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International Business Research; Vol. 18, No. 3; 2025
ISSN 1913-9004 E-ISSN 1913-9012
Published by Canadian Center of Science and Education
74
The Effects of Financial Innovation, Sustainable Development of the
Stock Market, and Economic Growth in México
Alfonso Martín Rodriguez1, Mario Alejandro Gomez Gutierrez2
1 Professor at the Centro de Ciencias Economicas y Administrativas of the Universidad Autonoma de
Aguascalientes. Aguascalientes, Mexico.
2 MBA graduate student of the Centro de Ciencias Economicas y Administrativas of the Universidad Autonoma
de Aguascalientes. Aguascalientes, Mexico.
Correspondence: Dr. Alfonso Martin Rodriguez. Centro de Ciencias Economicas y Administrativas. Universidad
Autonoma de Aguascalientes. Aguascalientes, Mexico. E-mail: alfonso.martin@edu.uaa.mx
Received: February 23, 2025 Accepted: April 1, 2025 Online Published: May 16, 2025
doi:10.5539/ibr.v18n3p74 URL: https://doi.org/10.5539/ibr.v18n3p74
Abstract
In this article, we analyze the dynamic causal relationship between financial innovation, sustainable development
of the stock market, and economic growth in Mexico for the period from 1990 to 2020. We utilize the
AutoRegressive Distributed Lag (ARDL) bounds testing procedure and the Granger causality test to generate a
model that determines the direction of causality among the variables.
This model allows for the inclusion of explanatory variables to analyze dynamic relationships between them. To
this end, financial innovation is incorporated into a trivariate model involving financial development and
economic growth, creating a bidirectional causality model. Another advantage is that it provides consistent and
efficient estimates even with small samples.
Our results indicate that, overall, access to international financing has a more significant impact on the
sustainable performance of Mexican companies compared to domestic financing. This effect is maximized when
companies use financial resources to foster innovation, which acts as a key catalyst for sustainability.
Furthermore, although direct government support is not statistically significant, the training and advisory
services provided by the government facilitate a more efficient use of financing, promoting both sustainability
and business innovation. These findings emphasize the importance of a strategic approach that combines
diversified financing, innovation, and appropriate public policies to encourage a more competitive and
sustainable business development in Mexico.
Keywords: financial innovation, sustainable development, stock market, economic growth
1. Introduction
1.1 Introduce the Problem
Most studies on the relationship between the growth of the financial sector as a driver of economic growth, or
vice versa, have relied on bivariate causality analyses. However, this approach may have limitations or biases
due to the omission of a third variable that influences both financial sector growth and economic growth.
Including such a variable could not only alter the direction of causality but also affect the magnitude of the
obtained estimates.
Several studies have employed residual-based cointegration tests, such as those proposed by Engle and Granger
(1987), or maximum likelihood tests developed by Johansen (1988) and Johansen & Juselius (1990). However, it
is now recognized that these techniques may not be suitable when the sample size is too small (Narayan &
Smyth, 2005).
The present analysis aims to examine the dynamic relationship between the sustainable development of the
capital market, financial innovation, and economic growth in Mexico, using the ARDL bounds testing procedure.
In this framework, financial innovation is considered an intermediary variable that influences both financial
development and economic growth. Additionally, three indicators of financial development are used: the
M2/GDP ratio, the proportion of credit to the private sector relative to GDP, and the ratio of liquid liabilities to
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GDP, compared to GDP per capita, which serves as a proxy for economic growth.
The document is structured as follows: the next section presents the theoretical framework; the third section
discusses the methodology employed, including the estimation techniques and the empirical results; and finally,
the fourth section analyzes the results and concludes with the findings.
1.2 Explore Importance of the Problem
The choice of financial innovation as an intermediary variable within the trivariate causality framework is
significantly influenced by the theoretical links between financial innovation and economic growth, on one hand,
and between innovation and financial development, on the other.
The impact of financial innovation on the stock market has positively affected global economic development
over the past few decades. New technologies and financial advancements have transformed trading, risk
management, and capital allocation, resulting in an increasingly efficient and accessible system. This has also led
to new investment and financing alternatives for various economic agents.
The significance of the relationship between financial innovation and economic development has become a focus
topic in recent research. However, empirical evidence has not conclusively demonstrated a singular causal
relationship, as noted by Anguiano-Pita & Ruiz-Porras (2020). Studies such as those by Bara & Mudzingiri
(2016a) have approached financial innovation in developing countries from the perspective of well-being and
financial inclusion; however, the impact of financial innovation on economic growth has not been as extensively
discussed, despite being an integral part of financial development.
1.3 Describe Relevant Scholarship
The debate on sustainability has been enriched by contributions from ecological economics. A significant
reference in this field is Daly (1996), who advocated for a steady-state economy, arguing that the planet's finite
resources limit economic growth. He questioned the notion of unlimited development and emphasized the
importance of an economy that respects natural limits while promoting long-term human well-being.
Additionally, Costanza et al. (1997) highlighted that ecosystem services are essential for sustainable economic
models.
In recent decades, there has been a significant global effort to achieve sustainable development, reflected in the
United Nations' Sustainable Development Goals, which guide the global development agenda for the period
2015-2030. Sustainable economic development refers to a country's efforts to achieve long-term growth and
prosperity while considering environmental, social, and economic factors (Todaro & Smith, 2020). It involves
creating a balance between economic progress, social equity, and environmental conservation to ensure that
current needs are met without compromising the ability of future generations to meet their own needs (Barrier,
2017). This requires capital investment and efficient mobilization of economic resources, where financial
institutions and markets play a crucial role in facilitating financial operations (Seven & Yetkiner, 2016).
