Technology and accounting convergence: factors influencing cloud accounting information systems adoption by accountants in the era of accounting 4.0

The growth of modern firms depends on their capability to invest in emerging technologies and other infrastructures that enable the exploration of business opportunities and the optimization of their operations (Jena, 2024; Mugwira, 2022). Researchers have identified two distinct phases of technological effects on accounting. The first phase involved the introduction of computerized systems, while the second phase saw the rise of the internet, integrated information systems like ERP, cloud computing, and more (Al-Okaily et al., 2023; Knudsen, 2022). The existing literature categorizes the main drivers behind changes in accounting procedures into three groups: increasing globalization, advanced information technology, and improved practices (Knudsen, 2022). Technological progress, different types of accounting information, and the volume of data have caused paradigm shifts throughout accounting history (Mugwira, 2022). The advent of Accounting 4.0 coincided with Industry 4.0, a phenomenon have helped organizations improve production and business processes. Accounting 4.0 utilizes various technologies and infrastructures, including “intelligent sensors,” the “Internet of Things (IoT),” “Cyber-Physical Systems (CPS),” “Big Data,” “Cloud computing,” “Artificial Intelligence (AI),” and “Blockchain” (Al-Okaily et al., 2023; Dai and Vasarhelyi, 2023; Jena, 2024; Yang, 2025).

Research on what predicts an accountant's intention to use CAIS is still in its early stages. There are several factors to consider when deciding whether or not to embrace CAIS. Companies usually choose based on how it will help them or fit their current process, not just how others have used it. The need for the study is as follows: (1) There are four primary categories of factors that impact the latest technology adoption: technology, user, organization, and environment (Seshadrinathan and Chandra, 2021). Most of the past research on CAIS focuses primarily on factors at the organizational level (Baiod and Hussain, 2024; Gao and Brink, 2019; Permatasari et al., 2024). (2) There are only a few studies that specifically studied the intentions of individuals to adopt cloud services in accounting, regardless of the type, size, and reputation of the firm (Altin and Yilmaz, 2022). (3) Further, previous studies have primarily analyzed the impacts of different aspects of accounting information systems adoption, examining them separately across various categories (e.g. organization, technology, user, and environment) (Afsay et al., 2023; Bachtiar et al., 2023). (4) Moreover, much of the study has been conducted in developed countries. Developed countries have distinct economic, social, and cultural circumstances, as well as advanced technological infrastructure, in comparison with developing ones (Afsay et al., 2023). These differences have a role in the use of technology in accounting and auditing in both advanced and emerging countries. (5) Furthermore, there is a dearth of studies investigating the diverse factors, at the individual, environmental, and organizational levels, that may influence an accountant's intention to adopt a CAIS, especially in India. Thus, it is crucial to expand the cloud accounting research to include the significant factors across the organizational, environmental, and personal levels that impact CAIS adoption among accountants in India. Based on the discussion above, the following objectives are outlined.

1. To identify different individual-level, environmental-level, and organizational-level factors for CAIS adoption

2. To develop a framework integrating the TAM and TOE models to predict the significant antecedents responsible for CAIS acceptance.

3. To empirically test and validate the study framework using PLS-SEM.

The study offers several valuable contributions to the readers and practitioners. This study is unique in offering and validating a conceptual framework that integrates the TAM and TOE models to examine the different categories of factors (technology, user, organization, and environment) that influence cloud adoption in accounting practices. It also expands the existing literature on information system adoption to include both cloud computing technology and the accounting practices (Altin and Yilmaz, 2022; Baiod and Hussain, 2024; Gao and Brink, 2019; Ma et al., 2021). Additionally, understanding the factors that impact CAIS adoption provides a greater understanding of why accountants are willing to integrate advanced technologies into their accounting processes. This study is among the first investigations into the factors affecting accountants' intentions to utilize CAIS, specifically in India. Finally, understanding the factors that are crucial for CAIS adoption can provide valuable insights for various stakeholders that have not yet transitioned to the cloud.

