Introduction

SSCM drives firms’ value and success within a competitive business environment while strictly adhering to sustainable business practices. SSCM, in a broad sense, is “the administration of environmental, social, and economic impacts alongside promoting good practices by corporations. It seeks to establish, safeguard, and enhance long-term corporate value for all the stakeholders concerned throughout the production of goods and services” (Rasche and Kell, 2010). Thus, it could be argued that firms that embrace the UN Global Compact principles in their SSCM have a better chance of achieving business ethics and good governance while promoting sustainable development goals. In the same vein, the evolving trends in climate change and global warming have become a top priority for governments across the globe, raising global environmental awareness and redirecting the attention of investors and other stakeholders to environmental sensitivity. Notably, financial institutions nowadays go beyond generating profits for providers of capital, other stakeholders such as the market regulators, investors, financial experts, and civil society groups, among others, increasingly demanding information of a non-financial nature (Sustainability reporting disclosure) to make more rational and informed investment decisions (Doktoralina and Apollo 2019).

SSCM practice was found to be cost-effective and environmentally friendly, as shown in agroforest, manufacturing, and tourism industries, particularly in developed economies (Parthiban et al. 2021; Mageto 2021; Zhou et al. 2021; Huang et al. 2021). In addition, Su et al. (2021) affirm that SSCM has been influenced by the proliferation of scholarly write-ups and practitioners in recent times as organizations acknowledge the significance of sustainable development and the execution of sustainable supply chain management strategies to achieve their social, environmental, and economic objectives. Conversely, minimal focus was directed towards emerging market economies such as Nigeria. This indicates that there is less knowledge of SSCM and the performance of enterprises in Nigeria’s banking sector. This study addresses the gap by offering empirical insights into the relationship between SSCM and banks’ financial performance, specifically focusing on the Nigerian banking sector.

Their business operations essentially informed the rationale for choosing the banking sector because of their involvement in the finances of business enterprises, irrespective of the nature of their business undertaking. As such, it creates sustainability risks, which poses a more significant challenge to SSCM (Dim and Ezeabasili 2015). The banking sector plays a significant role and serves as the financial hub of the economy. It finances companies in the extractive, oil and gas, and manufacturing sectors. Hence, they are susceptible to sustainability threats (Nwobu et al. 2017). Accordingly, the civil society organization financial research group Proshare Nigeria claim that lending to oil and gas companies accounted for about 40% of Nigeria’s bank loan assets. In addition, Fitch ratings opined that as of September 2019, oil and gas represented about 30% of Nigeria’s bank gross loan (Fitch Rating 2020). These indices suggest that one-third of the Nigerian total loan assets were channeled to one segment of the economy, neglecting other important sectors. Similarly, the pollutant effects of this industry, such as oil spills, deforestation, air pollution, and soil and water contamination, would continue to negatively impact the well-being of the people and environment (Mageto 2021). Hence, this study argues that the banking sector in Nigeria is directly or indirectly susceptible to SSCM risks due to channeling a substantial part of their loan assets to an industry prone to sustainable development.

The need for empirical quest in this area becomes necessary because previous studies such as Olaniyan et al. (2021), Adeyemi and Akanji (2020), Inoue and Lee (2011) concentrate more on corporate social responsibility as a sustainability measure of supply chain management. Some studies focused on sustainability reporting (SR), which is the financial performance nexus among firms in various economic sectors, and even in these studies, the banking sector has not received attention in their analysis. For example, Nwaiwu and Oluka (2018) investigated the impact of sustainability reporting (SR) on the financial capacity performance of industrial firms. They found a significant influence of SR on the firms’ performance over time. Nnamani et al. (2017) examined and found a positive impact of SR on firms’ performance in developing countries. However, others conducted firm-level analyses based on SR and financial performance indicators (Igbekoyi et al., 2021; Nazim et al. 2015; Othman and Ameer 2009).

While research on SR and firms’ financial performance is prominent, there is a dearth of empirical literature on the impact of SSCM on financial outcomes. The few extant literature that examined the effects of SSCM on the firms’ financial performance were primarily conducted in developed economies (see Seuring and Müller 2008; Doktoralina and Apollo 2019; Lee 2021; Mageto 2021). Studies conducted on emerging economies focused on the manufacturing, information technology, and transport sectors (see Zailani et al. 2012; Schaltegger and Burritt 2014; Jum’a 2020; Zimon 2020; Jum’a et al. 2021). In Nigeria, Dim and Ezeabasili (2015), Ojo, Mbohwa, and Akinlabi (2014) examined the effectiveness of strategic SSCM in the construction industry associated with procurement and firms’ management.

Given the importance of the finance and banking sectors in an economy, previous studies have ignored the role of SSCM in the financial industry. Specifically, how SSCM environmental disclosure financing impacts the performance of DBMs. To the best of the researchers’ knowledge, there is a limited empirical study in this direction, and to complement the extant literature, our study focuses on the impact of SSCM on the financial value of DBMs in Nigeria. Thus, research on this issue is imperative due to the emerging national and international importance of actualizing the United Nations’ SDGs. Against this background, the study seeks to provide answers to the question: Does SSCM environmental disclosure has dynamic effect on the financial performance of DMBs in Nigeria? To answer this question, the study uses annual data from 2005 to 2023 from seven DBMs in Nigeria with national, regional, and international authorizations. Using the panel cointegration test and CS-ARDL model, the empirical results show that SSCM environmental disclosure improves banks’ financial performance (value and profitability). However, the effect is higher in the short-run as sustainable financing is gaining momentum in the banking industry.

