Understanding accounting professionals’ intention to adopt robotic process automation: a TOE-based empirical assessment from an emerging country

The proliferation of the Fourth Industrial Revolution (4IR) is transforming the accounting landscape, with technologies such as Robotic Process Automation (RPA) changing the face of traditional accounting processes. This study investigates the level of RPA adoption among accountants in South Africa and examines how technological–organizational–environmental (TOE) factors influence the behavioral intention of RPA adoption. The study employed an exploratory cross-sectional survey comprising responses from 100 professional accountants in practice to analyze its data, combining descriptive statistics with a multiple linear regression model supported by correlation tests to determine significant predictors of RPA adoption intention. The robustness of the model, which was verified by multiple pre- and post-analysis checks, indicated that institutional support, particularly normative pressure, has the strongest influence on adoption intention, with an adjusted R² value of 0.27 highly significant. This highlights the crucial role that organizational readiness, managerial support, and technology readiness play in enabling RPA adoption. On the other hand, mimetic pressure showed a negative influence, indicating that the industry-wide adoption of RPA technology may raise concerns and anxiety about job displacement. Overall, the findings reinforce the importance of organizational capacity-building in fostering RPA adoption while also revealing the complexity of environmental and technological factors that influence the adoption decisions of professional accountants in a developing-economy context. The findings support SDG 9 by emphasizing capacity building and inclusive digital transformation.

1 Introduction

The accounting profession, which has long been grounded in established rules and practices, is now experiencing rapid transformation owing to digitalization and the proliferation of Fourth Industrial Revolution (4IR) technologies (Taib et al., 2023). Among these, Robotic Process Automation (RPA) —a software program that interacts with existing applications to automate repetitive rule-based accounting tasks—artificial intelligence (AI), blockchain, and cloud computing are no longer theoretical concepts for this profession but are now actively reshaping its landscape by automating traditional tasks, invoice processing, bank statement reconciliation, and redefining the skills required for professional processes and outcomes (Judijanto et al., 2025; Kokina and Blanchette, 2019; van der Aalst et al., 2018). RPA tools offer improved efficiency, minimized error, operational cost savings, and provide professionals ample time to focus on high-value analytical and advisory roles instead of the traditional repetitive tasks common to the profession (Doolin et al., 2025; Perdana et al., 2023).

Beyond efficiency, recent research has shown that professional accountants and auditors now integrate RPA with other emerging tools such as machine learning (ML) and generative AI systems to perform intelligent process automation, consequently transforming their roles and skills requirements (Kokina et al., 2025). Furthermore, recent studies find that RPA adoption depends largely on technological, organizational, and environmental (TOE) factors that significantly influence its successful adoption and application within the field of financial accounting (Doolin et al., 2025; Rovaris et al., 2025; Thangapandian et al., 2025). For instance, Rovaris et al. (2025) stressed that technological readiness in the form of perceived usefulness, technological reliability, and system compatibility significantly shapes adoption outcomes. Organizational support in terms of digital culture, training, resource availability, and leadership/management buy-in drives RPA implementation and adoption intention (Thangapandian et al., 2024). Kunene (2023) noted that environmental factors in the form of competitive pressure within the industry, regulatory expectations, supplier support, and client demands create external pressures that shape adoption decisions in accounting firms. Such research demonstrates that behavioral intention toward RPA is not random nor merely a determination by an individual; rather, it is a structured and quantifiable antecedent based on TOE factors.

Despite RPA’s transformative potential and the recent scholarly evidence on how RPA adoption and behavioral intention are shaped by TOE factors, its adoption rate remains uneven across countries and sectors. A report by Fortune Business Insights (2025) recorded that RPA has gained wide recognition in developed nations, with North America leading with the largest market share owing to its large corporate automation investments and government support for digital transformation. They further noted that countries in the Asia-Pacific are recording a rapid growth rate, while the adoption rate in the Middle East and Africa (MEA) is still slow. Similarly, in Cameroon, Fossung and Manfo (2025) reveal that the use of RPA technologies in accounting for rendering auditing services is still under-utilized, with an adoption rate of only 28%, despite firms using other basic accounting tools widely. The rate of adoption in South Africa is, however, yet to be determined, even though the country has been recording gradual growth in digital transformation. In addition, existing studies so far within this context (Kunene, 2023; Mlambo, 2022; Mongwe, 2024; Phage, 2023) have been mainly qualitative in approach and have focused only on financial institutions (banks and insurance companies), while empirical evidence from other sectors is still scarce and fragmented. This existing evidence, though, has failed to report on the level of RPA adoption; such studies have revealed that adoption is largely influenced by skills shortages, governance structures, organizational culture, legacy systems, and job displacement. Another study on the automotive industry within this context produced mixed findings, with some respondents also expressing concern about skills obsolescence and job displacement, but other respondents report efficiency gains (Dlamini, 2024). From the Brazilian credit cooperative system, Rovaris et al. (2025) similarly reported that motivators of RPA include increased operational efficiency, ease of technology use, risk mitigation, cost reduction, and external competitive pressure, while hindrances to its implementation are initial employee resistance and the absence of clear guidelines.