The paper is organized as follows: Section 2 discusses the literature and the formulation of the theoretical framework. Section 3 provides a comprehensive elucidation of the methodology used in this research. The study findings and their interpretation are reported in Section 4. Subsequently, Section 5 provides a detailed discussion of the findings. The conclusion section summarizes the study, elucidates its limitations and provides recommendations.

The “National Institute of Standards and Technology (NIST)” defines “Cloud Computing (CC)” as a model that facilitates convenient and immediate access to a shared reservoir of computing resources, which can be rapidly provisioned and decommissioned with minimal interaction between service providers. According to the NIST definition, there are three service models: “Infrastructure as a Service (IaaS),” “Platform as a Service (PaaS),” and “Software as a Service (SaaS).” The SaaS service models are well-suited for accounting because they include accounting applications and record management tools. A CAIS is a set of cloud computing resources and applications used to process financial information (Rawashdeh and Rawashdeh, 2023; Yau-Yeung et al., 2020). The cloud solutions migrate the accounting information system's installation, data storage, and data management from on-premises to cloud vendors (Adjei et al., 2021; Al-Okaily et al., 2023; Dai and Vasarhelyi, 2023).

Accounting bodies, such as the “American Institute of Certified Public Accountants (AICPA),” have responded to the growing focus on cloud-based accounting information systems by providing a variety of assurance services and guidance. Accounting firms, such as “Deloitte,” are also using CAIS. An increasing number of qualitative surveys and technical reports released within the past two years indicate a growing trend toward adopting and utilizing cloud software in the accounting domain (Al-Okaily et al., 2023; Hung et al., 2023). Thus, to pinpoint the crucial factors that impact the CAIS usage, it is necessary to employ an appropriate technology adoption model.

Several theoretical frameworks are well recognized and used in the fields of Information Systems adoption, including the “Theory of Reasoned Action(TRA),” “Technology-organization-environment (TOE),” “Social Cognitive Theory(SCT),” “Technology Acceptance Model (TAM),” “Theory of Planned Behavior (TPB),” “Model of PC Utilization(MPCU),” “Diffusion of Innovation (DOI),” “the Motivational Model,” the “combined TAM and TPB (C-TAM-TPB),” the “Unified Theory of Acceptance and Use of Technology(UTAUT)”. The above frameworks/models can be classified as individual-level and organizational-level models. The individual-level models, which include the TRA, SCT, TAM, TPB, MPCU, C-TAM-TPB, and UTAUT, are all widely recognized and applied in different domains. The widely used noteworthy organization-level models are TOE and DOI. TAM is the most effective standard ground theory in the literature on technology adoption (Davis et al., 1989), advocating for further investigation into the influence of factors on the perceived value and usability of a product/service. Scherer et al. (2019) asserted that the TAM alone could not elucidate the correlations between technological systems and adoption behaviors. Because of this, many experts have put together TAM and TOE frameworks to make a robust conceptual framework. Due to TAM's ability to predict how a particular technology will be used, this study used it as a base model and added relevant factors from the TOE framework to determine how accountants intend to use CAIS.

The TAM and the TOE have been utilized separately in past research focusing on adopting innovation and information technology. The TAM allows for the selection of external factors that can effectively capture individuals' adoption behavior (Saad et al., 2022; Sulaiman, 2023). On the other hand, the TOE framework used the technical, organizational, and environmental elements that influence technology adoption at the organizational level. Combining the TAM and the TOE can use both models' strengths and comprehensively capture the various levels of adoption behaviors. Wang et al. (2023) integrated the TOE and TAM frameworks to examine the many system factors that influence the adoption of “Enterprise Resource Planning (ERP)” systems within manufacturing organizations. This study integrated TOE with TAM, building on previous research on technology adoption. Past studies have identified eight TOE factors that impact technology adoption (Handoko et al., 2020; Perera and Abeygunasekera, 2022; Seshadrinathan and Chandra, 2021; Thottoli, 2022). These categories are as follows: technology (perceived usefulness, perceived compatibility, perceived complexity, and security concern); management (organizational competency, organizational readiness, learning capability, and management support); and environment (CA, GS, trading partner readiness, and uncertainty in standards). This study consulted a panel of six experts from central India (two academics, three accounting professionals, and one cloud computing expert working in the FinTech domain) to determine the most influential factors related to CAIS adoption. The Delhi method was used to rank factors influencing CAIS adoption in India. Finally, all these five relevant factors – security concern from the technology dimension, organizational competency, organizational readiness from the management group, competitive advantage and government support from the environmental dimension – are used to develop a theoretical framework (Figure 1).