Furthermore, the study contributes to theory and has practical significance. Theoretically, it contributes to the extant theoretical submission linking sustainable financing and financial performance by extending the debate to the banking industry. Although the SSCM-performance nexus is gaining momentum in the theoretical and empirical literature, the current analysis covers the financial hub of the economy (banking industry), which mobilizes and channels funds for investment purposes. The empirical findings complement the literature on sustainable financing and firms’ performance. Practically, the findings provide a beacon light for stakeholders, including financial practitioners, investors, and policymakers, by informing their decision-making and enabling better-informed actions regarding sustainable practices. Additionally, the study contributes to the extant knowledge base on the SSCM-performance link within the Nigerian banking context, which can guide the monitoring and evaluation efforts of non-governmental organizations and civil society groups. The rest of the paper is structured as follows: Section two reviews relevant and related literature on SSCM and financial performance; section three discusses the data and empirical methods; section four dealt with the empirical analysis and results; section five discusses the empirical findings; section six provides the concluding remarks and recommendation.

Literature review

The Global Reporting Initiative (GRI) G4 guidelines issued in 2016 require financial organizations to be held responsible for their operations’ environmental, social, and economic impacts on society, which affect a diverse range of stakeholders. This has put the banking sector disclosure measures in place for banks’ compliance. The corporate existence of banking sectors around the world is mainly service delivery. However, because of the role they play in funding business enterprises, irrespective of the nature of their business operations, has posed a more significant challenge to SSCM practice as they indirectly affect the environment by providing funds to companies engaged in mining and extractive industries, oil and gas sectors, manufacturing sectors that are prone to sustainable environment risks (Nwobu et al. 2017; GRI, 2013). In the same line of argument, Usenko and Zenkina (2016) affirm that the financial outcome of business entities could not be adequately ascertained without examining their impact on economic, social and environmental factors, and the disclosures of positive and negative environmental externalities. Thus, this established that the financial institutions are not directly exposed to environmental degradation, greenhouse gas emissions, alternative energy, or climate change. This may be due to the choice of enterprises whose operations are financed. For example, oil and gas operations require intensive capital sum and as such, substantial parts of funding such massive projects are financed mainly by the banking sectors, which often pose a tremendous amount of risk to the host community because of adverse environmental impacts such as the oil spillage, CO2 emissions, and discharge to water.

The banking sector principally funds the operations of oil and gas, extractive, and manufacturing companies. Hence, this exposed their financiers to risks that could lead to environmental hazards (Nwobu and Iyoha 2018). In emerging markets such as Nigeria, SSCM practice is still voluntary. Hence, this research argues that socially responsible investments are critical for attaining sustainable development goals because financial institutions in Nigeria often fund and derive huge profits from unsustainable business activities that negatively impact the environment. The United Nations Environment Program on Finance Initiatives (UNEP-FI) roundtable (2020) report further buttressed the need for banks to incorporate sustainability issues into their financial reporting. This means incorporating SSCM practice into strategic planning and evaluation by developing sustainability management systems would help create awareness and mitigate environmental hazards. The UNEP Finance Initiative (UNEP 2011) offered banks directives for integrating sustainability reporting into their corporate operations and processes. The indices indicate that financial institutions that neglect to integrate sustainability issues into their operations and processes are susceptible to social, environmental, and financial repercussions (Lee 2021). The indices indicate that cost reduction and financial risk avoidance are among the advantages of enhanced SSCM. Consequently, the sustainable development strategy for banks presents issues that must be addressed by any enterprise seeking to maintain relevance among stakeholders (Neckel 2017).

Financial institutions in advanced economies such as the USA, UK, France, Spain, and other European nations have responded to the need to be accountable for sustainability reporting in different operations. This is further shown in Peeters (2012). The involvement of European banks in environmental issues commenced in the early 1990s. Financial organizations, including banks and insurance corporations, have shown interest in sustainable development due to the imperative to mitigate environmental risks associated with lending. Freeman and Phillips (2002) postulated the stakeholders’ theory, which upholds that so many interest groups within a business circle are affected by its operations. These categories encompass customers, suppliers, employees, and government entities. Amran and Haniffa (2011) assert that the theory addresses the dynamic and intricate link between firms and their environment and the company’s capacity to reconcile the often-conflicting needs of its diverse stakeholders. From another perspective, Abdulsalam (2017) posits that when businesses recognize their responsibility for accountability and transparency to stakeholders, sustainable business practices emerge as a crucial mechanism for fulfilling these duties. Previous research on SSCM demonstrated that environmental, social, and economic challenges arise from stakeholder pressure for transparent, accountable, and sustainable development, thereby safeguarding the interests of future generations. It proposed that organizations consider the aspirations of various stakeholders, with some solutions manifesting as strategic opinions.

Furthermore, Michael and Becker (1973) formulated the signaling theory based on the organizational setting. The author upholds that the board of directors (insiders) may deliberately withhold some vital information from other parties (outsiders) to gain an undue advantage because such information may send signals of the most likely situation of the company. Signaling theory focuses on bridging the information asymmetry by transmitting relevant information to market participants on SSCM practice. Signaling theory has four themes: signaler, signals, receiver, and feedback. Signalers are the executives, directors, managers, etc., with privileged information about the organization. Signals transmit information about stock price developments, dividend distributions, and environmental financing. Receivers are external parties who lack knowledge of the insider information. The form of investors, investment analysts, etc., whereas feedback reflects the interaction between signaler and receiver. Signaling is broadly classified as positive and negative. A positive signal improves firms’ value and performance. Meanwhile, negative signals decrease product demand and the firms’ stock prices. Therefore, organization performance lies in how the quality of information on SSCM and its reliability reach diverse stakeholders. Thus, there is a close link between SSCM and signaling and stakeholder theories such that stakeholder engagement, proactive environmental strategy, climate change commitment and other ethical issues serve as motives and drivers for firms to voluntarily integrate and report on the SSCM namely, environmental social and economy aspects into their annual reports and account.

Given the above, therefore, this study anchored on the stakeholders and signaling theories in understanding the motives and drivers of SSCM, which enhanced firms’ performance. The study investigates how SSCM influences firms’ performance. Accordingly, what signals does SSCM convey to the diverse stakeholders? The stakeholder theory and signaling theory, therefore, underpinned this research. The Stakeholders theory upholds the view that companies should be accountable to various stakeholders by recognizing the importance of integrating SSCM to strengthen the relationship between firms and the host communities in which they operate. Thus, ignoring the stakeholder interests might adversely impact the firm’s public image and financial performance and put the firm’s ongoing concerns at stake. The Signaling theory focuses on bridging the information asymmetry (communication gap) by sending relevant and quality information to different market participants on SSCM. The availability of relevant information on SSCM sends a signal to diverse users of accounting information and, thus, would change their perceptions about the firm’s commitment to implementing sustainable business practices.

