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Article

The Role of Organizational Culture in Digital Transformation and Modern Accounting Practices Among Jordanian SMEs

by
Elina F. Hasan
1,*,
Mohammad Abdalkarim Alzuod
2,
Khalid Hasan Al Jasimee
3,
Sajead Mowafaq Alshdaifat
1,
Areej Faeik Hijazin
4 and
Laith T. Khrais
4
1
Financial and Accounting Sciences, Faculty of Business, Middle East University, Amman 11831, Jordan
2
Business Administration Department, Faculty of Business, Middle East University, Amman 11831, Jordan
3
College of Biotechnology, University of Al-Qadisiyah, Al Diwaniyah 58001, Iraq
4
Faculty of Business, Middle East University, Amman 11831, Jordan
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(3), 147; https://doi.org/10.3390/jrfm18030147
Submission received: 28 January 2025 / Revised: 24 February 2025 / Accepted: 27 February 2025 / Published: 10 March 2025
(This article belongs to the Special Issue The Future of Sustainable Finance: Digital and Circular Synergies)
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Abstract

:
This study investigates the impact of digital transformation on modern accounting practices among Jordanian SMEs, focusing on the moderating role of organizational culture. Digital transformation using AI, blockchain, and cloud computing improves operational efficiency, real-time financial reporting, and decision making. However, the integration of these technologies poses challenges such as skill gaps, cost constraints, and cultural resistance. A quantitative survey of 480 employees in managerial roles from Jordanian SMEs shows that organizational culture plays a dual role as a driver and moderator of digital transformation. The findings confirm the role of digital transformation in reshaping modern accounting practices. Also, this study shows that to get the most out of digital transformation in accounting, a culture of innovation and continuous learning is required.

1. Introduction

In today’s fast-paced business world, digital transformation is the key driver of innovation and redefining of business processes in many fields including accounting. This means combining physical and digital processes into decentralized systems which change the way financial information is collected, processed, and used (Alshdaifat et al., 2024). Companies using advanced technology not only get more efficient but also change their strategy. The use of AI, blockchain, cloud, and big data has changed the face of accounting (Al Rob et al., 2025; Sharif et al., 2025; Ghaith et al., 2024). These advanced technologies drove automating monotone procedures, enhanced the financial reporting process, and brought forth modern data analysis for efficient strategic decision making (Hijazin et al., 2023). For example, AI programs have overperformed traditional techniques by their ability of pointing out financial record asymmetry, forecasting patterns, and providing timely insights (Sharif et al., 2025; Alkan, 2022). Blockchain technology attributed as transparent and secure altered the process of recording and validating financial transactions, enhancing financial systems’ reliability (Masa’deh et al., 2024; Nakamoto, 2008). Cloud computing brings more fixability and responsiveness to accounting through its seamless collaboration and immediate data access (Bhimani, 2020).
However, the term digital in accounting extends beyond technology. It is a comprehensive new approach to assessing and managing financial data (Al-Matari et al., 2022; Mohamed et al., 2024) that has a big impact on the quality of accounting information (QAI) which is key to decision making, stakeholder confidence, and regulatory compliance (Ab Aziz et al., 2024). QAI has the attributes of accuracy, reliability, relevance, and timeliness that are required for good reporting (Alharasis, 2024; Alkhwaldi et al., 2022). When performed right, digital transformation can reduce errors, data consistency, and real-time analysis. Even though digital transformation has many benefits, it also introduces challenges. Accountants need to keep up with the technology which requires ongoing learning and new digital skills (Sutton et al., 2016). And, concerns about data security and the ethics of increased automation in accounting are still an issue (Vial, 2021). From an accounting professional’s perspective, digital transformation can be seen as a threat or an opportunity. The automation of tasks and processes traditionally performed by accountants can be seen as a threat and displace traditional roles. Or, it frees accountants from repetitive tasks and allows them to focus on more strategic value-added activities and enhance their role in the organization.
Organizational culture is expected to play a critical role in the relationship between digital transformation and accounting information quality (QAI). As the shared values, beliefs, and practices in the organization, culture determines how employees and management perceive, adapt to, and implement technological changes (Ababneh, 2021). A culture of adaptability, continuous learning, and innovation will enhance the benefits of digital transformation by encouraging the proactive adoption and integration of digital tools, and hence, QAI (Ferdaus et al., 2024). A rigid or resistant culture will hinder these efforts and lead to the underutilization of technology and poor outcomes. This is more important for Jordanian SMEs where cultural factors influenced by leadership style, workforce diversity, and market conditions can be very different. A culture that aligns technological adoption with organizational goals and encourages collaboration is key to maximizing the impact of digital transformation on QAI.
Jordanian SMEs are the backbone of the country’s economy and are going digital to stay ahead. Despite the growing body of research on digital transformation in accounting, there remains a notable gap in understanding its impact on QAI, particularly in the context of Jordanian SMEs. Previous research has focused on larger companies in developed markets, leaving SMEs in emerging markets untouched (Alharasis, 2025; Ahmad et al., 2024). The primary aim of our study is to fill this gap by looking into how digital transformation affects accounting information quality in Jordanian SMEs with organizational culture as a moderator. To achieve this goal, a quantitative study was undertaken. The survey included companies specialized in IT software, consulting and accounting, and tax services. Unlike previous research, this study offers perspectives from both professional accountants and IT managers on digital transformation in Jordanian small- and medium-sized enterprises. This dual perspective is important to know how digital transformation is being implemented and the challenges it poses.
After this introduction, Section 2 presents a review of the pertinent literature. Section 3 describes the research methodology, detailing the data collection and analysis methods. Section 4 discusses the study’s results, while the final section summarizes the main conclusions, addresses the study’s limitations, and offers suggestions for future research in this area.