Cloud computing requires IT to be scalable and flexible, enabling the online provision of computing services as part of customer service. Studies have identified intention based on TAM, or the likelihood that a person will use IS, as the primary output variable in the proposed framework. BI plays a major role in utilizing the latest technology (Davis et al., 1989) and is sometimes viewed as an attitude (Qin et al., 2020).

Cloud computing provides corporations with operational and tactical advantages, which we refer to as perceived usefulness. These advantages encompass portability, diminished computing expenses, straightforward installation and maintenance, and facilitated online evaluation of data (Arpaci, 2017). CC can provide complete services online so that users do not have to be there to analyze and work with data. The use of CC means that companies do not have to spend much money to build an IS because CC providers install, manage, and update the system (Sulaiman et al., 2023). This lowers the cost of IT. Thus, this study suggests that.

Cloud services enable employees to work remotely, providing simple data access via mobile devices — a significant benefit (Eldalabeeh et al., 2021; Raut et al., 2017; Sulaiman et al., 2022). CAIS supports automatic adoption as online transactions rise, necessitating cloud computing solutions (Chiregi and Navimipour, 2018). The cloud information system outsources accounting and finance tasks, allowing organization to focus more on strategic initiatives (Alshurafat et al., 2021). There is a shift from traditional accounting to cloud-based accounting (Lutfi, 2022b), allowing small businesses to avoid hardware upgrades and maintenance costs associated with various technologies. The simplicity of replacing file transfer protocol with cloud-based uploads eliminates the administrative burden and enables access at all locations, devices, and organisations (Al Shbail et al., 2021). In addition, the user's PEOU influences PU positively; therefore, this study proposes that.

Security denotes the extent to which an internet-based system fails in safeguarding online transactions and data sharing (Abed, 2020). Cloud services encompass a security feature that scrutinizes client emails and online traffic, external data storage, web services, and individual applications. Several studies have studied the implications of “information and communication technology (ICT)” on security and privacy, identifying them as the primary factors influencing innovation adoption (Chen and Chen, 2021). Conversely, prior studies indicate that the principal challenge service providers encounter with security threats is the establishment of client trust concerning privacy and confidentiality issues (Ardagna et al., 2014). The reluctance of enterprises to adopt cloud solutions can be attributed to the need for robust security protocols and standardized identity management practices (Abed, 2020; Oliveira et al., 2014). Thus, the following hypotheses are posited.

According to Tornatzky and Fleischer (1990), organizational competency refers to the notion of workers' skills, knowledge, and other pertinent characteristics necessary for effective job performance. Competence is enhanced by enhancing the performance of the company's employees. The TOE framework indicates the importance of organizational competence (Eldalabeeh et al., 2021). The concept of competency encompasses numerous facets. Employee competency contributes to organizational competency. Therefore, competent personnel are required to ensure a performance-orientated philosophy in a company. Further, if users are competent in using a system, the organization is deemed competent (Veliu and Manxhari, 2017). However, an organization's employees must utilize technology to perceive its utility (Veliu and Manxhari, 2017). An employee of a competent organization has always intended to adopt the technology. Based on the preceding dialogue, the following hypotheses are framed.

According to Iacovou et al. (1995), an organization's readiness is characterized by the necessary organizational resources that facilitate the adaptation of new processes. Certain organizational traits, such as the organization's size and the resources availability, impact the adoption of innovative technologies in any organization. An organization's size directly impacts its readiness to implement innovative technology (Saad et al., 2022). Larger organizations require additional financial and technical resources (Chatterjee et al., 2021; Saad et al., 2022). An organization must prepare to implement a new system, such as artificial intelligence (accounting 4.0), to ensure accountants feel supported using the CAIS system and realize its value. Further, organizational willingness to adopt new technology helps users enhance their intention to use technology. The preceding discourse helps the development of the following hypotheses.