SSCM entails a “sequence of activities in an organization designed to manage different stakeholders across the realm of supply chain efficiently” (Li et al. 2006). Several studies investigated the effect of SSCM on firms’ or organizational performance. For example, Menor et al. (2007), Boon-itt and Wong (2011), and Zimon (2020) examined the impact of diverse SCM-related organizational activities on a firm’s performance (both financial and market operations). Li et al. (2006) analyzed the impact of SCM practices on enhancing an organization’s competitiveness and overall market performance. The company’s performance is positively impacted by competitiveness, which stems from SSCM. The effect of SSCM practice on financial performance is contingent upon the supply chain’s position inside the firm (Cook et al. 2011; Jum’a, 2020). A sustainable environment is positively influenced by SSCM techniques, encompassing supplier relationships, processes, customer services, and human resource management (Bendehnezhad et al. 2012). Furthermore, Green et al. (2019) and Yildiz Çankaya and Sezen (2019) demonstrated a significant correlation between the consistent improvement of business operations, the Just in Time (JIT) delivery system, and green SCM practices, as well as their collective impact on the environmental; similarly, sustainability performance was notably affected by the dimensions and practices of green SCM. Le (2020) and Zimon (2020) discovered that green SCM activities, such as environment-friendly product development, positively influence three performance categories: social, financial, and environmental performance, with a substantial link between green supply chain practices and sustainable environment. Likewise, specific research indicated that sustainable/green performance was considerably and favorably affected by product design, customer interactions, equipment, and supplier connections (Iramanesh et al. 2019; Le, 2020). Previous research by Yang, Hong, and Modi (2011) found a positive and significant correlation between environmental management practices and lean manufacturing.

In contrast, Hofer, Eroglu, and Rossiter (2012) examined the influence of SSCM on corporate performance, as assessed by environmental management practices. The data demonstrated the negative effect of environmental management techniques on market and business performance, which diminished with enhanced environmental performance. The author discovered that inventory capacity partially mediated the relationship between production and sustainable financing. A study by De et al. (2020) utilized qualitative analysis and used sustainability-oriented innovation (SOI) as input criteria, while environmental, economic, social and organizational factors were evaluated as output criteria. The findings indicate that the integration of lean principles with SOI contributes to SMEs’ SCM sustainability. SCM practices and strategies, including ISO 14001 certification, reverse logistics, and adaptable sourcing methods that focus on waste reduction, supply chain risk management, and the adoption of Cleaner Production (CP) methodologies, positively influence sustainable development (Govindan et al. 2014; Ikram et al. 2020).

Researchers developed various performance measures grounded in market and financial success. Financial performance has garnered significant attention from researchers as it quantitatively represents the efficacy of a firm’s strategies and operations (Yang et al. 2011; Hofer et al. 2014; Feng et al. 2018). It can be defined as the extent to which an organization achieves profit-oriented goals, such as sales and investment returns (Yang et al. 2011). Numerous metrics exist to evaluate an organization’s financial success. Hofer et al. (2014) assessed the firm’s economic success through revenue and sales growth, whereas additional metrics, including return on assets (ROA), have been employed in some studies (Jum’a et al. 2021). This study examines the financial performance of individual DMBs using two indicators: ROA and Tobin’s Q, as suggested by Yang et al. (2011) and Jum’a et al. (2021), to provide a more accurate representation of the firm’s financial condition and performance.

Recently, supply chain management has been associated with environmental development and sustainability. Environmental sustainability denotes the “practices, methods and actions that exert a discernible positive impact on the ecosystem” (Sendawula et al. 2020). Empirical literature posits three primary elements of sustainability: environmental, economic, and social, which are directly interconnected and significant in operations and supply chain management (Marshall et al. 2014). The World Commission on Environment and Development (WCED) defines sustainability as economic practices that meet the demands of the current generation without jeopardizing the capacity to satisfy the needs of future generations (Imperatives 1987). Baliga et al. (2019) and Panigrahi et al. (2019) have shown that an organization’s economic success directly results from implementing sustainable practices, particularly the integration of SSCM in their operations. Chardine-Baumann and Botta-Genoulaz (2014) noted that the impact of SCM on sustainability must be assessed through the three fundamental characteristics of sustainability: environmental consideration, social responsibility, and economic benefits. Marshall et al. (1987) observed that prior literature on sustainability has emphasized environmental factors more prominently. This shift towards environmental sustainability is influenced by different stakeholders, including government regulators, investors, consumers, employees, NGOs, and local communities, which compel businesses to mitigate their environmental impact and adhere to environmental standards (Bendehnezhad et al. 2012; Jum’a et al. 2021).

Moreover, Wisner et al. (2009) have shown that effective environmental procedures substantially affect enterprises’ internal performance. Consequently, managers should employ SSCM methods to enhance the organization’s performance. Certain studies have identified SSCM environmental practices as determinants of industrial or company performance. Goyal et al. (2018) identified twelve determinants of environmental sustainability and categorised them into four principal types of supply chain practices. Näyhä and Horn (2012) categorised sustainable environmental practices into high and low impact to establish an appropriate environmental assessment for an industry. Previous studies on environmental sustainability have primarily addressed issues related to indicators such as the reduction of air emissions, waste minimization, decrease in solid waste and hazardous materials, mitigation of environmental risks, and enhancement of a firm’s overall environmental performance (Vu and Dang 2020; Singh et al. 2019; Ikram et al. 2019). Numerous studies have correlated environmental practices with corporate environmental commitment.

