The effect of competitiveness on the equity value of SMEs: evidence from a Central European country

The concept of competitiveness is one of the oldest in economic history, with its roots tracing back to Adam Smith and David Ricardo. Since then, it has been developed from the national level to the firm, regional and industry levels. Over the past decades, the competitiveness debate has been dominated by Michael Porter and Paul Krugman, who have engaged in a discourse about the meaning and rationality of competitiveness at the macro (national) level (Krugman, 1994; Porter, 1998). Nevertheless, even they concurred that the foundation of competitiveness is the performance of the firms.

Although there is no general agreement about the definition of firm-level competitiveness, there is a consensus that it relates to the competitive rivalry position of a particular firm in comparison to other businesses. However, competitive advantage can be interpreted in several ways, from an input and output perspective. The output-related view examines the business position by relying on financial performance-based measures, such as profitability, market share, efficiency, productivity and so forth (Chatfield and Vangermeersch, 2014; Oral et al., 1999; Tangen, 2003). The input perspective considers the firm’s resources, skills and capabilities to be the basis of competitiveness. The resource-based view (RBV) asserts that the value, uniqueness, inimitability and un-substitutability of a resource, in conjunction with its fit within the organisational context, determine the long-term competitive position of a business (Barney, 1991; Lafuente and Szerb, 2021; Lin and Wu, 2014; Nason and Wiklund, 2018).

The combination of the input and output sides of competitiveness in one model is a rare occurrence, and their relationship is largely unexplored in the literature (Chikán et al., 2022; Laureti and Viviani, 2011). This is particularly true for smaller businesses, which often lack reliable financial data and represent a very heterogeneous group (Lafuente et al., 2020a; Lafuente et al., 2020b).

Another line of literature deals with the evaluation of businesses (Damodaran, 2012; Fernandez, 2002; Koller et al., 2020). The International Valuation Standards (IVS) distinguish three valuation methods that can be applied to smaller businesses (International Valuation Standard Council [IVSC], 2022). These valuation techniques are all based on the principle of relying solely on financial performance data. However, the value is not contingent upon the competitive position and resources of the business. This study aims to address this gap by examining the relationship between the equity value and the competitiveness of businesses. In particular, we are interested in the manner and extent to which competitiveness and its components influence the value of the business.

Our findings collectively suggest that support policies for small- and medium-sized enterprises (SMEs) should be selective and evidence-based. These policies ought to target industries where enhancements in competitiveness have a proven impact on firm value. While general agendas for competitiveness remain pertinent for productivity and innovation, policy instruments that are focused on internationalisation, access to the home market, digital connectivity and management decision-making have the highest promise for enhancing both competitiveness and financial valuation of SMEs.

The remainder of the study is divided into three sections. Section 2 covers the relevant literature on RBV-based competitiveness and the latest scholarly findings on the field of valuation. Section 3 combines two lines of literature: the competitiveness measure and business evaluation methods. Competitiveness measurement based on the Global Competitiveness Project (GCP) methodology considers competitiveness as the configuration of ten competencies. To calculate the equity value, we utilise the IVS methodology and apply the principle of “highest and best use”. The results and discussion section, which forms Section 4 of the paper, presents the findings. Finally, the paper concludes.

In recent decades, the RBV approach has become the dominant view in examining corporate resources and capabilities for competitiveness (Wernerfelt, 1984; Prahalad and Hamel, 1990; Peteraf, 1993; Barney, 1991; Barney, 2001; Rugman and Verbeke, 2002). According to the RBV theory, performance differences among firms and the distribution of industry performance stem from resources and capabilities that firms successfully acquire, develop and utilise. The core competence concept highlights the importance of a few key resources and capabilities where the business has a comparative advantage over others. Besides the level of resources and capabilities, the configuration is also important: the configurational view puts emphasis on the proper balance of the resources and capabilities (Miller, 1986, 1996). According to these concepts, the measure of competitiveness also varies. Some researchers have examined the separate determinants of firm competitiveness according to RBV, while others (such as researchers from the GCP) suggest the application of composite RBV indicators. The advantage of composite competitiveness approaches is that they allow for a systematic examination of competitiveness factors, as proposed by Miller (1986, 1996). The application of a composite, systemic RBV approach is particularly relevant for SMEs, as it is known that the resources and capabilities possessed and utilised by SMEs are generally not unique in themselves and are easily replicable or acquirable (e.g. Luo and Child, 2015).

