F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.178285.1 Research Article Articles The Impact of Digital Transformation on the Financial Performance of Banks: An Applied Study on a Sample of Commercial Banks Listed on the Iraq Stock Exchange

[version 1; peer review: awaiting peer review]

 Al-Nuaimi Saadallah Mohammed Obaid Conceptualization Data Curation Investigation Methodology Project Administration Software Validation Writing – Review & Editing https://orcid.org/0009-0008-9729-7854 a 1 Hamood Methaq Noaman Resources Supervision Visualization Writing – Original Draft Preparation Writing – Review & Editing 2 Kh. Hasan Abdul Razaq Validation Visualization 3 University of Kirkuk, Kirkuk, Kirkuk Governorate, Iraq University of Kirkuk, Kirkuk, Kirkuk Governorate, Iraq Northern Technical University, Mosul, Nineveh Governorate, Iraq saedalnuaimy@uokirkuk.edu.iq

No competing interests were disclosed.

 21 5 2026 2026 15 775 29 4 2026 Copyright: © 2026 Al-Nuaimi SMO et al. 2026 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Digital transformation is one of the most serious contemporary strategies founded on the acceptance of digital systems that can meet the growing needs of customers, who demand banking services quickly, precisely, of top quality, and at an affordable price. This helps lower operational expenses and allows banks to gain a competitive edge that increases their performance level. This study seeks to establish how much digital transformation impacts the performance of banks, given the idea of digital transformation as a pressing requirement for the survival, existence, and competitive ability of banks in the Fifth Industrial Revolution era. To achieve this objective, a descriptive–analytical approach was adopted to determine the nature of the relationship between the two variables. Digital transformation was measured based on the number of disclosures related to it in the management reports of the sampled banks, according to what is known as textual data mining, using the LIWC (Linguistic Inquiry and Word Count) program as the best methodology to express it. Financial performance was measured using both return on total assets (ROA) and Tobin’s Q indicators. The Regression analysis method was applied using panel data to determine the effect of digital transformation on financial performance. The study concluded that digital transformation has a statistically significant inverse effect on financial performance. Therefore, commercial banks listed on the Iraq Stock Exchange are required to increase their investments in digital technologies and enhance their cybersecurity to maintain their market share amidst rising competition, given that digital transformation is inevitable for their survival and continuity.

 : digital transformation; financial performance; return on assets (ROA); Tobin's Q; Iraq Stock Exchange The author(s) declared that no grants were involved in supporting this work. 1. Introduction

The banking industry plays a critical role in the distribution of economic resources in nations, as it may be regarded as the heartbeat of the economy facilitating the transfer of funds between depositors and investors and, consequently, promoting investment and economic growth ( Al-Nuaimi & Hassan, 2021). Though the process of digitization transformation is still rather recent, the shift to digital from analog and physical processes has increased significantly during the last few years. The digital transformation has its roots in the 1950s when General Electric launched the UNIVACI computer the first to be used in the payroll systems in the United States leading to increased efficiency and low costs of operation. Since, the digitization process has achieved impressive strides in the direction of globalization, interconnection, and the easy accessibility of the world ( Gul et al., 2023).

This trend was further boosted by the COVID-19 pandemic. Lockdown policies, universal social restrictions and quarantine were introduced by governments ( Coryanata et al., 2023), leaving the digital transformation more rapid and pervasive in all industries ( Do et al., 2022) because the demand of contactless products and services grew exponentially. Pandemic has also shown that the efficient digital infrastructure is necessary to sustain the fast service delivery of banks and other financial institutions ( Doran et al., 2022), as remote working demands the change of operational methods with the necessity to access digital services immediately ( Do et al., 2022), which enabled individuals to ensure their economic and social activity using digital channels to work, study, communicate, and shop ( UNCTAD, 2021). 1

The digital breakthroughs include artificial intelligence, big data, 5G networks, blockchain technologies, and are slowly transforming the global economy, moving the world to a single digital era ( Shanti et al., 2023). Such developments have radically changed social and industrial practices, and digital transformation is a natural fact of modern business ( Doğan, 2024).

