Strategic Management of Environmental, Social, and Governance Scores and Corporate Governance Index: A Panel Data Analysis of Firm Value on the Istanbul Stock Exchange

Abstract

This study investigates how Environmental, Social, and Governance (ESG) scores and the Corporate Governance Index (CGI) jointly influence firm value in Türkiye. To address the contextual limitations of global ESG metrics, this study incorporates the CGI, a country-specific governance measure developed by Capital Markets Board of Türkiye, as a complementary indicator. Using panel data from 44 non-financial firms listed on the Istanbul Stock Exchange between 2019 and 2023, the study applies a random effects regression model with robust standard errors. The findings indicate that both ESG and CGI scores are positively and significantly associated with firm value, along with profitability (ROA), while financial leverage and liquidity (CR) show negative effects. The results underscore the strategic value of aligning sustainability performance with governance quality, particularly in emerging market contexts. This study contributes to the literature by providing empirical evidence for an integrated ESG–CGI framework and offers practical insights for corporate managers, investors, and policymakers.

1. Introduction

Over the last decade, non-financial performance metrics, particularly Environmental, Social, and Governance (ESG) scores, have drawn increasing attention from various stakeholders, such as investors and regulators [1,2]. While financial performance remains essential, attention has gradually expanded to include issues like carbon emissions, labor conditions, and board transparency, all of which now influence firm valuation [3]. Yet, recent studies have raised concerns that ESG scores alone may fall short due to certain methodological inconsistencies and heavy reliance on self-reported disclosures [4]. Additionally, national legal systems and regulatory frameworks often shape governance standards differently, which may cause varying ESG outcomes across countries [5,6]. Particularly in emerging markets, the effectiveness of ESG metrics is often constrained by contextual mismatches, as many models and theories originated from developed countries and thus fail to reflect local institutional capacity, external market pressures, and resource constraints [7]. Subsequently, the standardized global governance approach may not align with national compliance standards or regulatory enforcement of an emerging economy, so it becomes necessary to complement ESG metrics with more locally grounded governance indicators, particularly in emerging markets like Türkiye, where regulatory structures and investor expectations differ significantly from those of developed economies [8,9].

In this context, this study introduces a Corporate Governance Index (CGI) as a complementary indicator. CGIs are commonly used to evaluate how companies apply key governance principles such as transparency, accountability, and risk oversight [10,11]. Despite differences in design across countries, CGIs are typically constructed using core governance components, such as board independence, ownership structure, and other mechanisms that reflect internal control and accountability [12]. The CGI used in this study is grounded in Türkiye’s domestic legal framework and is calculated by institutions authorized by the Capital Markets Board of Türkiye (CMBT), capturing the implementation of governance principles in a localized and regulator-defined context [13,14].

Accordingly, this study adopts a dual approach by integrating ESG and CGI to provide a more comprehensive understanding of how both global and local governance dimensions jointly influence firm value. The combined approach contributes to the literature by offering an empirically grounded model that integrates ESG and CGI scores, two dimensions often analyzed in isolation [10,15].

The analysis is based on firm-level variables including ESG and CGI scores, Tobin’s Q, return on assets, current ratio, leverage, and total assets from companies listed in the CGI of the Istanbul Stock Exchange (ISE) between 2019 and 2023. The data are obtained from Refinitiv EIKON, the Corporate Governance Association of Türkiye, and the Public Disclosure Platform [13,16,17]. The appropriate estimation method is selected using the Hausman and Breusch–Pagan tests, and the hypotheses are tested using a random effects model with robust standard errors.

The findings demonstrate that both ESG and CGI scores are significantly and positively associated with firm value, indicating that firms with strong governance frameworks and sustainability performance are more likely to earn favorable investor evaluations. By integrating two key non-financial metrics into a unified model, this study highlights the strategic importance of ESG and CGI for firms operating in the emerging market context.

The remainder of the paper is structured as follows: Section 2 presents the theoretical background and literature review; Section 3 outlines the methodology, including data sources, model specification, and analytical techniques; Section 4 reports the empirical findings; Section 5 provides a detailed discussion of the results in relation to previous research; and finally, Section 6 concludes the study, outlines its limitations, and offers recommendations for future research.

2. Literature Review

Corporate governance is a set of internal and external guidelines that assist a business in accomplishing its objectives. It also helps to establish and maintain relationships with the state, its laws, the board of directors, and the public sector [11]. It has evolved significantly depending on changing societal expectations and the need for sustainable development. The transformation emphasizes a more integrated approach that considers all stakeholders, including customers, employees, communities, and the environment, rather than prioritizing shareholder primacy [18]. As corporate governance frameworks evolve, institutional investors, particularly passive funds, have increasingly shaped governance dynamics [19]. Today, corporate governance principles necessitate transparency, accountability, and stakeholder trust, which depend on key measures such as board diversity, independent oversight, executive remuneration transparency, and compliance mechanisms [20].

2.1. Environmental Social Governance Scores

In recent years, ESG reporting has become a common practice among companies aiming to meet the growing expectations of investors and other stakeholders [21]. The environmental (E) aspect of ESG typically addresses how firms manage natural resources, emissions, and related ecological risks. The social (S) component focuses on issues such as labor practices and relationships with employees, customers, and local communities. Meanwhile, governance (G) reflects the internal structures of oversight, such as board composition, shareholder rights, and decision-making transparency [5]. The logic behind the ESG scores aligns with key strategic management theories. For instance, the Triple Bottom Line (TBL) framework encourages businesses to consider environmental and social responsibility while pursuing financial success [22]. Similarly, Stakeholder Theory argues that firms must address the concerns of diverse groups, including employees and local communities [23]. From another aspect, the Resource-Based View (RBV) highlights intangible assets, such as firm reputation and governance culture, as drivers of competitive advantage [24,25].

ESG remains a valuable reference point in corporate strategies, as recent studies show that ESG performance is positively associated with firm value. For instance, ref. [26] showed ESG’s consistent contribution to firm value based on materiality classification, focusing on the banking sector in OECD countries. Similar results were obtained in various panel data analyses, such as [27], which reported a significant positive relationship between ESG scores and firm value. Ref. [28] reached the same conclusion in the context of the airline industry, and [15] further noted that this relationship intensified after the COVID-19 pandemic.

While the majority of studies support a positive relationship between ESG and firm value, not all the findings are consistent. For instance, inconsistencies between traditional financial reporting and ESG metrics raise concerns about how accurately such scores reflect actual company performance [29]. Additionally, ref. [30] points out that various internal and external factors, such as political systems, disaster risks, and ownership structures, can influence how firms disclose ESG information. Ref. [31] extended this perspective that industry-specific factors, such as concentration and growth rate, may moderate the relationship between ESG and firm value. In another aspect, ref. [32] concluded that environmental issues tend to outweigh social and governance aspects in the natural resource sector.