This limited evidence suggests that while RPA offers significant benefits, its adoption is influenced by TOE factors and that its impacts on the roles, skills, job security, and career progression of accounting professionals are far from deterministic. They also highlight a significant void between potential and actual deployment. Such complexity underscores the need for a study from an emerging context, where adoption decisions and the ultimate effect on accounting professionals may be differently shaped by organizational readiness, institutional supports, governance structures, resource constraints, technological capability, regulatory conditions, training, and skills shortages (Da Silva Costa et al., 2022; Moloi and Obeid, 2024; Perdana et al., 2023; Rawashdeh et al., 2022).

Therefore, this study investigates the extent to which RPA technologies are being adopted in South Africa, particularly by accounting professionals, and into factors shaping their behavioral intention to adopt the technology. This is crucial, as the majority of existing studies (such as Kokina et al., 2025; Tiberius and Hirth, 2019; Yang et al., 2024) have been based on developed economies, while the few from South Africa, an emerging country (Dlamini, 2024; Kunene, 2023; Phage, 2023) are largely qualitative and contextual, having their focus on case-based investigations in automotive and financial industries, limiting their generalizability. Additionally, studies that have examined accounting, such as da Silva Costa et al. (2022) and Thangapandian et al. (2024), Thangapandian et al. (2025), employed a systematic literature review approach, while Alassuli (2025) only used descriptive methodology. Empirical evidence with a specific focus on professional accountants from different industries in an emerging context is scarce; likewise, studies based on a quantitative research approach employing the TOE framework are close to non-existent.

Given this, our study investigates the following objectives.

i. Determine the extent of RPA adoption among professional accountants in South Africa.

ii. Assess professional’s perception of RPA technologies for executing accounting tasks.

iii. Examine which TOE factors influence professionals’ behavioral intention to adopt RPA technologies.

1.1 Research questions

RQ1: What is the current level of RPA adoption among accounting professionals in South Africa?

RQ2: How do accounting professionals perceive the TOE implications of RPA technologies in executing accounting tasks?

RQ3: To what extent do TOE factors influence the behavioral intention of accounting professionals in South Africa to adopt RPA?

This study offers both theoretical and practical contributions. Theoretically, by providing empirical evidence on the interplay of the technological–organizational–environmental (TOE) framework, we offer insights into how organizational and technological contexts influence the behavioral intentions of accounting professionals to adopt RPA technologies. The study also examines how environmental attributes stemming from the expectations of normative clients and professional bodies and mimetic pressures within the industry shape the technological readiness and willingness of organizations to adopt emerging technologies, as well as the influence of coercive pressures from government policies. On a practical level, the results will guide firm-level decisions (about training, governance mechanisms, change management, etc.) and policy recommendations (regarding specific institutional supports) that hinder RPA adoption. The study, therefore, considers how the findings may impact innovation-driven policies to advance Sustainable Development Goal (SDG) 9 to build resilient infrastructure, promoting inclusive and sustainable industry development to foster innovation. Through the evidence-based insights from this emerging country context, our study contributes to the growing literature and expands digital transformation discourse in the accounting profession, therefore, providing recommendations for professional accounting practitioners, regulators, and policymakers seeking to promote responsible RPA adoption.

The remainder of this study is organized as follows. Section 2 focuses on the concept explanation of RPA, a review of related literature on RPA, gaps in the literature, and explains the TOE framework. Section 3 presents the methodology, Section 4 provides detailed analysis, results, and discussion, and Section 5 concludes the study.

2 Literature review

2.1 Robotic process automation in the accounting and auditing profession

RPA involves the use of automated technologies that rely on software to perform tasks that are generally executed by humans. Contrary to popular belief, RPA does not involve the use of physical robots but, rather, bot application programming in the form of software (Kokina and Blanchette, 2019; Moffitt et al., 2018) to perform assigned digital labor at the user interface level following structured input programs/commands (Plattfaut and Borghoff, 2022). Using the software program/command, RPA executes repetitive tasks by interacting with existing applications and user interfaces.

RPA and other automation technologies have, for over a decade, gained popularity in the field of professional accounting and auditing owing to the debate that they enable for structured transaction processing, data extraction, cost reduction, human-error elimination, and time-saving by streamlining repetitive tasks and reconciliations (Lacity et al., 2015; Moloi and Obeid, 2024; Perdana et al., 2023; Sethibe and Naidoo, 2022). Therefore, studies have begun to investigate its adoption, benefits, and contextual dynamics across different organizations and regions.

Recent studies on the value creation of RPA have found that it fosters improved operational efficiency, process standardization, evaluation of the quality and accuracy of information, and cost reduction across key organizational value-chain activities such as internal operations and procurement; it thus has demonstrated that it can add value beyond mere task automation (Durão and dos Reis, 2024; Sethibe and Naidoo, 2022). Similarly, in the accounting domain, RPA’s proven potential regarding its ability to improve accuracy have allowed professionals make informed decisions by offering them time to concentrate on strategic duties and adversarial roles (Doolin et al., 2025; Perdana et al., 2023). It is now considered a core component of digital transformation by most modern organizations and serves as a practical solution to streamlining workflows (e.g., accounting, audit, and payables/receivables) and improving data accuracy (Rovaris et al., 2025; Thangapandian et al., 2025).

