Abstract
This study examines the association between fintech development and corporate financing and investment policies among non-financial listed firms in ten MENA countries over 2010–2024. Using firm-level financial and market data together with country-year measures of fintech development, the analysis evaluates whether digital financial development is associated with firms’ external financing and investment activity. The baseline estimates indicate positive and statistically significant associations between fintech development and both external financing and corporate investment. Additional analyses examine heterogeneity related to information asymmetry, corporate governance, financial constraints, and ownership structure, as well as associations with the cost of debt, investment efficiency, and firm value. Robustness tests employ alternative fintech proxies and complementary specifications. The findings contribute to the literature by providing firm-level evidence from the MENA region and by documenting conditions under which the association between digital financial development and corporate financial policies varies. The results are interpreted as conditional associations rather than causal effects.
1. Introduction
Fintech has become increasingly integrated into payment systems, lending, information processing, and other financial activities. Technologies such as data analytics, artificial intelligence, blockchain, and cloud computing can alter how financial information is collected, processed, and transmitted. These changes are relevant to corporate finance because access to external capital is partly shaped by the information available to investors and lenders and by the effectiveness of monitoring arrangements (Gomber et al., 2017; Lee & Shin, 2018; Thakor, 2020). They are also closely related to the information frictions described in the corporate-finance literature (Healy & Palepu, 2001; Myers & Majluf, 1984).
A stronger digital information environment may also affect how external stakeholders monitor firms. Faster access to more structured information can facilitate the assessment of corporate conditions by investors, lenders, and other market participants. Earlier work on fintech and digital finance highlights the role of technology in information processing, access to financial services, and the organisation of financial intermediation (Jagtiani & Lemieux, 2018; Haddad & Hornuf, 2019; Philippon, 2016). These arguments support examining whether differences in national fintech development are systematically related to firms’ financing and investment activity.
Despite the rapid expansion of the fintech literature, a substantial part of the evidence has focused on banking, financial inclusion, household behaviour, and technological innovation. Firm-level financing and investment decisions of listed non-financial companies have received comparatively less attention, particularly in the MENA region. The present study therefore considers whether variation in a country-year fintech measure is statistically associated with financing and investment outcomes at the firm level.
Financing and investment decisions are shaped not only by firms’ operating conditions but also by information frictions and agency considerations. When external investors and lenders have limited information, firms may face less favourable financing conditions and may postpone or scale down investment. Agency conflicts can create an additional source of inefficiency when managerial objectives diverge from those of shareholders. These mechanisms provide the basis for testing whether the fintech–corporate-policy relationship differs across information and governance environments.
The empirical analysis focuses on two related mechanisms. The first is an information mechanism, whereby digital financial infrastructure can facilitate the collection, processing, and transmission of information relevant to financing and investment. The second is a monitoring mechanism, through which greater availability of timely information may strengthen oversight by external stakeholders. The interaction tests are designed to assess whether the estimated relationship varies across these conditions; they are not intended to establish a causal mechanism.
In the reported full specifications, the fintech coefficient is positive for both FINANCING and INVESTMENT. Given the fintech standard deviation of 0.372, the estimated changes associated with a one-standard-deviation increase are 0.0082 for FINANCING and 0.0115 for INVESTMENT. Relative to the corresponding sample means, these amounts represent approximately 25.6% and 25.1%, respectively. These figures describe the economic size of the estimated associations and should not be read as causal effects.
The information-asymmetry and governance specifications are interpreted as tests of heterogeneous associations. The KZ specification examines whether the estimated fintech relationship differs with financial constraints, while the ownership specification compares state-controlled and non-state-controlled firms. The analyses of debt cost, investment efficiency, and firm value provide additional outcomes. FINANCING and INVESTMENT are not treated as formal mediators of firm value because no separate indirect-effect test is estimated.
The study adds to the existing evidence in three respects. First, it links firm-level financing and investment outcomes to a country-level measure of fintech development within a MENA panel. Second, it examines whether the relationship changes with information opacity, governance, financial constraints, and ownership. Third, it evaluates several related corporate outcomes while maintaining a clear distinction between statistical association, mechanism-consistent heterogeneity, and causal identification.