However, studies on SSCM practices and firms’ performance, as highlighted above, concentrated on specific industries (building, manufacturing and industrial sectors), mainly in developed economies. There is a dearth of empirical studies on the banking sector, which is critical in mobilizing and channeling funds for investment in various sectors of the economy. Environmental disclosure and SSCM commitment in the banking sector can significantly influence environmental sustainability practices, further influencing the banks’ market value and operational profitability. The current study seeks to bridge this gap by empirically examining the dynamic effect of SSCM environmental disclosure on the financial outcomes of DBMs in Nigeria.

Methods

Data

The study employed the ex-post research design for its suitability as the events occurred over time; hence, the research was conducted retrospectively. As widely used by previous studies (see Kerlinger and Lee 2000), annual statements reports and statements were used by this study to investigate the potential impact of SSCM environmental disclosures on the financial performance of DMBs in Nigeria. However, the ex post facto design is not without some limitations. It lacks random assignment, making it difficult to rule out alternative explanations, leading to issues with internal validity (Shadish et al. 2002). The retrospective nature of ex-post facto research can introduce biases and errors in the data collection (Gall et al. 2007). Nonetheless, we intend to minimize the effect of these issues using appropriate empirical analysis and diagnostic tests in Section “Empirical Analysis and Results”. The study used annual data from the London Stock Exchange Group (LSEG) workspace, formerly Refinitiv Eikon DataStream workspace. The data span from 2005 (after the consolidation exercise in the banking sector) to 2023 based on available data. The study’s population consisted of the specified deposit money banks operating in Nigeria. However, the sample was selected through a three-level filtering process: (i) banks that had been in operation throughout the period from 2005 to 2023, (ii) banks with national, regional and international authorization, (iii) banks that are listed on the Nigerian Stock Exchange, and (iii) banks with the available data on corporate annual reports, accounts, and sustainability reporting for the period covered. Based on the assessment, seven banks met the selection criteria for the study. These include Access Bank Plc, Fidelity Bank Plc, First Bank Nigeria Plc, First City Monument Bank Plc, United Bank of Africa Plc, Guaranty Trust Bank Plc, and Zenith Bank Plc.

Variables definitions and measurement

To account for the heterogeneity of the DMBs in Nigeria, the study used ROA and Tobin’s Q (market value) as measures of financial performance. The independent variables included in the model were SSCM environmental disclosure, bank size, capital structure, and corporate social responsibility. The use of ROA is well-justified in the literature. ROA is a widely accepted and commonly used metric to assess the profitability and efficiency of a firm’s asset utilization (Acharya et al. 2013; Athanasoglou et al. 2008), and it provides a valuable indicator of how effectively a bank generates profits from its assets, which is particularly relevant for the banking sector (Siddik et al. 2017; Sufian and Habibullah 2009). Furthermore, prior studies examining the financial performance of DMBs in Nigeria have employed ROA, demonstrating its suitability as a comprehensive measure that captures a bank’s ability to generate returns from its asset base (Obansa and Ohiri 2013; Nworji et al. 2011).

Tobin’s Q (TQ) reflects a company’s prospective market value and enduring profitability. This metric can signify investors’ opinions of a company’s future and intangible economic potential (Lee and Park 2009). Yoon and Chung (2018) posited that Tobin’s Q and ROA might be disconnected due to the conflicts arising from enterprises’ pursuit of short-term vs long-term economic objectives. The influence of SSCM on these variables would vary. We employed the methodology of Chung and Pruitt (1994) as follows:

$${\rm{TQ}}={MV}+{OPS}+{DEBT}/{TA}$$

MV denotes the product of stock price and number of outstanding common shares; OPS signifies the liquidating value of issued preferred stock; DEBT is the value of short-term liabilities less short-term assets, in addition to the value of long-term assets and inventories; and TA represents total assets.

Accordingly, the use of TQ is well-documented in the literature. It is a widely recognized metric that assesses a bank’s value and efficiency in utilizing shareholders’ equity (Akhtar et al. 2011; Flamini et al. 2009). It is particularly relevant for the banking sector, as it measures a bank’s capacity to generate returns for its equity holders, a crucial aspect of its business performance (Dietrich and Wanzenried 2011; Athanasoglou et al. 2008). Additionally, numerous studies on the financial capacity in the banking industry have employed Tobin’s Q, further reinforcing its suitability as a robust and well-established measure for the current research (Olokoyo 2013).

However, the use of the Environmental Disclosure Index to measure the environmental disclosure practices of DMBs in relation to their supply chain management is established. This index, ranging from 0 to 100%, captures the DMBs’ sensitivity to sustainable environmental practices, such as ecological impact, emissions, waste management, and decommissioning, providing a comprehensive assessment of their environmental sustainability efforts (Yang et al. 2011; Carter and Easton 2011). Testing the effect of SSCM-related index on the financial performance of DMBs is supported by the existing literature, which highlights the potential benefits of SSCM, including improved operational efficiency, cost savings, enhanced reputation, and better stakeholder relationships, all of which can positively influence a bank’s financial performance (Brandenburg et al. 2019; Carter and Rogers 2008).

In addition, we also included the banks’ size and capital structure as potentially relevant factors determining financial performance. The existing literature suggests that larger banks perform better than smaller ones, often exhibiting lower costs, higher returns, and greater net profit margins (Gelles and Mitchell 1996). This study employed total revenue to measure bank size, as in Koh et al. (2009) and Yoon and Chung (2018). To normalize the distribution of the total revenue variable, the researchers applied a natural log transformation to the data before incorporating it into the analysis.

Including bank capital structure as an additional variable in the analysis is imperative. Capital structure, which represents the mix of debt and equity financing a bank uses, is a critical determinant of financial performance (Rajan and Zingales 1995; Myers 2001). Accordingly, financial institutions with higher equity levels tend to have greater financial stability and better withstand economic downturns, leading to improved profitability (Berger and Bonaccorsi di Patti 2006; Tan and Floros 2013). Conversely, banks with higher debt levels may face greater financial risk and higher borrowing costs, which can negatively impact their financial performance (Abor 2005; Zeitun and Tian 2007). Therefore, understanding the link between capital structure and banks’ performance, as measured by ROA or TQ, is essential for evaluating the banking sector’s overall financial health and sustainability.