2.2 Empirical findings on RPA

Evidence has shown an increasing number of RPA adopters among practitioners. However, empirical research differs across industries and contexts. Table 1 below summarizes findings from the extant literature.

Table 1

Table 1. Summary of empirical literature.

2.3 Gaps in the literature

Overall, existing studies suggest that RPA adoption is growing, emphasizes its benefits, and demonstrates how it can be implemented (Perdana et al., 2023; Zhang et al., 2023). However, they fall short in providing explanations on the critical determining factors of its adoption. For instance, there is limited evidence to explain the behavioral intention behind the adoption of RPA technologies. Likewise, perceptions toward RPA technologies and organizational readiness have been noted as challenges by some studies, but researchers rarely employed behavioral intention as a predictor of RPA adoption when testing empirically. Additionally, existing studies rarely consider institutional support in the form of coercive, normative, and mimetic pressures as influences on technology adoption in professions such as accounting. Significant contextual gaps exist for emerging economies, including the South African accounting landscape, so studies have yet to investigate the dynamics within this context. Our study focused on South Africa to address these gaps by empirically exploring accountants’ perception of RPA and their behavioral intention to adopt it in order to ascertain whether RPA adoption is shaped by technological context, organizational support, and environmental influence. By employing a quantitative exploratory approach, our study offers evidence of behavioral and contextual factors that prior literature has ignored, providing a deep insight into RPA adoption in a professional setting from an emerging economy.

2.4 Theoretical underpinning

This study is grounded in the technology–organization–environment (TOE) framework propounded by Tornatzky and Fleischer (1990). TOE recognizes that in a professional organizational setting, technology adoption extends beyond individual perceptions and technology alone; it intersects with technological, organizational, and environmental factors. This framework has been widely applied to explain the adoption of emerging technologies such as AI, blockchain, cloud computing, and other digital technologies. Proponents of this theory argue that adoption behavior is generally shaped by several forces that fall under the umbrella of TOE, and they can either be internal or external forces or both contingencies at the same time. Recent studies have demonstrated that the TOE framework is suitable for modeling RPA adoption since it integrates technological readiness with organizational attributes and environmental influences (Fossung and Manfo, 2025; Thangapandian et al., 2025). This interplay is crucial to understanding the multifaceted influences on the behavioral intention of technology adoption in any professional domain. This is particularly crucial for the accounting profession, which is currently experiencing rapid digital transformation, regulatory shifts, and skills-related pressure arising from the automation of its routine tasks. We believe this framework will offer good ground to theorize and understand how and why professional accountants react to technology adoption.

2.4.1 Technological dimension

The perceived usefulness of RPA technologies can be captured under the lens of the technological features that a new technology possesses as this has a higher likelihood of influencing professional accountants’ adoption intention. The extant literature noted that RPA has the capacity to eliminate errors, save time, improve consistency in accounting task execution, and increase throughput. These attributes suggest that when users of RPA technologies find it useful for effectively completing accounting tasks, they will more likely be convinced of the relative advantage it has to offer, consequently influencing their behavioral intention to adopt it (Durão and dos Reis, 2024; Rawashdeh et al., 2022; Rovaris et al., 2025; Syed et al., 2020). Based on this, we propose the following:

H1

H1. Perceived usefulness of RPA technologies is positively associated with professional accountants’ behavioral intention to adopt RPA.

H2

H2. Perceived ease of use of RPA technologies is positively associated with professional accountants’ behavioral intention to adopt RPA.

H3

H3. Perceived reliability of RPA by professional accountants is positively associated with their behavioral intention to adopt the RPA technologies.

H4

H4. Perceived cost–benefit of RPA by professional accountants is positively associated with their behavioral intention to adopt RPA technologies.

2.4.2 Organizational dimension

The organizational aspect captures the internal firm capabilities, support, and readiness shaping automation adoption. This aspect covers the organizational attributes influencing RPA adoption, especially within accounting domains. At a time when automation is rapidly transforming accounting work processes, organizations must now invest in capital and digital skills. In support of this, some evidence exists for how organizational support, digital training of employees, investment in digital tools and human resources, resource availability for the maintenance of digital tools, and management strategy are critical for the implementation of automated technologies (Fossung and Manfo, 2025; Thangapandian et al., 2024). This is even crucial because technology adoption is hindered by a lack of both financial and human resources (Perdana et al., 2023; Remlein and Nowak, 2025). Given these submissions, we hypothesize the following:

H5

H5. The training resources provided by organizations in support of RPA technologies will create a significant impact on the behavioral intention of professional accountants, thus influencing them to adopt RPA.

H6

H6. The managerial support provided by organizations for RPA technologies will create a significant impact on the behavioral intention of professional accountants, influencing them positively to adopt RPA.

H7

H7. A high level of digital readiness by an organization has a positive influence on the behavioral intention of accounting professionals to adopt RPA technologies.

