Sustainable Performance Drivers in Central and Eastern European IT Firms: A Multi-Theoretical and Empirical Analysis

Abstract

This study investigates the determinants of financial and market-based sustainability among listed Information Technology (IT) firms in Central and Eastern Europe (CEE) between 2018 and 2024. Drawing on Agency Theory, Stakeholder Theory, Resource-Based View Theory, Dynamic Capabilities Theory and Legitimacy Theory, it examines how leverage, profitability, growth and earnings quality shape firm performance and valuation outcomes. Using a balanced panel of 266 firm-year observations from Poland, Romania, Hungary and Croatia, the analysis applies fixed-effects Ordinary Least Squares (OLS) regressions with heteroscedasticity-robust (HC3) standard errors. The results reveal that lower leverage significantly enhances return on equity, confirming agency-based governance effects, while revenue growth and earnings per share (EPS) are strong positive predictors of profitability. On the contrary, rapid growth increases Stock Price Volatility, reflecting a risk–return trade-off typical of emerging technology markets. Market valuation ratios (P/E) show weak sensitivity to fundamentals, suggesting that investor confidence in CEE IT firms remains partially institutionally constrained. Overall, the findings emphasise that sustainable performance in transitional economies depends more on internal capability deployment and governance discipline than on market perception, highlighting the maturity gap between operational excellence and valuation transparency in the regional IT sector.

1. Introduction

1.1. CEE IT Landscape

The Information Technology (IT) sector has become one of the defining engines of economic modernisation and structural convergence across Central and Eastern Europe (CEE). Since the early 2000s, the region has undergone a rapid digital transformation fuelled by post-accession European Union (EU) investment, the diffusion of broadband infrastructure and the globalisation of software and data-driven services. According to Eurostat and the European Commission’s Digital Economy and Society Index (DESI), nearly all CEE economies have made substantial progress in digital public services, cloud adoption and e-commerce capacity between 2015 and 2024 [1,2]. At the same time, the IT sector now contributes between 5 and 8% of GDP in leading CEE economies and employs more than one million professionals across the region [3]. Countries such as Poland, Romania and the Czech Republic have emerged as export hubs for software development, cybersecurity and fintech services, while smaller markets like Croatia, Estonia and Slovenia increasingly specialise in AI applications, IT consulting and platform integration [4,5,6].

Despite these achievements, the CEE IT landscape remains heterogeneous. While Poland’s Warsaw Stock Exchange hosts several mid-cap technology listings, other exchanges, such as Bucharest and Budapest, have only recently developed liquidity for IT equities [7]. Many innovative firms remain privately held or venture-funded, creating an information gap between accounting performance and market valuation. Empirical analyses by the European Investment Bank and World Bank highlight that limited capital market depth and asymmetric information are persistent obstacles to digital sector scaling in the region [8,9]. Consequently, firm performance often depends not only on innovation capability, but also on governance quality, financing access and transparency, all of which condition the sustainability outcomes.

CEE economies differ noticeably from Western Europe in terms of institutional maturity and corporate governance enforcement. Transition-era privatisations and ownership concentration produced governance systems that sometimes lack minority investor protection [10]. In the IT sector, characterised by rapid intangible accumulation and high R&D intensity, these governance structures play a crucial role in aligning managerial incentives with long-term innovation objectives. Recent studies confirm that stronger corporate governance practices improve both accounting and market performance in CEE listed companies [11]. However, disparities remain: while Polish and Czech firms often comply with OECD governance standards, smaller markets show mixed adherence, affecting investor confidence and valuation stability [12].

Sustainability discourse has increasingly intersected with digital transformation in CEE. The integration of environmental, social and governance (ESG) criteria is accelerating, partly due to the EU Green Deal and the Corporate Sustainability Reporting Directive (CSRD). Research published in open-source indexed journals reports that ESG adoption in the region correlates with firm profitability and access to capital, but it remains uneven across industries [13,14,15]. For technology firms, the sustainability sequence extends beyond environmental impact to encompass digital ethics, data security and social innovation. The literature emphasises that ESG-aligned IT companies tend to exhibit higher resilience and reputation capital, translating into lower risk premiums and volatility [16].

Sustainability has become a defining determinant of corporate value and competitive positioning, especially in emerging and transitional markets across CEE. Recent findings demonstrate that firms with stronger ESG risk management and sustainability integration trade at valuation premiums, indicating that sustainability performance contributes to higher market credibility and reduces perceived investment risk [17,18]. On the contrary, firms with weaker ESG practices face valuation discounts, confirming that sustainability risk is financially material even where ESG adoption is still developing [17]. These dynamics underscore that sustainability is no longer a peripheral concern but an essential factor shaping investor behaviour and long-term financial outcomes.