Finally, the inclusion of CSR as another control variable is well-justified. Stakeholder theory suggests that effective stakeholder management, as reflected in CSR, can enhance long-term financial success (Jamali 2008). The proponents of resource-based theory posit that CSR can be a valuable, rare, and inimitable resource providing a competitive advantage (McWilliams and Siegel 2011). CSR can also improve a bank’s legitimacy, reputation, and risk mitigation, positively influencing its financial outcomes (Hasan et al. 2018). Furthermore, as banks increasingly integrate CSR into their strategic approach, examining this relationship can provide valuable insights (Eyasu and Arefayne 2020). Based on the earlier discussion, the variables captured by the study are summarized in Table 1.

Table 1 List of variables and their measurement.
Full size table

Specifications

Following the work of Yoon and Chung (2018), this study specifies the functional form model as follows:

$$x=f(sscm,bs,bcs,csr)$$
(1)

Where x represents banks’ financial performance measured as ROA and TQ (market value), sscm is the sustainable supply chain management, bs stands for bank size, bcs is the banks’ capital structure and csr is the corporate social responsibility.

Thus, Eq. (2) presents the analytical model for Eq. (1):

$${x}_{it}={\alpha }_{0}+{\alpha }_{1}ssc{m}_{it}+{\alpha }_{2}b{s}_{it}+{\alpha }_{3}bc{s}_{it}+{\alpha }_{4}cs{r}_{it}+{\mu }_{it}$$
(2)

Where: \(x,sscm,bs,bcs,csr\) are defined earlier in Eq. (1); α0 is the intercept; α1 to α4 represent the slopes of the estimated parameters; i is the cross-section of banks under consideration; t represents the period considered by the study and \({\mu }_{it}\) denotes the error term. Equation (2) suggests that the performance of DBMS in Nigeria at a given time t is a function of the bank’s sustainable supply chain management, bank size, capital structure, corporate social responsibility, and other unobserved factors captured in the error term.

Furthermore, this study employed the appropriate pre-estimation techniques for panel data to improve the modeling and empirical analysis. One pre-estimation test employed was the Lagrange Multiplier (LM) test for cross-sectional dependence by Breusch and Pagan (1979). The LM test statistic is calculated as:

$${{CD}}_{{LM}}=T\mathop{\sum }\limits_{i=1}^{N-1}\mathop{\sum }\limits_{j=i+1}^{N}{\tau }_{{ij}}^{2}$$
(3)

Equation 3 captures the LM test, which frequently produces inconsistent results when the sample size (N) is large (Balli et al. 2021). To address this issue, as (N, T) → ∞, the presence of cross-sectional dependence (CD) among the series can be assessed using Eq. 4 as proposed by Pesaran (2004). The null hypothesis of zero dependence across the panel is computed as follows:

$${CD}=\sqrt{\frac{1}{N(N-1}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^{N}(T{\tau }_{{ij}}^{2}-1)}$$
(4)

N and T denote the number of selected banks and the time considered. The null hypothesis posits the absence of CD. The test statistics \({\tau }_{{ij}}^{2}\) represents the pair-wise correlation coefficient of the residuals derived from the OLS regressions. Following the cross-section dependence test, we estimated the 2nd generation panel unit root tests. The methods employed are cross-sectional augmented Dickey-Fuller (CADF) and CIPS. The CADF test, as referenced in Chen et al. (2024), assesses panel stationarity. The formula for the CADF test statistics is expressed as follows:

$${\Delta Z}_{{it}}={\alpha }_{i}+{\rho }_{i}{Z}_{i,t-1}+{\beta }_{i}{\bar{Z}}_{t-1}+{\sum }_{j=0}^{k}{\gamma }_{{ij}}{\Delta \bar{Z}}_{i,t-1}+{\sum }_{j=0}^{k}{\delta }_{{ij}}{\Delta Z}_{i,t-1}+{\varepsilon }_{{it}}$$
(5)

\({\alpha }_{i}\) and \({\bar{Z}}_{t-1}{t}_{i}(N,{T})\) measures the unit root’s deterministic term \(\left(\frac{1}{N}\right){\sum }_{i=1}^{N}{Z}_{i,t-1}\), and \({\rho }_{i}\) estimates the ADF individual statistics. However, the CIPS developed by Pesaran (2007) tests unit roots in heterogeneous panels. It resolves the challenges of cross-sectional dependence by allowing for individual dynamic specifications in each regression. The model is given as:

$${\rm{D}}\,{\_}\,{{\rm{y}}}_{t}={{\rm{a}}}_{t}+{{\rm{b}}}_{t}\,* \,{{\rm{y}}}_{t-1}+{{\rm{c}}}_{i}\,*\, {\rm{MEAN}}\,{\_}\,{{\rm{y}}}_{t-1}+{{\rm{d}}}_{t}\,* \,{\rm{MEAN}}\,{\_}\,{\rm{D}}\,{\_}\,{{\rm{y}}}_{t}+{{\rm{e}}}_{t}$$
(6)

In this model, \({\rm{D}}\_{{\rm{y}}}_{t}\) is the differenced response variable for panel member i at time t, yt-1 represents the lagged dependent variable, MEAN_yt-1 measures the cross-sectional mean of the lagged value of the response variable, MEAN_D_yt represents the cross-sectional mean of the differenced response variable, and et represents the error term. The CIPS test allows for individual dynamics specifications in each regression, capturing potential heterogeneity across panel members. The p-value of the serial correlation Breusch-Godfrey Lagrange multiplier test is utilized to assess the individual regressions and determine the presence of unit roots in the panel. Accordingly, The study employed the method of Pesaran and Yamagata (2008), derived from Swamy (1970), to assess the homogeneity of slope coefficients by calculating the delta (Δ) and adjusted delta (Δ-adj.) statistics. Δ statistic is an altered variant of Swamy’s (1970) test (S), estimated as follows:

$$S=\mathop{\sum }\limits_{i=1}^{N}({\gamma }_{i}-{\gamma }_{{WFE}})\frac{{X}_{i}^{{\prime} }{M}_{i}{X}_{i}}{{\sigma }_{i}^{2}}({\beta }_{i}-{\beta }_{{WFE}})$$
(7)