In addition to these inconsistent findings, recent studies explicitly argue the need for sector- and country-specific models, particularly in the G dimension [5,6]. Also, a growing body of literature suggests incorporating complementary measures to address the limitations of ESG frameworks. For instance, refs. [33] and [34] advocate for complementary metrics that account for health, circularity, and broader social equity dimensions, while [35] proposes the inclusion of a ‘Missing Information’ pillar to address data gaps in ESG assessments. Similarly, ref. [36] demonstrate that ESG scores do not always align with real eco-efficiency, emphasizing the need for additional evaluation frameworks. Ref. [37] underlines the need to redesign ESG evaluation structures around principles of resilience, materiality, and stakeholder inclusion, particularly in light of systemic disruptions such as the COVID-19 pandemic. Ref. [38] further emphasizes the disconnect between ESG indicators and sector-specific sustainability practices, especially in areas like green computing, calling for technology-sensitive and socially inclusive ESG metrics. In a similar vein, ref. [39] proposes a software-based ESG maturity framework that adjusts for firm size, geography, and industry characteristics, highlighting the inadequacy of static, one-size-fits-all ESG tools. The need for complementary metrics is particularly salient in developing countries, where institutional structures, resource limitations, and stakeholder dynamics differ significantly from developed markets, and traditional ESG models have limited applicability without customized contextualization [7,8].

Another critical point regarding ESG scores is that some firms communicate their ESG initiatives strategically instead of implementing them substantively. Management research uses the term ‘greenwashing’ to describe these strategies [40]. Greenwashing refers to a PR strategy using selective disclosure, symbolic gestures, or exaggerated claims to project a false image of environmental responsibility to gain legitimacy, while maintaining business-as-usual operations, without meaningfully reducing environmental impact [41]. For instance, certifications and eco-labels lacking strong verification often serve symbolic purposes, with reporting shaped more by strategic image management than actual performance improvements [41,42]. Regarding this point, ref. [43] highlights the need for stringent government regulations and credible third-party monitoring to combat false or misleading claims.

Subsequently, the literature collectively reinforces the need to integrate complementary indicators, such as sector-specific, governance-focused, or performance-oriented measures, to enhance the accuracy and credibility of sustainability assessments.

2.2. Corporate Governance Index as a Complementary Metric

The Corporate Governance Index (CGI) is commonly used to evaluate how companies apply core governance principles such as transparency, accountability, and risk oversight [10]. While different CGI designs exist depending on the specific regulatory and market environments, the frameworks are grounded in international standards [10,11,44,45]. The conceptual basis for CGI aligns with Agency Theory, which promotes the implementation of internal monitoring mechanisms to reduce potential conflicts between corporate managers and shareholders [46]. The theory also highlights that governance mechanisms should consider the interests of all stakeholders, which is also emphasized in Stakeholder Theory [23,46]. Building on theoretical foundations, effective corporate governance structures with clear roles, accountability, and transparency helps align strategic decisions with organizational objectives, mitigate risks, and balance stakeholder interests [47]. Therefore, a well-constructed Corporate Governance Index measures the impact of internal governance mechanisms on corporate firm value [48,49], as institutional investors play a growing role in shaping corporate governance standards [19].

Empirical studies in various markets confirm the positive relationship between corporate governance measures and firm value, such as [50], which found that corporate governance quality positively affects firm value in Turkish companies. Similarly, ref. [51] revealed that ownership concentration, institutional ownership, and board independence have positive effects on firm value for Indian financial services firms. In the Nigerian stock market, ref. [52] confirmed that internal governance mechanisms are significantly associated with higher firm valuation, whereas external mechanisms showed insignificant results. For European financial institutions, ref. [53] found that firms with more gender-diverse boards and CEOs who hold stock in the company may affect the firm valuation positively. Finally, ref. [54] concluded that strong corporate governance mechanisms can effectively contribute to firm value by mitigating the negative impact of opportunistic managerial behavior.

Despite the known emphasis of corporate governance mechanisms in enhancing firm value, the literature also acknowledges several limitations associated with Corporate Governance initiatives. For instance, excessive governance provisions, such as large boards, may lead to coordination inefficiencies and slower decision-making, ultimately diminishing strategic agility and firm value [53,55]. Similarly, high ownership concentration, while potentially reducing agency conflicts, can suppress minority shareholder rights and hinder transparency [53]. Furthermore, the benefits of strong governance may diminish when combined with other high-control mechanisms, such as strict accounting transparency, leading to overregulation and lower firm value [56].

While ESG and CGI scores are often examined separately, they are interconnected dimensions of corporate sustainability. From the Stakeholder Theory perspective, ESG indicators reflect how firms demonstrate their commitment to societal expectations and stakeholder legitimacy by aligning with internationally standardized sustainability metrics [57], whereas CGI focuses on internal governance quality and managerial accountability within a localized regulatory framework shaped by country-specific institutional conditions [12,48]. Agency Theory further supports the integration of ESG and CGI, as their combination helps reduce reputational and regulatory risks while mitigating information asymmetry and agency costs through more comprehensive oversight [5,58]. Integrating both dimensions aligns with the Resource-Based View, where firm-specific capabilities, such as sustainable operations and robust governance, are considered intangible strategic assets [30,59]. Accordingly, a dual approach provides more robust and context-sensitive information, helping firms to better leverage their non-financial capital for sustained value creation.

Recent studies suggest that corporate governance mechanisms significantly moderate the negative impact of ESG controversies on firm value [60,61]. For instance, board independence, share incentive, and board gender diversity allow firms to mitigate the adverse effects of controversies on firm value [61,62]. The role of board size, however, remains contested. While [61] finds no significant effect, ref. [63] reports that larger boards reduce ESG controversies, attributing the effect to enhanced advisory capacity and stakeholder engagement.

To summarize, despite evidence from previous studies showing a connection between ESG and CGI, joint evaluation of these two metrics remains underexplored in empirical research, particularly in emerging markets. Thus, there is a need for an integrated approach that captures both external sustainability engagement and internal governance quality. In response, this study proposes a unified model that assumes ESG and CGI metrics serve complementary functions in capturing different yet interconnected dimensions of corporate sustainability and value creation.

3. Methodology

This study examines how ESG and CGI affect firm value in a combined model by employing panel data analysis. The sample includes the companies listed in the Corporate Governance Index (CGI) on the Istanbul Stock Exchange (ISE) from 2019 to 2023. Türkiye, as an emerging market, offers a meaningful context for this investigation. Its corporate governance system has been evolving in recent years [64], and regulatory attention toward sustainability has noticeably increased [65]. In addition, the country shows a higher potential for sustainable governance practices, with its strong production capacity [9].

The core contribution of this study is evaluating the combined influence of ESG and CGI scores on firm value. Unlike previous papers that assess ESG and CGI separately [28,48], this study integrates both dimensions in a single model. Given that country-specific conditions can have a strong effect on ESG performance [5,6], CGI scores structured by institutions authorized by Capital Markets Board of Türkiye (CMBT) were included [13], for a more contextualized and reliable interpretation of governance practices.