2.4.3 Environmental dimension

The environmental construct covers external pressures and institutional influences that often shape technology adoption. In the context of a developing country such as South Africa, this will include client expectation and market demand, regulatory support, professional standards, competitive pressure, institutional expectations, industry, and technological trends. These factors can be summarized as three forms of institutional pressure—coercive, mimetic, and normative pressures—which are generally believed to influence the adoption and extent of technology use, especially in the accounting field.

Clients’ expectations for value-added services beyond traditional compliance, or market demand for real-time data and prompt reporting in this era of digitalization, can compel accounting firms to adopt automation technologies to meet client expectations and evolving market demands (Moloi and Obeid, 2024; Timmis, 2025). We, therefore, argue that client expectations and market demand for improved professional services will act as normative pressure to compel accountants to adopt RPA technologies for enhanced accounting services and role delivery (Alassuli, 2025; Rawashdeh et al., 2022; Yang et al., 2024). Hence, we hypothesize the following:

H8

H8. Normative pressure arising from the client’s expectation for digitally enabled accounting services has a significant and positive influence on the behavioral intention of accounting professionals to adopt RPA.

Another crucial institutional force compelling firms to embrace automation is the mimetic pressure formed through broader technological and industry trends the such as use of automation and digital technologies, system integration, and the evolving demands for digital service delivery. This dynamic is reinforced by the increasing interest in integrating RPA with AI, commonly termed as “intelligent process automation,” in the field of accounting and finance (Durão and dos Reis, 2024; Thangapandian et al., 2024; Wang et al., 2025). Firms may adopt RPA to stay competitive in a market, especially if their competitors within the same industry use automation to provide cheaper and prompt services to clients or deliver advisory services. Given this, one can argue that mimetic pressure formed due to the desire to gain a competitive advantage is a core determinant of RPA adoption in accounting. It has also been found by researchers that competitive pressure, such as the need to match industry trends about digital efficiency benchmarks at national, regional, and global levels, can force accountants to adopt automation technologies (Ansari, 2025; Durão and dos Reis, 2024; Rovaris et al., 2025). To this end, we hypothesize the following:

H9

H9. Mimetic pressure existing in the accounting market positively influences accounting professionals’ behavioral intention to adopt RPA.

Furthermore, the activities of accounting professionals regarding auditing and financial reporting are often subject to stringent regulatory requirements, compliance standards, and high expectations from professional bodies, especially when operating in a well-regulated setting. Professionals may, therefore, find RPA a desirable alternative when the regulatory environment in which they operate requires them to adopt such technologies for improved accuracy and transparency in reporting. This form of regulatory requirement highlights how professionals can be coerced into adopting automation owing to increasing digital expectations from regulatory authorities. Numerous studies identify coercive or regulatory pressure as a motivator of RPA (Hsiung and Wang, 2022; Rovaris et al., 2025; Thangapandian et al., 2024). In an environment such as South Africa that is currently undergoing significant digital transformation, this form of institutional pressure may be instrumental in fostering the adoption of RPA technology. On this basis, we propose the following hypothesis:

H10

H10. Coercive pressure due to stronger regulatory compliance has a positive influence on the behavioral intention of accountants to adopt RPA.

This integrated approach is suitable for our study as it allows for a multidimensional understanding of RPA adoption by South African accounting professionals and firms, extending beyond individual perceptions and mere technology, to align with the organizational and institutional environment in which the accounting profession operates. This, combined with behavioral intention as an outcome variable, can indicate professionals’ readiness to adopt RPA. Moreover, recent studies such as Perdana et al. (2023) and Zhang et al. (2023) recommend the TOE framework as a strong model that any study on digital transformation ought to consider, including studies from the accounting field, because it captures both internal and external factors that shape innovation outcomes. Therefore, by employing this framework for our study situated in the accounting field in an emerging context such as South Africa, the study offers a comprehensive insight into the factors influencing accountants’ behavioral intention to adopt RPA technologies and use them for executing their tasks. Drawing on this, we conceptualize RPA adoption in Figure 1.

Figure 1

Figure 1. Conceptual framework of RPA adoption based on TOE perspective.

3 Methodology

3.1 Research design, population, sample technique, and instrument

This exploratory study employed a quantitative, cross-sectional survey design approach, allowing for statistical testing of the association between the constructs examined and accounting professionals’ perception of RPA adoption at a particular time. The study targeted accounting professionals working in different areas of specialization within South Africa. However, because there is no central database of accountants within South Africa, a purposive sampling technique was employed to reach the targeted groups via social media platforms (LinkedIn and WhatsApp) with verified user profiles displaying details of the individuals’ professional status and role and through professional associations such as the South African Institute of Chartered Accountants (SAICA), South African Institute of Professional Accountants (SAIPA), and the Institute of Internal Auditors South Africa (IIA SA). This ensured that only qualified professionals in South Africa were invited. Through these media we distributed the questionnaire developed on Google Forms to the targeted groups with a detailed explanation provided on the purpose of the research, eligibility criteria for the individuals to determine whether they are eligible to respond, a confidentiality statement, consent, and estimated completion time. Furthermore, the study had some questions to screen respondents; through the screening responses to the survey questions, we were able to confirm the employment status of the respondents, their designated roles, location in South Africa, basic awareness of automation technologies, and an assessment of their RPA usage with response rated (use: never, rarely, moderately, frequently, or always). This assessment further allowed us to separate the active users of RPA technologies from non-active users. This approach was deemed appropriate given the exploratory nature of the study and the need to obtain information-rich cases from professionals who are knowledgeable about RPA adoption, implementation, and challenges often faced by organizations seeking automation.