Digital transformation is the use of technology to alter business models and introduce new ways to generate revenue, as well as to generate value ( Pratiningsih and Wardhani, 2024; Doğan, 2024). This is a process in which information is coded into organized digital representation using unique units of data identified as bits using decimal or other numeral systems hence facilitating the effective management, storage, and transfer of information with integrity among others ( Sultan et al., 2023). Digital transformation can further be described as the process of converting information products, services, and operations from a physical to a digital state, thereby generating new value and enhancing business models, customer experiences, and customer loyalty ( Zhou et al., 2023).

The transformation of the digital environment improves business models, introduces new revenue streams, and opens value-added opportunities for production and business operations through emerging digital technologies that prompt companies and industries to offer new services, review working approaches, and continuously deliver added value. Digital transformation has been applied extensively in the banking sector, giving rise to new digital financial services such as smart asset management, online credit issuance, and online account management ( Doğan, 2024), in addition to traditional digital banking channels, including Internet banking, mobile banking, cloud computing, QR code payment, and electronic money ( Coryanata et al., 2023).

The financial and banking literature presents conflicting evidence regarding whether digital transformation significantly impacts bank performance. Do et al. (2022) argue that digital transformation positively influences bank performance by enabling banks to serve a larger number of customers simultaneously, digitizing employee procedures, improving human resource efficiency, shortening transaction processing times, thereby increasing productivity, and minimizing costs through reduced headcount and processing time. Research has also indicated that digital transformation offers various advantages in different industries, including the minimization of risks, improvement of crisis management, enhancement of competitiveness and investment capacity, and increased profitability in the banking sector ( Umba et al., 2024).

Pratiningsih and Wardhani (2024) add that digital transformation does not greatly impact the cost-to-income ratio of banks, but that it boosts non-traditional product and service income, meaning that digital transformation is a crucial driver of performance improvements in terms of efficiency, productivity, and decision-making ( Guo & Xu, 2021).

However, some studies have indicated that digital transformation has no significant effect on performance ( Al Mulhim, 2021). Digital transformation can also bring risks and challenges that adversely affect performance, owing to the expenses involved and the lack of requisite technical skills. Liu et al. (2019) argued that these costs place a significant financial burden on banks during the transformation process ( Yavuz et al., 2025). In addition, digital transformation may not generate a sustainable competitive advantage in the initial years of development, as competitive advantages are eventually eroded when the technology becomes ubiquitous and affordable to competitors ( Doran et al., 2022).

The net effect of digital transformation on performance is therefore uncertain a phenomenon referred to in the literature as the digital transformation paradox, the Solow paradox, or the productivity paradox ( Zeng et al., 2022) signifying the gap between investments in digital technologies and corresponding improvements in productivity. Meaning, although the digital investments are increasing, it is not quite certain that the increased investments will be translated into performance improvement ( Yavuz et al., 2025). Digital transformation is not always associated with an improvement in performance, partly due to the fact that it involves increased IT management and support staff to transform digital investments into actual performance increases ( Pratiningsih & Wardhani, 2024), and in part because of lack of organizational fit, managerial inefficiency, and poor integration of processes. Productivity paradox has been a thorn in the flesh and especially to banks being digitized ( Yavuz et al., 2025).

As a result, there is no empirical research on the impact of digital transformation on the performance of banks, which is the strength of the research problem of this study. Empirical data and close research on the correlation between digital transformation and the financial performance of the banks are urgently needed. To fill this gap, the current research focuses on the extent to which the use of digital transformation affects the financial performance of commercial banks listed on the Iraq Stock Exchange an environment where not many studies have explored both variables at the same time. The need of the study is also reinforced by the fact that the process of digitalization of the Iraqi commercial banks and especially smaller ones is fraught with many difficulties, such as capital requirements, development of science and technologies, legal system reform, and development of infrastructure in accordance with modern digital business models.

 2. Methodology

Digital transformation and financial performance were studied in a quantitative descriptive-analytic context. Since the research is founded on quantitative data and actual financial indicators, the econometric approach was used to evaluate the effects of digital transformation on the performance of banks and measure them. The description of methodology is presented below.

2.1 Data collection method

The research data were gathered through the secondary sources in particular, the annual financial reports containing financial statements and management reports of the sampled banks and published on the official website of the Iraq Securities Commission.