3.1. Data Acquisition and Introduction of Sources

The ESG scores were retrieved from a widely recognized database for corporate metrics, Refinitiv EIKON DataStream [66,67]. In calculating the ESG scores, the Environmental, Social, and Governance pillars were weighted at 34%, 35.5%, and 30.5%, respectively. While the Environmental and Social weights are adjusted by sector, the Governance weighting changes based on the country where the firm’s headquarters are located [68].

Given the scope of this research, which focuses on firms listed on the Istanbul Stock Exchange (ISE), the framework of the Corporate Governance Index (CGI) in this study has been set by a government institution, the Capital Markets Board of Türkiye (CMBT). The methodology of the CGI is presented below [14,69].

At first, weight distribution is implemented as shareholders (25%), public disclosure and transparency (25%), stakeholders (15%), and the board of directors (35%). Secondly, the section grades are later adjusted to a coefficient of 10, resulting in an overall CGI rating ranging from 0 to 10. The results regarding compliance with the principles of CMBT are indicated as follows [14]:

• Grade Range 9–10: Largely compliant
• Grade Range 7–8.9: Significantly compliant
• Grade Range 6–6.9: Moderately compliant
• Grade Range 4–5.9: Minimally compliant
• Grade Range 0–3.9: Unable to comply

The CGI dataset initially includes 74 companies; however, after excluding financial institutions and newly listed firms, the final sample consists of 44 companies. Financial statement data for these firms, covering the period from 2019 to 2023 (5 years), were obtained from the consolidated Public Disclosure Platform [17]. Although the inclusion of 2024 data was intended, they could not be incorporated, as the relevant disclosures were not yet available. The companies for which CGI scores were calculated are those listed on the Corporate Governance Index. Since the number of firms with regularly calculated CGI scores has been quite limited since 2010, companies that do not currently report CGI scores or that discontinued their participation in previous periods were excluded from the sample. Accordingly, the most recent years with the highest data availability were included in the analysis to ensure consistency and data integrity. Finally, the stock prices of the companies were obtained from the Refinitiv EIKON Data Stream database [16].

Table 1. Companies and trading codes.

Trading Code	Company	Trading Code	Company	Trading Code	Company
AGHOL	AG Anadolu Grubu	DOAS	Doğuş Otomotiv	PETUN	Pınar Entegre Et ve Un
AKENR	Akenerji	ENJSA	Enerjisa	PINSU	Pınar Su
AKSGY	Akiş Gayrimenkul	ENKAI	Enka İnşaat	PINSUT	Pınar Süt
AKSA	Aksa Akrilik	EREGL	Ereğli Demir ve Çelik	QUAGR	Qua Granite
AKSEN	Aksa Enerji	FROTO	Ford Otomotiv	TATGD	Tat Gıda
ALARK	Alarko	GWIND	Galata Wind	TAHVL	TAV Havalimanları
AEFES	Anadolu Efes	GLYHO	Global Yatırım	TOASO	TOFAŞ
ARCLK	Arçelik	HLGYO	Halk Gayrimenkul	TUPRS	TÜPRAŞ
ASEL	Aselsan	ISDMR	İskenderun Demir ve Çelik	TTKOM	Türk Telekom
ASUZU	Anadolu Isuzu	KLKIM	Kalekim	TTRAK	Türk Traktör
AYDEM	Aydem	KONTR	Kontrolmatik	SISE	Türkiye Şişe ve Cam
AYGAZ	Aygaz	LOGO	Logo Yazılım	VKGYO	Vakıf Gayrimenkul
BIOEN	Biotrend	MGROS	Migros	VESTL	Vestel
CCOLA	Coca-Cola	OTKAR	Otokar	YUNSA	YÜNSA
DOHOL	Doğan Şirketler Grubu	PGSUS	Pegasus

shows the names of the companies in Türkiye with their stock exchange codes [69].

Furthermore, the total assets of the companies may be important in evaluating their firm values [70]. 

Table 2. Annual total assets (Turkish liras) (2019–2023).

	2019	2020	2021	2022	2023
AG Anadolu Grubu	67,131,708,000	73,272,993,000	111,816,191,000	377,714,512,000	391,799,428,000
Akenerji	6,872,973,181	6,734,536,777	13,111,835,774	37,804,276,653	31,949,666,370
Akiş Gayrimenkul	6,298,884,591	6,104,877,531	7,729,416,023	23,574,159,761	23,931,249,060
Aksa Akrilik	4,187,921,000	4,926,012,000	9,443,050,000	25,372,757,000	25,001,684,000
Aksa Enerji	8,501,122,048	9,502,694,305	20,649,538,050	55,203,703,045	55,806,281,421
Alarko	3,196,615,293	4,093,383,761	7,131,820,762	51,831,923,975	59,358,898,142
Anadolu Efes	45,956,475,000	50,561,368,000	85,037,222,000	268,244,570,000	265,225,686,000
Arçelik	34,729,500,000	46,549,044,000	85,078,606,000	232,422,279,000	258,137,907,000
Aselsan	25,633,043,000	34,094,229,000	46,413,298,000	144,802,627,000	150,577,885,000
Anadolu Isuzu	1,576,444,150	1,929,394,228	3,607,247,112	14,676,550,024	19,099,501,671
Aydem	null	12,470,698,190	3,820,356,887	59,788,434,916	55,606,785,506
Aygaz	4,954,859,000	5,395,240,000	8,333,131,000	45,770,964,000	46,188,584,000
Biotrend	null	814,750,232	1,826,083,907	6,644,030,002	6,668,203,235
Coca-Cola	15,959,755,000	19,147,331,000	32,786,241,000	107,259,230,000	110,157,984,000
Doğan Şirketler Grubu	11,240,591,000	13,693,203,000	22,294,738,000	83,805,566,000	93,417,723,000
Doğuş Otomotiv	4,664,944,000	11,719,008,000	9,754,599,000	49,591,167,000	67,866,864,000
Enerjisa	23,395,458,000	24,675,505,000	31,333,641,000	126,123,660,000	132,096,404,000
Enka İnşaat	49,409,979,000	62,053,819,000	121,324,988,000	160,441,430,000	276,940,651,000
Ereğli Demir ve Çelik	46,672,625,000	57,993,912,000	126,442,297,000	174,893,623,000	310,033,249,000
Ford Otomotiv	16,406,372,000	24,349,179,000	42,792,853,000	174,307,138,000	217,007,029,000
Galata Wind	null	1,620,618,640	1,767,586,090	8,761,974,105	9,159,539,055
Global Yatırım	7,056,432,382	9,406,379,211	15,077,311,142	41,514,675,474	44,521,524,710
Halk Gayrimenkul	3,039,209,130	3,514,726,080	4,578,221,287	24,390,715,825	28,072,224,715
İskenderun Demir ve Çelik	21,622,201,000	28,343,423,000	55,755,271,000	74,689,216,000	135,571,219,000
Kalekim	null	470,434,548	984,137,844	3,393,031,515	3,859,138,689
Kontrolmatik	null	320,073,376	940,425,952	7,336,763,617	11,622,164,447
Logo Yazılım	775,732,272	1,097,519,976	1,807,471,818	4,900,858,000	5,343,020,000
Migros	14,460,875,000	15,378,059,000	18,100,325,000	81,620,198,000	92,129,481,000
Otokar	2,677,717,000	4,334,175,000	5,989,526,000	26,632,326,000	32,484,677,000
Pegasus	21,059,321,198	29,070,672,791	52,896,598,949	95,803,046,438	201,955,079,557
Pınar Entegre Et ve Un	849,703,284	1,106,954,705	1,839,221,341	6,843,630,407	6,678,173,904
Pınar Su	325,284,640	425,279,979	666,930,212	2,038,328,300	1,867,909,789
Pınar Süt	1,548,975,104	1,919,215,560	3,387,323,742	12,610,549,593	12,000,869,016
Qua Granite	null	706,804,617	2,857,126,029	14,832,305,669	14,486,978,388
Tat Gıda	1,026,363,393	1,148,390,814	1,637,839,590	8,206,506,284	6,607,570,975
TAV Havalimanları	25,556,843,000	32,024,208,000	51,969,566,000	84,335,435,000	154,795,518,000
TOFAŞ	12,809,287,000	19,475,621,000	23,473,341,000	63,799,094,000	78,667,295,000
TÜPRAŞ	55,511,558,000	61,168,522,000	102,535,001,000	347,700,482,000	366,793,673,000
Türk Telekom	39,909,286,000	44,722,520,000	58,337,635,000	187,304,063,000	194,720,878,000
Türk Traktör	2,915,771,429	4,592,005,561	6,110,646,593	26,006,098,032	32,456,340,727
Türkiye Şişe ve Cam	38,722,780,000	58,684,418,000	74,216,129,000	288,985,246,000	291,211,843,000
Vakıf Gayrimenkul	1,781,467,349	2,928,981,416	4,619,815,011	12,387,757,758	14,938,242,772
Vestel	19,452,405,000	40,855,945,000	63,971,343,000	112,100,710,000	125,180,830,000
YÜNSA	325,927,846	323,951,610	391,856,418	1,837,179,819	3,540,798,655