The remainder of this paper is structured as follows. Section 2 presents the study background, the literature review, and hypotheses. Section 3 describes the data, variables, and methodology. Section 4 presents the empirical results and robustness checks. Section 5 concludes with implications and directions for future research.
2. Literature Review
Information asymmetry is a long-standing concern in corporate finance because outside investors and lenders may not observe the same information as firm managers. Such differences in information can affect the terms and availability of external finance and may influence firms’ investment choices (Healy & Palepu, 2001; Myers & Majluf, 1984). Evidence on analyst coverage also indicates that the information environment can matter for financing conditions and real corporate decisions (Derrien & Kecskés, 2013). Financial constraints can further restrict firms’ ability to undertake otherwise attractive projects (Han & Qiu, 2007).
Agency considerations provide a complementary explanation for differences in corporate financial policies. Managers may have objectives that do not fully coincide with shareholder interests, creating scope for inefficient financing, investment, or resource allocation (Jensen & Meckling, 1976). Related governance research shows that managerial incentives and monitoring can influence corporate behaviour (Bertrand & Mullainathan, 2003). Taken together, information asymmetry and agency costs provide a useful framework for examining whether improvements in the financial information environment are associated with corporate financing and investment decisions.
Fintech has broadened the technological infrastructure through which financial information and services are produced and delivered. Digital platforms, data analytics, artificial intelligence, and related technologies can modify information processing, intermediation, and access to financial services (Gomber et al., 2017; Lee & Shin, 2018; Thakor, 2020). Alternative data and machine-learning applications may also expand the information available to lenders when evaluating borrowers (Jagtiani & Lemieux, 2018). These developments provide a basis for investigating whether stronger fintech environments coincide with differences in firms’ access to external capital and investment activity.
The potential relevance of fintech is particularly pronounced where conventional financial intermediation is affected by information or access constraints. Digital channels can broaden the range of information used in credit assessment and can alter the way financial institutions screen and monitor borrowers (Jagtiani & Lemieux, 2018). At the same time, the development of fintech markets depends on economic and technological conditions and therefore may be associated with broader institutional and financial changes (Haddad & Hornuf, 2019; Philippon, 2016). In the MENA context, these considerations motivate an empirical examination of whether national fintech development is related to corporate financing and investment.
Fintech may also influence the monitoring environment surrounding firms. Greater access to timely financial information can improve the ability of outside stakeholders to evaluate corporate decisions, while digital financial services can change the information flows between firms and financial intermediaries (Gomber et al., 2017; Thakor, 2020). From an agency perspective, stronger information and monitoring can reduce some of the frictions associated with managerial discretion (Jensen & Meckling, 1976). The empirical tests therefore examine whether the estimated fintech association varies with governance characteristics rather than assuming that fintech automatically resolves agency problems.
Prior research provides several reasons to expect a relationship between fintech development and corporate financial outcomes. Studies of digital finance emphasise changes in financial intermediation, information availability, and access to financial services (Gomber et al., 2017; Lee & Shin, 2018; Thakor, 2020). Evidence on alternative data and fintech lending likewise points to changes in credit assessment and financing access (Jagtiani & Lemieux, 2018). The present study extends this perspective by testing the association with firm-level financing and investment outcomes in a MENA sample.
Hypothesis Development
Two empirical channels are considered in the heterogeneity analysis. The first concerns information opacity: when the information environment is weaker, changes in digital financial infrastructure may have a different relationship with financing and investment outcomes. The second concerns governance: the value of additional external information and monitoring may depend on existing governance arrangements. These considerations motivate interaction terms, which are interpreted using the relevant conditional relationships rather than as independent percentage effects.