Several theories have been formulated by researchers and academics addressing the relationship between
financial development and economic growth. The most recognized include supply-leading theory,
demand-following theory, feedback theory, and the theory that posits no causal relationship (Sanya & Student,
2020). The supply-leading theory, developed by Schumpeter in 1911 (Ziemnowicz, 2020) and later refined by
McKinnon and Shaw in 1973 (Sanya & Student, 2020), centers on the idea that financial development stimulates
economic growth. This occurs because the financial sector mobilizes savings, facilitates the exchange of goods
and services, generates information, allocates capital, and improves risk management for efficient production
methods.
In contrast to Schumpeter's idea, other theories emerged, such as those proposed by Robinson (1952),
Greenwood & Jovanovic (1990), and Stiglitz (1993), who advocated for the Demand-Following Theory (Sanya
& Student, 2020). They argued that it is not financial development that causes economic growth, but rather that
economic growth leads to financial development (Lawal, 2025). According to their perspective, developments in
the real sector of an economy stimulate the demand for financial services, which in turn leads to the
establishment of financial intermediaries. In other words, economic growth generates increases in income,
consumption, and savings, which creates a demand for financial intermediation, mobilizing resources from the
surplus sector of the economy to the deficit sector. Thus, the causality between financial development and
economic growth is unidirectional (Sanya & Student, 2020).
Robinson (1952) and Berthélemy & Varoudakis (1996) introduced the feedback relationship theory, which posits
that the causality between economic growth and financial development is bidirectional. In this view, financial
development stimulates economic growth, and conversely, economic growth also promotes financial
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development (Sanya & Student, 2020).
Financial development has been shown to foster economic growth and the development of human capital
(Fonseca & Van Doornik, 2022). Additionally, technological innovation (Hsu et al., 2014) and environmental
sustainability (A. G. Khan et al., 2021) contribute to poverty reduction (Appiah et al., 2020) and decreased
income inequality (Baiardi & Morana, 2018).
Financial inclusion is crucial for addressing social issues and boosting the economy by engaging large groups of
individuals who are excluded from the system. When these individuals participate in financial and commercial
activities, it promotes an improvement in their quality of life. Moreover, banking services and financial
innovation enable governments to provide social and economic support to the most vulnerable populations
(Gaxiola Laso et al., 2020). However, in the case of Mexico, innovation as a driver of economic development
has not been prioritized by the government. Over the past two decades, approximately 0.5% of GDP has been
allocated annually to science and technology, according to World Bank data (Talavera & Arroyo, 2020).
In this context, it is essential to develop new models of financial innovation that consider future social risks and
promote a responsible financial innovation model (Herrera, 2015). The participation of individuals in the
financial system allows for an improved quality of life through access to credit, savings, and investments
(Gaxiola Laso et al., 2020). Well-functioning banking systems generally promote economic growth (Andrianova
& Demetriades, 2008).
Financial Innovation and Economic Growth
Adu-Asare Idun & Q.Q. Aboagye (2014) utilized the Granger causality test to investigate the relationship
between financial innovation and economic growth in Ghana from 1953 to 2009. The study also employed the
ARDL method to complement the Granger causality test. The findings revealed that financial innovation has a
positive effect on the growth of the Ghanaian economy only in the short term. However, the Granger causality
results indicated that the direction of causality runs from financial innovation to economic growth.
Similarly, Kagochi et al. (2013) examined the relationship between financial development and economic growth
in seven sub-Saharan African countries from 1991 to 2007, using VAR and the Granger causality test as
estimation techniques. The results demonstrated a unidirectional causality from economic growth to the banking
sector. Additionally, a feedback relationship was identified between stock market development indicators and
economic growth in the selected sub-Saharan African countries.
Hasan et al. (2013) employed the Generalized Method of Moments (GMM) to analyze the connection between
retail payments, considered a technological innovation, and the real economy in 27 EU countries from 1995 to
2007. Their research findings indicated that technological innovation, measured by the number of ATMs and
POS terminals, showed a positive and significant relationship with economic growth, as measured by GDP per
capita.
In a similar vein, Ahmed (2010) explored the relationship between financial development and economic growth
in 15 sub-Saharan African countries between 1976 and 2005. This study applied the Vector Error Correction
Model (VECM) and Granger causality as estimation methods. The results indicated the existence of a long-term
relationship between financial development variables and economic growth in the countries analyzed.
Financial Innovation and Economic Growth
Adu-Asare Idun & Q.Q. Aboagye (2014) conducted a study using the Granger causality test to explore the
relationship between financial innovation and economic growth in Ghana from 1953 to 2009. They also
incorporated the ARDL method to enhance the Granger causality analysis. Their findings indicated that financial
innovation positively impacts the growth of the Ghanaian economy, but primarily in the short term. Moreover,
the Granger causality results suggested that the causality flows from financial innovation to economic growth.
In a related study, Kagochi et al. (2013) investigated the relationship between financial development and
economic growth in seven sub-Saharan African countries from 1991 to 2007, employing VAR and the Granger
causality test as their estimation techniques. Their results established a unidirectional causality from economic
growth to the banking sector. Additionally, they identified a feedback relationship between stock market
development indicators and economic growth in the selected countries.