2. Literature Review and Research Hypotheses

Based on the Technology–Organization–Environment (TOE) Framework (Tornatzky & Fleischer, 1990) and Contingency Theory (Lawrence & Lorsch, 1967) this study examines the role of digital transformation in shaping digital accounting practices and improving accounting information in Jordanian SMEs. TOE framework argued that the successful adoption and use of digital technologies are not only dependent on the technology itself but also on three interrelated factors: the firm’s technological infrastructure and capabilities, its organizational readiness (which includes leadership, culture, and internal resources), and the pressure from the external environment, industry standards, competition, and regulations. This approach highlights the multi-dimensional nature of digital transformation where firms must align their technological and organizational factors to obtain the best outcomes. In addition, contingency theory argues the success of any organizational initiative, including digital transformation, is contingent upon the fit between internal and external factors. Specifically for SMEs, the effectiveness of digital transformation is largely moderated by organizational culture. If an organization has a culture of innovation and adaptability, digital technologies are more likely to be adopted and integrated into the business processes and thus, digital accounting practices and higher accounting information.

2.1. Digital Transformation

Digital transformation means the full integration of digital technologies across the whole business, changing how businesses operate and deliver value to their customers. It is about adopting digital tools and solutions to simplify processes and improve customer experience and innovation within the organization (Vial, 2019; Bharadwaj et al., 2013; Schallmo et al., 2020a). Digital transformation has been around for a few decades. First, it was about the transition from analog to digital, the introduction of computers, and basic software applications in the 1980s and 1990s. Then, it became more popular in the 21st century with the internet, cloud, big data, AI, and Industry 4.0 (Vial, 2019; Iansiti & Lakhani, 2020; Schallmo et al., 2020b; Khrais & Alghamdi, 2022; I. Abu-AlSondos & Salameh, 2020).
Digital transformation is not limited to adopting new technology; it is a comprehensive remodeling of business operations and competition in the digital era. Remaining competitive is fundamental in a dynamic digital market (Vial, 2019; Iansiti & Lakhani, 2020). The accounting profession is redefined, as merging technologies such as artificial intelligence (AI), blockchain, and cloud accounting is redefining the processing, analysis, and reporting of financial data. These innovations enhance the accuracy of prompt financial reporting and improved decision making and operational efficiency (Bhimani, 2020; Appelbaum et al., 2017). This research illustrates technological innovation through four main aspects: technological innovation itself, process automation, employee skills, and cost of implementation. Technological innovation in the scene of digital transformation denotes developing and deploying technologies that lead to crucial amelioration or automation in business processes. Examples include AI, blockchain, big data analytics, and cloud (Hatamlah et al., 2023; I. A. Abu-AlSondos et al., 2024; Al-Araj et al., 2022).
Technology has always steered business change. The early 2000s witnessed the ascension of telecommunication technologies like the internet and mobiles; presently, we are observing digital technology with AI and blockchain. Machine learning, natural language processing, and predictive analytics proofs AI transformation from being a concept to a practical application (Bharadwaj et al., 2013; Iansiti & Lakhani, 2020; Schallmo et al., 2020a). Blockchain technology emerged as Bitcoin which was initiated by Satoshi Nakamoto in 2008, and its capacity to provide secure and transparent digital ledgers drove the change in financial transactions and record keeping (Nakamoto, 2008; Vial, 2019; Iansiti & Lakhani, 2020). These technologies are fundamental for financial reporting. AI and machine learning allow the processing of loads of financial data instantly, outperforming humans by pointing out the patterns and deviations in the provided data. Blockchain provides more security and transparency to financial transactions enhancing real-time reporting reliability and less exposure to fraud. Cloud computing promotes making timely and informed decisions through real instant accessibility and analysis of financial data from anywhere (Trigo et al., 2014; Bhimani, 2020; Quattrone, 2016). Process automation is a major ingredient of digital transformation where human interference is eliminated by implementing technology to perform repetitive tasks (M. Alzuod et al., 2019; Moffitt et al., 2018). Accountingwise, routine tasks such as data entry, invoicing, payroll, and compliance checks are automated for leveling up efficiency and accuracy (M. A. Alzuod et al., 2017; Sutton et al., 2016; Schallmo et al., 2020a).
Process automation goes back to industrial automation in the 20th century when repetitive tasks in manufacturing previously performed by humans were replaced by machines. The 21st century witnessed a movement towards the digital space after introducing robotic process automation (RPA). RPA mimics human actions through applying software robots, or “bots”, to perform tasks across multiple digital platforms (Moffitt et al., 2018). Adapting RPA and other automation technologies by businesses led to growth in the scope of accounting process automation. Automation has transformed the basic spreadsheet into sophisticated workflows integrating various systems and processes (Moffitt et al., 2018; Busulwa & Evans, 2021; Appelbaum et al., 2017). Automating Process is fundamental to accounting operations. Reduced human error and costs and increased productivity can be obtained through automating repetitive and time-consuming tasks (Sutton et al., 2016), giving accountants the opportunity to focus on strategic, value-added activities such as financial analysis and business planning, rather than routine processes (Moffitt et al., 2018). Employee skills—defined as the knowledge, skills, and competencies that employees possess—are critical in the context of digital transformation. Employee competencies are important in employing progressive digital tools and technologies such as AI, blockchain, and data analytics platforms (Bharadwaj et al., 2013). Such competencies in a technology-driven business environment enable leveraging digital innovation, driving business growth and competitiveness.
Skilled employees have always served as fundamental to business success; however, their importance is more significant today due to the accelerated technological progress in the 21st century. Demand for professionals possessing skillsets both in traditional accounting and digital skills has grown significantly due to embedding digital technologies in business processes (Jardak & Ben Hamad, 2022). However, a skill gap in many small- and medium-sized enterprises (SMEs) especially in countries like Jordan is present. This gap represents a challenge to implementing digital transformation initiatives, as effective usage of new technologies relies on training and experience which they lack (Vial, 2021). Employee skills set up the foundation for digital transformation in accounting (Trigo et al., 2014). Adopting and integrating new technologies are easier for a workforce with high digital skills, leading to more accuracy, operational efficiency, and improved decision making. On the contrary, a workforce lacking digital skills indicates the underutilization of technology, reduced productivity, and resistance to change (Bharadwaj et al., 2013). In addition, implementation cost plays a significant role in digital transformation. This pertains to the financial resources necessary to adopt and integrate modern technologies such as hardware and software expenses, employee training, and system maintenance. The success and sustainability of technologies within the organization depend on managing these costs effectively (Bhimani, 2020).
Historically, adopting new technology was associated with high cost; today, digital transformation cost has become more critical due to its complex nature and broad scale (Bhimani, 2020). The cost of implementing new technology represents a barrier to digital transformation, especially for small- and medium-sized enterprises (SMEs) in emerging markets like Jordan. Some of these financial burdens were alleviated by the usage of cloud computing and software as a service (SAAS), allowing businesses to avoid large upfront investments by adopting a pay-as-you-go approach. But, other costs like employee training, change management, and ongoing system maintenance can still be significant and may hinder adoption.The cost of implementation is, therefore, a key factor in whether an SME can integrate new accounting technology. Managing these costs is key to making digital transformation accessible and sustainable for smaller businesses. High costs can deter a business from going digital and limit its ability to improve accounting practices (Schallmo et al., 2020a). While investing in technology can lead to long-term savings and operational efficiency, those benefits are only achievable if the initial costs are manageable (Bhimani, 2020).