The RBV-based SME competitiveness methodology used by the GCP was developed by Szerb et al. (2014). In this study, they suggest that flow variables representing financial performance should not be included among the stock resource and capability variables describing SME competitiveness. This later provided an impetus for GCP researchers to explore complex relationships between resources and capabilities and financial-accounting data and performance indicators. Moreno-Gómez and Lafuente (2019) measured economic performance with ROA, while Varga et al. (2024) formed short- and long-term financial performance composite financial indicators. Márkus and Rideg (2021) analysed the relationship between competitiveness and cash-flow generation. Overall, this is the first GCP study to analyse the relationship between resources and capabilities determining SME competitiveness and equity value.

In this study, applying the GCP methodology, we capture SME competitiveness based on the characteristics and closely related corporate resources and capabilities of human capital, product, domestic market, networks, international markets, online presence, marketing, decision-making, strategy and technology. The high performance of these resources and capabilities, and their appropriate combinations, enable SMEs to compete effectively with other businesses and offer high-value products/services to consumers, while adhering to economic, competitive and cultural regulations.

One of the least explored areas is the relationship between firm competitiveness and financial performance (Chatfield and Vangermeersch, 2014). Research predominantly focuses on large and publicly listed companies (Brem et al., 2008). Financial performance is measured partly in cash generation capabilities (Greenberg et al., 1986) and partly in relation to stock prices (Damodaran, 2012). The financial performance of the SME sector constitutes a minority of research studies, mainly focusing on creditworthiness-related issues (Berger and Udell, 2006).

In a meta-analysis covering 290 articles, Miller et al. (2013) identify three schools of thought capturing financial performance as the separated financial performance dimensions, the latent financial performance and the aggregated (composite) financial performance views. However, none of them include equity value examinations. Yet, enriching empirical understanding through analysis of the relationship between RBV-based SME competitiveness and equity value is crucial. It assists SME owners and managers in decision-making (e.g. investment returns in resources and capabilities, borrowing, acquisitions) and aids banks, investors, capital funds and insurance companies in areas such as lending, investment planning and risk assessment.

Leading theorists in business valuation categorise potential methodologies differently. Damodaran (2012) distinguishes intrinsic valuation, relative valuation or pricing and contingent claim valuation. Intrinsic valuation assesses an asset’s value based on its inherent qualities, including its ability to generate cash flows and associated risks. This is often done through discounted cash flow (DCF) analysis, where the asset’s value is determined by the present value of expected future cash flows. Relative valuation, on the other hand, determines an asset’s value by comparing it to similar assets based on variables like earnings, cash flows, book value, or sales. Contingent claim valuation employs option pricing models to evaluate the value of assets that exhibit option-like characteristics. Fernandez (2002) distinguishes between balance sheet, income statement, mixed, DCF, value creation and options modelling approaches. Koller et al. (2020) present DCF-based, accounting income-based, relative and option pricing models.

The IVS Council, operating since 1981, aims to build confidence and public trust in valuation by producing standards and securing their universal adoption and implementation. The IVS define three valuation approaches: market-based valuation, income-based valuation and cost-based valuation (International Valuation Standard Council [IVSC], 2022). In the SME valuation methodology development (session 3.3.), a detailed discussion is provided on the specifics of these valuation approaches, combined with the highest and best use principle.