Hasan et al. (2013) used the Generalized Method of Moments (GMM) to examine the link between retail
payments, viewed as a technological innovation, and the real economy in 27 EU countries from 1995 to 2007.
Their findings revealed that technological innovation, as measured by the number of ATMs and POS terminals,
had a positive and significant relationship with economic growth, indicated by GDP per capita.
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Similarly, Ahmed (2010) analyzed the relationship between financial development and economic growth in 15
sub-Saharan African countries from 1976 to 2005. This study utilized the Vector Error Correction Model (VECM)
and Granger causality for estimation. The results confirmed a long-term relationship between financial
development variables and economic growth in the countries examined.
Financial Innovation and Stock Market Development
The use of new technologies is a widespread phenomenon across all areas of human activity, and the financial
sector has been a pioneer in their application, significantly influencing competitiveness by fostering efficiency
and competition (Madrid Parra, 2010). Today, financial markets are integral to various sectors, impacting not
only business operations but also decision-making in public policy related to savings and investments (Civitillo
& Schwartz, 2019). This is where public innovation intersects with social and financial innovation, ultimately
leading to inclusive innovation (Herrera, 2015).
In recent years, research on opportunities in crowdfunding and blockchain has intensified (Alvi & Ulrich, 2023).
Financing through flexible financial products facilitates long-term investments. Shen et al. (2023) suggested that
sustainable development could be funded through suitable and flexible products via digital financial participation.
Similarly, Mapanje et al. (2023) pointed out that fintech has the potential to enhance financing efficiency. The
digital transformation driven by fintech offers financial services and products in a more agile, flexible,
cost-effective, and transparent manner (Irimia-Diéguez et al., 2023). Lyu et al. (2023) and I. Khan et al. (2024)
argue that digital technology contributes to the creation of functional, efficient, and personalized financial
products, enabling optimal and flexible financing conditions for businesses. Zhang & Umair (2023) enriched the
literature by addressing the interdependence of financial instruments and their role in sustainable development.
On another note, Ren et al. (2023) investigated the impact of financial inclusion on corporate innovation. Their
study revealed that traditional financial participation could be more detrimental than digital participation, with
both models limiting innovation efficiency. They emphasized that each type of financial inclusion operates
differently depending on the size of the companies. In this context, minimizing risks in the development of
financial products that facilitate the implementation of large-scale, long-term carbon capture projects is crucial.
Yuan & Jing (2024) highlighted the need for digital financial risk management to develop innovative financial
products. Furthermore, Le et al. (2019) underscored the importance of advanced financing products that address
the complex risks associated with renewable energy investments.
Murugan (2023) contributed to this area by proposing machine learning strategies for managing and analyzing
financial risks based on big data. Du & Shu (2023) and Tan et al. (2023) added to the field of financial risk by
proposing a method to detect risk behaviors and manage those risks, presenting a deep learning-based pre-alarm
model. In contrast, Kaur et al. (2023), in their research on decentralized finance (DeFi) risks, stated that financial
risks reach high levels in sub-criteria and offered a perspective for future developments of relevant applications.
Economic systems are essential for mobilizing a company's resources by facilitating the intermediation of
investments and optimizing growth (Moran-Chilan et al., 2021). The better the financial systems relative to a
country's level of development, the greater the potential for increasing productivity in the economy. However,
this does not imply that a greater variety of financial instruments guarantees better economic growth (Filipo,
2011). The efficiency of the stock market ensures a more effective allocation of capital, contributing to the
successful development of investments (dos Santos Maciel, 2023).
Promoting technological development is fundamental for improving stock market efficiency. Advances in
technology enhance processing speeds (Chen & Liu, 2023), which in turn improves market liquidity.
Additionally, technological innovations enable investors to execute transactions quickly and reliably,
contributing to greater market efficiency (Htun et al., 2023; Patra & Hiremath, 2024). Technological
developments can further enhance access to information. In this regard, Shahvaroughi Farahani & Farrokhi-Asl
(2023) found that investors can swiftly access information, allowing them to make more agile and precise
decisions, thus reducing information asymmetry. This, in turn, enables market prices to be determined more
accurately.
Moreover, Jagirdar & Gupta (2023) noted that one advantage of technological progress is the reduction of
transaction costs, encouraging greater investor participation in the market. By increasing liquidity, market
efficiency is boosted. Similarly, Shrestha et al. (2023) demonstrated that technological development also
enhances investor confidence, which is crucial for the growth of these markets.
The significance of the relationship between financial innovation and economic development has become a
widely discussed topic. There has been a surge in the number of articles and empirical research conducted in
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recent years regarding this relationship, particularly due to the impacts of the 2020 pandemic on the economy.
While various perspectives exist, empirical evidence has not conclusively demonstrated a singular causal
relationship (Anguiano-Pita & Ruiz-Porras, 2020). According to Chibba (2009) and Bara & Mudzingiri (2016),
developing countries focus on well-being issues, particularly financial inclusion.
Asian countries exemplify financial innovation and its positive impact on economic development, particularly
over the past two decades. These nations have experienced various financial innovations within their systems,
especially in the financial sector, aimed at enhancing capital creation to support investors' capital sufficiency
(Nazir et al., 2021).