The governance channel follows the same logic. Digital financial systems can increase the quantity and timeliness of information available to external stakeholders, potentially changing the monitoring environment around corporate decisions (Gomber et al., 2017; Thakor, 2020). From the agency perspective, stronger monitoring can reduce opportunities for managerial actions that are not aligned with shareholder interests (Jensen & Meckling, 1976). The resulting hypothesis concerns heterogeneity in the fintech association across governance conditions, rather than a claim that fintech itself eliminates agency problems.
H2a: The association between fintech development and corporate financing and investment varies with the degree of information asymmetry. H2b: The association between fintech development and corporate financing and investment varies with corporate governance characteristics. H2c: The association between fintech development and corporate financing and investment is stronger among firms facing greater financial constraints. H2d: The association between fintech development and corporate financing and investment differs between state-owned and non-state-owned firms.
H1.
Fintech development is positively associated with corporate financing and investment among non-financial listed firms.
H2.
The association between fintech development and corporate financing and investment is consistent with information and governance channels and varies across firms with different financial constraints and ownership structures.
3. Research Design
3.1. Data
Table 1 summarizes the geographic coverage of the empirical sample. The empirical population comprises non-financial firms listed on stock exchanges in ten MENA countries over 2010–2024. Financial institutions, including banks and insurers, are excluded because their balance-sheet structures, regulation, and financing activities differ substantially from those of non-financial corporations. Observations with missing or internally inconsistent firm-level information are removed from the estimation sample. After these screening procedures, the unbalanced panel contains 45,820 firm-year observations.
Table 1.
Geographic coverage of the empirical sample.
Firm-level accounting and market information is drawn from Refinitiv Eikon, whereas country-level macroeconomic variables are taken from the World Bank World Development Indicators. The two databases are used for distinct purposes: Eikon supplies the firm observations and WDI supplies the country-level macroeconomic information. To limit the influence of extreme observations, continuous firm-level variables are winsorized at the 1st and 99th percentiles.
The sample period is selected to cover a broad phase of financial and digital development in the MENA region. The 2010–2024 window includes the expansion of digital financial services, the acceleration of fintech adoption during the 2010s, the COVID-19 period, and the subsequent recovery. Restricting the analysis to non-financial listed firms also improves comparability because financial institutions operate under substantially different regulatory and balance-sheet conditions.
3.2. Variables
FINANCING represents the amount of external capital raised relative to firm assets. It is calculated as the sum of equity issuance and changes in short- and long-term debt, divided by total assets. The measure is intended to capture the firm’s use of both debt and equity markets as external funding sources (Derrien & Kecskés, 2013).
INVESTMENT measures expenditure on productive assets relative to the firm’s asset base. It is defined as capital expenditure divided by total assets. The ratio provides a scale-adjusted measure of the resources devoted to long-term investment.
Fintech development is represented by a country-year index based on the MENA text-mining methodology of Kharrat et al. (2023). The underlying approach identifies FinTech-related terminology in financial-sector annual reports and combines the resulting information into a composite index. For the present study, the country-year score is transformed using its natural logarithm and matched to each listed non-financial firm according to headquarters country and fiscal year. Thus, firms located in the same country and observed in the same year share the same national FinTech value. Country-year observations for which the index is unavailable are excluded rather than imputed.
ROA is calculated as net income divided by total assets and is used as a profitability measure. CF denotes operating cash flow scaled by total assets and represents internally generated funds available to the firm. These measures capture two distinct aspects of internal financial capacity (Fama & French, 1998; Almeida et al., 2004).
LEV is measured as total liabilities divided by total assets and captures the firm’s reliance on debt financing. MB is the market-to-book ratio, calculated as the market value of equity relative to its book value, and is used as an indicator of growth opportunities (Rajan & Zingales, 1995; Baker et al., 2003).
SIZE is the natural logarithm of total assets and controls for scale differences across firms. CASH is cash and cash equivalents divided by total assets and reflects liquid internal resources. GROW denotes the annual percentage change in revenue, while PPE is net property, plant, and equipment divided by total assets. These controls capture differences in scale, liquidity, growth, and asset composition (Frank & Goyal, 2009; Opler et al., 1999; Campello, 2006). Table 2 defines all variables.
Table 2.