Contributions to the valuation literature increasingly highlight the overlooked role of intangible resources and knowledge-based assets in shaping firm value. Chen et al. (2005) and Zéghal and Maaloul (2010) apply the value-added intellectual coefficient framework to show that intellectual capital, particularly human and structural components, positively correlates with both financial performance and market value, although sectoral differences may influence its relevance. Corrado et al. (2009) extend this perspective to the macroeconomic level, arguing that intangible capital is a key driver of productivity and growth, yet remains underrepresented in national accounts. Upton (2001) further critiques the disconnect between traditional financial reporting and the value creation mechanisms of knowledge-intensive firms, calling for greater disclosure of nonfinancial performance drivers. Collectively, these works underscore the growing recognition that standard valuation approaches may systematically undervalue firms with strong intangible or competency-based foundations. This concern is particularly salient for SMEs in emerging economies, where such factors may be central to competitiveness yet underreported.

In recent examinations of valuation, Boonlert-U-Thai et al. (2022) analysed the significance of earnings, book value and dividends in the market value of Asian exchange-listed companies. Begley et al. (2023), focusing on the role of depreciation in capital-intensive firms, concluded that the valuation emphasis on pre-depreciation income increases during industry upturns, while the emphasis on depreciation expense decreases. Liao and Errico (2023) found in their dynamic model that investment to boost market valuation is more pronounced when managerial stock ownership is high or earnings quality is low. Based on FTSE 100 constituents, Breedon and Larcher (2022) observed that equity market valuation of pension liabilities is consistent with discounting without considering credit risk, thus aligning with a valuation closer to their settlement value. Lambertides (2022) investigated the paradoxical relation between stocks with higher asset growth and lower returns, identifying a negative relation in undervalued stocks but not in overvalued ones. In corporate Initial Public Offerings (IPO) valuation, Massel et al. (2024) examines the roles of revenues and earnings, while Schnyder et al. (2022) explore the difference between the effect of positive law and the effect of the perception of law. Johannesson and Ohlson (2023) developed a framework linking static perspective valuation to the dynamics explaining returns and vice versa. Hamilton (2023) noted that investors price depreciation-related deferred tax liabilities as economic burdens during investment evaluation. Liu et al. (2023) assert that revenue serves as a performance metric in executive compensation contracts when it provides additional information on equity valuation beyond earnings. Ferris et al. (2022) concluded that the cultural distance between CEOs and a firm’s directors enhances shareholder value. Rajgopal (2022) found a correlation between valuation discount and the presence of large block holders in European firms, but not with the poorer disclosure record of US firms on environmental and social dimensions. This suggests that poorer governance in continental Europe may erode more shareholder value than improved environmental and social dimensions can add.

In the context of the recent scholarly literature on the field of evaluation, it is apparent that there is a gap in research regarding the influence of SMEs’ RBV-based competitiveness on equity value. This suggests an area where further investigation is warranted, as understanding the relationship between these factors could provide valuable insights into the drivers of SME performance and valuation.

The empirical investigation is based on the Hungarian SME data set of the GCP (Link to the website of smeLink to the website of sme). A questionnaire was developed to measure the performance of SMEs’ resources and capabilities. The applied data were collected through primary data collection by the GCP, an international research collaboration composed of research teams from 11 universities and business schools (GCP, 2025).

The survey was conducted by a professional vendor company following a standard procedure: after an initial telephone call for approval, the face-to-face survey with personal support was carried out with one of the business owners who also participated in the management. Similar to Irwin et al. (1998) and Douglas and Ryman (2003), the managers were asked to value the individual importance of a series of resources and capabilities along a five-point scale (see Priem and Butler, 2001). In the proposed Likert-type uniform quantification, the value of “0” indicates no strategic value (Douglas and Ryman, 2003), while the rest of the scale is evaluated from “1” (low relevance) to “4” (high relevance). This scale allows sufficient differentiation in the valuation of the analysed variables (Lederer et al., 2013). Overall, it is possible to obtain information for 44 variables (Lafuente et al., 2020a; Lafuente et al., 2020b) about each of the ten groups of SME’s resources and capabilities. The questionnaire has been used relatively widely in Hungary and in other GCP countries for various research purposes since 2013.