Awa and Ojiabo (2016) define CA as the degree to which a technical factor provides organizations with a more significant benefit. The competitive benefits of technology compared to its alternatives, play a pivotal role in an organization's decision to adopt it. These external factors are linked with the perception that advantages are at risk of being lost. Yang et al. (2015) contend that the competitive pressure is a significant factor in innovative technology diffusion. Accounting 4.0's ability to promote innovation is regarded as an accounting firm's most advantageous competitive advantage (Chatterjee et al., 2021; Saad et al., 2022). The impact of gaining a competitive advantage will be felt by the organization's employees, who become more relaxed when they are in favourable situations (Yang et al., 2015). Thus, organizations establish a socio-environmental aspect of their competitive advantage through advanced technology (ALzoubi et al., 2023). These technologies will facilitate organizations acquiring competitive advantages. Then, trained workers will recognize the utility and usability of the technology. The preceding discussion facilitates the formation of the subsequent hypotheses.

The term “government policy” in this research pertains to the perceptions of accountants about the efficacy of the government's technological practice policy (David et al., 2023). According to Jackson and Allen (2024), organizations' adoption of the latest technology was influenced by the alignment of jobs and norms about technology usage. Several scholarly investigations have examined the implications of legislation that facilitates individuals' use of technology. Further, government policy has a significant role in influencing organizations' adoption of specific technologies (David et al., 2023). Furthermore, Purnomo and Kusnandar (2019) revealed a direct correlation between GP and ICT adoption. Additionally, the study identified that PU and PEOU mediate connecting government policy and regulations with ICT (Al Mudawi, 2021). The subsequent content outlines the proposed hypotheses of this investigation.

To accomplish the study objectives, a quantitative methodology was used (Figure 2) A questionnaire was used to collect data. The questionnaire consisted of multiple-item scales for each proposed construct derived from prior research. The survey consists of two parts. The first part pertains to the individuals' demographic data and their basic understanding of cloud accounting information systems. Conversely, the subsequent part pertains to the constructs related to the proposed framework. The items related to PU, PEOU, GP, OC, and OR were adopted from Chatterjee et al. (2021). The items for government policy were taken from Sulaiman et al. (2023). The behavioral intention and security concern scales were adapted from Yousafzai et al. (2007) and Abed (2020), respectively. The instrument consisted of 33 items encompassing all pertinent constructs in the proposed model. The item details can be found in Appendix A.

To address clarity and language disparities, a pilot study was conducted among accountants (N = 30) hailing from Nagpur, India. Participants were requested to offer comments if they encountered any challenges in comprehending and responding to the survey (Hair et al., 2023). Subsequently, the items on the questionnaire underwent a thorough reassessment concerning their length, scales, clarity, and linguistic simplicity. Finally, the items were restructured to improve the readability and time taken to complete the response. All items were measured using a five-point Likert scale, where the endpoints signified “1: disagree” and “5: agree.”

The present study utilized convenience sampling, a method known for its cost-effectiveness (Dwivedi et al., 2006). This sampling approach enables a more appropriate generalization of the findings, including a diverse range of profiles (Dwivedi et al., 2006). The survey questionnaires were sent to accountants of different accounting firms in India through online web links utilizing the Qualtrics online survey platform during August 2023 and July 2024. The potential participants were provided with information regarding the nature and objectives of this research. The individuals were assured that their privacy and confidentiality would be rigorously upheld. The participants were also given guidelines to complete the response sheet. The participants were requested to respond within 30 days of receiving the questionnaire. The determination of the sample size for this research was based on considerations of SEM criteria. According to earlier studies (Boomsma and Hoogland, 2001), it is advisable to stick with a minimum sample size of 300 to reduce any bias in the results. Based on the recommendations above, a sample size of more than 300 would effectively mitigate bias in the findings and yield a dependable structural model. A total of 393 replies were obtained, indicating a response rate of 43.7%. A thorough examination was conducted on all 393 responses. Out of the 393 responses received, 22 were identified as inadequate and excluded from subsequent analysis. Finally, a total of 371responses that were deemed both usable and legitimate were used for subsequent analysis. Table 1 presents the demographic information of the 371 individuals who participated in the study.