3.1.1 Respondents’ composition and sample size justification

The study population was indeterminable, so invitations were sent across the media noted in the preceding section, and participation was voluntary. The study recorded an initial response of 127, comprising both active and non-active users of RPA technologies as recorded in the self-assessment screening question. Therefore, to capture only responses of actual RPA users for further analysis, we screened out non-active users (27 in total) to arrive at the final sample of 100 responses from active users. The respondents in the group worked in various organizations with roles across finance, accounting and audit, taxation, public sector, insurance, and technology service providers supporting RPA deployments. The final sample of 100 responses was deemed sufficient for this study as this is within the recommended minimum acceptable threshold of Hair et al. (2010).

3.1.2 Research instrument

The structured questionnaire (see appendix) was composed of items on a five-point Likert scale: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. The construct comprised technological, organizational, and environmental contexts, with behavioral intention as outcome variable, and adoption level to ascertain the extent of adoption in South Africa. Table 2 summarizes the final items used in the study analysis.

Table 2

Table 2. Survey construct employed for data preparation and analysis.

3.2 Instrument reliability (Cronbach alpha)

The study adapted a validated instrument (see Appendix) used in previous technology adoption studies (Attuquayefio and Addo, 2014; Kim et al., 2024). We assessed the internal consistency of the instrument using Cronbach’s α (Table 3) where necessary because some constructs had multiple items, while others had only a single item. The combined alpha was 0.8. Under the technological dimension, we had constructs measuring technology perceived ease of use (TEOU), technology perceived usefulness (TU), technology perceived reliability (TR), and technology perceived costs–benefits. For our multi-item constructs (TU and TR), we followed standard psychometric guidance (Nunnally and Bernstein, 1994; Tavakol and Dennick, 2011). Table 3 summarizes the results of the reliability, validity testing, and exploratory factor analysis. TU with α = 0.778 demonstrated good internal consistency and was retained with its composite mean score (Hair et al., 2010). TR showed an alpha value of 0.63. While this is slightly below the conventional 0.70 threshold, research noted that an α value of ≈0.60 is acceptable for exploratory scale development and early-stage research (Nunnally and Bernstein, 1994; Taber, 2018). Hence, based on the acceptable values of the item-total correlations and a single-factor structure that the four item scales produced, the mean composite score was retained. Behavioral intention, BI1 and BI3, produced α = 0.503 when combined. Eisinga et al. (2013) noted that the reliability indices of two-item scales are sometimes lower due to scale length but are conceptually coherent. All other constructs were regressed separately and interpreted individually, allowing for more precise interpretation. All analyses covering both descriptive and inferential statistics were conducted in SPSS version 29.

Table 3

Table 3. Reliability, validity, and factor analysis output table.

3.3 Instrument validity

A KMO value of 0.81 (Table 3) indicated sampling adequacy, and Bartlett’s test was significant (p < 0.001), confirming factorability. Exploratory factor analysis using Promax rotation revealed a clear four-factor structure consistent with the TOE theoretical model. TU (6 items) with KMO = 0.773, Bartlett’s χ² (15) = 169.62, and p < 0.001 showed a single factor explaining 35.7% of variance (α = 0.778). TR (four items) also with KMO = 0.679, Bartlett’s χ² (6) = 46.48, and p < 0.001 had one factor explaining 31% variance (α = 0.628), both suggesting their suitability and acceptability for exploratory research (Nunnally and Bernstein, 1994; Taber, 2018). All reflective items loaded strongly (0.53–0.82) on their respective dimensions with no problematic cross-loadings. Behavioral intention items clustered together as expected. Institutional support indicators were excluded because they represent conceptually distinct pressures (normative, coercive, and mimetic) and are treated as separate theoretical variables. Overall, the results confirm the construct validity of the measurement instrument.

The regression model employed is

$\begin{array}{ll}\hfill \mathrm{B}\mathrm{I}1\_3& ={\beta }_0+{\beta }_1\left(TEOU\right)+{\beta }_2\left(TU\right)+{\beta }_3\left(TR\right)+{\beta }_4\left(TCB\right)+{\beta }_5\left(DR\right)\hfill \\ \hfill & +{\beta }_6\left(MS\right)+{\beta }_7\left(TRes\right)+{\beta }_8\left(NP\right)+{\beta }_9\left(CP\right)+{\beta }_{10}\left(MP\right)+ϵ.\hfill \end{array}$

3.4 Ethical consideration

All respondents provided their consent to participate in the study and were assured of their anonymity. The study also received ethical approval with code SAREC20241024/05.