The data set consisted of 1,433 Hungarian SMEs that participated in the survey between 2016 and 2020, filling out the survey once. To ensure sample homogeneity, some filtering was conducted. First, we excluded micro-enterprises with fewer than five employees and large firms with 250 or more employees as they did not meet the SME profile specified for this research. As a result, 313 firms were not included, followed by a further 22 with no available employee data. Out of the remaining 1,098 companies, 67 had to be removed because of characteristics that did not meet the research’s scope. The reasons for removal included state-owned firms, firms that were liquidated before the end of the examined period, subsidiaries of larger parent companies and nonprofit organisations. Additionally, there were duplications that had to be addressed, leaving 1,031 observations in the sample. Then, 4 firms were excluded due to missing financial data, and 4 more because their industry classification was deemed ambiguous.

The final sample comprises 1,023 SMEs from Hungary, possessing all the required data for computing the competitiveness index as well as financial figures used for determining the equity value. These enterprises are classified into three main regions of Hungary, namely, the central region (including Budapest, the capital city), the Western region and the Eastern region, in ascending order of development level. Additionally, the final sample includes firms from 15 distinct industries, with each categorised into one of two size groups: small (including micro) enterprises with 5–49 and medium-sized enterprises with 50–249 employees. Non-response bias was tested for early and late respondents in terms of business size (employees), business age and industry sectors. Table 1 presents a summary of the sample composition.

It is important to note that, even though the final sample includes observations from five distinct years, each firm was examined only once within the period of 2016–2020. Hence, the sample can be analysed and tested as a cross-sectional database.

The methodology for assessing the competitiveness of SMEs developed by GCP is a comprehensive RBV-based approach that takes into account the entire spectrum of relevant resources and capabilities. It incorporates a total of over 200 indicators to compile 44 (typically complex) competitiveness variables ( Appendix), which constitute the ten pillars.

While Figure 1 outlines a comprehensive and integrative conceptual framework we developed to capture the multi-layered nature of SME competitiveness, Figure 2, adapted from the GCP (2025) research project, highlights the specific firm-level dimensions that are empirically examined in relation to equity value. Presenting both figures together clarifies how the broader competitiveness architecture informs and is operationalised through the analytical focus of this study.

Figure 1 presents an expanded model of SME competitiveness that builds upon the framework developed by Lafuente et al. (2020a). This enhanced model integrates external macroeconomic and meso-level market/industry conditions with competitiveness outcomes. The macroeconomic dimension adopts the STEEPLE framework, encompassing social, technological, economic, environmental, political, legal and ethical factors (Wilkins et al., 2024), to capture the broader institutional and environmental context influencing SMEs. The market/industry component reflects the meso-environment, drawing on elements such as market structure, competitive rivalry, clustering, supply chain dynamics and targeted policy supports, including grants (Delgado et al., 2010; Organisation for Economic Co-operation and Development [OECD], 2017; Porter, 1980). These supports are particularly relevant in the context of emerging economies such as Hungary.

While traditional firm-level models often emphasize internal outcomes such as value creation, growth, profitability, or productivity, contemporary frameworks increasingly recognize the broader societal contributions of SMEs (Indris and Primiana, 2015). These include fostering regional economic development, generating employment and advancing sustainability goals (Fajarika et al., 2024; Organisation for Economic Co-operation and Development [OECD], 2023). Notably, these outputs are not merely end results; they exert a recursive influence on SME competitiveness by reshaping capabilities and strategic orientations over time (Cantele and Zardini, 2018; Oduro and Haylemariam, 2025). The external factors could also directly affect the SMEs outputs.

The pillars, representing the ten aspects of various competencies, are the core elements of the model. Their dynamic interaction could lead to different competitiveness configurations and effects/outputs as shown in Figure 1. It is important to note that while we provide a comprehensive conceptual model of competitiveness, our primary focus is to examine the effect of overall competitiveness on equity.