The error terms are presumed to follow a normal distribution under the null hypothesis as (N, T) approaches infinity. The Δ test may be performed utilizing the subsequent expression:

$$\Delta =\sqrt{N}\left(\frac{{N}^{-1}S-k}{\sqrt{2k}}\right)$$
(8)

The expression for the delta in a small sample can be derived from Eq. (9).

$${\Delta}_{{adj}}=\sqrt{N}\left[\frac{{N}^{-1}S-E({Z}_{{iT}})}{\sqrt{{Var}({Z}_{{iT}})}}\right]$$
(9)

Subsequently, after assessing the variables’ CD and panel unit root, the study utilized the Pedroni and Kao cointegration method to ascertain the long-term relationship among the variables. The study assessed the dynamic short- and long-term impacts of SSCM on the financial performance of DBMs utilizing the augmented autoregressive distributed lag (ARDL) model. The model incorporates CD within the panel framework, as Chudik and Pesaran (2015) established in their Cross-sectional Dependence ARDL (CS-ARDL) model. This method resolves the problems associated with CD in panel estimations. Consequently, following Chen et al. (2024), the CS-ARDL equation is articulated as follows:

$${D}_{i,t}=\mathop{\sum }\limits_{I=0}^{p}{\theta }_{I,i}{D}_{i,t-I}+\mathop{\sum }\limits_{I=0}^{q}{\gamma }_{I,i}{X}_{i,t-I}+{\varepsilon }_{i,t}$$
(10)

Where Xit is the vector of independent variables, to address the issues of CD and slope homogeneity, the augmented form of Eq. (10) is expressed as follows:

$${D}_{i,t}=\mathop{\sum }\limits_{I=0}^{p}{\theta }_{I,i}{D}_{i,t-I}+\mathop{\sum }\limits_{I=0}^{q}{\gamma }_{I,i}{X}_{i,t-I}+\mathop{\sum }\limits_{I=0}^{r}{{\partial }_{i}^{{\prime} }I\bar{Z}}_{t-I}+{\varepsilon }_{i,t}$$
(11)

\({\bar{Z}}_{t-I}=({\bar{D}}_{i,t\bar{I}}\,{\bar{X}}_{i,t\bar{I}}\)) measures the averages of the explained and explanatory variables. Consequently, the lags are represented by \(p,{q},{r}\) and \({D}_{{it}}\) represents the dependent variable, and \({X}_{{it}}\) is the vector of explanatory variables. \(\bar{Z}\) is a dummy variable measuring the time effect. Equation (12) represents the model for estimating the long-run coefficients:

$${\hat{\varphi }}_{{CS}-{ARDL},i}=\frac{\mathop{\sum }\limits_{I=0}^{\rho }{\hat{\gamma }}_{I,i}}{1-\mathop{\sum }\limits_{I=0}^{q}{\hat{\theta }}_{I,i}}$$
(12)

Equation (13) estimates the mean group coefficients:

$${\hat{\bar{\varphi }}}_{{MG},i}=\frac{1}{N}\mathop{\sum }\limits_{i=1}^{N}{\hat{\varphi }}_{i}$$
(13)

Similarly, the equations for estimating the short-run dynamics and coefficients can be expressed as:

$$\begin{array}{ll}{\Delta D}_{i,t}={\theta }_{i}\left[{D}_{i,t-1}-{\varphi }_{i}{X}_{i,t}\right]-\mathop{\sum }\limits_{I=0}^{{pD}-1}{\theta }_{I,i}\,{\Delta }_{I}{W}_{i,t-I}\\\qquad\qquad+\,\mathop{\sum }\limits_{I=0}^{{qX}}{\gamma }_{I,i}{{\Delta}_{I}X}_{i,t-I}+\mathop{\sum }\limits_{I=0}^{{rZ}}{{\partial }_{i}^{{\prime} }I\bar{Z}}_{t-I}+{\varepsilon }_{i,t}\end{array}$$
(14)

Accordingly, the short-run coefficients are estimated using the following expressions. All parameters are defined earlier in Eqs. (11), (12).

\(\begin{array}{ccc}\hat{{\alpha }_{i}}=-\left(\mathop{\sum }\limits_{I=1}^{\rho D}{\hat{\theta }}_{I,i}\right); & {\hat{\varphi }}_{i}=\frac{\mathop{\sum }\limits_{I=0}^{{qX}}{\hat{\gamma }}_{I,i}}{{\hat{\alpha }}_{i}}; & {\hat{\bar{\varphi }}}_{{MG}}=\mathop{\sum }\limits_{i=1}^{N}{\hat{\varphi }}_{i}\end{array}\)

Descriptive statistics

The statistical features of the data are described and summarized in Table 2. The summary statistics provide some preliminary information on the characteristics of the variables used to study the link between financial outcomes and sustainability practices of the banking sector in Nigeria. Based on the results, TQ and the ROA have a mean value of 0.034 and 0.112, respectively. This implies that the value and profitability of the sampled banks recorded a growth rate for the period covered. The analysis indicates that the average value relative to total assets is about 3.44%, while the return on equity suggests a return of around 11.21%, indicative of effective utilization of shareholders’ equity.

Table 2 Summary statistics of the variables.
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The average score of 51.782 reflects moderate SSCM practices in the sampled banks. Furthermore, the results indicate that CSR has the highest standard deviation (36.26) in the distribution, while TQ has the lowest standard deviation (0.01). This implies that CSR is the most volatile variable in the distribution, while return on asset is the least volatile series. The summary statistics suggest that the distributions of the variables are non-normal, with varying degrees of skewness and kurtosis. The coefficients of the Jarque-Bera test are significant, rejecting the null hypothesis of non-normally distributed series. This is expected because financial variables are subject to various degrees of volatility. Knowing the series’ descriptive nature, we further estimate the correlation coefficients summarized and reported in Table 3.