 2.2 Study population and sample

The population under study includes the commercial banks that are listed on Iraq Stock exchange. In order to choose the sample of the studied, banks whose data is not available, delisted, and newly listed banks were eliminated. The ultimate research sample was composed of ten commercial banks. The research was conducted during eight years, i.e., 2017–2024. This time frame was chosen as the digital transformation is still a rather new phenomenon in the banking industry in Iraq. Moreover, the political uncertainty of the occupation of certain Iraqi regions by the ISIS terrorist organizations in 2017 before 2017 might have confounded the study variables in a negative manner, thus, justification of the exclusion of previous years.

 2.3 Description of Study Variables

2.3.1 Dependent variable: Financial performance

Industries vary in their strategies of measuring performance depending on the extent of capital utilization. The use of working capital is of particular concern to banks, insurance firms and securities firms, therefore the securities performance of these companies has widely been gauged using the most relevant indicator namely, the return on assets (ROA). ROA is calculated as the net profit/total assets ( Yongjie & Shanyue, 2025) and is considered to be an accounting-based performance indicator. Besides ROA, the study uses the Tobin Q, which is the market value of the shareholders equity plus the book value of the debt divided by the book value of the total assets. Q is a performance metric of the market that indicates the expectation of the market about the value of a bank and the reported investments and business activities including investments in information and communication technology ( Nguyen & Le Thi, 2025). These variables will be operationalised in the following way: ROA = NI / TA Tobin ' s Q = ( MVE + BVD ) / TA

Where: NI = Net Income; TA = Total Assets; MVE = Market Value of Equity; BVD = Book Value of Debt.

2.3.2 Independent variable: Digital transformation

Despite the trend of increasing body of literature on digital transformation, there is still a considerable gap in effective quantitative indices of digital transformation at the firm level. Most of the studies that have been done on the topic apply qualitative research techniques like the survey and as mentioned, the survey is good in terms of capturing the perceptions of the managers, however, it is limited in scale, objectivity and temporal consistency ( Yavuz et al., 2025). As the financial reports present the current position, tendencies, and approaches of banks, text mining as a means of assessing the digital transformation is a legitimate and growing practice in science ( Zeng et al., 2022; Chen & Srinivasan, 2024).

As such, the paper utilizes content analysis (textual data mining) Kriebel and Debener (2019) using Linguistic Inquiry and Word Count (LIWC) program. The words that mention digital transformation in the banking operations were listed and totaled, such as digitization, digital, technology, technological, electronic, technical, automated, mobile banking or services, Internet banking or services, automated teller machine, ATM, debit card, credit card, electronic clearing, CT system, RTGS system, ACH system, POS, point of sale, Visa card, MasterCard, automatic response system, electronic wallets, e-wallets, QR Pay, remote account opening, blockchain, and artificial intelligence.

To calculate a standardized digital transformation index of each bank, the number of words that contained the digital transformation-related words to the total number of words in the management report were divided as follows: Dig = ( Number of words indicating digital transformation ) / ( Total number of words in the management report ) .

2.3.3 Control variables

The model also had three control variables, bank size, the natural logarithm of total assets; capital adequacy (CAR), a ratio of capital and assets; and credit risk (CRisk), a ratio of Current assets and Current liabilities. Contrary to the other control variables, credit risk is likely to be negatively correlated with the performance of the bank.

 2.4 Study model

Three estimation methods of digital transformation on the performance of sampled banks were examined using a panel data regression model, utilizing the fixed effects, random effects, and pooled regression methods. This model would suit the study design given that it entails cross-sectional information (ten banks) and time-series information (eight years), resulting into a total of 80 observations. The regression equation is given as follows: Perf _ it = β 0 + β 1 Dig _ it + β 2 Size _ it + β 3 CAR _ it + β 4 CRisk _ it + ε _ it

Where: Perf_it = bank performance (ROA or Tobin’s Q); Dig_it = digital transformation; Size_it = bank size; CAR_it = capital adequacy; CRisk_it = credit risk; ε_it = random error term; β 0–β 4 = model coefficients.

 3. Study results

The panel data models were used to analyze the impact of digital transformation on the financial performance of commercial banks. As was established below, the results of the fixed effects, random effects and pooled regression models were compared to determine the best estimation method to be used in describing the relationship between the variables.