The structure and content of the pillars (Figure 2) are as follows:

• In the human capital pillar, variables describing employee and managerial excellence, as well as related human resource management functions, are featured (4 variables).

• In the product pillar, variables encompass new or improved products and services, their performance in the target market segment, related inventions, intellectual properties and uniqueness (4 variables).

• In the domestic market pillar, variables describing the geographical reach of sales, prospects for market development, levels and intensity of competition and the ability to respond to changing customer demands are included (5 variables).

• In the networks pillar, the presence, stability, uniqueness and contribution of economic and other external relationships supporting corporate development and innovation were examined (4 variables).

• In the international markets pillar, variables related to the number of foreign customers, their contribution to revenue, fulfilment of conditions for selling to foreign customers and the uniqueness of the location are utilised (4 variables).

• The online presence pillar includes variables reflecting the technical characteristics, services, content and uniqueness of the company’s website (3 variables).

• In the marketing pillar, characteristics such as the uniqueness of products and services, characteristics of distribution channels, pricing levels, marketing communication tools and characteristics of marketing methods and innovations are expressed (6 variables).

• In the decision-making pillar, variables related to information management, decision-making and administrative procedures are presented (5 variables).

• The strategy pillar includes variables describing the direction and dynamics of changes in the operational scope, entrepreneurial capabilities of management and the uniqueness of long-term proactive strategy (4 variables).

• The variables in the production pillar represent the level of technological advancement, modernity, age, the level of related innovations, the sophistication of production management and quality assurance systems, the application of ICT tools and their uniqueness (5 variables).

The competitiveness index, the pillars and the variables were formed from questionnaire data using the following six-step methodology:

1. Identifying variables and calculating values [0;4].

2. Normalisation of variable values to the range [0;1].

3. Calculation of pillar values by averaging given normalised variables [0;1].

4. Normalisation of pillar values to the range [0;1].

5. Adjusting the normalised pillar values to the common average of the pillar averages by increasing the values to the same kth power [0;1].

6. Calculation of competitiveness indexes by summation of the adjusted pillar values [0;10]. ^[1]

The IVS, issued by the International Valuation Standard Council (IVSC), serves as the primary guide for business valuations used by professional bodies, individual experts and relevant professional firms. Updated continually, the standards “serve as the key guide for valuation professionals globally to underpin consistency, transparency and confidence in valuations” (International Valuation Standard Council [IVSC], 2023). In accordance with relevant literature, IVS outlines three fundamental approaches for valuation: cost-based, income-based and market-based approaches.

In the context of a cost-based valuation, the equity value (i.e. the value attributable to shareholders, or in simpler words, the value of the 100% ownership in the company) is determined by the net assets held by the company on the date of valuation. Koller et al. (2020) mention that for companies operating under international accounting systems such as US GAAP or IFRS, using the most recent book value of assets and liabilities is usually acceptable for the calculations, arguing that following the valuation rules required in those systems, the book value provides a reasonable estimation of market value. In certain cases, such as non-listed entities or SMEs operating under local, historical cost-based accounting systems, asset and liability revaluation may be required. Nevertheless, this process requires market data and information that are not included in the published financial statements. Consequently, valuers typically deem the book value of shareholders’ equity, as indicated in the most recent balance sheet, to be an adequate indicator of equity value.

The second main approach used to derive the value of a business is income-based valuation. This method estimates the present value of future profits or cash flows of the firm, applying discounting techniques. Equity value can be computed by discounting the net income of future periods with the cost of equity (Takács et al., 2020). Depending on growth expectations, valuers may use different models to determine the equity value, such as perpetuity, growing annuity or multi-stage models (Damodaran, 2012).