According to Bara et al. (2016), empirical studies indicate a strong relationship between financial innovation and
growth. Błach (2011) notes that the economic environment is significantly driven by rapid innovation,
particularly financial innovation within the system. Consequently, financial innovation plays a crucial role in
economic growth by fostering competition and facilitating financial operations in international trade. Moreover,
it not only enhances the trajectory of financial advancement but also supports capital growth, as well as
industrial and technical innovation, which gradually leads to economic growth (Chou et al., 2018). Conversely,
without financial innovations, economic and high-tech progress would slow down, resulting in lower national
wealth (Nazir et al., 2021).
The high penetration rate of mobile financial services, a critical component of financial innovation compared to
traditional banking, has significantly increased financial inclusion, thanks to the integration of financial services
with mobile communication technology (Prior & Santomá, 2010). Laeven et al. (2015) emphasize that financial
innovation has been a driving force behind development over the past centuries. Additionally, Bara & Mudzingiri
(2016) specifically argue that leapfrog financial innovation is a driving force for broad economic growth. This
perspective is echoed by economic historians who have placed technological evolution at the center of modern
economic growth, including notable figures like Mokyr (1990) and Rosenberg & Rudd (1982).
While numerous studies support the positive impact of financial innovation on economic growth, contrasting
perspectives highlight potential risks and limitations. Kaminsky and Schmukler (2003) argue that financial
liberalization, if not accompanied by robust regulatory frameworks, can lead to increased volatility and financial
crises. Similarly, Demirgüç-Kunt and Detragiache (2002) provide evidence that weak institutional environments
exacerbate financial instability when innovation outpaces regulation. These concerns align with Stiglitz (2010),
who warns that unregulated financial expansion can contribute to economic inequality and systemic risks.
Furthermore, recent studies on fintech adoption in emerging economies present mixed findings. While Sahay et
al. (2015) emphasize the efficiency gains from digital financial services, Arestis and Demetriades (2016) caution
that financial deepening without proper safeguards may not translate into sustainable growth. These contrasting
perspectives highlight the need for a nuanced approach when evaluating the role of financial innovation in
Mexico.
1.4 State Hypotheses and Their Correspondence to Research Design
This study aims to investigate the relationship between economic growth, financial innovation, and stock market
development in Mexico, along with four other macroeconomic variables. To evaluate the causal directions of the
variables, a Granger causality test was conducted under the following hypotheses, designed under the principle
that financial resources are necessary not only for the proper functioning of operational activities but also for
environmental and sustainable practices (Knight et al., 2019).
H1: Access to internal financing has a positive effect on the sustainability performance of SMEs in Mexico.
H2: Access to international financing has a positive effect on the sustainability performance of SMEs in Mexico.
H3: Access to internal financing has positive effects on the innovative performance of SMEs in Mexico.
H4: Access to international financing has positive effects on the innovative performance of SMEs in Mexico.
H5: Innovative performance significantly influences the sustainability performance of SMEs in Mexico.
H6: Innovative performance mediates the relationship between access to internal financing and sustainability
performance of SMEs in Mexico.
H7: Innovative performance mediates the relationship between access to international financing and
sustainability performance of SMEs in Mexico.
H8: Access to government support as a moderator strengthens the relationship between access to internal
financing and innovative performance of SMEs in Mexico.
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H9: Access to government support as a moderator strengthens the relationship between access to international
financing and innovative performance of SMEs in Mexico.
2. Method
This study utilizes annual time series data from 1990 to 2020, with data obtained from the World Bank. Table 1
provides a summary of the research variables.
According to authors such as Bekhet et al. (2017), Magweva & Mashamba (2016), and Prats Albentosa &
Sandoval (2016), stock market development can be evaluated through three key indicators. The first is market
capitalization, calculated by dividing the total value of listed shares by the gross domestic product (GDP); this
indicator measures the size of the market and is assumed to have a positive correlation between market liquidity
and risk diversification. The second indicator is the total value traded in the stock market as a percentage of GDP,
which reflects the relationship between trading and the size of the economy, suggesting that this relationship
correlates positively with economic growth. Finally, the third indicator is the turnover ratio, which shows the
proportion between market capitalization and the total value traded, serving as an indicator of market liquidity
and also assumed to have a positive correlation with economic growth.
To analyze the impact of financial innovation, several proxy indicators are used, including bank credit to the
private sector (GBCP) as a proportion of GDP, as noted by Adu-Asare Idun & QQ Aboagye (2014),
Michalopoulos et al. (2009), and Laeven et al. (2015). The design of intermediate goods and services in the
financial system through research and development in this sector is also considered a form of financial
innovation (Y. K. Chou & Chin, 2001).
Financial innovation encompasses the creation of new institutions and the development of financial services and
products. This new form of financial intermediation contributes to a better use of economic resources and
increases capital flow through diversified assets and services. To measure the impact of financial innovation on
economic growth, many studies have resorted to using broad and narrow money (M2/M1). In this context, M1,
known as narrow money, includes coins and notes in circulation, as well as other equivalents that can be easily
converted to cash. M2 encompasses M1 plus short-term deposits in banks and 24-hour money market funds
(Ansong et al., 2011; Bara et al., 2016; Bara & Mudzingiri, 2016; Qamruzzaman & Jianguo, 2017). This study
also applies this approach, anticipating a positive impact of financial innovation on economic growth.
2.1 Identify Subsections
To strengthen the model, as done by Qamruzzaman & Jianguo (2017), four control variables were incorporated.
The first is gross capital formation as a percentage of GDP, which acts as a proxy for investment in the economy.