3.1 Descriptive statistics and correlation matrix

3.1.1 Descriptive statistics

Table 1 shows the descriptive statistics of all the variables of the study. The observations are 80 as the product of the number of sampled banks (10) and the number of years in study period (8) shows that the sample size is sufficient to conduct a panel data regression analysis ( Park & Yu, 2017). The average digital transformation (Dig) is 0.001, which is also a relatively new event that commercial banks in the Iraqi market have embraced digital systems. The average value of Tobin Q is 0.912 which implies that the sampled banks were not very highly valued in the stock market compared to the stock market value of their assets. The average profitability of the sample is 0.0872 in the form of the mean return on assets (ROA). The standard deviation of the various variables is indicative of a middle level of dispersion about the mean.

Descriptive statistics of study variables.
Variables Observations Mean Standard Deviation Min Max
Dig 80 0.001 0.024 0.0001 0.008
Tobin’s Q 80 0.912 0.142 0.215 1.351
ROA 80 0.0872 0.021 −0.021 0.012
Size 80 0.912 0.21 0.521 1.123
CAR 80 0.428 0.135 0.142 0.621
CRisk 80 0.281 0.254 0.181 0.487

Note: Dig = digital transformation index; ROA = return on total assets; Tobin’s Q = market-to-book value ratio; Size = natural logarithm of total assets; CAR = capital adequacy ratio; CRisk = credit risk (non-performing loans/total loans).

3.1.2 Correlation matrix

Table 2 shows the pairwise correlation coefficient of the research variables. The findings reveal that the correlation coefficients fell within a range between −0.423 and 0.492 which means that there was no significant multicollinearity between the variables, which is within the recommended threshold by Cohen (1992).

Correlation matrix between study variables.
Variable Dig Tobin’s Q ROA Size CAR CRisk
Dig 1.000
Tobin’s Q −0.423 ** 1.000
ROA 0.312 ** 0.062 ** 1.000
Size 0.083 * 0.054 ** 0.283 * 1.000
CAR 0.123 ** 0.492 ** 0.417 ** 0.392 * 1.000
CRisk −0.294 * −0.341 ** −0.416 * −0.419 ** −0.085 ** 1.000

Note:

p < 0.05;

p < 0.01. Empty cells represent symmetric matrix entries.

 3.2 Variance Inflation Factor (VIF) test

Table 3 presents the Variance Inflation Factor (VIF) values for all independent and control variables to formally assess multicollinearity. All VIF values are close to 1, confirming the absence of harmful multicollinearity among the predictors.

Variance Inflation Factor (VIF) results.
Variable VIF 1/VIF
Dig 1.02 0.870372
Size 1.08 0.818826
CAR 1.13 0.775846
CRisk 1.21 0.894665

Note: All VIF values are below the conventional threshold of 10, confirming the absence of multicollinearity.

 3.3 Model comparison

After estimating the fixed effects, random effects, and pooled regression models, the restricted F-test was conducted to compare the fixed effects model with the pooled regression model to determine which better represents the relationship between the variables. Table 4 presents the test results.

Restricted F-test (Fixed effects vs. Pooled regression) results.
Effects Test ROA Statistic ROA d.f. ROA Prob. Tobin’s Q Statistic Tobin’s Q d.f. Tobin’s Q Prob.
Cross-Section F 9.1162 (2, 96) 0.0002 7.9254 (2, 54) 0.008755
Cross-Section Chi-square 23.6251 4 0.0000 4 0.0000

Note: Prob. values below 0.05 indicate that the pooled regression model is inappropriate, necessitating the Hausman test.

It is evident from Table 4 that the Prob. values reached 0.0002 and 0.008755 for ROA and Tobin’s Q, respectively both of which are below the 5% significance level. This indicates that the pooled regression model is not appropriate for representing the relationship between the variables, and that the Hausman test is warranted to choose between the fixed effects and random effects models. Table 5 presents the Hausman test results.

Hausman test results (Fixed effects vs. Random effects).
Test Summary ROA Chi-sq Statistic ROA Chi-sq d.f. ROA Prob. Tobin’s Q Chi-sq Statistic Tobin’s Q Chi-sq d.f. Tobin’s Q Prob.
Cross-Section Random 0.000011 4 0.06500 0.0000548 4 0.125484

Note: Prob. values above 0.05 indicate that the random effects model is preferred.