2.2. Modern Accounting Practices

Digital transformation means integrating digital across the whole business, changing how the business operates and what is delivered to customers (Caron et al., 2024). In accounting, digital transformation is changing traditional practices by making data more accurate, better decision making, and real-time financial reporting (Atta et al., 2024). Many studies highlight the importance of digital transformation in modernizing accounting practices. Appelbaum et al. (2017) talk about how business analytics and enterprise systems improve managerial accounting by making data-driven decisions. Bhimani (2020) looks at the disruption in accounting due to digitalization, specifically the impact of technologies like AI and blockchain. The literature consistently says digital transformation is key to efficiency and effectiveness in accounting especially in SMEs where resource optimization is critical.
Modern accounting is all about advanced methods and digital tools to obtain more accurate, efficient, and relevant financial information (Bhimani, 2020). Modern accounting is real-time financial reporting, advanced data analytics automation and AI (Quattrone, 2016). We have gone from manual ledger entries to digital systems that can handle complex financial data. Introducing personal computers and accounting software like QuickBooks and SAP in the late 20th century initiated such a transformation (Jardak & Ben Hamad, 2022). The integration of these systems with cutting-edge technologies like AI, blockchain, and big data analytics took place in the 21st century (Bhimani, 2020; Iansiti & Lakhani, 2020). Obtaining a deeper insight into financial performance and improved decision making, as recent accounting is based on obtaining real-time data using automated processes and advanced tools (Appelbaum et al., 2017). This is all about accuracy, efficiency, and value in financial management.
Real-time financial reporting is generating and providing financial data in real time so stakeholders can have the most up-to-date financial information to analyze and make decisions (Warren et al., 2015). This is enabled by technology such as cloud computing and automated accounting systems that increase data accuracy and support better business decisions.Accounting efficiency is optimizing accounting tasks to obtain faster and more accurate results with fewer resources (Caron et al., 2024). This efficiency is significantly driven by technologies such as robotic process automation (RPA) and enterprise resource planning (ERP) systems. Reducing operational costs and improving financial processes can be achieved by automating manual tasks like data entry and reporting (Hanandeh et al., 2023; Granlund, 2011). Adopting up-to-date accounting practices for SMEs in Jordan is crucial in this digital era. Enhanced financial management, transparency, and business responsiveness are factors of these practices (Schallmo et al., 2020a). However, vast investment in technology and employee skills is required for transitioning to modern accounting practices, creating a major challenge for firms, especially small firms with limited resources.