presents the total asset amounts of the companies in Turkish liras (2019–2023).

Table 2 presents the annual total asset values for the 44 firms included in the sample, covering the period from 2019 to 2023. While 38 companies reported available total asset data in 2019, six firms had missing data, which primarily reflects their absence from public trading during that year or delayed inclusion in the Corporate Governance Index. Over the 5-year period, total assets showed substantial growth, with the aggregated value rising from 557.7 billion to over 3.84 trillion Turkish liras, representing more than a sixfold increase. The trend was significantly attributed to conglomerates, such as Koç Holding and Sabancı Holding, which consistently ranked among the top firms by asset size. Conversely, firms like İhlas Gazetecilik and Dardanel maintained modest but stable asset levels. The significant variance in firm sizes highlights the necessity of including firm-level controls in the regression analysis to ensure comparability across differently scaled operations [12].

Market value of companies may be affected by CGI [71]. The market values of companies traded on the stock exchange are calculated using the closing prices of their stock on the last day [72]. The values are obtained by multiplying the stock market closing prices by the number of shares [28]. Therefore, the formula is as follows:

$$\mathit{Market} \mathit{Value}=\mathit{Stock} \mathit{Price}\times \mathit{Number} \mathit{of} \mathit{Outstanding} \mathit{Stock}$$

(1)

As taking the market value as the nominal value will negatively affect the analysis, the natural logarithm (ln) is included in the analysis, since the change range of the companies’ total assets is quite high (see Table 2) [73].

3.2. Model Development

This study examines the impact of ESG and CGI scores on market value.

Table 3. Overview of model variables.

Variables	Code	Type	References
Value	value	Dependent	[71,72]
ESG Score	esg	Independent	[29,30,32]
CGI	cgi	Independent	[12,74,75]
Financial Leverage	lev	Independent	[71,76]
Return on Assets	roa	Independent	[12]
Current Ratio	cr	Independent	[77]
Tobin’s Q	tobinsq	Independent	[74]

provides an overview of the model variables, including dependent and independent variables.

Prior studies have observed that companies with stronger ESG performance take advantage of more investor trust and faceless financial exposure, which in turn may contribute to better overall financial outcomes [28,29,30,32]. Therefore, ESG scores are incorporated into the model to assess their relationship with firm value. CGI scores are also included, as they have a significant effect on both ESG performance and firm value [52,53,54].

In empirical models examining the relationship among ESG, corporate governance, and firm value, researchers have commonly included financial control variables to account for firm-specific characteristics that could influence valuation outcomes. These typically include indicators such as firm size, return on assets, leverage, current ratio, and Tobin’s Q—each reflecting dimensions like profitability, operational efficiency, financial risk, liquidity, and market perception [11,12,62]. Thus, including such controls ensures that the estimated effects are not confounded by differences in firms’ financial health or structural characteristics. Thus, the model also considers firm-level financial indicators.

Firstly, the leverage (LEV) ratio measures the extent to which a company’s assets are financed through debt. There is a well-established relationship between a firm’s debt level and its value. If the cost of debt is lower than the cost of equity, firms may prefer to meet their financing needs through borrowing. In this case, the marginal cost of debt is compared to the marginal return generated from the borrowed funds. When the cost of borrowing remains low, firms are more inclined to increase their leverage. Indeed, debt financing can enhance firm value up to an optimal point; however, excessive borrowing may eventually have a detrimental effect on firm valuation, as the marginal cost of additional debt surpasses the marginal benefit [71,76,78].

As firms utilize their assets more efficiently, their overall value is expected to increase. Various financial ratios are employed to assess the effectiveness of asset utilization. Return on assets (ROA) is one of the most common metrics in profitability assessment, calculated by dividing net income by total assets. A higher ROA generally indicates greater operational efficiency and stronger financial health [12,75]. An increase in ROA is important not only for firms themselves, but also for investors, as improved financial performance often increases demand for the firm’s shares, thereby enhancing market valuation.

In addition to profitability, a firm’s liquidity level can have a positive impact on its value. Current ratio (CR) is among the most widely used measures for evaluating a firm’s liquidity position. CR represents a company’s ability to cover its short-term liabilities with its current assets. A higher current ratio is typically interpreted as a sign of better liquidity management, which can positively influence investor perception [77]. Maintaining sufficient liquidity reduces the firm’s risk of default and enables the firm to take advantage when there is any alternative investment opportunity.

Tobin’s Q (TOBINSQ) compares a company’s firm value to the replacement cost of its assets. It helps to explain whether the market sees the company as undervalued or overvalued. Tobin’s Q is defined as [79,80].

$$\mathit{TOBINSQ}=\left(\mathit{Total} \mathit{Debts}+\mathit{Market} \mathit{Value}\right)/\mathit{Total} \mathit{Assets}$$

(2)

Some earlier research has applied the natural logarithm transformation of TOBINSQ to normalize data distribution [74]. However, since TOBINSQ is already a ratio, taking its logarithm may distort the results. Therefore, following the approach used in another study, the untransformed TOBINSQ ratio is included in the model [76].