The Hausman test results presented in Table 5 indicate that the Prob. values reached 0.06500 and 0.125484 for ROA and Tobin’s Q, respectively both of which exceed the 5% significance level. This confirms that the fixed effects model is not statistically superior, and that the random effects model is the most appropriate specification for representing the relationship between the variables.

 3.4 Study model estimation results

Having established that the random effects model is the most suitable specification, the model was estimated with digital transformation as the independent variable, bank size, capital adequacy, and credit risk as control variables, and financial performance (ROA and Tobin’s Q) as the dependent variables. The estimation results are presented in Tables 6 and 7, after the References section.

Random effects model estimation results – Dependent variable: ROA.
Variable Coefficient Std. Error t-Statistic Prob.
C (Constant) 32.54926 8.654392 6.982738 0.0001
Dig −0.010586 0.220981 −7.012861 0.0000
Size 0.22873 0.218651 8.172881 0.0000
CAR 0.196235 0.032471 1.821735 0.2357
CRisk −0.364925 0.156324 −1.432625 0.2976
R-Squared 0.32067
Adjusted R-Squared 0.243297
F-Statistic 6.197825
Prob. (F-Statistic) 0.000000
Durbin–Watson 2.017852

Note: Dig = digital transformation index; Size = bank size; CAR = capital adequacy ratio; CRisk = credit risk.

 Random effects model estimation results – Dependent variable: Tobin’s Q.
Variable Coefficient Std. Error t-Statistic Prob.
C (Constant) 29.67815 6.945872 6.187541 0.0001
Dig −0.162110 0.194541 −6.855416 0.0001
Size 0.408050 0.301005 3.972881 0.0654
CAR 0.196235 0.032471 2.421885 0.1875
CRisk −0.537724 0.354844 −2.945845 0.5881
R-Squared 0.35877
Adjusted R-Squared 0.26954
F-Statistic 7.546851
Prob. (F-Statistic) 0.000000
Durbin–Watson 1.981487

Note: Dig = digital transformation index; Size = bank size; CAR = capital adequacy ratio; CRisk = credit risk.

 4. Discussion

Based on the obtained estimation results in Tables 6 and 7, it is clear that digital transformation affects the bank financial performance negatively in a statistically significant manner in the two metrics of return on assets (ROA) and the Q of Tobin. In the case of ROA, the coefficient on the digital transformation is −0.010586 (p < 0.001) which means that a one unit increase in the digital transformation index is linked to a one unit decrease in ROA. For Tobin’s Q, the coefficient is −0.162110 (p < 0.001), indicating a reduction of approximately 0.162 units. These results are consistent with the conclusion that digital transformation currently has an inverse effect on the financial performance of commercial banks in the Iraqi market.

This finding is consistent with that of Hussein and Neama (2025), who established that digital transformation has a negative effect on the short-term performance of Iraqi banks, as well as with the findings of Nguyen-The-Huong et al. (2023), who determined that digital transformation has a negative influence on bank financial performance in terms of both ROA and ROE. Similarly, Doğan (2024) established that digital transformation has adverse effects on the financial results of Turkish banks in terms of ROA, ROE, and capital adequacy.

This adverse impact may be explained by the fact that digital transformation is a relatively new experience in the Iraqi banking industry, and the high cost of transformation, limited technological maturity, and the cautious attitude of the majority of banks towards significant digital investments owing to the lack of adequate financial and human resources collectively suppress short-term financial returns. Moreover, low customer awareness of digital banking, lack of trust in digital banking because of cyber security threats and lack of advanced security infrastructure may make digital transformation a liability and not a growth opportunity making banks that embrace digital systems to lose their competitive advantage in the short term.

Moreover, digital systems and their resulting differences in terms of financial performance are not directly related to investments in them because of the high cost of their adoption, the lack of technological infrastructure, and the low number of qualified human resources. Even in cases where qualified digital personnel may be available, the cost of employing them is high which means that the digital transformation has a negative marginal effect on the financial performance of the Iraqi commercial banks presently.