The third widely accepted valuation approach is market-based valuation, also known as relative valuation. This method utilises particular multiples obtained from observed market data of other similar, publicly traded companies, which are referred to as the peer group. The multiple expresses the average ratio between the market value and a selected performance indicator, such as sales revenue or net income, for the firms in the peer group as of the valuation date. The estimated market value of the subject company is attained by multiplying its appropriate performance indicator by the computed multiplier from peer group data. The IVS emphasise the importance of ensuring comparability between the subject company and its peers in the peer group for a relative valuation to be dependable. This entails a significant degree of similarity in terms of profile, size, diversification, growth prospects, geographical location and other factors (International Valuation Standard Council [IVSC], 2022). Therefore, in smaller markets, the use of relative valuation may be limited due to insufficient comparable firms and information availability. Additionally, Koller et al. (2020) suggest that multiples are rather a corroboration tool to test the credibility of income-based valuation and to explain variations in company value compared to its competitors.

To select the appropriate method, one should consider the principle of “highest and best use” (International Valuation Standard Council [IVSC], 2022). This principle presumes that a rational investor would select the form of utilisation (such as liquidating or continuing the company’s operations) that generates the highest income. Accordingly, the firm’s market value should be associated with the greatest of the values determined by various methods.

In this study, we will use the outlined valuation approaches and principles as follows: The cost-based equity value of each firm will be determined by subtracting the total liabilities (TL_i) from the book value of total assets (TA_i), which is equivalent to the shareholders’ equity (SHE_i) presented in the balance sheet during the most recent financial year prior to the primary data collection:

To determine the income-based equity value, we utilise the perpetual annuity model. This involves assuming a constant amount of annual net income for the firm, identical to the most recent closed year before data collection and discounting it by the industry’s average cost of equity for an infinite period. The chosen model, which excludes growth, was based on an objective evaluation of the raw data from the sample. The majority of firms in the sample could not achieve net income growth in their last three years, with many reporting decreasing profits. Additionally, SMEs commonly use creative tax optimisation techniques. Given that all Hungarian firms, including SMEs, are required to use online invoicing systems directly connected to the National Tax Office and provide real-time data on all outgoing invoices according to Order No. 48/2013 issued by the Ministry of National Economy in 2013, revenue manipulation options are extremely limited. Hence, tax optimisation strategies concentrate on the recognition of expenses. To address potential distortions resulting from tax optimisation, the net income presented by each firm (NI_i) was compared with a benchmark net income calculated by multiplying the firm’s sales revenue (Sales_i) by the standard net margin in the industry where the company operates (NetMargin_i^ind). Then the higher of the two was discounted by the usual cost of equity in the industry (CostOfEquity_i^ind), using the perpetual annuity formula to determine the income-based equity value:

This step necessitated additional parameters, namely industry-specific net margins and cost of equity, which were not included in the primary data collection. Aswath Damodaran’s public database (Damodaran, 2023) was utilised for collecting the essential data. The net profit margin and cost of capital data were collected for every industry in the sample for each year of the period 2016–2020. For each company, the variables $NetMarginiind$ and $CostOfEquityiind$ were determined using the Damodaran database values for the year of primary data collection.

The third approach, market-based valuation was excluded from this analysis due to the unavailability of sufficiently comparable firms in the Hungarian market. Therefore, in accordance with the “highest and best use” principle, the equity value of each firm was determined as the higher between its cost-based and income-based values. This value was subsequently logarithmised to derive the dependent variable for use in the regression analysis:

This combined valuation technique should be regarded robust, as the surrounding accounting standards and valuation norms have been stable in time based on the following reasons: First, Hungary, as an EU member state, adopted IFRS for large companies and has made significant efforts to converge its local GAAP (to be applied by SMEs) to IFRS, a widely accepted and reliable global accounting system. Secondly, IVS is a set of valuation standards which set its fundamental principles in 1981, and since then, despite the regular updates, has kept its basic approach and methods to valuation.