By incorporating financial indicators such as TOBINSQ, ROA, LEV, and CR into the model, the analysis controls for firm-level financial factors that could otherwise bias interpretations of the relationship between non-financial indicators and firm value [27,81,82,83,84].

3.3. Analysis Methods

Econometric research typically involves three data types. Time series data refers to observations collected over time for a single entity. Cross-sectional data provide a snapshot of different entities at a specific point in time. Panel data, on the other hand, combine both cross-sectional and time-series dimensions, making it possible to track changes across multiple firms over several periods. This study employs panel data analysis, which enables examination of how ESG and CGI scores impact firm value over time, while controlling for firm-specific heterogeneity. Panel data methods provide higher statistical efficiency by capturing variations within and between firms and reducing issues related to omitted variable bias [85,86].

A general panel data regression model can be expressed as follows [86]:

$$Y_{it}=a_{it}+{\beta }_{\mathrm{i}\mathrm{t}}{X}_{it}+u_{it} i=1, \dots , N; t=1, \dots , T$$

(3)

Here, Y is the dependent variable, $X_{it}$ is the independent variables, $a$ is the constant parameter, $\beta$ is the slope parameters, and u is the error term. The subscript $i$ represents units, such as city, firm, or country, and the subscript $t$ represents time, such as day, month, or year. The fact that the variables, parameters, and error term carry subscripts indicates that they consist of a panel data set [86].

Panel data analysis can be conducted using either fixed effects (FE) or random effects (RE) models. The Hausman test is employed to determine the appropriate model specification by testing whether individual firm effects are correlated with the independent variables. If these effects are correlated, the fixed effects model is preferred; otherwise, the random effects model is appropriate [87].

Given that the dataset spans 5 years (2019–2023), with a relatively large number of firms (N > T), it qualifies as a micro panel dataset, for which unit root testing is generally not required [88].

The random effects method is suitable when variations across units are assumed to be random and influence the dependent variable. The model is expressed by the following equation [87]:

$$Y_{it}={\beta X}_{it}+a+u_{it}+{\epsilon }_{it}$$

(4)

where $Y_{it}$ presents the dependent variable and $X_{it}$, denotes the independent variable for unit $i$ at time $t$. The coefficient $\beta$ captures the effect of $X_{it}$ on $Y_{it}$. The term $a$ is a constant that remains the same across all units and time periods. Additionally, $u_{it}$ accounts for unobserved heterogeneity across units that is not related to the independent variables and is therefore treated as a random effect. Finally, ${\epsilon }_{it}$ represents the traditional error term, capturing the impact of random factors not included in the model and explaining variations that the model cannot account for [28,87].

The model used in this study is structured as follows:

$${\mathit{Value}}_{\mathit{it}}={\beta }_1\mathit{tobinsq}+{\beta }_2\mathit{esg}+{\beta }_3\mathit{cgi}+{\beta }_4\mathit{lev}+{\beta }_5\mathit{roa}+{\beta }_6\mathit{cr}+{\alpha }_i+u_{it}+{\epsilon }_{it}$$

(5)

In the model, the dependent variable ‘Value’ represents firm value, while the independent variables include esg, cgi, lev, roa, cr, and tobinsq. The coefficients $\left(\beta \right)$ quantify the impact of these independent variables. The term ${\alpha }_i$ denotes the unit-specific intercept, which accounts for individual characteristics that do not change over time. Lastly, $u_{it}$ represents the error term that states unexplained variations [28,87].

4. Findings

This section presents the results of the analysis, including descriptive statistics, correlation analysis, multicollinearity assessment, the Hausman test, autocorrelation diagnostics, and panel data regression results. All statistical analyses were conducted using Stata 15 (StataCorp LLC., College Station, TX, USA).

Table 4. Descriptive statistical findings.

Variable	N	Mean	Std. Dev.	Min	Max
Value	214	22.55332	2.15384	16.69	26.72
TOBINSQ	214	1.27363	1.27447	0.29	11.93
ESG	175	65.1485	17.48959	9	94.00
CGI	187	94.4993	2.48050	80.05	97.60
LEV	214	1.40835	0.86617	0.21	8.51
ROA	214	0.07756	0.07763	−0.19	0.32
CR	214	0.55292	0.27154	0.06	3.33

presents the number of observations (N), mean values, standard deviations, and minimum and maximum values for the variables included in the analysis. The dataset consists of 214 firm-year observations for market value, 175 for ESG scores, and 187 for the CGI index. Since firms’ market values exhibit wide variation, the natural logarithm of market value is used to normalize the distribution and improve the interpretability of the regression models. In contrast, the ESG scores (ranging from 9 to 94) and CGI index values (ranging from 80.05 to 97.60) are included in their original form, as their distribution characteristics do not necessitate a logarithmic transformation. It is noteworthy that the number of observations differs across ESG, CGI, and other variables. Firms lacking either ESG or CGI scores were excluded from the analysis. In the constructed dataset, missing values were present for only two variables; consequently, the panel data analysis was conducted with 164 observations using Stata 15. To address the issue of missing data, an unbalanced panel data model was also applied, and the results were compared for robustness (see Tables 10 and 11 under the Section 4.6).

4.1. Correlation Test

Correlation analysis assesses the direction and strength of relationships between variables [89]. In regression modeling, correlation analysis helps evaluate potential multicollinearity issues, ensuring that the independent variables do not exhibit excessive linear dependence. High correlations, typically exceeding 0.8 or 0.9, can lead to multicollinearity, which distorts coefficient estimates and reduces model reliability [90].

Table 5. Correlation test findings.

	Value	TOBINSQ	ESG	CGI	CR	ROA	LEV
Value	1.0000
TOBINSQ	0.3173	1.0000
ESG	0.4857	0.0135	1.0000
CGI	0.2952	−0.0286	0.3503	1.0000
CR	0.1747	0.0256	0.1139	−0.0865	1.0000
ROA	0.1063	−0.0177	−0.0393	−0.1750	0.1825	1.0000
LEV	−0.0650	0.1524	0.0717	0.1742	−0.4725	−0.3835	1.0000

presents the correlation matrix for the variables used in this study.

The correlation coefficients in Table 5 indicate several noteworthy relationships. First of all, firm value exhibits a positive correlation with ESG score (0.4875), TOBINSQ (0.3173), and CGI index (0.2952). So, it is clear that firms with higher ESG and corporate governance ratings tend to have greater market valuation. Also, a negative correlation exists between firm value and financial leverage (−0.0650), which indicates that excessive leverage may increase financial risk and reduce firm value. Finally, the relatively low correlation levels among the independent variables indicate that multicollinearity is unlikely to be a concern.

4.2. Multicollinearity Test

Multicollinearity is one of the critical challenges in regression analyses. It arises in regression analysis when independent variables exhibit strong linear relationships, leading to inflated standard errors, reduced coefficient reliability, and misleading statistical significance. This issue is commonly assessed using the Variance Inflation Factor (VIF) and its reciprocal (1/VIF), which measure how much the variance of a regression coefficient is inflated due to collinearity. The VIF formula is expressed as follows [90]:

$$VIF={\displaystyle \frac{1}{1-r_{2 3}^2}}$$

(6)

where $r_{2 3}^2$ represents the coefficient of determination between the independent variables $X_2$ and $X_3$. As $r_{2 3}^2$ approaches 1, the VIF value increases, indicating stronger collinearity and potential estimation issues. If there is no correlation between independent variables, the VIF equals 1, meaning multicollinearity is absent [90].