4 Result presentation, interpretation, and discussion

4.1 Pre-test diagnosis and normality assessment

Before proceeding with the analysis, we checked for missing items; none were missing. We also checked to detect outliers, but only two cases were flagged as mild outliers; since these were valid responses, we retained them in the analysis. Normality was assessed using the Shapiro–Wilk test and Q–Q plots. Both Kolmogorov–Smirnov and Shapiro–Wilk tests were significant (p < 0.05), indicating an over-sensitivity to minor deviations, especially given that the study sample size exceeded 50 observations (Ghasemi and Zahediasl, 2012; Shatz, 2024). Hence, it has been noted that this is not an indication of non-normality when taken alone (Arnastauskaitė et al., 2021; Avram and Măruşteri, 2022). Using recommendations from recent literature offering methodological guidance, we plotted the Q–Q plots for visual inspection of the data distributions. The Q–Q plots (Supplementary Appendix A1), however, showed that the distribution of all items was closely aligned to the diagonal line with no substantial deviations, indicating that the distributions were approximately normal. Hence, they were suitable for regression-based analyses that are robust to mild departures from normality (Ghasemi and Zahediasl, 2012; Shatz, 2024). On this basis, we proceeded with the parametric testing.

4.2 Result analysis and interpretation

4.2.1 What is the current level of RPA adoption among accounting professionals in South Africa?

Table 4 below provides a summary of the RPA adoption level among accounting professionals in South Africa.

Table 4

Table 4. RPA adoption indicators.

The results indicate a varying level of RPA adoption among the accounting professionals in South Africa. Table 4 shows that 37% of the respondents noted that their organizations are currently using RPA, while 30% planned to adopt RPA within the year and 33% had no plans of adoption. Overall, approximately 63% of respondents could be categorized as prospective adopters and non-adopters of RPA. This suggests a relatively low momentum of RPA adoption for accounting and auditing processes by South African accountants, though moving gradually toward aligning with global digital transformation trends. This answers the first research question and confirms the recent report that RPA adoption level is still low in the African region (Fortune Business Insights, 2025).

4.2.2 What are accounting professionals’ perceptions about RPA technologies for executing accounting tasks?

The results from Table 5 had values that demonstrated some organizational readiness and generally mild-to-moderately positive perceptions of RPA’s technological features; the weakest from all constructs is MS (managerial support), which had a mean of 2.91(SD = 1.26) under the organizational context. This implies the respondent’s perception that management providing necessary support for automation uptake is very low. Most likely, management support is not yet visible or not available in most organizations. For the technological context, TU had a mean of 3.40 (SD = 0.86), indicating that respondents had a mildly positive perception of RPA usefulness for accounting tasks. TR had a mean of 3.38 (SD = 0.88), indicating that respondents generally agreed that RPA would be reliable for repetitive tasks. TEOU had a mean of 3.22 (SD = 1.17), reflecting modest agreement that RPA is easy to use. PCU had a mean = 3.34 (SD = 1.27), revealing that respondents generally agreed that RPA would offer some benefits that would save costs in the future when it is adopted. For organizational context, DR averaged 3.24 (SD = 1.14), indicating modest agreement by respondents that organizations are digitally prepared for automation. TRs had a mean = 3.01 (SD = 1.1,4), also suggesting modest agreement that organizations provide training resources to expose professionals to RPA. For constructs under the environmental/institutional context, NP had a mean value of 3.03, CP had a mean value of 2.94, and MP’s mean = 2.98, with their SDs ≈1.16–1.24 collectively reflecting a neutral to slightly positive perception of institutional pressures.

Table 5

Table 5. Descriptive statistics.

The bivariate associations revealed in Table 6 show that BI1_3 (behavioral intention) had a positive and significant correlation with all constructs except Tres, CP, and MP. The largest and most meaningful is found with NP (normative pressure). The favorable mean values and significant positive correlation that TU and TR had with BI suggest that professional accountants perceive that RPA technologies can be beneficial, useful, eliminate errors, and are reliable for repetitive tasks, consistent with previous research (Durão and dos Reis, 2024; Rawashdeh et al., 2022; Rovaris et al., 2025; Thangapandian et al., 2025).

Table 6

Table 6. Correlation matrix.

Overall, all three TOE contexts reveal that professionals have a relatively positive perception of RPA technology, although their perception was uneven across all contexts. For instance, perception about NP (normative pressure from clients) showed the most meaningful factor likely to influence BI, followed by TR, TCB, and TU. This implies that the reliability of a technology, presumed cost–benefit (future cost saving), and perceived usefulness are all very crucial in determining intention to adopt and use RPA for executing accounting tasks. This aligns with the TOE framework that a single construct does not by itself influence behavioral intention to adopt new technologies; rather, a triad of factors is a key determinant (Dlamini, 2024; Tornatzky and Fleischer, 1990). It is also consistent with extant research (Durão and dos Reis, 2024; Fossung and Manfo, 2025; Perdana et al., 2023; Rovaris et al., 2025). Meanwhile, the individual factors might not drive intention to use RPA because the level of influence they exert alone as a single construct might not be sufficient compared to when they all work together. A correlation heatmap is included in the appendix.

4.2.3 To what extent do TOE factors influence the behavioral intention of accounting professionals in South Africa to adopt RPA?