2.3. Impact of Digital Transformation on Modern Accounting

Technological innovation is vital to digital transformation, and it means enhancing the accuracy and timeliness of financial reporting. Processing and reporting financial information have dramatically shifted due to AI, blockchain, and big data analytics technologies. The quick and accurate analysis of large data sets provided by AI systems extends to pointing out data patterns and deviations that may be overlooked by human analysts. Blockchain technology, with its focus on transparency and security, means financial transactions are recorded and verified in real-time (Nakamoto, 2008). Quattrone (2016) and Moll and Yigitbasioglu (2019) found that technology directly improves the financial reporting process. They show how adopting new technology in accounting gives real-time insights so businesses can make better decisions. The literature says technology not only makes financial reports more accurate but also reduces the time to produce them so overall efficiency is increased.
Automation is a key part of digital transformation in the accounting profession. Automation tools like RPA and AI have been shown to make accounting processes more efficient. By automating tasks like data entry, invoice processing, and compliance checks, firms can reduce human error, reduce costs, and free up accountants to do more strategic work (Moffitt et al., 2018). The literature shows that process automation leads to big gains in operational efficiency. Research by Busulwa and Evans (2021) and Dutta et al. (2020) shows automation reduces time for various accounting tasks and increases accounting department productivity. Alkan (2022) also highlight the role of automation in improving financial reporting speed and accuracy which is key to staying competitive in today’s fast-paced business environment. Based on the above, the following hypotheses are proposed:
Hypothesis 1: 
Digital transformation positively impacts the implementation of modern digital accounting practices among Jordanian SMEs.
Hypothesis 2: 
Digital transformation enhances the quality of accounting information in Jordanian SMEs.

2.4. Organizational Culture

Organizational culture is key to successful digital transformation. It determines how new technology is adopted and integrated into existing practices and whether employees welcome or resist change. The literature suggests that a supportive culture amplifies the positive effects of digital transformation on accounting practices and a resistant culture hinders progress (Cameron & Quinn, 2011; Denison, 1990). Iansiti and Lakhani (2020) and Henderson et al. (2012) highlight the link between digital transformation success and cultural readiness. Companies that have a culture of innovation and continuous learning are more likely to adopt new technology and get the most out of it (Kotter, 2012; Schein, 2010). A culture resistant to change can undermine digital transformation efforts, especially in accounting where precision and consistency are key (Hofstede, 2001; Cameron & Quinn, 2011). Thus, a positive organizational culture is key to getting these technologies adopted, which encourages innovation and responsiveness to market change (Cameron & Quinn, 2011; Kotter, 2012). Vial (2019) and Granlund (2011) suggest that technology innovation will not reach its full potential if not supported by the organization.
The automation of tasks with technology like robotic process automation (RPA) and enterprise resource planning (ERP) systems has made accounting tasks much more efficient. Moffitt et al. (2018) show how RPA reduces manual tasks like invoice processing and data entry, so processing time and human error. Sutton et al. (2016) found that ERP systems simplify financial reporting and auditing, so organizational efficiency. Sutton et al. (2016)found that ERP systems simplify financial reporting and auditing, so organizational efficiency. Therefore, organizations that adopt automation technology get a competitive advantage by increasing productivity and reducing operational costs.. But as Schallmo et al. (2020b) point out, the success of process automation is all about having a culture that values efficiency and continuous learning.
A flexible and innovative culture is key to digital transformation. Denison (1990) and Cameron and Quinn (2011) say organizations that are adaptable and innovative are more likely to adopt new technology. In accounting, a culture of collaboration and openness is essential for employees to use tools like AI-driven analytics and cloud-based systems. According to Schein (2010) and Bhimani (2020), a learning and experimentation culture is critical to the successful integration of new technology. Iansiti and Lakhani (2020) say companies that are continuously improvement-focused are more likely to adopt digital solutions that enable real-time financial reporting and automation. Vial (2019) notes the relationship between digital transformation and culture is dynamic successful technology adoption backed by a strong culture increases an organization’s innovation capacity.
On the other hand, cultures that are stuck or risk-averse will stifle digital transformation. Hofstede (2001) and Schein (2010) reported that cultural resistance, where organizations hold onto the status quo and will not adopt new technology, will slow down accounting modernization. Henderson et al. (2012) found that organizations with change-resistant cultures are slower to adopt automation and real-time financial reporting, which means operational inefficiencies. Kotter and Cohen (2012) say resistance to change is often caused by a lack of leadership commitment or insufficient employee training, both of which are key to new technology implementation. Arkhipova et al. (2024) say leaders must create a culture of innovation and calculated risk-taking to overcome this resistance. Iansiti and Lakhani (2020) show that companies with a digital-ready culture get the most out of digital transformation. These organizations see big benefits in real-time financial reporting, automation, and overall accounting efficiency. Kotter and Cohen (2012) and Schein (2010) say leadership is key to creating a culture open to digital transformation which is required to integrate new tools into accounting operations. Granlund (2011) reported that cultural characteristics such as openness to innovation and continuous learning are key to getting the most out of process automation and real-time reporting. Bhimani (2020) says organizations with a supportive culture are more likely to invest in training so they can better adopt new accounting systems.
Hypothesis 3: 
The effectiveness of digital transformation in improving digital accounting practices is moderated by organizational culture.
Hypothesis 4: 
Organizational culture moderates the relationship between digital transformation and the quality of accounting information in Jordanian SMEs.