To assess nonresponse bias, the guidelines proposed by Armstrong and Overton (Armstrong and Overton, 1977) were used. The study employed independent sample t-tests to analyze the data, focusing on the initial and final 100 responses. The results did not reveal significant differences between the groups (t (370) = 1.61; p = 0.14).

Moreover, the research relies on participant data collected through structured questionnaires encompassing all relevant components. Therefore, there is a possibility of respondent bias. To mitigate the potential influence of bias, participants in the survey were explicitly guaranteed the preservation of their identity and confidentiality. This reassurance was provided as a proactive approach to guaranteeing impartial responses. However, a typical method of common bias analysis was conducted to ensure the absence of bias. Harman's single-factor test was used to assess common biases in the data. According to the findings, the first factor only accounted for 39.9% of the variance, which is less than the acceptable threshold of 50% set by Podsakoff (Podsakoff et al., 2003). Therefore, it can be inferred that the data does not introduce any bias into the prediction. The study used the programming language's R packages for data analysis.

All constructs are defined and evaluated using reflective measurement models by looking at factors like “factor loadings (FL)”, “internal consistency”, reliability, “convergent validity”, and “discriminant validity” (Hair et al., 2019).

According to the data presented in Table 2, the factor loadings exceeded the recommended threshold of 0.7, except for two instances [SC1 (0.68) and BI4 (0.69)], which were still close to the recommended value (0.7). Therefore, the fundamental factor accounts for over 50% of the variance in each indicator. Subsequently, internal consistency and reliability were evaluated, as presented in Table 2. The internal consistency of all latent variables was found to be satisfactory, as indicated by Cronbach's alpha >0.7. Further, the “average variance extracted (AVE)” >0.5 and “composite reliability (CR)” values exceeding 0.70 confirm the reliability of the constructs (Hair et al., 2023).

Convergence validity (CV) was evaluated by analysing the AVE values, with all constructs surpassing the 0.50 benchmark (Hair et al., 2019). Consequently, the CV of all constructs was established. Discriminant validity (DV) was confirmed based on the “Fornell-Larcker” criterion (Fornell and Larcker, 1981). In Table 3, diagonal values represent the square root of AVE; lower diagonal values represent inter-construct correlations. This rule states that the main diagonal of Table 3 must exceed the maximum correlation with other constructs. The study conducted a “heterotrait-monotrait (HTMT)” test to further validate the discriminant validity established by the Fornell and Larcker criteria. The HTMT values are presented in the upper diagonal elements (italicised) of Table 3. All HTMT values were less than 0.85, hence confirming the DV (Voorhees et al., 2016).

There are several steps used to evaluate structural models. An assessment was conducted to ensure the absence of collinearity by examining the values of VIF. The VIF values in the study were all below 5, with the highest value was 4.1 (Table 2). This indicates that multi-collinearity was not a concern in the analysis. A bootstrapping process was then used to assess the structural models' relevance and importance. In this procedure, 5,000 subsamples were generated, and subsequently, a two-tailed test was performed.