The predictive contribution of our TOE variables on behavioral intention was tested using multiple regression analysis (see output in Table 7). The final model explained R² = 34.3% of the total variance in the BI composite, with adjusted R² = 27%. Overall, the model was significant, F (10, 89) = 4.650, p < 0.0. Cohen’s f² for assessing overall effect size for the model based on R² = 0.343 is 0.522, implying a large effect size for the predictor variables collectively. However, after accounting for the sample size and number of predictors, the adjusted R² value (≈0.27) shows that roughly 27% of the variance in behavioral intention is explained by the predictors. Multicollinearity was tested with VIF (values <2) and tolerance (values > 20), both indicating no multicollinearity issues. Heteroskedasticity was also assessed using the Breusch–Pagan (BP) test, p = 0.24, and scatterplots (see Figure 2) with residuals approximately revealing random dispersion. Both results showed no significant heteroskedasticity, indicating the error variance across the observations was constant. Furthermore, we assessed the linearity assumption. The Durbin–Watson value of 2.24 was within the acceptable threshold, indicating no autocorrelation or linearity assumption violations. Overall, all diagnostic tests confirmed that the study data met the essential assumptions for multiple regression analysis, strengthening the validity and reliability of the model.

Table 7

Table 7. Multiple regression output (OLS).

Figure 2

Figure 2. Scatterplot of residuals with BI as dependent variable.

From the regression output (Table 7), variables with the most significant influence were normative pressure (NP), a construct under institutional context (β = 0.297; p = 0.004) having the strongest predictor, followed by managerial support (MS) (β = 0.222; p = 0.027), also exhibiting substantive influence under organizational context. Digital readiness (DR) and RPA perceived cost-benefit (TCB) have β = 0.174 and p = 0.078 and β = 0.189, p = 0.072, respectively, both implying marginal influence. Mimetic pressure (MP) (β = −0.196; p = 0.058) showed a mild negative influence. However, despite RPA’s usefulness (TU) and reliability (TR) having a significant positive bivariate correlation with BI (Table 6), they did not exhibit independent predictive power in the regression model.

The visual display in Figure 2 is the scatterplot from the regression output. A close examination of the standardized residuals showed no evidence of non-linearity or heteroscedasticity. The residuals were randomly dispersed around zero, with a relatively constant spread across the predicted values. This suggests that all the assumptions of linearity and homoscedasticity were met as previously mentioned. The non-significant value of the BP test further supports this assumption, indicating that the behavioral intention is not distorted by any violations. Overall, this shows the reliability of the model interpreted. Hence, we are confident that all the key assumptions of linear regression are satisfied.

4.3 Discussion

The positive effect of NP implies that professional accountants are highly likely to use RPA technology if they perceive high expectations from clients, professional associations, or based on higher market demand for value-enhanced services. Normative pressure tends to grant legitimacy and normative approval by reducing perceived social risk and increasing behavioral adoption intention. This finding is consistent with the environmental context of TOE that normative pressures are central to the regulatory environment or institutional level shaping organizational and professionals’ actions. It also adds to prior TOE studies that document institutional influence in the form of normative pressure are enablers for RPA technology adoption in professional domains such as accounting (Alassuli, 2025; Rawashdeh et al., 2022; Yang et al., 2024). Moreover, clients’ expectations for value-added services beyond traditional compliance and market demand for real-time data significantly shape the adoption of accounting and auditing technologies in this digitalization era (Moloi and Obeid, 2024; Timmis, 2025). Hence, we accept H8: “Normative pressure arising from clients’ expectation for digitally enabled accounting services has a significant and positive influence on the behavioral intention of accounting professionals to adopt RPA.”

This finding also links to SDG 9 by shaping professional values toward innovation-driven practices. Normative pressure exerted on professional organizations can hasten the adoption of digital technologies such as RPA that enhance data-driven services, industrial efficiency, and innovation diffusion, thereby advancing SDG9 targets for resilient infrastructure and innovation.

The positive significant effect of managerial support (MS) implies that support provided by top management of an organization in terms of management buy-in, leadership readiness in terms of commitment and encouragement, or even sponsor-led advocacy will significantly influence behavioral intention. Additionally, management support through strategic priority-setting and investment in human and capital resources can signal to professional accountants that the organization supports automation. This will influence their perception and reduce any barrier to automation, consistent with Moffitt et al. (2018), Rovaris et al. (2025), and Zhang et al. (2023). The findings align with the organizational context of TOE that management support is an essential element of organizational readiness. On this basis, we accept H6 that managerial support provided by organizations for RPA technologies will create a significant impact on the behavioral intention of professional accountants, influencing them to adopt RPA. Furthermore, if NP, in terms of professional standards and guidance, is now combined with investment decisions under MS, they can both act as enablers needed to scale RPA adoption, foster industrial processes, innovation capability, and productivity, linking with the core aims of SDG 9.