Table 6. Findings of the multicollinearity test.

Variables	VIF	1/VIF
LEV	1.55	0.645944
CR	1.35	0.742236
CGI	1.20	0.836309
ROA	1.19	0.838851
ESG	1.18	0.847668
TOBINSQ	1.04	0.960016
Mean (VIF)	1.25

presents the VIF values for all independent variables in the regression model. The VIF values range between 1.04 and 1.55, with a mean of 1.25. A VIF below 10 is generally considered acceptable, indicating that multicollinearity does not pose a concern [89]. Given that all VIF values are well below this threshold, the analysis confirms the absence of significant multicollinearity among the model variables.

4.3. Hausman Test

In panel data analysis, the Hausman test is used to determine whether a fixed effects or random effects model should be employed instead of the ordinary least squares (OLS) method [64,87]. The test compares the coefficient estimates of the fixed effects and random effects models to assess whether there is a systematic difference between them. The Hausman statistic is computed as follows [85]:

$$q=\widehat{\beta }-\tilde{\beta }$$

(7)

$$x^2={q^{\prime }(V\left(q\right)}^{-1})q$$

(8)

where $\widehat{\beta }$ represents the fixed effects estimators and $\tilde{\beta }$ denotes the random effects estimators. The term $q$ refers to the difference between these two estimators. Also, $V\left(q\right)$ represents the asymptotic variance-covariance matrices derived from both models [28].

Table 7. Findings of the Hausman test.

	Coefficients
	b	B	(b − B)	sqrt(diag(V_b − v_B))
	(fe)	(re)	Difference	S.E.
TOBINSQ	0.5044368	0.5025104	0.0019264	0.0123468
ESG	0.0227377	0.0291773	−0.0064396	0.0029186
CGI	0.4845882	0.4129578	0.0716304	0.0640319
LEV	−3.7315140	−3.2623620	−0.4691515	0.3398445
ROA	3.5259280	3.5833100	−0.0573811	0.3209526
CR	−0.4103157	−0.2775350	−0.1327808	0.0869690
chi2 (6)=	10.91
prob > chi2=	0.0912

presents the results of the Hausman test, which compares the fixed effects (FE) and random effects (RE) models. The test yields a chi-square (chi2) value of 10.91 with a prob > chi2 value of 0.0912. Since the p-value exceeds the 0.05 threshold, the null hypothesis (which favors the random effects model) cannot be rejected. Thus, the random effects model is the preferred specification for this analysis. To further verify whether random effects regression is appropriate compared to ordinary least squares (OLS), the Breusch and Pagan Lagrangian Multiplier (LM) test is conducted [27,87].

Table 8. Findings of Breusch and Pagan Lagrangian Multiplier Test for Random Effects.

	Var	sd = sqrt(Var)
Value	3.347225	1.829542
e	0.3627966	0.602326
u	2.000897	1.414531
tVar (u)chibar2 (01)	111.87
prob > chibar2	0.0000

presents the findings of the Breusch and Pagan Lagrangian Multiplier Test for Random Effects. The results confirm that the random effects model is more suitable for this dataset [87].

4.4. Autocorrelation Test

Autocorrelation can distort standard error estimates, which in turn compromises the validity of test statistics and p-values. As a result, testing for autocorrelation is essential to improve both the predictive accuracy of the model and the trustworthiness of the findings [66]. In this test, the null hypothesis assumes no autocorrelation.

Table 9. Findings of the autocorrelation test.

Value
Durbin–Watson	=	1.2494076
Baltagi–Wu LBI	=	1.9127959
Prob > F	=	0.0000

displays the outcomes of the autocorrelation test.

The autocorrelation test results indicate a p-value below 0.05, which supports the absence of serial correlation in the model. The finding is further reinforced by the Durbin–Watson and Baltagi–Wu LBI statistics, both of which suggest that autocorrelation is not present in the data [91].

4.5. Heteroskedastic Test

Heteroskedasticity occurs when the variance of residuals is not constant across observations, which can reduce the efficiency of regression estimates and distort standard errors. In this study, the issue was examined using F-tests and Gaussian distribution-based methods. Levene’s test (1960), later refined by Brown and Forsythe (1974) [86], offers a more robust approach by adjusting for outliers through alternative estimators centered around the mean. Accordingly, the Levene, Brown, and Forsythe tests are used to assess variance stability in the context of the random effects model. The formula is presented below:

$$W_0={\displaystyle \frac{\sum _i{n}_i\frac{{\left(\overline{Z_i}-\overline{Z}\right)}^2}{\left(g-1\right) } \sum _i }{\frac{\sum _i \sum _i \left(Z_{ij}-\overline{Z}\right)}{\sum _i{n}_i-1}}}$$

(9)

Here,

$X_{ij}$ represents the j’th observation of X within the i’th group,

$Z_{ij}=|X_{ij}-{\overline{X}}_i|$ is the absolute deviation of each observation of the group mean ${\overline{X}}_i$,

$\overline{Z}$ $=\mathrm{the}\mathrm{overall}\mathrm{mean}\mathrm{of}Z \mathrm{value}$,

$\overline{Z_i}$ $=\mathrm{the}\mathrm{group}-\mathrm{level}\mathrm{mean}\mathrm{of}\mathrm{variable}Z$,

$n_i$ is the number of observations,

$g_i$ is the number of groups.

Brown and Forsythe (1974) [86] proposed two tests for assessing heteroskedasticity. In the first test ($W_{50}$), the group mean ${\overline{X}}_i$ is replaced with the group median. In the second test ($W_{10}$), the group mean is replaced with the group’s 10% trimmed mean. Critical values for $W_0$ are determined using the Snedecor F distribution table, with degrees of freedom g−1 ve $\sum _i \left(n_i-1\right)$.

In the random effects model, the Levene, Brown, and Forsythe tests are conducted to assess heteroskedasticity. The results are as follows:

• $W_0$ = 1.61138545 (df(43, 120), Pr > F = 0.0229129)
• $W_{50}$ = 0.84412327 (df(43, 120), Pr > F = 0.73337762)
• $W_{10}$ = 1.61138545 (df(43, 120) ve Pr > F = 0.0229129)

According to the $W_0$ test statistic, the variances across groups are not equal. Therefore, the presence of heteroskedasticity in the model is confirmed, indicating variance instability.

4.6. Panel Data Random Effects GLS Method

To test the relationships among ESG, CGI, and firm value, panel data regression analysis was conducted using the random effects model, as determined by the Hausman test results. Additionally, to account for heteroskedasticity and potential autocorrelation issues, the robust variance estimator (heteroskedasticity-consistent standard errors) was applied in the analysis. The results of the panel data random effects GLS regression are presented in

Table 10. Findings of the panel data random effects GLS regressions.