Beyond investor perspectives, sustainability also influences firm performance through stakeholder channels, particularly customer behaviour. Empirical evidence shows that customers are more loyal to socially responsible firms and are willing to pay higher prices for products from companies that communicate credible sustainability actions [19]. Such behaviour directly enhances revenue stability and profitability, reinforcing Stakeholder Theory’s premise that sustainable practices generate shared value. However, customers’ favourable responses depend on transparency, credible reporting and mechanisms such as external assurance of sustainability disclosures. Firms that provide assured corporate social responsibility or sustainability reports benefit more strongly from customer-driven financial gains, demonstrating that disclosure quality and accountability strengthen the sustainability–performance link [19].

At a broader ecosystem level, sustainability has become tightly integrated with innovation and technological development. Deep tech ecosystems characterised by breakthrough scientific advances are emerging as critical enablers of sustainable solutions, supporting the digital and green transitions simultaneously. European economies increasingly emphasise that investment in scientific infrastructure, R&D and advanced technology is fundamental for sustainable development and resilience [20]. However, fully harnessing deep tech for sustainability requires supportive governance, financing mechanisms and regulatory frameworks that accelerate commercialisation and reduce technological risk. These conditions are particularly relevant for countries such as Poland, where deep tech innovation is growing rapidly but faces persistent structural barriers [20].

The global policy environment reinforces this trend. The International Platform on Sustainable Finance outlines an international shift toward interoperable taxonomies, transition finance standards, biodiversity integration and strengthened Do-No-Significant-Harm (DNSH) requirements, signalling that sustainability considerations are becoming embedded in global financial regulation [21]. These initiatives contribute to harmonising sustainability criteria across jurisdictions and strengthen the expectations placed on firms to manage climate and environmental risks responsibly.

Finally, sustainability governance has emerged as a foundational enabler of credible transition strategies. Strong governance, integrating structure, culture, oversight and accountability is increasingly seen as the essential mechanism through which environmental and social ambitions can be translated into real outcomes [22,23]. Without robust governance, ESG efforts risk fragmentation, symbolic compliance or greenwashing. Institutions such as the European Bank for Reconstruction and Development stress that governance capabilities are now prerequisites for resilience, competitive positioning and market access, particularly amid geopolitical uncertainty and escalating climate risks [22]. Recent analyses also highlight that governance failures frequently underpin ESG breakdowns, reinforcing the need for integrated, non-siloed governance systems [23].

In this study, the concept of sustainable performance refers to the financial and market-based capacity of firms to generate stable, resilient outcomes over time. Due to the limited availability and inconsistency of ESG disclosures among CEE IT firms during 2018–2024, sustainability is operationalised through financial sustainability (ROA and ROE) and market sustainability (P/E ratio and volatility). This follows prior emerging market research where financial and market indicators are considered valid proxies for sustainability resilience in environments characterised by lower reporting transparency and institutional volatility.

1.2. Research Gap and Contribution

Against this backdrop, the present study addresses two primary research gaps:

• The absence of integrated, multi-theory testing of financial and sustainability drivers among CEE IT firms;
• The need to reconcile accounting-based and market-based measures of performance within transitional institutional contexts.

By merging theories of governance, capability and legitimacy into one empirical model, this paper advances understanding of how internal efficiency and external perception jointly determine sustainability. Methodologically, it employs outlier-adjusted panel regressions with fixed effects and heteroscedasticity-robust errors. Mainly, it clarifies whether the mechanisms that drive profitability also foster valuation stability, thereby bridging the “performance-legitimacy” gap in CEE capital markets.

The aim of this study is to identify the financial and market drivers of sustainable performance in Central and Eastern European IT firms, using a multi-theoretical framework integrating Agency Theory, Stakeholder Theory, Resource-Based View Theory, Dynamic Capabilities and Legitimacy theories. By combining these perspectives with panel-data econometrics, the study provides a comprehensive explanation of performance and valuation outcomes in a rapidly transforming regional sector.

2. Literature Review and Theoretical Background

2.1. Theories Analysed

To explain performance and sustainability in this context, five interrelated theories are relevant. Agency Theory postulates that effective governance and reduced leverage lower agency costs and enhance profitability [24]. In emerging markets, where monitoring mechanisms are weaker, financial discipline (e.g., lower debt ratios) is often a proxy for governance quality [25]. Stakeholder Theory extends performance assessment beyond shareholders to include employees, customers and communities, arguing that firms balancing stakeholder expectations achieve more durable success [26]. The Resource-Based View Theory links unique, inimitable assets, such as intellectual capital and R&D capacity, to sustain competitive advantage [27]. In IT, human capital and software IP are important strategic resources [28]. Dynamic Capabilities Theory explains how firms adapt to turbulence by sensing and reconfiguring resources; this flexibility is especially vital in fast-changing technological markets [29]. Legitimacy Theory emphasises the social contract between corporations and their environments: consistent financial performance, transparency and ESG behaviour enhance legitimacy and investor trust [30].

These perspectives complement each other. Agency Theory and Resource-Based View Theory explain internal efficiency; Stakeholder, Dynamic Capabilities and Legitimacy theories address adaptation and external validation. Together they form the conceptual foundation for analysing how governance, resource utilisation and market interaction influence sustainable performance in the CEE IT sector.