3. Methodology

3.1. Sampling, Data, and Participant Profile

The study’s target population consisted of 7600 employees from Jordanian SMEs. A random sample of 650 employees was selected to participate by filling out a self-administered questionnaire. After removing incomplete responses, 480 fully completed questionnaires were included in the analysis, yielding an effective response rate of 73.8%.
To strengthen the data collection process, the questionnaires were emailed to the chosen participants. The survey included 26 questions focusing on digital transformation, modern accounting practices, and organizational culture, aiming to gather insights into organizational dynamics and strategic decision making within the industry. The respondents were employees in managerial roles such as directors, general managers, team leaders, experts, and senior staff across the Jordanian SMEs. The selection of employees in managerial roles because they have a broad view of the operational and strategic parts of their organization, including culture. As decision makers and drivers of strategic initiatives like digital transformation, these people are best placed to tell us how culture affects the adoption and success of digital transformation initiatives and how it moderates the relationship between technology and accounting information quality. Managers also have a deeper understanding of the organization’s overall objectives, the implementation of technological changes, and the dynamic interplay between these factors that shape organizational outcomes.
The study’s sample population demographics included gender distribution, age range, educational background, job roles, and years of experience in the telecom sector. Male participants outnumbered females, comprising 60.9% of the sample. In addition, 74.3% of the participants held bachelor’s degrees, with seniors in the department representing the largest portion at 45.6%. Nearly half of the participants had between 5 and 10 years of experience, making up 48.2% of the sample. Overall, Appendix A provides valuable insights into the demographic profile of the sample population, offering a comprehensive understanding of key characteristics.

3.2. Research Instrument

The constructs in the proposed model were assessed using multiple items adapted from the established studies. Each statement in the instrument was evaluated on a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). To ensure internal consistency, Cronbach’s alpha was calculated for each scale to verify reliability.
Digital transformation was measured through three main dimensions: technological innovation, process automation, and employee skill level, encompassing 13 items in total. Technological innovation was gauged using 3 items adapted from Parasuraman (2000), Singh et al. (2021), Pingali et al. (2023), and Nguyen et al. (2024). Process automation was assessed with 7 items based on Groover (2002), Davenport and Kirby (2015), and Vial (2019). Employee skill level was operationalized through 3 items adapted from Rogers (2001) and subsequently employed by Singh et al. (2021), Halpern et al. (2021), and Nguyen et al. (2024).
Modern accounting was measured using 9 items, divided between digital transformation in accounting with 4 items from Li (2022), and Nguyen et al. (2024), and the quality of accounting information with 5 items adapted from Phornlaphatrachakorn and NaKalasindhu (2021) and Nguyen et al. (2024). Organizational culture was operationalized using 4 items derived from Singh et al. (2021), Wokurka et al. (2017), and Martínez-Caro et al. (2020).
To maintain linguistic accuracy and cultural suitability, the questionnaire was translated using the back-translation method. Two bilingual business professors proficient in English and Arabic performed this task. The instrument was then subjected to comprehensive testing and refinement, involving academic review and field piloting. Question clarity and reliability of the applied measurement were ensured by conducting a pre-testing on a small, representative sample. A list of instruments including the individual measures are provided in Appendix A.

4. Results

4.1. Measurement Model

The higher-order construct (HOC) model combined with the Disjoint Two-Stage Approach was applied for analysis to handle data complex relationships. The measurement model was assessed through Confirmatory Factor Analysis (CFA) illustrated in Table 1, which was performed before testing the hypothesis with Smart-PLS. As illustrated in Table 1 and Figure 1, all the indicators loaded above 0.70, so indicator reliability was ensured (Hair et al., 2020). Further model testing with Rho-A, Rho-C, and Cronbach’s alpha all scored above 0.70 (Ebrahimi et al., 2021; Nekmahmud et al., 2022). Each construct’s AVE was above 0.5 (Hair et al., 2020; Shmueli et al., 2019). Indicating the appropriateness of the measurement model used in this study for further analysis.
Discriminant validity was tested using Fornell and Larcker’s (1981) method. This involves comparing the square root of each construct’s average variance extracted (AVE) with the correlations among the constructs. Discriminant validity is confirmed when the square root of the AVE for each construct is greater than the highest correlation involving that construct. In Table 2, the diagonal values are the square root of the AVE for each construct (DTA, ESL, OC, PO, and QAI). These are greater than the off-diagonal values in their respective rows and columns so discriminant validity is established for all constructs.
To test for non-response bias, we performed a non-response bias test by comparing the responses of early and late respondents, following the method by Holtom et al. (2022). There were no significant differences in the mean values between the two groups so non-response bias was not a problem. We also tested for common method bias (CMB) by looking at the variance inflation factor (VIF) values within the inner model. The VIF values ranged from 1.00 to 1.402, all of which were below the threshold of 3.3 as suggested by Kock (2015). So, the research model was not affected by CMB and our findings are reliable.