${\mathit{r}}^2$	=	0.3143		Wald chi2(6)=	215.14
Number of obs.	=	164		Prob > chi2=	0.0000
Number of Companies	=	44
Value		Coef.	Robust Std. Error	z	p > |z|
TOBINSQ		0.5025104	0.0806132	6.23	0.000
ESG		0.0291773	0.007343	3.97	0.000
CGI		0.4129578	0.0933106	4.43	0.000
LEV		−3.262362	0.7070166	−4.61	0.000
ROA		3.58331	1.411669	2.54	0.011
CR		−0.277535	0.091481	−3.03	0.002
Cons.		−16.79137	8.849584	−1.90	0.050
Sigma_u		1.4145308
Sigma_e		0.60232602
Rho		0.84651287

.

The $r^2$ value (0.3143) suggests that approximately 31.43% of the variation in firm value is explained by the independent variables included in the model. Additionally, rho shows that 84.65% of the variance is attributed to differences between panels, indicating substantial variability across firms. The values for p > |z| of all independent variables indicate statistical significance, which means the variables have an impact on firm value.

A positive and significant relationship is observed between TOBINSQ and firm value. Since TOBINSQ measures the ratio of market value to asset replacement cost, this result implies that companies with higher TOBINSQ values tend to have stronger market valuations, as investors price their assets at a premium [80].

The results confirm a positive and significant impact of ESG scores on firm value. This suggests that, as companies improve their ESG performance, their market valuation also increases, which reflects investor confidence in sustainable business practices [15,23,27].

A positive and statistically significant relationship is found between CGI scores and firm value. This result aligns with the literature, suggesting that investors value companies with strong governance structures and transparency, which reduces investment risk and increases firm valuation [50,51,52].

A negative and significant relationship is found between financial leverage (lev) and firm value. This suggests that higher debt levels negatively affect market value, likely due to increased financial risk and diminished investor confidence [92]. In addition, taxes, as well as the cost of debt, affect investors’ confidence and decisions [93].

ROA exhibits a positive and significant effect on firm value, implying that firms with higher profitability tend to be more highly valued in the market, as they efficiently utilize their assets to generate earnings [94,95]. There is evidence that profitability has a significant and positive effect on firm value [96].

The findings indicate a negative and significant relationship between current ratio (CR) and firm value [95]. This result suggests that firms with excess liquidity may experience lower valuations due to inefficient capital allocation or reduced growth potential [97].

Although the GLS model accounts for heteroskedasticity, missing values in some variables may have affected the model’s overall robustness. In such cases, the Maximum Likelihood (ML) Estimation Method enables validation of the results [98].

Table 11. Findings of the panel data random effects ML regressions.

Log Likelihood	=	−213.16069		LR chi2(6)=	162.53
Number of obs.	=	164		Prob > chi2=	0.0000
Number of Companies	=	44
Value		Coef.	Std. Error	z	p > |z|
TOBINSQ		0.5026272	0.513203	9.79	0.000
ESG		0.028948	0.0060231	4.81	0.000
CGI		0.4159404	0.073569	5.65	0.000
LEV		−3.283202	0.6396908	−5.13	0.000
ROA		3.579101	1.0444331	3.43	0.001
CR		−0.2820691	0.117924	−2.39	0.017
Cons.		−16.99779	6.89283	−2.47	0.014
Sigma_U		1.429571	0.1675324	chibar2 (01) = 153.08
Sigma_E		0.5922509	0.0387208	LR Test Sigma_u = 0
Rho		0.8535099	0.0346036	Prob ≥ chibar = 0.0000

presents the ML findings.

The Prob ≥ chibar value of 0.000 confirms that the model is statistically meaningful. The effect of all independent variables on the dependent variable and the likelihood ratio (LR) test are statistically significant. Also, rho (0.8535) reinforces the conclusion that firm-specific effects are substantial. Since the p > |z| value is 0.000, all independent variables are considered significant predictors of firm value. Additionally, 85.35% of the variance is attributed to differences between panels, indicating substantial variability across firms. In an unbalanced panel, the weights depend on the length of the time series available for each unit, unlike in a balanced panel where all units have the same number of observations. The analysis results indicate that both the balanced and unbalanced panel models yield highly similar outcomes, with all independent variables maintaining their statistical significance.

5. Discussion

This study explores how CGI and ESG scores influence firm value, based on data from companies listed in the relevant segment of the Istanbul Stock Exchange (ISE). The results indicate that firms with stronger ESG performance usually have greater investor confidence and exhibit better financial performance, supporting earlier findings [15,27,28]. A possible reason for this finding is that alignment with ESG practices can lead to more efficient operations and stronger stakeholder engagement [23]. In some cases, high ESG scores can help firms to access capital at lower costs or improve compliance with regulatory frameworks [99,100]. Even so, the impact of ESG on firm value may not be the same in all cases. Some studies point out that this relationship can differ depending on the country or sector and recommend context-specific studies [5,29,101]. For example, ref. [32] reported that, in the natural resource sector, environmental scores outweighed social and governance dimensions in shaping market value. Similarly, ref. [102] emphasized that ESG factors do not always translate to financial efficiency unless embedded into core business strategies. These findings further reinforce the importance of developing integrated, context-aware models, also stated by [7], such as the one adopted in this study.

The findings also show that higher CGI scores are associated with increased firm value. Thus, this supports the argument that strong corporate governance mechanisms can improve investor trust and financial stability [50,51,52,103]. In other words, companies with robust governance structures are more likely to maintain transparent processes, uphold accountability, and demonstrate ethical leadership, which can lead to stronger firm valuation [47]. In addition to their direct impact, corporate governance mechanisms may also mitigate the reputational and financial risks associated with ESG controversies. For instance, refs. [61,62] found that board independence and gender diversity help buffer the negative effects of ESG controversies on firm value. In this regard, the CGI serves as a stabilizing internal mechanism that complements ESG strategies, particularly in environments in the emerging markets where stakeholder scrutiny and data disclosure inconsistencies remain high.

From a theoretical perspective, Agency Theory suggests that effective governance aligns managerial and shareholder interests, reducing internal conflicts [46]. The observed positive effect of ESG–CGI integration on firm value, particularly in mitigating risks such as greenwashing, supports this principle [40,41,42,43]. Stakeholder Theory emphasizes meeting the expectations of multiple constituencies. Combined ESG–CGI performance provides a more holistic signal of accountability, and can thus strengthen stakeholder trust and legitimacy, as emphasized in Stakeholder Theory [23]. The Resource-Based View (RBV) highlights the strategic value of intangible assets, including sustainability practices and governance quality, in securing long-term competitive advantage [24,25]. Firms that integrate both dimensions can maintain investor confidence and utilize their non-financial capital more effectively [59]. The explanatory power of ESG–CGI integration in relation to firm value arises from the influence of contextual factors such as regulatory oversight, legal enforcement, media independence, and institutional investor presence [6,104,105,106]; therefore, governance mechanisms must be assessed in light of institutional settings [106]. Accordingly, governance indicators tailored to national conditions can offer reliable signals, as a complement to standardized global metrics [12], since there is still room to improve ESG metrics, particularly in the context of developed versus developing countries, as institutional challenges and appropriate solutions vary widely by country [104,105,106,107]. Subsequently, this study contributes by jointly analyzing ESG and CGI, two indicators that are typically examined separately [12,74,75]. The findings show that both are significant and complementary in explaining firm value, supporting a more comprehensive and context-sensitive evaluation.