Table 3 Correlation matrix.
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Correlation analysis

The correlation analysis in Table 3 reveals some fascinating degree of association between the banks’ financial performance and sustainability practices. The TQ has a positive correlation (0.10) with sustainable supply chain management, indicating a positive association between TQ and sustainable supply chain practices. Accordingly, the ROA positively correlates (0.17) with sustainable supply chain management, suggesting a positive association between profitability and sustainable supply chain management.

Moreover, regarding the association between financial performance and BCS, the result indicates a positive correlation (0.25) between return on assets and capital structure of the sampled DBMs, suggesting a positive association between profitability and the composition of BCS. Conversely, TQ negatively correlates (−0.02) with banks’ capital structure, indicating a negligible inverse association between the value of the sampled DBMs and the structure of capital. Finally, the correlation between financial performance and CSR reveals a negative correlation (−0.12) between ROA and corporate social responsibility, implying an inverse association between profitability and CSR. In contrast, TQ has a positive correlation (0.08) with CSR, suggesting an infinitesimal positive association between the market value of banks and CSR.

Based on the results of the correlation coefficients (Table 3), it is evident that no multicollinearity exists among the variables. The threshold commonly used to detect the presence of multicollinearity is a correlation coefficient above 0.9 (Hair et al. 2014; Gujarati and Porter, 2009). The absence of any correlation coefficients above 0.9 suggests that multicollinearity is not a concern among the variables included in the analysis. This indicates that the variables measure distinct and independent concepts and can be used together in a model without the risk of multicollinearity issues.

Empirical analysis and results

CD test results

The CD test results show substantial cross-sectional dependence evidence (see Table 4). All three test statistics - Breusch-Pagan LM, Pesaran scaled LM, and Pesaran CD - exhibit statistically significant probabilities at the 1 and 5% levels. The CD test indicates that the variables correlate across the sample’s cross-sectional units (e.g. banks). Due to the presence of CD, first-generation unit root tests, which presume cross-sectional independence, are considered unsuitable for analysing the dynamic behavior of the variables. The research acknowledges that second-generation unit root tests incorporating cross-sectional dependence are appropriate for the analysis.

Table 4 Results of CD test.
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Panel unit root tests

The unit root tests result provides the stationarity properties of the variables. The series exhibits a mix of stationarity characteristics (see Table 5). The CADF test results show that TQ, bank size, bank capital structure, and corporate social responsibility are stationary at the level values I(0), while SSCM is stationary at the I(1) level. Similarly, the cross-sectional augmented IPS (CIPS) test found that TQ, bank size, and BCS are stationary at level I(0). In contrast, ROA, SSCM, and CSR are stationary at the I(1) level. These results imply that the variables are stationary based on I(0) and I(1) levels. Given this mix of stationarity properties, the study identifies that the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is suitable for examining the effect of SSCM on the financial performance of DBMs. The CS-ARDL approach is well-suited for analyzing datasets with variables integrated into different orders, as it can accommodate both I(0) and I(1) variables within the same model.

Table 5 Second generation panel unit root tests.
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Panel cointegration test

Assessing cointegration among the variables is a critical step before estimating both the long-run and short-run relationships. This study employed two cointegration techniques by Pedroni and Kao to ascertain the long-run dynamics among the variables, and the findings are summarized in Table 6. The Pedroni (2004) test found strong evidence of cointegration among the variables. The null hypothesis is rejected at the 1% significance level for six test statistics. This suggests that the series share a common long-run dynamic equilibrium relationship. Furthermore, the Kao ADF co-integration test also affirmed the earlier findings of the Pedroni test. The Kao ADF test is significant at a 1% level, providing additional evidence of cointegration among the series.

Table 6 Panel cointegration tests.
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Slope homogeneity test

The result of the slope homogeneity test formulated by Pesaran and Yamagata (2008) is presented in Table 7. It shows evidence of heterogeneity (variability) among the variables. This is attributable to the statistically significant values of the Delta and Adjusted Delta. The finding rejects the null hypothesis of slope homogeneity. Rejection of the null hypothesis signifies significant disparities in the slope coefficients among the variables analyzed in the study. The effect of these variables on the dependent variable is expected to vary among different subgroups or cross-sectional units within the dataset.

Table 7 Slope heterogeneity test.
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CS-ARDL results

Table 8 presents the result of the short- and long-run dynamics of the CS-ARDL model. The results found that sustainable supply chain management significantly impacts the financial performance (measured by ROA) of DBMs in Nigeria. This implies that increased environmental disclosure by banks increases their short- and long-term profitability. This implies that positive changes in SSCM practices drive the financial performance of DMBs, given that the coefficient of SSCM (Long-run: β = 0.1795, p < 0.05; Short-run: β = 0.2025, p < 0.01) is significant. The coefficient of BS has a positive and significant impact on ROA, supporting the notion that the size of a bank positively affects its operational profitability.

Table 8 CS-ARDL estimations.
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The findings presented in Table 8 suggest a nuanced picture when examining the short-term impacts on the performance of DBMs in Nigeria. In the short term, the findings indicate that the effect of SSCM on financial performance is prominent and higher. This implies that an increase in the implementation of SSCM practices can lead to an increase in the financial performance of the banking industry in the near term. Conversely, the findings indicate that bank size and corporate social responsibility do not significantly impact the banks’ performance. This implies that the scale or size of the banks and their engagement in CSR activities may not be the primary drivers of immediate financial performance, at least within the context of the Nigerian banking industry.

However, the error correction term exhibits a negative sign and is statistically significant. The coefficient implies that in case of any disruptions or shocks that cause the banking sector’s performance to divert from its long-term equilibrium path, the error correction mechanism will work to restore the system to equilibrium. The speed of this adjustment process is estimated to be around 46% annually, suggesting that the banking sector in Nigeria has a relatively fast self-correcting mechanism to address any imbalances or deviations from the long-run sustainable path.