Table 4 presents the results of the estimations, which include the size and importance of the path coefficients. The findings indicate that all direct paths, except for the one from organizational readiness (OR) to perceived ease of use (PEOU), are not statistically significant (p = 0.13). Thus, hypothesis H5b is not supported. The effect sizes, as measured by f², range from medium to high, as shown in Table 4 (Cohen, 1988). The observed path coefficient for the association between security concern (SC) and behavioral Intention (BI) is negative (β = −0.36), falling within the allowed range of effect size (f² = 0.33), and is deemed statistically significant (p < 0.05). Therefore, hypothesis H3 is substantiated. The path coefficient of PU to BI was shown to be statistically significant (β = 0.65, p < 0.05). The evidence presented in this study provides support for hypothesis H1. The results indicate that there are significant path coefficients between the variables. Specifically, the path coefficient between PEOU and BI is 0.48 (p < 0.05), the path coefficient between PEOU and PU is 0.44 (p < 0.05), the path coefficient between organisational competence (OC) and PU is 0.34 (p < 0.05), the path coefficient between organisational readiness (OR) and PU is 0.29 (p < 0.05), and the path coefficient among competitive advantages (CA) with existing systems. PU is 0.41 (p < 0.05), the path coefficient between government policy (GP) and PU is 0.39 (p < 0.05), the path coefficient between CA and PEOU is 0.65 (p < 0.05), and the path coefficient among GP and PEOU is 0.33 (p < 0.05). Thus, the hypotheses H2b, H2a, H4a, H5a, H6a, H7a, H6b, and H7b have been supported. The suggested model showed substantial explanatory power, accounting for 67% of the variation in BI (R² = 0.67), 61% of the variance in PU (R² = 0.61), and 57% of the variance in PEOU (R² = 0.57), as suggested by Hair et al. (2019). These results suggest a moderate level of predictive ability within the sample.

Finally, “Root Mean Square (SRMR)” values were used to assess the model's fitness. According to Henseler et al. (2014), it is recommended that the SRMR should be less than 0.08 for a good fit. Similarly, Cho et al. (2020) suggested that a cutoff value of SRMR below 0.08 is appropriate for sample sizes over 100. An SRMR score of 0.071 (Table 4) demonstrates a satisfactory model fit for this study.

Furthermore, the evaluation of the R² values, which serve as a metric for predictive accuracy, was supplemented by the calculation of Stone-Geisser's Q² value, which serves as a criterion for predictive relevance. The blindfolding methodology was adopted (Hair et al., 2023). The methodology involves the computation of the “CV-communality index (H²)” and the “CV-redundancy index (Q²)” for both constructs and indicators.

According to Alharbi and Sohaib (2021), the confirmation of the forecasting power of the proposed model is indicated by H² and Q² values that should be greater than zero. As indicated in Table 5, all H² values are more than 0, with an average value of 0.357. Similarly, all Q² values exceed 0, with an average value of 0.286. The measurement model exhibits a superior level of quality in comparison to the structural model. Further, according to Alharbi and Sohaib (2021), a Q² value of 0.02 represents a small effect size, while a value of 0.15 shows a medium effect size, and 0.35 suggests a high effect size. Based on results (Table 5), the proposed model exhibits high predictive power.

For mediation analysis, the indirect path coefficients are presented in Table 6. All the indirect impacts of OC, OR, CA, and GS on BI through PU and PEOU are significant. Thus, the hypotheses H4b, H5c, H6c, H6d, H7c, and H7d, except H5d, are supported. Finally, the hypothesis testing results are shown in Table 7 (Appendix B).

This research examines the individual, organizational, technological, and environmental factors that affect the Indian accountants' intentions to adopt CAIS by employing a hybrid TOE-TAM framework. The study findings offer significant insights into the behavioral and organizational factors that influence CAIS adoption in a developing country context, specifically India.

Security concerns, PU, and PEOU were the main technology predictors of CAIS adoption. The security concern was a significant negative predictor of CAIS adoption. The executives expressed concern regarding the potential risks associated with privacy and security due to the rapid and automatic nature of data processing (Saad et al., 2022). They identified data security as the primary risk associated with CAIS, as the data becomes susceptible to targeted attacks. The users express apprehensions regarding the potential theft of their own or their customers' data during its transmission over the Internet. Therefore, this issue pertains to the amplification of system vulnerabilities, which reduces the user base. This study's results are in line with past studies (Rababah et al., 2017; Saad et al., 2022; Usman et al., 2019). Given the potential effects of security concerns on adoption intentions, it is imperative to prioritize CAIS adoption through proper awareness program and precautions (Al-Okaily et al., 2023). In the initial step of making an adoption choice, it is advisable for adopters to prioritize their security concerns rather than focusing on the quality of sources and services. In general, CAIS providers need to be aware of the existence of SC as a potential obstacle to the adoption of CAIS.