From a practical side, integrating ESG with a CGI, structured and monitored by government authorities, can prevent misleading disclosures, such as greenwashing [43]. As ESG scores are largely based on self-reported and often unaudited data, they can be vulnerable to symbolic disclosures that mask a lack of substantive action [40,41]. This issue is particularly concerning in emerging markets, where weaker institutional accountability may allow firms to attain high ESG scores without demonstrating corresponding internal environmental or social performance [43]. Along this line, governance-focused mechanisms like CGI, especially when tied to enforceable national frameworks, offer a verifiable and locally relevant complement to ESG assessments. Subsequently, combining both metrics enhances the robustness and credibility of the analysis, particularly in emerging markets.

Furthermore, the financial indicators in this study align with earlier findings that highlight how company performance metrics influence firm value. The positive effect of TOBINSQ suggests that investors place more confidence in firms whose firm valuation exceeds their asset replacement costs [108,109,110]. Similarly, return on assets (ROA) indicates that firms with higher profitability levels achieve better firm valuations [81,111].

On the other hand, the results show that leverage (LEV) has a negative association with firm value. Companies that carry excessive debt may be seen as financially fragile, which could lead to concerns among investors. In such cases, the risks tied to repayments and interest obligations might outweigh the expected benefits of using debt for growth [86]. While a moderate level of leverage can still be useful for improving returns, the findings suggest that going beyond a certain threshold may hurt a company’s market valuation, especially during times of uncertainty [83]. Additionally, the current ratio (CR), which measures liquidity, also shows a negative relationship with firm value. Although high liquidity is generally considered a sign of financial health, having excess liquid assets might give the impression that a firm lacks productive investment capabilities [84].

The findings are consistent with prior empirical research. For example, ref. [95] found a negative relationship between current ratio and firm value in the food and beverage sector of the IDX, indicating that high liquidity may reflect capital inefficiency. Likewise, ref. [78] confirmed that excessive leverage reduces firm value, particularly when the cost of capital outweighs strategic benefits. By incorporating these financial indicators alongside ESG and CGI, the model controls for firm-level financial risks that could otherwise bias sustainability-related interpretations.

These findings carry practical implications concerning policy-makers, investors, and corporate managers. In emerging markets such as Türkiye, regulatory bodies could implement more comprehensive regulations regarding disclosures to encourage more transparent and accountable firm behavior [66,112,113]. For instance, CMBT and the Borsa Istanbul can promote coherent and integrated frameworks [114,115]. For investors, the combined use of ESG and CGI metrics can provide better insight into a company’s governance strength. Thus, this helps reduce non-financial risks, while supporting responsible investment [116,117].

The implications suggest that corporate managers should align governance practices with sustainability goals. Such alignment can improve not only firm value but also the company’s ability to adapt to shifting market conditions [5]. In particular, larger firms often face greater scrutiny from both the public and institutional stakeholders [118]. While this attention may create pressure to improve ESG practices [119,120], it can also provide operational advantages through economies of scale [121]. However, failure of good governance initiatives at large firms can lead to severe reputational damage and even scandals, which can trigger stock price crashes [122,123]. Subsequently, maintaining strong ESG and CGI scores is vital, particularly for high-profile, publicly traded companies.

6. Conclusions

This study examines how ESG performance and CGI scores relate to firm value, using panel data from companies listed in the Corporate Governance Index on the Istanbul Stock Exchange (ISE). By incorporating both indicators within a single analytical framework, this study captures the complementary aspects of governance that ESG scores might overlook. Given that the CGI in Türkiye is developed by CMBT-authorized rating institutions, this ensures regulatory consistency and offers a robust measure of governance quality to complement ESG scores.

The results of the random effects panel data model (R² = 0.3143, p < 0.001) indicate that Tobin’s Q, ESG, and CGI scores are all positively and significantly associated with firm value. These findings suggest that firms demonstrating strong sustainability performance and governance practices are more likely to be favorably perceived by investors, particularly in emerging markets. Additionally, financial indicators such as return on assets (ROA) positively influence firm value, while financial leverage (LEV) and current ratio (CR) exhibit negative effects. These results imply that, while profitability enhances valuation, excessive debt and surplus liquidity may raise concerns regarding financial risk and inefficient capital allocation.

These findings contribute to the literature by developing an empirically grounded model that integrates ESG and CGI, aligning with theoretical perspectives such as the Resource-Based View, Agency Theory, and Stakeholder Theory. Moreover, given increasing concerns about the credibility of ESG disclosures and the risk of greenwashing, especially in less regulated environments, the inclusion of CGI enhances the reliability of sustainability assessments by anchoring part of the evaluation in verifiable national governance practices. This integrated approach enables a more comprehensive and context-aware analysis of non-financial firm performance.

6.1. Limitations

The primary limitation of this study lies in its sample scope. Although the Istanbul Stock Exchange (ISE) lists 681 publicly traded firms, only 74 are included in the Corporate Governance Index (CGI). Among them, 18 financial institutions were excluded due to their distinct regulatory and reporting structures, and 12 new entrant firms lacked sufficient CGI data, resulting in a final sample of 44 non-financial firms. This limited sample may constrain the generalizability of the findings. Additionally, while the study uses a multi-year panel and integrates key financial and non-financial indicators, it does not fully capture informal governance practices not reflected in formal CGI scores.

6.2. Further Studies

Given the limited empirical exploration of combined ESG and CGI frameworks, future research can extend the scope of this study in multiple directions. First, researchers could apply the proposed model to larger and more diverse samples, either by incorporating sectoral comparisons or by conducting cross-country analyses. A particularly promising approach would involve a longitudinal analysis comparing firm performance before and after inclusion in the Corporate Governance Index, thereby identifying causal impacts of governance compliance.

Second, the cost efficiency of achieving high CGI compliance could be examined by analyzing whether the additional financial or operational costs incurred by firms are offset by gains in market valuation or access to capital. This line of inquiry would be especially relevant for firms balancing regulatory demands with shareholder expectations.

Third, future studies may benefit from investigating the role of investor perception in mediating the ESG–CGI–value relationship. Surveys or behavioral data could shed light on how different investor groups interpret the materiality of non-financial metrics in their decision-making processes.

Finally, expanding the methodological toolkit to include dynamic panel data models (e.g., system GMM), structural equation modeling, or mixed-method approaches could enhance the analytical depth. Future research may also integrate qualitative content analysis of corporate disclosures or case-based comparative studies to explore how ESG and CGI implementation strategies vary across organizational contexts.