Variable definitions and measurement.
3.3. Model Specification
The economic magnitudes are obtained using the fintech standard deviation of 0.372 and the coefficients from the fully controlled specifications: 0.022 for FINANCING and 0.031 for INVESTMENT. Multiplying each coefficient by the standard deviation gives estimated changes of 0.0082 and 0.0115. Relative to the respective sample means, these correspond to approximately 25.6% and 25.1%. The calculations are descriptive measures of coefficient magnitude and do not imply a causal interpretation.
The baseline regressions are estimated separately for FINANCING and INVESTMENT. Since the principal fintech variable is defined at the country-year level, the fixed-effects structure has direct implications for identification. The main specification contains industry and year fixed effects. Additional specifications introduce firm fixed effects with year effects or country fixed effects with year effects. Country-year fixed effects cannot be included alongside the country-year FinTech variable because they would absorb the explanatory variable through perfect collinearity.
The estimating equation is: Y_{i,t} = β0 + β1 Fintech_{c,t} + β′X_{i,t} + α_j + λ_t + ε_{i,t}. Here, Y denotes FINANCING or INVESTMENT; i identifies the firm, c the country, j the industry, and t the year; X contains the firm-level controls. The baseline specification uses industry and year effects, while robustness models use firm effects or country and year effects. Because only ten countries are represented, inference based on country clustering should be interpreted cautiously. The analysis therefore supplements conventional country-clustered inference with a small-cluster procedure, such as wild cluster bootstrap-t p-values.
The coefficient β1 represents the conditional relationship between national fintech development and the firm-level outcome after accounting for the controls and fixed effects in the model. Industry effects capture time-invariant industry differences, while year effects absorb shocks common across firms in a given year. Firm fixed effects additionally remove time-invariant firm characteristics. Given that the main explanatory variable is measured at the country-year level, the coefficients are interpreted as conditional associations rather than causal effects.
Financial constraints are examined through the KZ interaction. The index is calculated as KZ = −1.002 CF + 0.283 Q + 3.139 LEV − 39.368 DIV − 1.315 CASH, where CF is operating cash flow scaled by assets, Q is Tobin’s Q, LEV is leverage, DIV is cash dividends relative to assets, and CASH is cash holdings relative to assets (Kaplan & Zingales, 1997). The original sign convention is retained, so higher values correspond to tighter financial constraints. The interaction term is interpreted through the conditional fintech relationship rather than as a direct percentage effect.
The parameter β1 captures the estimated association between fintech development and corporate financial policies. The underlying framework predicts a positive coefficient if a more developed digital financial environment is associated with greater access to external funding and higher corporate investment.
4. Empirical Results
The firm-value regressions are interpreted as conditional relationships. The inclusion of FINANCING and INVESTMENT in the value equation does not, by itself, demonstrate mediation or an indirect effect. The analysis therefore avoids describing these variables as mediators in the absence of a formal mediation procedure. The relatively high adjusted R2 values, around 0.910–0.912, are also interpreted cautiously because the specification contains several market-based variables that may share information mechanically. See Table 3.
Table 3.
Descriptive statistics.
The descriptive statistics indicate meaningful dispersion across the observations. FINANCING has a mean of 0.032 and a standard deviation of 0.084, whereas INVESTMENT averages 0.046 with a standard deviation of 0.049. The fintech index has a mean of 5.215 and a standard deviation of 0.372. The control variables likewise display variation across firms. These statistics describe the composition of the sample and do not establish a causal effect of fintech.
Table 4 reports positive and statistically significant fintech coefficients in both sets of baseline regressions. In the fully controlled specifications, the coefficient equals 0.022 for FINANCING and 0.031 for INVESTMENT. These estimates are conditional on the controls and fixed effects included in the corresponding models and are therefore interpreted as positive statistical associations.
Table 4.
Baseline regression results.
The interaction terms between fintech and the two information-opacity measures are negative across the reported specifications (see Table 5). This pattern indicates that the estimated fintech relationship changes as ERROR and DISP increase. The coefficients should be interpreted as interaction effects; their economic meaning depends on the values of both the fintech variable and the relevant moderator.