The recent empirical literature supports many of these theoretical linkages. Achim et al. [11] found that Romanian firms with higher governance quality exhibit superior intellectual capital efficiency and valuation multiples. Buglea et al. [12] demonstrated that digital transformation enhances non-financial performance in CEE countries, especially when paired with robust management systems. Similarly, Dobos [31] reported that technological progress and structural reforms explain much of the variance in competitiveness and productivity across the region. At the macro level, studies reveal that IT investment intensity contributes significantly to GDP growth and total factor productivity in new EU member states [32]. However, the returns to such investment depend strongly on institutional readiness and innovation ecosystems. In countries where venture capital, education and research linkages are stronger, IT firms achieve higher performance persistence.

Despite ample research on innovation and productivity, few studies link accounting-based performance with market-based sustainability in the CEE IT context. Western evidence suggests that profitability and growth typically command valuation premiums [33], but emerging market investors often discount these signals due to perceived risk and transparency issues [34]. For instance, Bekaert and Harvey [35] show that emerging market equity volatility remains structurally higher even after controlling the fundamentals.

Furthermore, the transition to digital business models introduces new valuation dynamics. Firms with strong intangibles may underperform in short-term profitability while creating long-term option value. This asymmetry complicates sustainability assessment if investors focus on traditional metrics.

2.2. Hypotheses Development

Drawing on the five theoretical lenses discussed above, the following hypotheses are formulated to test the expected relationships between firm-level characteristics and sustainable performance outcomes:

Hypothesis 1. (Agency Theory). 

Higher leverage (DE ratio) is negatively associated with profitability (ROA and ROE).

Hypothesis 2. (Resource-Based View). 

Firms with higher earnings per share (EPS) achieve higher profitability (ROA and ROE).

Hypothesis 3. (Dynamic Capabilities). 

Revenue growth positively influences profitability but increases market risk, leading to higher Stock Price Volatility.

Hypothesis 4. (Stakeholder Theory). 

Higher profitability and operational margins are positively associated with market-based valuation (P/E ratio).

Hypothesis 5. (Legitimacy Theory). 

More stable performance (ROE) is rewarded by higher market valuations (P/E ratio), reflecting investor confidence and perceived organisational legitimacy.

These hypotheses guide the empirical models presented in the next section.

3. Materials and Methods

The methodological framework of this study was developed to empirically evaluate how organisational performance theories explain financial and sustainability outcomes in the CEE IT sector. The analysis was designed as a quantitative, theory-driven, hypothesis-testing approach, combining panel data econometrics with theoretical testing.

The empirical model draws on five theoretical foundations: Agency Theory, Stakeholder Theory, the Resource-Based View Theory, Dynamic Capabilities Theory and Legitimacy Theory. Each theory generates testable hypotheses linking specific financial variables (e.g., leverage, profitability, earnings, volatility) to sustainable performance outcomes. The purpose of this section is to describe the methodological steps through which these hypotheses were statistically examined.

3.1. Data Used

The dataset consists of 266 firm-year observations covering the period 2018–2024, encompassing listed IT companies from Poland, Romania, Hungary and Croatia. These firms were selected from official stock exchange filings and financial databases to ensure transparency and comparability of data. Romania contributes the largest share of observations, consistent with its growing cluster of publicly traded technology firms.

The period 2018–2024 was selected because it captures three distinct phases relevant to IT sector performance: the post-digitalisation expansion in CEE, the COVID-19 pandemic shock and the subsequent economic recovery. This provides a balanced and representative time frame for assessing sustainable financial and market outcomes.

The four included countries (Poland, Romania, Hungary and Croatia) were chosen because they are the only CEE markets with a sufficient number of publicly listed IT firms on the main markets, reporting complete financial and market data across the entire study period, enabling the construction of a balanced panel dataset. Because listed firms have better disclosure than unlisted ones, for the sole purpose of availability of data, only the listed firms were chosen for analysis.

The use of a multi-country panel structure allows both cross-sectional and temporal variability to be captured, an essential feature for identifying dynamic patterns in firm behaviour and market valuation.

All variables used in the analysis were derived from standard financial statements and market data, ensuring alignment with prior literature on firm performance [12,13,14,15], as they have been provided by the Refinitiv Eikon database. This ensures standardised reporting between firms and countries.

Dependent variables are:

• ROA (Return on Assets), which measures asset efficiency and overall profitability.
• ROE (Return on Equity), which reflects shareholder value creation and capital efficiency.
• P/E (Price-to-Earnings Ratio), which serves as a proxy for market-based sustainability, indicating the extent to which profitability is capitalised into firm value.
• Volatility (Stock Price Volatility), which captures market risk and investor confidence.

Independent variables are:

• DE (Debt-to-Equity Ratio), which is a proxy for financial leverage and governance discipline.
• Growth (Revenue Growth Rate), which is an indicator of expansion and dynamic capabilities.
• NPM (Net Profit Margin), which is an operational efficiency measure.
• EPS (Earnings per Share), which is the reflection of internal capability utilisation and value creation.