The findings presented in Table 9 show that SSCM and banks’ capital structure significantly impact banks’ financial performance in Nigeria, as measured by market value and long-term profitability (TQ). This further reinforces the notion that implementing sustainable supply chain practices drives Nigeria’s banking sector’s performance. This finding conforms with the conventional wisdom that a well-structured and optimized capital composition is a key driver of bank financial outcomes. Conversely, the findings show that CSR has a negative and significant effect on the performance of the DBMs in the short run. However, over the long term, the impact of CSR on banks’ market value is positive and significant. This implies that banks allocate more resources towards CSR activities, affecting their overall financial outcomes. The results imply that a 1% increase or decrease in the banks’ CSR engagement will lead to approximately a 0.78% decrease in the short run but an increase of 0.0047% in the long run. Notably, the results suggest that any disturbance or disequilibrium in the Nigerian banking sector will be corrected relatively. Specifically, the analysis shows that approximately 78% of any error or distortion in the economy, particularly within the banking sector, will be corrected within the subsequent period.

Table 9 CS-ARDL estimations.
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Discussion and implications of the findings

The study examined the potential effect of SSCM practices on the financial performance of DBMs in Nigeria. To achieve this, the authors disaggregated financial performance into banks’ operational profitability and market value and assessed how variation in SSCM activities related to environmental disclosure influences each performance indicator. To determine how the SSCM practices impact banks’ performance, the study used a panel co-integration test and CS-ARDL on a panel of seven filtered banks with national, regional and international authorization and data from 2005 to 2023. Based on the empirical analyses, the study finds that SSCM influences the financial performance of DMBs in two ways. On the long-run equilibrium relationship, SSCM and financial performance co-move, implying that banks’ initiatives toward SSCM environmental disclosure on various stakeholders (e.g. employees, managers, clients, government) effectively increase the banks’ long-term profitability and value. The finding aligns with those of Baliga et al. (2019) and Panigrahi et al. (2019), who suggested that the long-term economic success of an enterprise has a direct link with implementing sustainable practices, explicitly integrating SSCM in their operations. Accordingly, the banks’ attention to SSCM through the natural environment, consumer well-being and community development could raise their intangible resources (e.g., goodwill, reputation, positive consumer feedback) (Jum’a et al. 2021; Zimon 2020).

However, on the long-run dynamics of SSCM and financial performance, the study found a positive impact of SSCM practices on the market value of DMBs in Nigeria. More specifically, the environmental dimension in SSCM positively affected Tobin’s Q of banks. The impact of SSCM on the banks’ operational profitability is positive and significant. The findings suggest that The incorporation of SSCM practices by DMBs in Nigeria creates favorable indicators that enhance their value and profitability. The findings are in tandem with Margolis et al. (2009), Yang et al. (2011), Yildiz Çankaya and Sezen (2019) and Green et al. (2019), who found a positive impact of environmental sustainability on firm’s performance. Conversely, Buallay et al. (2019) found an inverse relationship between sustainable environmental practices and financial performance in a cross-sectional study. The co-movement between SSCM and DBMs’ financial performance of Nigeria’s DMBs raises concerns regarding mitigating environmental risks associated with lending to some specific economic sectors. The financial performance of banks is properly ascertained by examining the effects of environmental, economic, and social aspects, along with the disclosures of both favorable and unfavorable environmental externalities about their supply chain management. This is a crucial component that influences the sustainable development of DMBs.

Moreover, this study has theoretical and practical implications. Deducting from Freeman’s (1984) stakeholder theory, a bank’s commitment toward its stakeholders promotes short-term and long-term strategies and corporate value. In addition, the research findings have some implications for bank managers, public sector officials, policy analysts, market participants and other stakeholders in the country that are directly or indirectly linked to the banking sector. The effect of SSCM practices on the performance of DMBs in Nigeria suggests that banks need to integrate SSCM in their critical decisions, remain committed to environmental sustainability, and implement such decisions. Furthermore, stakeholders (communities and government) need to appreciate environmental initiatives from banks and adopt more strategies leading to sustainable development and socially responsible activities. These will go a long way in upgrading sustainable financing and environmental quality at local and national levels through the lens of profile and selective financing to reduce costs and increase SSCM practices in the economy.

Conclusions

Following the empirical findings, the study concludes that there is a positive and significant relationship between SSCM and the financial performance of DMBs in both short- and long terms. Banks adhering to SSCM principles stand a chance to create and sustain their shareholders’ wealth while taking into cognizance all possible steps in mitigating risk tied to their corporate identity and value. SSCM communicates environmental practices to various stakeholders, indicating the responsibility of corporate entities, such as banks, through environmental awareness and assessment. Consequently, the research recommends that DMBs incorporate SSCM principles and enhance the regulatory framework to address challenges and mitigate information asymmetry among various stakeholders. Strategic goals of SSCM through profile and selective financing are imperative in mobilizing and channeling funds to environmentally friendly investment, which is in tandem with sustainable financing and sustainable development goals.

However, the study used non-probability sampling (availability sampling) and concentrated on the environmental aspect of sustainable financing of only seven banks based on available data filtered on authorization levels. So, due to these challenges faced by the study, generalization of the empirical findings may be challenging. Future studies can overcome these challenges by applying large samples and accommodating the social and governance indices of sustainable financing on financial institutions with available data.

Nevertheless, a rising trend of literature was identified in the areas of sustainability reporting and financial outcomes (Adams and Narayanan 2010; Amran and Haniffa 2011; Adeyemi and Akanji 2020), and SSCM and environmental sustainability to performance (Jum’a 2020; Zimon 2020; Jum’a et al. 2021). As such, future studies may investigate the potential effects of environmental, social, and governance aspects of SSCM and sustainability reporting strategies on the performance of DMBs. The current study examined and concentrated only on the environmental aspect of the SSCM related to Nigeria’s banking sector. Therefore, future studies may replicate it in other countries and industries.