4.2. Validation of Higher-Order Constructs

The higher-order constructs were validated as part of the measurement model assessment. Each construct was tested for reliability and convergent validity. The higher-order constructs were also tested for discriminant validity against the lower-order constructs in the study as per Sarstedt et al. (2019). The results showed that both reliability and validity were achieved for the higher-order constructs. Reliability was confirmed with values above 0.70 and AVE was above 0.50 (Ebrahimi et al., 2021; Nekmahmud et al., 2022) (see Table 3). In addition to testing reliability and validity, the discriminant validity of the higher-order constructs with lower-order constructs was also examined. Following Fornell and Larcker’s (1981) criterion, the results showed that the square root of AVE for each construct was higher than its correlations with other constructs (Al Jasimee and Blanco-Encomienda, 2024) (see Table 4). This thorough testing confirmed the higher-order constructs used in the study.

4.3. Structural Equation Model

A robust model was built using PLS with pairwise elimination to handle missing data. Table 1 shows that all the VIF scores are below 5, no multicollinearity issue, and ideal values are around 3 or lower (Hair et al., 2020). The structural model was evaluated with R-square, Q-square, and path significance. The R-square values measure the proportion of variance explained by the model for each endogenous construct, ranging from 0 to 1, and higher values mean stronger explanatory power (Hair et al., 2020). The model’s R-square values are above the benchmark, so the model is good, as shown in Table 5.
Q-square was used to test the endogenous constructs’ predictive relevance, and values above 0 mean predictive relevance (Dul, 2016). SRMR was used to assess the model fit to minimize model misspecification. The SRMR was calculated by integrating the correlation coefficients from the sample and the predicted covariance matrices. An SRMR value near 0.05 is indicative of an acceptable model fit (Hu & Bentler, 1999).
To determine the statistical significance of path coefficients, the analysis utilized 10,000 bootstrapped samples. A confidence interval that does not include zero was taken as evidence of significant relationships, following a one-tailed test at the 0.05 significance level.

4.4. Analysis of Direct and Moderating Effects

The analysis, as illustrated in Figure 2 and Table 6, confirms the significance of all examined causal relationships at the 0.01 level. Digital transformation was found to have a positive impact on digital transformation in accounting (DTA), with a standardized coefficient of β = 0.764 (t = 22.271, p < 0.001), and on the quality of accounting information (QAI), showing β = 0.622 (t = 11.647, p < 0.001), thus confirming hypotheses H1 and H2.
The moderating analysis in Table 6 reveals that OC negatively impacts the direct relationship between digital transformation and both DTA and QAI. Specifically, the interaction effects show coefficients of −0.273 (S.E. = 0.041, 95% CI = [−0.359, −0.198]) for DTA, and −0.117 (S.E. = 0.044, 95% CI = [−0.200, −0.028]) for QAI. This indicates that while OC supports DTA and QAI, it can also moderate these relationships, potentially constraining their effects when acting as a boundary condition (Schein, 2010; Kotter, 2012).
The use of higher-order differentiation (HOD) provided a more nuanced understanding of these interactions, highlighting the dual role of OC as both a driver and a moderator within the digital transformation framework. This is in line with Vial (2019), who reported that digital transformation can bring about significant operational improvements but the degree of impact is often limited by organizational culture.

5. Conclusions, Implications and Limitations, and Further Research

This study examined the role of organizational culture in the relationship between digital transformation and the modernization of accounting processes in Jordanian small- and medium-sized enterprises (SMEs). The results show that digital transformation changes everything in accounting by improving the quality, reliability, and timeliness of financial data. The automation of processes and technology innovation reduces operational redundancy and allows accountants to focus on added-value activities. The study highlights the importance of organizational culture in the relationship between technology adoption and accounting information quality. Digital transformation improves operational efficiency, real-time financial reporting, and informed strategic decision making by combining modern technologies such as artificial intelligence (AI), blockchain, and cloud computing. But, the study concludes that the full potential of these technologies can only be realized in an organizational culture that values adaptation, creativity, and continuous learning. Despite the benefits, the talent gap, economic constraints, and cultural resistance still exist, especially among Jordanian SMEs. Strategic investments in training, technology infrastructure, and cultural transformation are needed to overcome these challenges and reap the benefits of digital transformation.
The study has practical, policy, and theoretical implications. Practically, organizations need to promote adaptability and continuous learning to fully benefit from digital transformation. This means investing in employee training for digital skills and creating an environment that encourages technological innovation. Policymakers can support small and medium enterprises (SMEs) by providing financial incentives, regulatory backing, and public–private partnerships to help them adopt digital technologies. The research contributes to the academic discussion on digital transformation by emphasizing the relationship between technology advancements and organizational culture, particularly in emerging markets. However, the study has limitations. The use of self-reported survey data may introduce bias, and the focus on Jordanian SMEs may limit the applicability of the findings to other contexts. Future research should address these limitations by using longitudinal designs to study the long-term effects of digital transformation on accounting practices. Comparative studies across various industries, regions, and organizational sizes could further validate and expand on these findings. Qualitative approaches could also provide deeper insights into how cultural resistance and other barriers impact technological adoption.