Control dummies for year (γ_t) and country (δc) were introduced to capture unobserved heterogeneity stemming from macroeconomic cycles or national institutional differences.

Each dependent variable is modelled separately, and variables such as ROE and Volatility appear as regressors only in models where they are conceptually justified.

3.2. Data Processing

Financial data in emerging market contexts frequently contains extreme values due to firm size heterogeneity, market shocks and reporting anomalies. To mitigate distortion from outliers, this study applied outlier management at the 1st and 99th percentiles, a robust data-trimming technique recommended for financial econometrics [14].

The outlier data transformation is expressed as:

$$X_{it}^{*}=\{\begin{array}{cc}{Q}_{0.01}\left(X\right),& X_{it}<Q_{0.01}\left(X\right)\\ Q_{0.99}\left(X\right),& X_{it}>Q_{0.99}\left(X\right)\\ X_{it},& \mathrm{otherwise}\end{array}$$

where $Q_{0.01}\left(X\right)$ and $Q_{0.99}\left(X\right)$ denote the empirical 1st and 99th percentiles of variable $X$.

This approach preserves all observations and limits the leverage of extreme values on estimated coefficients.

Descriptive statistics (mean, standard deviation and range) were computed before and after outlier-adjusting data to verify the stability of central tendencies and to ensure that the procedure primarily affected tails rather than the distribution core.

Four regression equations were estimated to test the theoretical relationships among performance indicators and sustainability outcomes. Each model includes fixed effects for year (γ_t) and country (δc) to account for unobserved heterogeneity:

• Model 1 (ROA):

  $$RO{A}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}Growt{h}_{it}+{\beta }_{3}NP{M}_{it}+{\beta }_{4}EP{S}_{it}+{\gamma }_t+{\delta }_c+{\epsilon }_{it}$$
• Model 2 (ROE):

  $$RO{E}_{it}={\beta }_0+{\beta }_{1}D{E}_{it}+{\beta }_{2}Growt{h}_{it}+{\beta }_{3}NP{M}_{it}+{\beta }_{4}EP{S}_{it}+{\gamma }_t+{\delta }_c+{\epsilon }_{it}$$
• Model 3 (P/E):

  $$P{E}_{it}={\beta }_0+{\beta }_{1}Growt{h}_{it}+{\beta }_{2}NP{M}_{it}+{\beta }_{3}RO{E}_{it}+{\beta }_{4}EP{S}_{it}+{\beta }_{5}Volatilit{y}_{it}+{\gamma }_t+{\delta }_c+{\epsilon }_{it}$$
• Model 4 (Volatility):

  $$Volatilit{y}_{it}={\beta }_0+{\beta }_{1}Growt{h}_{it}+{\beta }_{2}NP{M}_{it}+{\beta }_{3}D{E}_{it}+{\gamma }_t+{\delta }_c+{\epsilon }_{it}$$

These specifications allow hypothesis testing across multiple theoretical perspectives simultaneously. Parameters ${\beta }_1...{\beta }_k$ capture the marginal effects of independent variables on firm performance, while ε_it represents an idiosyncratic error term assumed to have zero mean and finite variance.

All models were estimated using Ordinary Least Squares (OLS) with heteroscedasticity-consistent standard errors (HC3), which provide superior small-sample properties in panel settings. The HC3 version is used for finite-sample panel data because it provides improved small-sample correction relative to the original HC0 formulation.

Formally, the covariance matrix of estimated coefficients is computed as:

$${\widehat{V}}_{HC3}=(X^{\prime }X{)}^{-1}(\sum _i x_i{x}_i^{\prime }{\displaystyle \frac{{\widehat{\epsilon }}_i^2}{(1-h_{ii}{)}^2}})(X^{\prime }X{)}^{-1}$$

where $h_{ii}$ denotes the diagonal elements of the hat matrix and ${\widehat{\epsilon }}_i$ represents model residuals.

We included fixed effects for year and country (as categorical dummy variables) controls for common shocks, such as macroeconomic cycles or policy changes [15]. This fixed-effects approach improves internal validity by isolating within-group variation across firms and time periods.

The empirical analysis used a dual-environment approach combining SPSS and Python:

(1)

SPSS Statistics 29 was used for:

• Initial data inspection and descriptive statistics;
• Correlation matrices and normality diagnostics;
• Cross-tabulations by country and year to identify structural gaps.

(2)

Python 3.11 was used for:

• Data preprocessing via the pandas library (handling of missing values, outlier-adjust the data and merging of multi-year datasets);
• Estimation using the statsmodels package’s OLS function with the cov_type = ‘HC3’ option to produce robust standard errors;
• Encoding of fixed effects through dummy variables for year and country;
• Automated export of results to an Excel workbook for reproducibility.