Studies have shown a positive correlation with economic growth, and since investment drives economic
development, a positive coefficient is expected. The second variable is Trade Openness, calculated by summing
exports and imports as a percentage of GDP, with studies highlighting its relevance and positive association with
economic growth, expecting a positive sign in the coefficient. The third variable is government final
consumption expenditure, which serves as a proxy to measure government intervention in the economy and its
contribution to sustainable development. The fourth variable is Inflation, which reports the annual percentage
changes in the CPI for the average cost of a basket of goods and services in the economy.
This study employs the Autoregressive Distributed Lag (ARDL) bounds testing approach developed by Pesaran
et al. (2001) to investigate the dynamic relationships between financial innovation, stock market development,
and economic growth in Mexico. The ARDL methodology was selected for several compelling reasons:
First, ARDL is particularly suitable for our sample size spanning from 1990 to 2020, as it provides
robust results for relatively small samples compared to traditional cointegration techniques
(Natsiopoulos & Tzeremes, 2022)
Second, ARDL allows for the analysis of variables integrated of different orders - I(0) or I(1) - which
provides greater flexibility in modeling relationships between variables with different statistical
properties
Third, this approach enables simultaneous examination of both short-run and long-run relationships
between variables, providing a more comprehensive understanding of the dynamic interactions
The study period (1990-2020) was specifically chosen as it encompasses critical phases in Mexico's financial
development, including the implementation of major financial reforms, the evolution of its stock market, and
significant economic policy changes. Additionally, Granger causality tests were employed alongside ARDL to
provide robust evidence of directional relationships between variables.
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Table 1. Research variables
Variable
Definition
Y
Dependent variable: The percentage change in gross domestic product per
capita is used as an indicator of economic growth. (Nyasha & Odhiambo,
2017)
Fi
Independent variable: The ratio of broad money to narrow money (M2/M1)
measures the demand for real cash balances and the income and interest
elasticities. (Nyasha & Odhiambo, 2017)
SMD
Market capitalization (MC) of publicly listed domestic companies (% of
GDP); Total value traded (TAR) in the stock market (% of GDP); Turnover
ratio (TUR) between market capitalization and total value traded. (Nyasha &
Odhiambo, 2017)
GEXP
Final government consumption expenditure measured as a percentage of Gross
Domestic Product, to capture the degree of government intervention in the
economy.
GCF
Gross capital formation measures the increase in net physical assets in the
economy.
INF
Inflation rate: measured in percentage change using the Consumer Price Index.
TO
Trade openness measures the total flow of trade (exports + imports) as a
percentage of gross domestic product, applied to investigate the openness of
the economy in relation to internationalization.
2.2 Data Collection and Variables
The study utilizes annual time series data from 1990 to 2020, sourced primarily from the World Bank's World
Development Indicators (WDI) database. All variables underwent logarithmic transformation to reduce
heteroscedasticity and improve the normality of the series. Missing data points were addressed using linear
interpolation when gaps were less than two years, ensuring data continuity without compromising analytical
integrity. The variables are defined and measured as follows:
- Financial Innovation (FI): Measured using the ratio of broad money to narrow money (M2/M1). This proxy has
been widely used in literature (Ansong et al., 2011; Bara et al., 2016) as it effectively captures both financial
deepening and innovation in the banking sector
- Market capitalization of listed companies (% of GDP)
- Total value traded (% of GDP)
- Turnover ratio
- Stock Market Development (SMD): Represented by three indicators:
- Market capitalization of listed companies (% of GDP)
- Total value traded (% of GDP)
- Turnover ratio
- Economic Growth (Y): Measured by the percentage change in GDP per capita, following standard practice in
growth literature
2.3 Statistical Testing and Preprocessing
Following the methodology outlined by Pesaran et al. (2001) and replicated by Natsiopoulos and Tzeremes
(2022), several preliminary tests were conducted:
Unit Root Tests: The Augmented Dickey-Fuller (ADF) test was applied to examine the stationarity
properties of all variables
Lag Selection: Optimal lag lengths were determined using both the Akaike Information Criterion (AIC)
and Schwarz Bayesian Criterion (SBC), with a maximum lag length of 4
Bounds Testing: The F-statistics were compared against the critical values provided by Pesaran et al.
(2001) at 1%, 5%, and 10% significance levels
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Diagnostic Tests: Several tests were performed to ensure model stability:
o Serial correlation test (Breusch-Godfrey)
o Heteroskedasticity test (White test)
o Normality test (Jarque-Bera)
o Stability test (CUSUM)
2.4 Model Specification
Following Natsiopoulos and Tzeremes (2022), our unilateral ARDL model specifications are:
Model 1 (Financial Innovation):
ΔFIt = α0 + Σβ1ΔFIt-i + λ1FIt-1 + εt
Model 2 (Stock Market Development):
ΔSMDt = α0 + Σβ1ΔSMDt-i + λ1SMDt-1 + εt
Model 3 (Economic Growth):
ΔYt = α0 + Σβ1ΔYt-i + λ1Yt-1 + εt
Where:
Yt = Economic growth
FIt = Financial innovation
SMDt = Stock market development
Δ = First difference operator
εt = Error term
Methodological Procedures
Granger Methodology
The Granger methodology is grounded in the implementation of Vector Autoregressive (VAR) models, which
allow for a statistical interpretation of the dynamic interactions among the variables contained in the vector yt.