Author Contributions

Conceptualization, E.F.H. and S.M.A.; methodology, S.M.A., M.A.A. and A.F.H.; software, K.H.A.J., L.T.K. and A.F.H.; validation, E.F.H. and M.A.A.; formal analysis, K.H.A.J., A.F.H. and S.M.A.; investigation, E.F.H. and S.M.A.; resources, E.F.H.; data curation, S.M.A.; writing—original draft preparation, E.F.H. and S.M.A.; writing—review and editing, M.A.A., K.H.A.J. and A.F.H.; visualization, L.T.K.; supervision, E.F.H. and S.M.A.; project administration, A.F.H.; funding acquisition, E.F.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Middle East University, Amman, Jordan.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Middle East University. The Research Ethics Committee has confirmed that no ethical approval is required.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to the privacy of the research.

Acknowledgments

The authors are grateful to Middle East University, Amman, Jordan, for the financial support to cover this article’s publishing fee.

Conflicts of Interest

No potential conflicts of interest were reported by the author(s).

Appendix A

Demographic data
CategoryCount%
GenderFemale18839.1
Male29260.9
Total480100
Age<305010.4
30–3416434.2
35–3915933.2
40–448417.6
≥45234.6
Total480100
Educational LevelDiploma244.9
Higher Diploma316.5
Bachelor’s35674.3
Master’s6613.7
Ph.D.30.7
Total480100
Job RoleDirectors163.3
General Managers479.8
Team Leaders10020.8
Experts9820.4
Senior Staff21945.6
Total480100
Years of Experience<5 years9419.5
5–10 years23148.2
11–15 years9419.5
16–20 years479.8
>20 years142.9
Total480100
CodeVariables and ScalesReference
TITechnology invitation
TI1Enterprise systems are stable, up-to-date, and reliable.Singh et al. (2021); Pingali et al. (2023); Parasuraman (2000); Nguyen et al. (2024)
TI2The employees have access to a range of new technologies like cloud, mobile, social media, and big data analytics available to facilitate innovations.
TI3The employees believe that IT infrastructure is stable, up-to-date, and reliable to facilitate innovations.
PO Process automation
PO1The company support me in optimizing my daily workflows to be more efficient?Davenport and Kirby (2015); Vial (2019); Groover (2002)
PO2Consistency and Quality
PO3The company put in place measures to ensure that my work consistently meets high quality standards.
PO4The internal systems are designed to promote seamless integration and connectivity among teams.
PO5The employees receive to quickly adopt and effectively use new tools or processes.
PO6The company’s focus on cost efficiency benefits me and my team’s resources.
PO7The company’s growth plans create opportunities for my career development.
ESLEmployee skill level
ESL1The employees have the appropriate knowledge of technology, business processes, and organizational culture to facilitate innovations.Singh et al. (2021); Rogers (2001); Halpern et al. (2021); Nguyen et al. (2024)
ESL2The employees have the appropriate skills to facilitate innovations.
ESL3The employees have the appropriate adaptability to facilitate innovation.
DTDigital transformation in accounting
DT1The company aims to digitize everything that can be digitized (documents, reports, etc.).Li (2022); Nguyen et al. (2024)
DT2The company aims to achieve digital communication(external and internal reports).
DT3The company aims to create a stronger network betweendifferent implementation processes (in business andaccounting) with digital technology.
DT4The company aims to collect extremely large volumes of data (business and accounting) from different sources.
QAIQuality of accounting information
QAI1Understandability of accounting informationPhornlaphatrachakorn and NaKalasindhu (2021); Nguyen et al. (2024)
QAI2FAI Timeliness of accounting information
QAI3Relevance of accounting information
QAI4Comparability of accounting information
QAI5Representational faithfulness of accounting information
OCOrganizational culture
OC1The company accounting information is clear and straightforward.Singh et al. (2021); Wokurka et al. (2017); Martínez-Caro et al. (2020).
OC2The company reports use simple, jargon-free language.
OC3The format of company accounting data makes complex information easy to grasp.
OC4The company accounting communication aids quick, effective decision making.