The positive, significant but marginal influence exhibited by digital readiness (DR), a component of organizational context with BI, also reflects that when firms demonstrate that they are prepared for digital uptake, it will increase the likelihood that professional accountants will use RPA tools. This aligns with the organizational context of TOE, which noted that organizational digital readiness is a key determinant of automation adoption (Tornatzky and Fleischer, 1990). It is also consistent with prior evidence that leadership readiness in terms of commitment and top management support is requisite to successful automation transformation (Moffitt et al., 2018; Rovaris et al., 2025; Zhang et al., 2023). Hence, we accept H7 that a high level of digital readiness by an organization has a positive influence on the behavioral intention of accounting professionals to adopt RPA technologies. This result has implications for organizations. It encourages them to pair technical upgrades with management strategy and investment in core IT infrastructures and technology integration capabilities before implementing RPA on a large scale throughout the organization. This also links directly to SDG9 that digital readiness can foster technology diffusion and innovation uptake.

Furthermore, the marginally positive significant effect also revealed by RPA TCB, reflective of professionals’ perception and intention to use RPA adoption, is that it reduces cost and enhances efficiency. This finding aligns with the TOE technological context that cost–benefit/relative advantage is a core component of the technological context. It is also consistent with prior submissions that cost is essential for technology adoption (Dlamini, 2024; Durão and dos Reis, 2024), particularly in accounting and professional services, because it enhances performance through reconciliation and consistent data processing (Alassuli, 2025; Judijanto et al., 2025). With this finding, we accept H4 that the perceived cost–benefit of RPA by professional accountants is positively associated with their behavioral intention to adopt RPA technologies. The findings also align with SDG 9’s goals for a resilient, competitive industry, noting that the cost–benefit accruing from automated technologies can foster industry efficiency and boost productivity.

The negative effect from mimetic pressure (MP) suggests that professionals’ perception of automation uptake by their peers/competitors in the industry might drive fear and anxiety of job loss or insecurity, consistent with prior submissions that automation and increased digitalization can cause job losses in accounting domain (Dlamini, 2024; Mlambo, 2022; Thangapandian et al., 2025) and consequently reduce professionals’ willingness to adopt RPA technologies. Based on this finding, we reject H9 that mimetic pressure existing in the accounting market positively influences accounting professionals’ behavioral intention to adopt RPA. The implication is that industry imitation alone may not always be sufficient to encourage professional accountants to use RPA tools. As a matter of fact, it may even discourage them, especially when they fear job displacement due to automation.

5 Conclusion, research implications, and limitations

5.1 Conclusion

This study examined the perception of professional accountants in South Africa and their behavioral intention toward adopting RPA technologies, examining this relationship through TOE factors. The findings indicate that while perceptions about the value that RPA offers are generally positive, organizational and environmental (institutional) factors play a crucial role as they exert a stronger influence on actual adoption intention. Of all the TOE factors, normative pressure showed the strongest positive influence on behavioral intention to adopt RPA, while mimetic pressure revealed a significantly negative influence. This is indicative that beyond mere imitation within industry, professionals might have concerns with job displacement due to automation, and thus are not encouraged to support automation adoption. Overall, the model explained 27% of the variation in behavioral intention, indicating that beyond TOE factors, adoption decisions are also influenced by psychological factors.

The study, therefore, contributes novel evidence by applying the TOE framework to examine RPA adoption within an emerging economy professional accounting context. It challenges conventional assumptions in technology-adoption models by demonstrating that technological factors can have detrimental effects when perceived usefulness and perceived threat coexist. The findings that adoption may be discouraged by mimetic pressure further elucidate the limits of environmental influence in a professional, autonomy-driven industry such as accounting.

SDG 9 also strengthens this study’s theoretical claim that digital transformation is part of the broader developmental agenda rather than a mere change in technology. The findings, therefore, position RPA adoption as both an imperative of society and an organizational decision for fostering sustainable industry innovation.

5.2 Implication of findings and recommendation

Basically, the findings of this study strongly support prioritizing institutional support (normative pressure) along with management support, digital training, and technical resources because they are the most effective determinants that shape adoption. Mimetic pressure, negative influence, and the non-significance of technological support factors reinforce the need for structural change in management strategies to allay professionals’ fears and anxiety about job displacement. It is also important for professional bodies and organizations to encourage industry-specific RPA readiness guidelines rather than depending on competitive imitation since mimetic pressures fail to drive adoption intention. These recommendations align with SDG 9’s campaign for inclusive digital transformation and capacity building.

5.3 Limitations and suggestions for future research

This study is not without some limitations. A common method bias that is often introduced when a study relies on self-reported perception data limits causal inference. Moreover, the small sample size also limits generalizability. Additionally, the low adjusted R² of 27% suggests that some key variables, such as organizational culture, technological anxiety, and perception regarding threats to the job, were not captured, and they might have a stronger influence on actual intention to adopt technologies. Future research is encouraged to use a longitudinal approach by integrating other organizational constructs not captured in this study and psychological factors.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, upon reasonable request.

Ethics statement

The studies involving humans were approved by the School of Accounting Research Ethics Committee (SAREC) at the University of Johannesburg. The studies were conducted in accordance with local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

NG: Validation, Methodology, Writing – review and editing, Software, Visualization, Writing – original draft, Formal analysis, Data curation, Conceptualization. HC: Supervision, Conceptualization, Validation, Data curation, Writing – review and editing. KT: Investigation, Writing – original draft, Data curation, Conceptualization.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frobt.2025.1747539/full#supplementary-material

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