Several post-estimation tests were conducted to ensure robustness:

• Multicollinearity: Variance Inflation Factors (VIFs) were computed. All variables remained below the conventional threshold of 10, suggesting no critical multicollinearity;
• Heteroscedasticity: White’s test confirmed the presence of heteroscedasticity; hence, HC3 standard errors were maintained throughout;
• Influence Analysis: Cook’s D and leverage plots were inspected. No single observation exceeded the 4/n rule, confirming stability of results;
• Alternative Specifications: Models were re-estimated excluding pandemic period observations (2020–2021) to check for structural breaks. Coefficient signs and significance remained largely consistent;
• Cross-validation: Subsample regressions by country confirmed that the direction of relationships, particularly the negative effect of DE on ROE and the positive effect of Growth on ROE, held across markets, though magnitudes varied.

In combination, the dataset design, econometric specification and robustness procedures ensure that the estimated relationships are both statistically reliable and theoretically interpretable. The mixed use of accounting and market variables enables triangulation between internal efficiency and external valuation, offering a holistic assessment of sustainability in the IT sector.

The methodological rigor rests on three pillars:

(1)

Data reliability is ensured through verified, audited financial statements and outlier-adjusted data processing;

(2)

Model validity is supported by fixed effects and robust standard errors;

(3)

Replicability is offered by an open-source computational environment.

These measures align with widely accepted best practices in empirical corporate finance and comparative management research. The methodological structure thus provides a robust foundation for the hypothesis testing and interpretation of results presented in subsequent sections.

3.3. Variables Calculation

The following variables were imported from Refinitiv Eikon: Return on Assets, Return on Equity, Net Profit Margin, EPS, P/E Ratio, Revenue Growth Rate, Debt-to-Equity Ratio and Stock Price Volatility.

4. Results

4.1. Descriptive Statistics and Correlations

Table 1. Descriptive Statistics (outlier-adjusted data, 2018–2024).

Variable	Description	N	Mean	Std. Deviation	Min	Max
ROA	Return on Assets (%)	266	14.9	11.6	–5.8	48.5
ROE	Return on Equity (%)	266	13.0	10.8	–7.4	45.2
NPM	Net Profit Margin (%)	266	5.2	3.9	–1.5	18.4
EPS	Earnings per Share (currency units)	266	1.67	1.18	–0.11	6.47
DE	Debt-to-Equity Ratio (%)	266	33.3	27.1	0.0	138.5
Growth	Revenue Growth Rate (%)	266	16.6	14.8	–5.9	61.7
P/E	Price-to-Earnings Ratio	229	21.5	18.2	2.8	89.5
Volatility	Stock Price Volatility (annualised)	229	0.35	0.22	0.05	0.91

Source: Authors’ own analysis.

presents the descriptive statistics for the outlier-adjusted dataset (1st–99th percentiles). Currency values are standardised by each firm’s reporting denomination.

The mean ROA of 14.9% and ROE of 13.0% suggest moderate profitability across the sampled IT firms. However, wide standard deviations for both metrics indicate significant heterogeneity in firm performance, reflecting differences in maturity, capital intensity and internationalisation strategies among CEE IT firms.

The NPM median of roughly 5% reveals lean operational margins, consistent with the sector’s project-based nature and high wage cost structures. EPS averages 1.67, again with a large spread, implying that some firms exhibit extremely high per-share profitability, possibly due to one-off gains or low share-float effects.

The DE ratio averages 33.3% but ranges widely, from near zero to more than 400%, illustrating that some firms remain virtually debt-free while others leverage heavily for expansion. Growth shows an average of 16.6%, an indication of an expanding technology market. The P/E ratio averages 21.5, suggesting moderate market optimism, while Stock Price Volatility averages 0.35, indicating noticeable but not excessive risk exposure.

These statistics portray a sector in transformation: profitable yet volatile, innovative but heterogeneous. Such distributional patterns support the econometric strategy using robust standard errors and fixed effects to isolate true relationships from noise.

Preliminary Pearson correlations are presented in

Table 2. Correlation matrix (Pearson coefficients).

Variable	1	2	3	4	5	6	7	8
1. ROA	1.00
2. ROE	0.59 ***	1.00
3. NPM	0.21 **	0.25 **	1.00
4. EPS	0.33 ***	0.29 ***	0.18 *	1.00
5. DE	–0.08	–0.12 *	–0.06	0.02	1.00
6. Growth	–0.04	0.17 *	0.27 ***	0.09	0.03	1.00
7. PE	0.05	0.11	0.09	–0.03	–0.02	0.06	1.00
8. Volatility	–0.07	–0.09	–0.04	–0.06	0.05	0.29 ***	–0.10	1.00

Source: Authors’ own analysis. Note: * p < 0.10; ** p < 0.05; *** p < 0.01.

and reveal several interesting associations. ROA and ROE are highly correlated (r ≈ 0.59, p < 0.01), confirming internal consistency between the two profitability measures.

DE correlates negatively with ROE (r ≈ −0.12, p ≈ 0.08), indicating that more leveraged firms tend to deliver lower equity returns, a first hint supporting Agency Theory.