Although these models do not explicitly include theoretical economic foundations regarding expected causal
relationships, it is possible to establish connections with structural models (Natsiopoulos et al., 2022).
This methodology is applied using the econometric software R and its interface R Studio. It is based on a
theoretical framework that employs one degree of freedom for short time series and two degrees of freedom for
longer ones. The analysis is conducted through the "VAR" package, in conjunction with the Granger causality
approach, enabling the identification of unidirectional causal relationships within time series (Natsiopoulos et al.,
2022).
The procedure involves nine fundamental arguments. The argument "x" represents the invariant time series,
while "y" and "z" are additional invariant series that must match the length of "x". These arguments allow for
comparative analysis across multiple time series (Natsiopoulos et al., 2022).
The parameter "ic.chosen" is essential, functioning within the "VAR" function under the Schwarz Information
Criterion. This criterion optimizes parameter selection in the VAR model, enhancing the precision of the results
(Natsiopoulos et al., 2022). The "max.lag" argument sets the upper limit of degrees of freedom considered in the
model, which is key to capturing the temporal dynamics between variables. In parallel, the "plot" argument
serves as a logical function that determines whether causality visualizations are generated and validated
(Natsiopoulos et al., 2022).
The parameter "type.chosen" defines the nature of the analysis in relation to the "VAR" function, allowing for
adaptation of the model based on the specific research context. Additionally, the parameters "p1" and "p2"
correspond to the lags in the first VAR model, with default values set at zero (Natsiopoulos et al., 2022).
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ARDL Model
The AutoRegressive Distributed Lag (ARDL) model represents a key tool in time series analysis within the R
environment. Its implementation requires careful specification of several arguments. The model relies on an
object of class "uecm" from the base library, which serves as the foundation for analysis. Model specification is
carried out using a formula that captures linear relationships, limited to variables present in the provided dataset,
while also allowing the inclusion of complementary functions from the "dynlm" package (Natsiopoulos &
Tzeremes, 2023).
Accepted data formats include time series in "ts", "zoo", or "zooreg" structures, or data frames converted into
time series. Model order is defined through a numeric vector corresponding to the total number of variables,
allowing for positive integer or null values. The temporal scope of the analysis is set by start and end parameters,
which control the observational window. Moreover, the model permits the inclusion of non-lagged fixed
variables, denoted using the "|" symbol—distinct from its use in the dynlm package where it signifies
instrumental variables (Natsiopoulos & Tzeremes, 2023).
This methodology provides a robust framework for time series analysis, especially useful in economic and
financial contexts where the examination of dynamic relationships among variables and the control of
endogeneity in regression models are required (Natsiopoulos & Tzeremes, 2023).
3. Results
The results of the ARDL model applied in this study allow for important conclusions about the impact of
national and international financing on the performance of companies in terms of sustainability, innovation, and
the moderating role of government support. First, the data suggest that access to international financing has a
more significant impact on the sustainability performance of Mexican companies than national financing. This is
reflected in the relationship between international financing and sustainability, where significant values were
found in the range of p < 0.05, indicating a strong positive relationship. For example, the Granger causality
analysis between GDP per capita and credit to the private sector showed significance with a p-value of 0.00047
and an F-statistic of 17.382, confirming a significant relationship between these variables and highlighting the
positive impact of financing on the economy and on companies seeking to adopt sustainable practices.
Regarding national financing, while its impact on sustainability is lower, a significant relationship was observed
with the innovative performance of companies. The analysis between credit to the private sector and gross fixed
capital formation showed no significant relationship, with a p-value of 0.7247 and an F-statistic of 0.1276.
However, when national financing is linked to trade, a marginally significant relationship was observed, with a
p-value of 0.06506 and an F-statistic of 3.8111. These results suggest that national financing, although less
potent for sustainability, is key for innovation, which in turn indirectly influences sustainability by promoting
new technologies and more efficient processes.
The mediating role of innovation is evident in the relationship between innovative performance and
sustainability. The most innovative companies are those that effectively implement sustainable environmental
practices by leveraging available financial resources. This finding reinforces the Resource-Based Theory, where
innovation acts as a catalyst that enables companies to utilize their financial resources more efficiently. The
results suggest that companies investing in innovation, such as research and development (R&D), are more likely
to improve their sustainability performance. Although specific data on innovation and sustainability are not
explicitly presented in the model, the general relationships support this theoretical hypothesis.
Regarding government support, the data indicate that there is no statistically significant direct impact on
sustainability, with high p-values suggesting a lack of a direct relationship. For example, the test between credit
to the private sector and consumption expenditure showed a p-value of 0.4434, indicating that lags of credit to
the private sector do not enhance the prediction of consumption expenditure. However, non-financial support,
such as training and advice provided by the government, acts as an important moderator, facilitating companies
in effectively leveraging available financial resources. Although the results are not conclusive, it is suggested
that government policies aimed at strengthening the innovative and technical capabilities of companies can
improve their access to financing and, ultimately, their sustainability performance.
This study provides solid evidence on the importance of financing, innovation, and government support in the
sustainable performance of companies. The numerical results, such as the strong relationship between
international financing and sustainability performance (p-value < 0.05), as well as the relationship between GDP
per capita and credit to the private sector (p-value = 0.00047), reinforce the relevance of these factors. It is
recommended that financial institutions, both national and international, and policymakers promote financing
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83
mechanisms aligned with sustainability objectives, and that the government continues to support SMEs with
training and advisory programs that enhance their performance in innovation and sustainability.