Table 5.
Information asymmetry channel.
The coefficients on Fintech × SEPAR and Fintech × BOARD are negative in the reported models (see Table 6). The estimates therefore indicate that the fintech coefficient varies with the governance measures used in the interaction specifications. The interaction terms should not be interpreted independently as evidence that fintech offsets weak governance.
Table 6.
Corporate governance channel.
The Fintech × KZ coefficients are positive and statistically significant in the reported specifications (see Table 7). Conditional on the model, this indicates that the estimated fintech relationship is larger at higher KZ values. The interpretation of KZ as a measure of tighter financial constraints follows the sign convention specified in the methodology.
Table 7.
Financial constraints.
OWNER equals one for firms under state control and zero otherwise. State control is defined using a threshold of at least 50% of voting rights, including control exercised directly or indirectly through state-controlled entities. The interaction coefficient therefore measures the difference in the estimated fintech relationship between state-controlled and non-state-controlled firms (see Table 8 and Table 9).
Table 8.
Ownership structure.
Table 9.
Fintech development and cost of debt.
Tobin’s Q is calculated as the market value of equity plus the book value of total liabilities, divided by the book value of total assets. Market equity is obtained from the year-end share price multiplied by shares outstanding. Because both Q and MB incorporate market-value information, the high adjusted R2 values near 0.91 should be treated as a diagnostic point and checked for possible mechanical overlap or multicollinearity rather than interpreted as evidence of unusually strong explanatory power (see Table 10 and Table 11).
Table 10.
Fintech development and investment efficiency.
Table 11.
Fintech, corporate financial policies, and firm value.
The alternative FinTech measures are introduced as measurement-robustness checks (see Table 12). CityFintech is intended to capture more localised digital-finance development, whereas patent-based measures reflect financial-technology innovation activity. Comparing these measures with the main index tests whether the reported association depends strongly on one operational definition. These alternatives do not, by themselves, remove potential reverse causality or omitted-variable concerns because local fintech development and innovation can evolve with broader economic, institutional, and corporate conditions.
Table 12.
Robustness tests.
Because the principal FinTech variable is defined at the country-year level, it may move together with financial-sector reforms, digital infrastructure, institutional conditions, regulatory changes, and macroeconomic developments. Year fixed effects account for shocks shared across countries, while country fixed effects account for time-invariant country characteristics in the corresponding robustness specification. Time-varying country-level factors are not fully absorbed, and the limited number of country clusters also warrants caution in statistical inference. The results are therefore interpreted as conditional associations.
A principal limitation is the country-year construction of the FinTech measure. The coefficient reflects a firm-level association with a national indicator rather than a firm-specific causal treatment. National fintech development may change alongside regulation, digital infrastructure, institutional quality, and macroeconomic conditions, leaving room for time-varying confounding. In addition, the sample contains only ten countries, which limits the precision of country-level inference. These considerations support the use of alternative fintech measures and a deliberately cautious interpretation of the estimates.
5. Conclusions
The study investigates the relationship between fintech development and corporate financing and investment for non-financial listed firms in ten MENA countries over 2010–2024. The baseline models produce positive and statistically significant fintech coefficients for both outcomes. The heterogeneity analyses indicate that the estimated relationship differs across information environments, governance characteristics, financial constraints, and ownership structures.
Author Contributions
Conceptualization, K.S.; methodology, K.S., R.B. and A.A.; validation, K.S.; writing—original draft, K.S., R.B. and A.A.; supervision, S.B.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, grant number KFU266008. We are grateful for their financial support, which made this study possible.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The study uses firm-level accounting and market data obtained from Thomson Reuters, together with country-level macroeconomic data from the World Bank’s World Development Indicators (WDI). The country-year FinTech measure follows the text-mining methodology for the MENA region developed by Kharrat et al. (2023). Access to Thomson Reuters data is subject to the provider’s licensing conditions.
Conflicts of Interest
The authors declare no conflicts of interest.
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