Perceived usefulness significantly affects Indian accountants' desire to adopt CAIS, validating the applicability of the TAM in this scenario. Participants acknowledged CAIS as a mechanism for augmenting real-time data accessibility, automating compliance processes, and improving client service—features that are increasingly requisite in India's expanding digital economy and the evolving GST and TDS rules (Abrahams et al., 2024). Many past studies (Chatterjee et al., 2021; Sulaiman et al., 2023) support these findings. The advantageous effect of PEOU on both PU and BI suggests that user-friendly systems with minimal learning demands are more likely to be embraced, especially by individuals with limited IT expertise. This indicates that the personnel inside the organization have the requisite competencies, expertise, and capabilities, as well as other essential attributes, necessary for achieving effective performance, thereby benefiting the organization (Chatterjee et al., 2021; Kang and Lee, 2017).

The present investigation has discovered a positive association between OR and PU. The finding is consistent with earlier research by Ransbotham et al. (2017), which shows that the availability of sufficient resources and a skilled workforce eliminates any difficulties to the adoption of novel technologies. The study findings also indicate that competitive advantage is crucial to Indian professional accountants adopting cloud-based accounting information systems. The impact of competition intensity on the adoption or acceptability of innovations is significant and positive, which aligns with previous studies (Lutfi, 2022a). In essence, organizations are cognizant of the competitive edge cloud computing offers. It is worth noting that these competitive advantages are considered unattainable using traditional methods and technologies. In general, organizations need more awareness regarding the utility and advantages of cloud-based systems. In contrast, previous studies conducted by Khayer et al. (2020) and Oliveira et al. (2014) have provided evidence to support the notion that organizations possess knowledge and recognition of the adoption, utilization, and benefits of cloud-based systems.

The present investigation has discovered a positive association between OR and PU. These findings are consistent with earlier research by Ransbotham et al. (2017), which shows that the availability of sufficient financial and technical resources and a skilled workforce eliminates any barriers to the adoption of novel technologies. Additionally, the results did not support the association between OR and PEOU. This finding contrasts with previous research in developed economies, which generally indicates that well-resourced organizations are linked to a higher perceived ease of use for technology systems (e.g. Chou and Chang, 2008; Putri et al., 2025). In Indian conditions, the perceived value of CAIS emerges primarily from external incentives (policy mandates, client demand, market competitiveness) rather than internal transformation goals. Thus, cultural factors like jugaad (frugal improvisation), resistance to process re-engineering, and hesitation to invest in non-billable technologies may affect the perceived usability (Nagdev and Rajesh, 2018).

Further, this study presents empirical evidence about the importance of organizational readiness in resource allocation and engagement in work processes for making optimal decisions regarding adopting technology or systems, such as CAIS. Similarly, the research conducted by Lutfi et al. (2022) showed that technology management are a significant organizational component that enhances the acceptance and utilization of novel innovations or technologies. The research findings also indicated a significant and favorable influence of OR on adopting CAIS among professional accountants in India, which aligns with previous studies (Alharbi and Sohaib, 2021; Lutfi et al., 2022). This study's results show a noteworthy positive association between government support, precise policies, and technology's perceived usefulness. The study findings align with those of Purnomo and Kusnandar (2019), who concluded that government policy substantially impacts the uptake of ICT. Additionally, there was a substantial association between government support and the perceived ease of usage. This finding aligns with the findings of Li et al. (2019). This illustrates the effects of India's Digital India initiative, the transition to compulsory e-filing of taxes, and regulatory support for digitized recordkeeping as mandated by the Companies Act and Income Tax Act. The “Institute of Chartered Accountants of India (ICAI)” has facilitated the issuance of guidelines regarding technology standards for audit and assurance practices.