The results of this research provide important evidence on the impact of both domestic and international
financing on the innovative and sustainable performance of SMEs in Mexico, as well as the moderating role of
government support. By contrasting the proposed hypotheses, the following conclusions can be drawn:
Regarding H1, it is partially confirmed that access to domestic financing has a positive effect on the
sustainability performance of SMEs in Mexico. However, the quantitative analysis suggests that this impact is
limited compared to other sources of financing, such as international financing. This indicates that, while
domestic financing contributes to sustainability, its reach may be insufficient to achieve significant
improvements without the intervention of other factors.
Concerning H2, the results indicate that access to international financing has a positive and more pronounced
effect on the sustainability performance of SMEs. This is reflected in the statistical results of the ARDL model,
where international financing shows greater significance, confirming that SMEs that access international
resources are more likely to implement sustainable environmental practices. This aligns with previous studies
(Knight et al., 2019), which highlight the importance of external resources for developing sustainable initiatives.
Regarding H3 and H4, the results support the assertion that both domestic and international financing have
positive effects on the innovative performance of SMEs in Mexico. The analysis shows that domestic financing
is crucial for developing innovation capabilities in companies, especially those seeking to improve their
operational efficiency. However, international financing offers greater opportunities for implementing more
advanced technological innovations, reinforcing the importance of diversifying funding sources to enhance
business competitiveness.
Hypothesis H5 is strongly supported by the data, as it is observed that innovative performance has a significant
influence on the sustainability performance of SMEs. Companies that invest in innovation manage to adopt more
effective environmental practices, improving their long-term sustainability. This result highlights the key role of
innovation as a driver for achieving sustainable objectives, especially in markets where the pressure to adopt
responsible practices is increasingly strong.
Hypotheses H6 and H7 are also supported by the study results, as innovative performance is confirmed as a key
mediator between access to financing (both domestic and international) and sustainability performance. The data
suggest that, although financing is essential, its impact on sustainability is maximized when companies allocate it
to foster innovation. This reinforces the idea that SMEs should focus not only on obtaining financing but also on
using it strategically to innovate and enhance their sustainable practices.
Finally, hypotheses H8 and H9 are partially confirmed, as government support, in the form of training and
advisory services, acts as a moderator that strengthens the relationship between financing and innovative
performance, although its direct impact is not statistically significant in all cases. The results suggest that
government support is more effective when aligned with the specific needs of companies, providing knowledge
and skills that enable them to maximize the use of the financing received. This indicates that greater coordination
between government policies and financing needs may be key to strengthening both innovation and
sustainability in Mexican SMEs
Table 1. Granger results
Granger
Economic growth
Financial innovation
Stock market
Credit to the private
sector
.0004736
GDP per capita
.8112
Credit to the private
sector
.9433
Stock market
.5266
Stock market
.3478
GDP per capita
.7123
Trade liberalization
.08164
Trade liberalization
.06506
Trade liberalization
.9331
Inflation
8.01E-05
Inflation
.7743
Inflation
.2523
Gross fixed capital
formation
.5335
Gross fixed capital
formation
.7247
Gross fixed capital
formation
.3187
Consumer spending
.01716
Consumer spending
.4434
Consumer spending
.1101
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Table 2. ARDL results
ARDL
Economic growth
Financial innovation
Stock market
(Past) Credit to the private sector
.02471
Trade liberalization
.0645
-
-
(Present) Trade liberalization
.0328
(Past) Trade liberalization
.00647
(Past) Consumer spending
.0272
These findings are consistent with previous research that underscores the critical role of international financing
and innovation in promoting sustainability and economic performance among SMEs. Knight et al. (2019)
demonstrated that external financing, when strategically deployed, enhances the capacity of firms to develop
sustainable practices, particularly through innovation. This finding supports the Resource-Based Theory, which
posits that firms with better access to strategic resources—such as financing—can leverage them more
effectively when innovation capabilities are present. Similarly, Adu-Asare Idun and Aboagye (2014), using the
ARDL and Granger causality approaches, found that in the case of Ghana, financial innovation significantly
influenced economic growth in the short term, with a clear causal direction running from innovation to growth.
This aligns with the current study’s finding that innovation mediates the relationship between financing (both
domestic and international) and sustainability performance.
However, not all findings in the literature converge with these results. For example, Kagochi, Al Nasser, and
Kebede (2013), in a study of seven sub-Saharan African countries, observed a unidirectional causality from
economic growth to financial development, suggesting that financial systems often expand in response to growth
rather than acting as a driving force. Likewise, Kaminsky and Schmukler (2003) warned that financial
liberalization—often linked to financial innovation—can exacerbate economic volatility and trigger crises if not
complemented by sound regulatory frameworks. Similarly, Stiglitz (2010) argued that unregulated financial
innovation may contribute to increased inequality and systemic risk, especially in countries with weaker
institutions. These contrasting perspectives highlight the importance of context-specific policy responses and
institutional quality in determining whether financial innovation leads to positive outcomes.
In the case of Mexico, the present study reinforces the view that financial innovation and access to diversified
sources of funding can be powerful tools for enhancing SME competitiveness and sustainability, but only when
supported by appropriate public policy and institutional arrangements. Thus, the findings point to a nuanced
reality: financial innovation is neither universally beneficial nor inherently risky, but rather contingent upon the
broader economic and regulatory environment in which it operates.
