory support factors positively impacted AI adoption by the path coefficient values of 0.180 and 0.099, respectively, as they score less than 0.05 p-value. On the other hand, communication structure, competitive pressure and resources are unsupported with the path coefficient values of 0.594, 0.924 and 0.093, respectively. At the same time, the results of individual factors indicate that perceived usefulness and ease of use positively impacted AI adoption by the path coefficient of 0.120 and 0.199. In addition, accounting profit and behavioral intention as mediator variables partially supported perceived usefulness and ease of use by the path coefficient of 0.494 and 0.471, respectively; they scored less than 0.05 p-value. The R² value of the dependent variable AI adoption was 0.625, which indicates the extent to which the different organizational and individual factors significantly affect AI adoption in the accounting profession. Moreover, the SRMR score expresses a good fit with a value of 0.068, suggesting it is less than 0.085 (Benitez et al., 2020).

AI systems have significantly changed the accounting industry (Fast and Horvitz, 2017; Solaimani et al., 2020). However, few studies have examined the perception of accountants about AI adoption in the public sector (Bakarich and O'Brien, 2021). In addition, this industry is known for its high complexity, diversity and accountability, which may make it challenging to implement AI systems in accounting (Agostino and Arnaboldi, 2016).

Therefore, this study aims to examine the perception of accounting practitioners regarding the factors affecting using AI-based solutions in accounting processes in the public sector in Kuwait, where most Kuwaiti nationals work in public sector (Ghura et al., 2021). The study's factors are stemmed from different theories of AI adoption (Ma and Liu, 2004; Oliveira and Martins, 2011; Chao, 2019) and developed in a framework that includes organizational culture, effective communication channels among different management levels, keeping a competitive edge, supportive government policies for technology absorption, human and financial capital availability and the perceived usefulness and ease of use of AI adoption among accountants. Addressing these factors is critical for public organizations to successfully implement AI technology and reap the benefits of increased efficiency, accuracy and reduced costs.

The study’s findings found that organizational culture, regulatory support, perceived usefulness and ease of use have a direct positive effect on public accountants’ AI adoption, while perceived usefulness and ease of use also have an indirect positive effect through accounting profit and behavioral intention. The previous results are in line with the current literature, which highlights that several organizational and individual factors have a significant influence on the adoption of AI technologies (Kumar et al., 2016; Beauchamp et al., 2019; Kaya et al., 2019; Van Noordt and Misuraca, 2020; Chen et al., 2020; Abdallah et al., 2023).

However, contrary to expectations, the results showed that the availability of resources and effective communication channels have an insignificant impact on AI adoption. An explanation might be that the availability of resources (e.g. talent, assets and capabilities) and effective leadership structure have a long-term rather than a short-term effect on AI adoption goals. Also, this study did not prove the competitive factor's relevance because the government does not compete. No market share loss pressure exists either.

Based on the study's findings, adopting AI systems in accounting and auditing can provide many benefits to the public sector in Kuwait, such as using AI to automate repetitive tasks and reduce errors (Stancu and Dutescu, 2021). To promote the adoption of AI in accounting in the public sector, public organizations can consider implementing policies such as increasing investment in AI development (Mohmmad et al., 2020), educating and training different stakeholders (e.g. accountant practitioners) on the potential benefits of AI (Albawwat and Frijat, 2009) and addressing ethical and social dilemmas that may arise from AI adoption (Rkein et al., 2019; Nayak and Sahoo, 2021).

Moreover, developing an effective AI communication and governance strategy for public organizations involves several key steps, including assessing current AI capabilities, fostering different stakeholders’ collaboration and transparency (Mancini, 2021), evaluating the potential risks associated with AI (e.g. financial data leakage), taking measures to mitigate these risks and establishing a sound legal system and regulatory framework for AI technology to ensure its safe and effective use (Jin, 2022). Finally, leaders in the public sector must align the organization's culture, structure and working methods to support broad AI adoption. As a result, public organizations can improve efficiency, accuracy and decision-making in accounting and auditing processes (Hasan, 2022).

The study’s results have specific limitations that potentially open the door for future research. First, the current research did not include other variables that might affect accounting practitioners using AI in the public sector, such as age, gender, risk tolerance and ethical and social factors (Mancini, 2021). Second, the target population is focused on the context of Kuwait, and future studies are recommended to have a larger sample size and identify different determinants, such as a comparison between a cross-country study that can identify different perceptions in the public sectors (Noordin, 2022). Finally, future studies should focus on qualitative research to have a more in-depth analysis of the factors influencing AI adoption in accounting in the public sector (Rkein et al., 2019).

Overall, the study’s findings suggest that combining AI technology with accounting work is necessary to promote the further development of the accounting industry for the public sector in Kuwait. We hope that our research will contribute to a better understanding of the impact of AI on the accounting industry and provide valuable insights for financial practitioners in this field.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest concerning this article's research, authorship and publication.

Funding: The author(s) received no financial support for this article's research, authorship and publication.