Surprisingly, Growth correlates positively with both Volatility (r ≈ 0.29, p < 0.01) and NPM (r ≈ 0.27, p < 0.01), implying that expansion is often accompanied by higher operating margins but also by greater market risk. P/E exhibits weak or inconsistent correlations with financial fundamentals, suggesting that market valuation is influenced by factors beyond immediate profitability, such as investor sentiment, listing liquidity or macroeconomic events.

4.2. Regression Results

4.2.1. Model 1 on Determinants of ROA

To evaluate how firm-level financial characteristics influence operational profitability, Model 1 examines the determinants of ROA. The regression equation is presented as follows:

$$RO{A}_{it}={\beta }_0-0.075 D{E}_{it}-0.115 Growt{h}_{it}+0.022 NP{M}_{it}+5.087 EP{S}_{it}$$

$$(R^2=0.27; \mathrm{Adj}.R^2=0.23)$$

The model explains roughly a quarter of the variance in ROA after controlling for country and year effects. EPS emerges as a strong positive predictor (β = 5.09, p = 0.025). Each unit increase in EPS corresponds, on average, to a five-point increase in ROA. This is consistent with the Resource-Based View Theory, which links internal capabilities and intangible assets to superior profitability [5].

NPM is weakly positive (β = 0.022, p = 0.07), indicating that operational efficiency contributes modestly to asset returns. In contrast, DE and Growth have negative but statistically insignificant coefficients. The absence of a significant growth effect on ROA suggests that expansion may dilute asset productivity in the short run, particularly when firms reinvest heavily in R&D or human capital without immediate payoff.

These findings collectively support Resource-Based View Theory’s emphasis on capability deployment while indicating that aggressive growth may temporarily suppress accounting profitability.

4.2.2. Model 2 on Determinants of ROE

This model extends the analysis by focusing on ROE, capturing how leverage, revenue growth, operational efficiency and earnings capability affect shareholders’ returns. The regression equation is presented as follows:

$$RO{E}_{it}={\beta }_0-0.158 D{E}_{it}+0.318 Growt{h}_{it}+0.002 NP{M}_{it}+1.634 EP{S}_{it}$$

$$(R^2=0.29; \mathrm{Adj}.R^2=0.25)$$

This specification explains nearly 30% of the variation in ROE. The negative coefficient of DE (β = −0.158, p = 0.045) provides strong evidence for Agency Theory [3]: firms that rely less on debt achieve higher returns on equity. In CEE IT markets, where bank financing remains relatively expensive and capital markets are developing, this outcome suggests that lower leverage mitigates agency conflicts and interest burdens, enhancing shareholder returns.

The positive and highly significant Growth coefficient (β = 0.318, p = 0.002) confirms Dynamic Capabilities Theory [6]. Firms that adapt and expand revenue bases effectively transform dynamic opportunities into profitability gains. This association also supports the notion that learning and innovation processes are critical to value creation in fast-changing technological environments.

EPS again shows a robust positive effect (β = 1.63, p = 0.002), reinforcing Resource-Based View Theory arguments that superior internal resource utilisation, like intellectual capital, skilled labour and proprietary technology, translates directly into shareholder wealth.

The NPM variable remains insignificant, implying that pure operational efficiency does not necessarily raise equity returns when strategic factors such as growth and resource leverage are controlled. Overall, Model 2 yields the most theoretically coherent pattern: governance discipline (low DE), dynamic adaptation (Growth) and resource effectiveness (EPS) jointly drive equity performance.

4.2.3. Model 3 on Determinants of Market Sustainability (P/E Ratio)

This model explores the drivers of market-based sustainability by analysing the P/E ratio, which assesses whether profitability, growth, operational margins, earnings strength and Price Volatility translate into higher market valuations. The regression equation is presented as follows:

$$P{E}_{it}={\beta }_0-0.124 Growt{h}_{it}+0.0066 NP{M}_{it}+0.080 RO{E}_{it}-0.215 EP{S}_{it}-2.044 Volatilit{y}_{it}$$

$$(R^2=0.04; \mathrm{Adj}.R^2=-0.03)$$

This model’s explanatory power is low, indicating that market valuation ratios in CEE IT firms depend heavily on external, possibly behavioural factors rather than accounting fundamentals. Nevertheless, several patterns are noteworthy.

ROE is marginally positive (β = 0.080, p = 0.06), suggesting that profitability is weakly priced into valuation, providing tentative support for Legitimacy Theory [7]. Higher profitability appears to signal corporate credibility, yielding slightly higher P/E multiples. NPM shows a near-significant positive effect (β = 0.0066, p ≈ 0.10), partially validating Stakeholder Theory [4], where efficient firms may be perceived as more sustainable.

On the contrary, Growth and Volatility have negative but insignificant coefficients. This result contrasts with findings in mature markets where growth typically commands valuation premiums. In CEE, rapid expansion may raise uncertainty about earnings persistence, leading investors to apply a discount.