As indicated in Tables 6 and 7, the independent variables account about 32% and 36% variance in financial performance in terms of ROA and the Q of Tobin respectively. Both of the estimated models are statistically significant as Prob. (F-statistics) = 0.000000 indicates the same at 5% significance level. There is no autocorrelation in both models as evidenced by the value of the Durbin Watson statistics, with no significant differences expected to be so close to 2.

 5. Conclusions and recommendations

This paper has explored the effects of digital transformation on the financial performance of ten commercial banks listed on the Iraq Stock Exchange between 2017 and 2024. Based on textual data mining (LIWC) to generate a digital transformation index and the random effects model applied in a panel data model, the study finds that digital transformation is negatively statistically significant concerning the financial performance of Iraqi commercial banks, both in terms of ROA and the Q of Tobin. These results have been shown to be in line with the digital transformation paradox that is recorded in the wider international literature, which points to the fact that digital investment gains are not fully reflected in short-term financial performance.

On the basis of these findings, the following recommendations are offered:

For commercial banks: Iraqi commercial banks are highly advised to be more focused on investing in digital technologies and improve their cybersecurity systems to keep their market shares in the face of accelerating competition. The dependence on traditional banking services may lead to the loss of market share in the coming years as a result of the technological advancements, the influx of already digitally advanced competition, as well as the increased demands of the clients regarding fast, accurate, and efficient banking services all of which can be delivered only with the help of the continuous digital transformation. Banks which do not keep up with these trends are likely to be slowly drained of customers and competitive advantage.

For regulatory authorities: Regulatory authorities have been encouraged to make banks invested in digital systems gradual by focusing on digital projects that have short payoffs like e-banking and mobile payment solutions but not on more intricate digital infrastructure projects. Iraqi Banking Association is also urged to conduct workshops and conferences to sensitize the bank customers about the benefits of digital banking services.

For banks’ internal management: Banks can be urged to invest in employee training and development within the sphere of digital transformation, to update corresponding legal and compliance systems on the digital-level, and to encourage researchers to come up with effective digital performance measurement tools that could help measure the impact of digital transformation on productivity and efficiency.

For future research: It is recommended that researchers should replicate this study with regard to other market-based performance indices like the price-to-earnings ratio, which are not dependent on managerial actions, and are believed to be among the best that can be used to compare the performance of banks in an efficient market ( Al-Nuaimi, 2017). The future research can also be based on expanding the analysis to include a larger sample of banks, including other dimensions of digital transformation, or using longitudinal designs, which can help account for the lagging financial payoff of digital investment within a wider horizon.

The main limitation of this research consists in low levels of digital transformation seen in the Iraqi banks, the novelty of the phenomenon in Iraq, and the fact that most of digital banking users are employees of the public sector, which limited the timeframe of the research and could restrict the extrapolation of the results.

 Ethics and consent

This study relied exclusively on publicly available annual reports, institutional financial disclosures, and bank-level data published on the official website of the Iraq Securities Commission. It did not involve human participants, interviews, surveys, clinical materials, or personal identifiable information. Accordingly, formal ethical approval and participant consent were not required.

 Data availability statement Underlying data

Zenodo: Underlying data for: The Impact of Digital Transformation on the Financial Performance of Banks: An Applied Study on a Sample of Commercial Banks Listed on the Iraq Stock Exchange. https://doi.org/10.5281/zenodo.19003094 ( Mohammed Obaid, 2026).

This project contains the underlying data used in the statistical analysis of the study, including the panel dataset for the sampled Iraqi commercial banks and the variables employed in the empirical models.

Financial Data.xlsx A balanced panel dataset of 10 Iraqi commercial banks observed annually over 2017–2024 (80 firm-year observations), provided in Microsoft Excel format. Variables include: Company, Year, Dig (digital transformation index), Equity Capital, Current Assets, Current Liabilities, Book Value of Debt, Net Profit, Total Assets, and Market Value of Equity. All monetary variables are expressed in thousand Iraqi dinars (IQD ‘000). The dataset contains no missing values.

The data are available under the terms of the Creative Commons Attribution 4.0 International licence (CC BY 4.0).

 Extended data

No additional extended data are associated with this article.

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The United Nations Conference on Trade and Development (UNCTAD) is a permanent intergovernmental body established in 1964, operating under the auspices of the United Nations.