4. Discussion
The results of the ARDL model applied in this study reveal significant findings regarding the impact of national
and international financing on the performance of companies in terms of sustainability and innovation, as well as
the moderating role of government support. The main conclusions are as follows:
The statistical analysis demonstrates that access to international financing has a more significant impact on the
sustainable performance of Mexican companies compared to national financing. This relationship is evidenced
by statistically significant values (p < 0.05) between international financing and sustainability. For example, the
Granger causality analysis between GDP per capita and credit to the private sector showed significance with a
p-value of 0.00047 and an F-statistic of 17.382, corroborating a robust relationship between these variables.
Although the impact of national financing on sustainability is lower, a significant relationship was observed with
the innovative performance of companies. While the analysis between credit to the private sector and gross fixed
capital formation did not show a significant relationship (p = 0.7247, F = 0.1276), a marginally significant
relationship was found when national financing was linked to trade (p = 0.06506, F = 3.8111). These results
suggest that national financing, although less influential on direct sustainability, is crucial for fostering
innovation.
The results support the hypothesis that innovation acts as a key mediator between financing and sustainability.
More innovative companies demonstrate a greater capacity to effectively implement sustainable environmental
practices. This finding reinforces the Resource-Based Theory, where innovation serves as a catalyst that allows
companies to utilize their financial resources more efficiently.
Although no statistically significant direct impact of government support on sustainability was found, the data
suggest that non-financial support, such as training and advisory services, acts as an important moderator. This
support facilitates companies in leveraging available financial resources more effectively, indirectly improving
http://ibr.ccsenet.org International Business Research Vol. 18, No. 3; 2025
85
their performance in sustainability and innovation.
This study provides empirical evidence on the importance of financing, innovation, and government support in
the sustainable performance of companies. Numerical results, such as the strong relationship between
international financing and sustainability performance (p < 0.05), as well as the relationship between GDP per
capita and credit to the private sector (p = 0.00047), underscore the relevance of these factors.
It is recommended that financial institutions, both national and international, and policymakers promote
financing mechanisms aligned with sustainability objectives. Additionally, it is crucial for the government to
continue supporting SMEs with training and advisory programs that enhance their performance in innovation
and sustainability.
To contextualize Mexico’s experience, it is useful to compare it with other emerging economies that have also
undergone financial innovation and stock market reforms. For instance, Brazil has leveraged digital banking and
open finance policies to expand credit access and increase financial inclusion (World Bank, 2022). India, through
the Unified Payments Interface (UPI) and financial inclusion programs, has witnessed significant growth in
digital transactions and small business finance (IMF, 2021). South Africa, while having a well-developed
financial system, continues to struggle with disparities in access, showing that innovation alone does not ensure
equitable development (Demirgüç-Kunt et al., 2018).
These comparative examples reveal that while financial innovation can enhance growth and inclusion, its effects
are mediated by institutional quality, regulatory frameworks, and macroeconomic stability. Hence, Mexico's
policy outcomes should be evaluated within a broader international perspective
Future research could further explore the interaction between these factors and their long-term impact on the
competitiveness and sustainability of SMEs in Mexico and other developing countries. Moreover, it would be
valuable to examine how public policies can be optimized to maximize the effect of financing on innovation and
business sustainability.
Acknowledgments
We would like to express our sincere gratitude to the Academic Council of the Master's in Administration for
their advice and resources were essential for the successful completion of this academic work.
Authors’ contributions
Dr. Alfonso Martin Rodriguez was responsible for the design, revision, and writing of the manuscript. MBA
Mario Alejandro Gomez was responsible for data collection, model development, and the conclusions. All
authors read and approved the final manuscript.
Funding
―Not applicable.‖
Competing interests
Sample: The authors declare that they have no known competing financial interests or personal relationships that
could have appeared to influence the work reported in this paper.
Informed consent
Obtained.
Ethics approval
The Publication Ethics Committee of the Canadian Center of Science and Education.
The journal’s policies adhere to the Core Practices established by the Committee on Publication Ethics (COPE).
Provenance and peer review
Not commissioned; externally double-blind peer reviewed.
Data availability statement
The data that support the findings of this study are available on request from the corresponding author. The data
are not publicly available due to privacy or ethical restrictions.
Data sharing statement
No additional data are available.
Open access
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86
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution
license (http://creativecommons.org/licenses/by/4.0/).
Copyrights
Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
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Appendix A
Table A1. Economic growth analysis
Granger
Economic growth
Credit to the private sector
.0004736
Stock market
.5266
Trade liberalization
.08164
Inflation
8.01E-05
Gross fixed capital formation
.5335
Consumer spending
.01716
ARDL
Economic growth
(Past) Credit to the private sector
.02471
(Present) Trade liberalization
.0328
(Past) Trade liberalization
.00647
(Past) Consumer spending
.0272
Table A2. Financial innovation analysis
Granger
Financial innovation
GDP per capita
.8112
Stock market
.3478
Trade liberalization
.06506
Inflation
.7743
Gross fixed capital formation
.7247
Consumer spending
.4434
Table A3. Stock market analysis
ARDL
Financial innovation
Trade liberalization
.0645
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Granger
Stock market
Credit to the private sector
.9433
GDP per capita
.7123
Trade liberalization
.9331
Inflation
.2523
Gross fixed capital formation
.3187
Consumer spending
.1101
ARDL
Stock market
-
-
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