Finally, EPS enters negatively but insignificantly. This counterintuitive sign might reflect denominator effects, where high earnings can mechanically reduce the P/E ratio, or investor scepticism toward short-term earnings spikes.

Collectively, Model 3 underscores that market-based sustainability in the CEE IT context is less tied to financial metrics and more influenced by institutional credibility, liquidity and investor sentiment. It echoes prior research showing that in emerging markets, valuation ratios are often driven by macro factors rather than micro-level fundamentals [2,11].

4.2.4. Model 4 on Determinants of Stock Price Volatility

This model investigates the determinants of Stock Price Volatility, providing insight into how growth dynamics, operational efficiency and leverage influence market risk. Volatility is treated as the principal market risk indicator in this study. Volatility reflects both systematic shocks and firm-specific uncertainty. In emerging markets such as CEE, higher volatility typically signals elevated information asymmetry, lower liquidity and higher perceived risk, which makes it a relevant component of sustainable market performance. The regression equation is presented as follows:

$$Volatilit{y}_{it}={\beta }_0+0.0015 Growt{h}_{it}+0.00008 NP{M}_{it}-0.0004 D{E}_{it}$$

$$(R^2=0.34; Adj.R^2=-0.03)$$

The model explains one-third of total variation in volatility, a relatively high proportion for financial risk data. Growth is the only significant driver (β = 0.0015, p = 0.03), implying that firms with faster revenue expansion experience greater share price fluctuations. When ROE is included as an additional regressor, its coefficient becomes negative but remains insignificant (β = −0.0007, p = 0.29). This suggests that stable profitability provides some dampening effect but is insufficient to offset the volatility induced by rapid growth.

These findings suggest that the growth–risk trade-off dominates in CEE IT markets: investors reward innovation, but also price in uncertainty about scalability and earnings persistence. The lack of a significant leverage effect (DE) implies that capital structure plays a minor role in short-term price dynamics; instead, expectations about future growth drive market volatility. This is consistent with empirical evidence on emerging market technology stocks, where informational asymmetries amplify price reactions to earnings announcements and macro shocks [2,11].

A summary of the regression results for all four models is presented in

Table 3. Summary of Results.

Variables	ROA	ROE	P/E	Volatility
DE	−0.075 (0.052)	−0.158 ** (0.078)	—	−0.0004 (0.0005)
Growth	−0.115 (0.092)	0.318 *** (0.098)	−0.124 (0.086)	0.0015 ** (0.0007)
NPM	0.022 (0.013)	0.002 (0.017)	0.0066 (0.0040)	0.00008 (0.00008)
EPS	5.087 ** (2.241)	1.634 *** (0.545)	−0.215 (0.285)	—
ROE	—	—	0.080 * (0.043)	−0.0007 (0.0007)
Volatility	—	—	−2.044 (9.173)	—
Constant	11.25 (4.31) **	9.82 (3.88) **	17.6 (9.7) *	0.18 (0.07) **
Year FE/Country FE	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes
R2/Adj. R2	0.27/0.23	0.29/0.25	0.04/−0.03	0.34/0.30
Observations	266	266	229	229

Source: Authors’ own analysis. Notes: Robust standard errors (HC3) in parentheses; All models include fixed effects for year and country; * p < 0.10; ** p < 0.05; *** p < 0.01.

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5. Discussion

The most consistent empirical support is for Agency Theory and the Resource-Based View Theory. The negative association between leverage and ROE affirms that reducing debt exposure enhances shareholder alignment and performance. This mirrors findings from prior governance studies in CEE [8], where boards emphasising financial transparency and lower debt dependency achieved superior returns.

These results are consistent with Achim et al. (2023) [11], who found that governance discipline and financial structure play central roles in performance across emerging European firms. Similarly, Pistor et al. (2023) [10] emphasise that weak investor protection and limited transparency in transition economies constrain valuation efficiency.

The weak association between fundamentals and market valuation (P/E) aligns with findings by Kluza et al. (2025) [17], who report that emerging markets often display valuation inefficiencies due to lower liquidity, limited analyst coverage and narrower investor bases. This is consistent with the structural characteristics of CEE stock exchanges, where information asymmetry and low trading volumes can distort market signals.

The Resource-Based View Theory evidence is robust: EPS positively influences both ROA and ROE at high significance. This underscores the fact that the deployment of intellectual capital and technological know-how remains the strongest performance lever for IT firms. It also implies that internal capability building, and not short-term cost efficiency, determines sustainable profitability.

Dynamic Capabilities Theory receives partial validation: firms pursuing growth realise higher returns but face rising volatility. This trade-off aligns with Karanikic (2022) [6], who argued that dynamic renewal incurs risk but is essential for long-term advantage. In transitional markets, limited institutional depth exacerbates this tension: innovation fosters competitive advantage but invites greater uncertainty.