Based on study findings, organizations considering CAIS implementation may evaluate the compatibility of their organizational contexts with cloud accounting. Accountants can also assess whether their technical innovation characteristics, organizational elements, and factors associated with information systems are favorable for implementing cloud accounting. Examining the diverse sub-factors of cloud accounting enables managers to gain a deeper understanding, assess and explore numerous technological resources, and deliberate on their potential for increasing organizational performance (García-Sánchez, 2021). One of the prime considerations surrounding the adoption of cloud services is the Internet's vulnerability, given that such services' utilization necessitates a consistent and uninterrupted Internet connection. An additional problem pertains to the potential incongruity between the use of CAIS and the organization's existing infrastructure. Users assume the existing infrastructure could be more conducive to implementing cloud-based accounting systems. In this scenario, it is necessary to contemplate a shift in management approaches (Putri et al., 2025). One such strategy is educating accountants and top management about the benefits of utilizing accounting or cloud computing.

Additionally, garnering robust support from all stakeholders is crucial (Schneider and Sunyaev, 2016). Many accountants need an adequate understanding of cloud computing and accounting, particularly in India. Therefore, it is recommended that cloud computing service providers and technical support teams employ various strategies to enhance accountants' knowledge and understanding of technology's advantages through promotional seminars and workshops that effectively showcase the benefits of intangible resources. Further, when creating cloud-based systems for accountants, it is essential to prioritize user-friendly interfaces and functional utilities. This ensures that accountants with limited technological knowledge and competence may quickly deploy or use these systems.

This research theoretically contributes to the literature by proposing and validating an integrated model by combining two widely recognized models, i.e. TOE and TAM, that encompasses the factors influencing the adoption of CAIS among accountants in India. The findings of this study provide support for the TOE paradigm in the context of both individual and organizational perceptions for research that examines the factors influencing the adoption of CAIS. Furthermore, it demonstrates the suitability of the proposed integrated framework as a theoretical foundation for research investigating the factors impacting the adoption of CAIS. This study contributes to the existing body of cloud computing literature, specifically on CAIS. It expands upon utilizing the TOE-TAM model to elucidate the relationship between various technological, organizational, and environmental aspects and CAIS. Despite numerous studies on the utilization of cloud computing services, there needs to be more research explicitly focused on the CAIS, particularly in developing countries.

Cloud accounting is a significant technological advancement offering strategic and operational benefits. Nevertheless, the adoption rates of this technology within Indian accounting practices have yet to reach satisfactory levels. Therefore, this study employed an integrated research framework that combines the TOE and TAM models to identify the individual, technological, organizational, and environmental factors influencing the intention of Indian accountants to use CAIS. The findings suggest a significant relationship exists between security concerns, organizational readiness, competitiveness, government support, and CAIS usage intention. The study findings offer a solid foundation for decision-makers and the government to facilitate CAIS adoption. Organizations desiring to implement cloud accounting may choose a progressive approach, wherein the scale of operations is incrementally expanded through enhanced internet infrastructure and proper training.

Even with the contributions mentioned above, the study exhibits several shortcomings. Firstly, this study's instrument consisted of a questionnaire that gathered data from professional respondents from different private and public firms. In this context, individuals can exhibit bias in their beliefs despite the research tool having undergone rigorous testing to establish its validity and reliability. It would be advantageous to assess annual reports to verify the accuracy and reliability of some of the data provided by the participants. Secondly, it is important to note that the primary data were gathered and assessed solely on a single occasion during a specific timeframe and do not account for temporal variations. The consideration of long-term consequences is of utmost significance, particularly regarding the development and establishment of cloud accounting services and the availability of support and competitive pressures. Thus, a longitudinal study will be helpful in the future. Thirdly, the data gathered originates from a single country, namely India. Therefore, it is imperative to consider any cultural disparities between developed and developing countries since these disparities can significantly influence information system management behaviors and views toward technology use. To facilitate the generalization of results, it is essential to use diverse samples from other nations. In addition, it is recommended that further investigation be conducted to enhance the robustness of the findings by exploring additional constructs such as organizational culture, firm size, social responsibility and other significant psychological and social factors that need to be thoroughly studied in the current model in the adoption of other emerging technologies (e.g. AI, blockchain) in accounting (García-Sánchez, 2021; Jena, 2024; Norzelan et al., 2024). Finally, it is important to think about the evaluation of technology after its adoption (specifically, the optimal adoption or implementation) as a crucial aspect of future research.

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