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Jrfm 18 00147 g001
Figure 1. PLS-SEM algorithm.
Figure 1. PLS-SEM algorithm.
Jrfm 18 00147 g001
Jrfm 18 00147 g002
Figure 2. Structural equation model estimated.
Figure 2. Structural equation model estimated.
Jrfm 18 00147 g002
Table 1. Validity and reliability of the constructs.
Table 1. Validity and reliability of the constructs.
Construct/ItemItem LoadingVIFAlphaRh-ARh-CAVE
Digital Transformation in accounting (DTA)
DTA10.8922.9310.9130.9140.9390.793
DTA20.9083.217
DTA30.8842.707
DTA40.8792.581
Employee skill level (ESL)
ESL10.8912.3070.8720.8720.9210.796
ESL20.8832.177
ESL30.9032.588
Organizational culture (OC)
OC10.8832.8990.9330.9340.9520.833
OC20.9364.697
OC30.9193.671
OC40.912
Process automation (PO)
PO10.9125.1240.9610.9620.9680.812
PO20.9185.72
PO30.9295.926
PO40.8924.278
PO50.873.48
PO60.8924.223
PO70.8923.912
Quality of accounting information (QAI)
QAI10.8122.5730.9070.9130.9310.729
QAI20.8142.609
QAI30.8663.042
QAI40.8772.851
QAI50.8963.491
Technology invitation (TI)
TI10.9564.8860.9370.9550.96O0.888
TI20.9353.521
TI30.9364.485
Table 2. Discriminant validity of measures.
Table 2. Discriminant validity of measures.
Construct/ItemDTAESLOCPOQAI
DTA0.891
ESL0.8470.892
OC0.6760.6310.913
PO0.8490.8840.7050.901
QAI0.7440.7580.7060.820.854
Note. DTA: digital transformation in accounting; ESL: employee skill level; OC: organizational culture; PO: process automation; QAI: quality of accounting information.
Table 3. Reliability and convergent validity of higher-order constructs.
Table 3. Reliability and convergent validity of higher-order constructs.
Construct/ItemAlphaRh-ARh-CAVE
Digital Transformation0.9480.950.9670.906
Table 4. Fornell and Larcker (1981) criterion–discriminant validity of higher-order constructs.
Table 4. Fornell and Larcker (1981) criterion–discriminant validity of higher-order constructs.
Construct/ItemDTADigital TransformationOCQAI
DTA0.891
Digital Transformation0.9460.952
OC0.6760.7051.000
QAI0.7440.8130.7060.854
Note. DTA: digital transformation in accounting; OC: organizational culture; QAI: quality of accounting information.
Table 5. Q-square and R-square.
Table 5. Q-square and R-square.
ConstructQ-SquareRMSEMAER-SquareR-Square Adjusted
DTA0.4060.0770.5730.7760.774
QAI0.4060.0770.5990.7110.708
SRMR = 0.045; NFI = 0.858; d-ULS 0.704; d-G 0.684; Chi-square 1666.129
Note. DTA: digital transformation in accounting; QAI: quality of accounting information.
Table 6. Direct and moderated direct relationship testing.
Table 6. Direct and moderated direct relationship testing.
ConstructPath CoefficientS.D.t-Valuep-ValueConfidence Intervals
Direct relationshipsLowerUpper
H1: Digital Transformation -> DTA0.7640.03422.2710.0000.7040.817
H2: Digital Transformation -> QAI0.6220.05311.6470.0000.5290.705
Moderated direct relationships
H3: OC × Digital Transformation -> DTA−0.2730.0416.6050.000−0.359−0.198
H4: OC × Digital Transformation -> QAI−0.1170.0442.6780.007−0.200−0.028
Note. DTA: digital transformation in accounting; OC: organizational culture; QAI: quality of accounting information.
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MDPI and ACS Style

Hasan, E.F.; Alzuod, M.A.; Al Jasimee, K.H.; Alshdaifat, S.M.; Hijazin, A.F.; Khrais, L.T. The Role of Organizational Culture in Digital Transformation and Modern Accounting Practices Among Jordanian SMEs. J. Risk Financial Manag. 2025, 18, 147. https://doi.org/10.3390/jrfm18030147

AMA Style

Hasan EF, Alzuod MA, Al Jasimee KH, Alshdaifat SM, Hijazin AF, Khrais LT. The Role of Organizational Culture in Digital Transformation and Modern Accounting Practices Among Jordanian SMEs. Journal of Risk and Financial Management. 2025; 18(3):147. https://doi.org/10.3390/jrfm18030147

Chicago/Turabian Style

Hasan, Elina F., Mohammad Abdalkarim Alzuod, Khalid Hasan Al Jasimee, Sajead Mowafaq Alshdaifat, Areej Faeik Hijazin, and Laith T. Khrais. 2025. "The Role of Organizational Culture in Digital Transformation and Modern Accounting Practices Among Jordanian SMEs" Journal of Risk and Financial Management 18, no. 3: 147. https://doi.org/10.3390/jrfm18030147

APA Style

Hasan, E. F., Alzuod, M. A., Al Jasimee, K. H., Alshdaifat, S. M., Hijazin, A. F., & Khrais, L. T. (2025). The Role of Organizational Culture in Digital Transformation and Modern Accounting Practices Among Jordanian SMEs. Journal of Risk and Financial Management, 18(3), 147. https://doi.org/10.3390/jrfm18030147

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