Stakeholder and Legitimacy perspectives receive weaker support. Marginally positive effects of NPM and ROE on P/E suggest that investors value efficiency and profitability, but the explanatory power remains low. This may reflect incomplete ESG adoption and information asymmetry across CEE exchanges. Investors in emerging markets often rely on heuristic signals rather than structured sustainability metrics.

5.1. Geographical and Sectorial Interpretation

The divergence between accounting and market measures is a central insight of this study. While internal profitability (ROA/ROE) responds predictably to leverage and capability variables, external valuation (P/E) remains weakly connected to fundamentals. This split highlights that CEE equity markets have not yet fully priced in corporate efficiency and innovation outcomes, echoing institutional patterns identified by Kluza et al. (2025) [17].

Another noteworthy finding is the positive relationship between Growth and Volatility. High Growth increases both expected returns and uncertainty. In CEE’s developing capital markets, information diffusion is slower and investors may over-react to limited signals, producing amplified price swings. As such, Volatility here reflects both genuine risk and information inefficiency.

Country-level fixed effects reveal some heterogeneity (not tabulated): Polish and Hungarian firms tend to display more stable profitability, while Romanian firms exhibit faster growth but higher volatility. These variations mirror national differences in capital market depth and corporate governance enforcement.

For corporate managers, the results highlight three actionable insights:

(1)

Control leverage, as maintaining moderate debt ratios enhances equity returns and investor confidence;

(2)

Invest in capability development, as intellectual capital utilisation and innovation directly translate into profitability;

(3)

Balance growth with risk management, as aggressive expansion should be coupled with communication and transparency strategies to mitigate volatility.

For investors, the findings underscore that traditional valuation ratios in CEE IT markets may not yet fully reflect fundamentals; thus, due diligence on earnings quality and governance is crucial.

For policy makers, the weak linkage between fundamentals and valuation reinforces the need to strengthen disclosure standards, promote analyst coverage and enhance market liquidity. These steps would reduce information asymmetry and stabilise investor expectations.

5.2. Conceptual Implications and Limitations

The present findings resonate with Achim et al. (2023) [11], who report positive links between governance quality and performance, and with World Bank (2023) [9], who observe benefits of digital transformation on non-financial outcomes. However, this study adds value by explicitly quantifying how these effects are transmitted through financial channels and how they interact with market risk.

Compared with Western European evidence, where high growth typically lowers perceived risk, CEE results indicate a risk premium on growth, similar to patterns observed in other emerging markets [2,11]. This emphasises that sustainability perceptions evolve with market maturity and institutional credibility.

The cross-model synthesis reveals a dual-equilibrium system in CEE IT firms:

(1)

Internal equilibrium, driven by governance and resource deployment (Agency and Resource-Based View theories), leading to consistent profitability;

(2)

External equilibrium, mediated by investor perception (Legitimacy, Stakeholder and Dynamic Capabilities theories), characterised by valuation and volatility fluctuations.

Bridging these two requires alignment between managerial actions and market communication. Firms that effectively disclose innovative strategies and risk controls are more likely to convert operational excellence into valuation stability.

Although robust techniques were applied, certain caution remains. Heteroscedasticity and limited sample size constrain the power of hypothesis testing; additionally, missing data for some market variables (P/E, Volatility) reduce model N. The absence of ESG or qualitative indicators limits full evaluation of stakeholder and legitimacy effects. Nevertheless, the convergence of multiple models on consistent signs and significance levels enhances confidence in the results’ validity.

The empirical findings provide strong support for Hypotheses 1 and 2, partial support for Hypotheses 3 and 5, and limited support for Hypothesis 4. This pattern suggests that financial sustainability is more strongly driven by internal governance and capability factors, while market-based sustainability remains shaped by external institutional and perceptual dynamics.

To consolidate the multi-theoretical interpretation of the empirical findings, Appendix A provides a cross-theory synthesis comparing the expected relationships derived from Agency, Stakeholder, Resource-Based View, Dynamic Capabilities and Legitimacy theories with the observed regression results. This structured comparison highlights where theoretical predictions are fully supported, partially supported or contradicted in the CEE IT context.

Future research may incorporate ESG scores as more consistent disclosures emerge across CEE markets, enabling a full sustainability assessment. Additional work could expand the sample to include unlisted firms, analyse ownership structures or incorporate nonlinear models such as random forests or neural networks. A promising direction is to examine post-CSRD reporting changes and their effects on transparency, valuation and performance in the IT sector.

6. Conclusions

Overall, the expanded results confirm that financial discipline and capability strength are the principal sustainability drivers in CEE IT firms. These internal levers explain performance far better than market valuations do. The findings demonstrate that sustainability in transitional technology sectors is not yet market-priced; it must first be built internally through governance, innovation and adaptive growth strategies.

The coexistence of strong profitability with elevated volatility captures the transitional nature of the region: firms are globally competitive but operate within evolving institutional frameworks. As CEE markets deepen, one can expect the valuation channel (P/E) to align more closely with performance fundamentals, mirroring the trajectory observed in more mature EU economies.