The analysis uses the log value of equity (described in section 3.3) as the dependent variable and the competitiveness index as the main explanatory variable. As explained in section 3.2, the competitiveness index is a composite of the scores of ten pillars of competitiveness (human capital, product, domestic market, networks, international markets, online presence, marketing, decision-making, strategy and technology), following the methodology developed in the GCP (GCP, 2025). In addition, three control variables are included in the model: region, industry and number of employees. The region variable ranges between 1 and 3, each of which indicates the region of Hungary where the firm is headquartered, while the industry variable expresses the firm’s industry with a code between 1 and 15 (Table 1 contains the 3 regions and 15 industry names). The statistical number of employees at the time of the primary data collection is used as a third control variable. A summary of the variables is presented in Table 2.

The original linear regression model is built as follows:

Based on the characteristics of the model, the test is performed with ordinary least squares (OLS) regression using Gretl software. To check the stability of the model, the correlation between the independent variables was analysed. Figure 3 shows the correlation matrix.

The results indicate that there is no significant correlation between the independent variables, confirming that the model is stable and not biased by potential multicollinearity.

The original model was tested first, using the data of the total sample. Results are highlighted in Table 3 (here and in all other tables, the asterisks indicate the significance level of the variable: *p < 0.1, **p < 0.05 and ***p < 0.01).

The values displayed in the table indicate that there is a significant positive relationship between competitiveness and value. This is supported by the fact that the explanatory variable COMPINDEX is significant at the 1% level (p = 0.000), and its coefficient is positive (0.232), confirming the preliminary hypothesis that more competitive companies are worth more. The model’s explanatory power (R²) is 31.7%, which is proof of a good fit in the model, although the most important finding is the statistically significant relationship between the competitiveness components and the equity value. It is unsurprising that the variable NO_EMP is significant at the 1% level, indicating that equity value typically increases with firm size. However, a noteworthy finding is that a company’s industry affiliation, which refers to its main profile, is a relevant factor, whereas regional location does not have a statistically significant impact on equity value.

To gain a deeper understanding of the effects of company size and profile, the tests were replicated on appropriately constructed sub-samples. In the first step, firms were categorised by size: one sub-sample comprised small (including micro-) companies with 5–49 employees, while the other sub-sample consisted of medium-sized enterprises employing 50–249 people. The original model was executed on both sub-samples, excluding the NO_EMP variable from the independent variables. The outcomes of the test are outlined in Table 4.

The exclusion of the NO_EMP variable from the model resulted in a significant decrease in the adjusted R² in both subsamples compared to the original model. This suggests that the number of employees, which indicates the firm size, has a critical impact on the equity value of the firm. Another significant finding can be derived from the table data: the COMPINDEX coefficient demonstrates a higher positive value for small firms’ sub-sample (0.303) compared to medium-sized firms (0.237). This indicates that small companies can achieve a greater increase in equity value by enhancing their competitiveness than medium-sized enterprises.

Furthermore, it should be highlighted that the industry category continued to be a significant independent variable. As a result, it would be prudent to conduct additional analysis to identify potential differences between various firm profiles. To this end, the entire sample was reclassified, resulting in a manufacturing sub-sample comprised of 388 firms and a retail and service sub-sample consisting of the remaining 635 firms. The classification was based on the industry names listed in Table 1. Firms in the Engineering/Construction, Farming/Agriculture, Machinery or Metals and Mining industries were classified under the manufacturing sub-sample. On the other hand, firms in the remaining industries listed (Business and consumer services, Education, Entertainment, Financial services, Hospitals/Health-care services, Hotel/Gaming, Information services, Real Estate, Retail, Transportation and Utility) were categorised under the retail and service sub-sample. The initial model, excluding the INDUSTRY variable, was tested on these two sub-samples. Table 5 presents the findings.

Table 5 data reveal considerable disparities between manufacturing and retail and service enterprises in terms of how competitiveness affects equity value. The COMPINDEX variable conveys a higher level of statistical significance for retail and service sectors (p = 0.000) than for the manufacturing sub-sample (p = 0.024). Furthermore, the positive coefficient is three times higher (0.304) for retail and service organisations in comparison to manufacturing companies (0.104). This evidence indicates that enhancing competitiveness is a more effective means of increasing firm value in the retail and service sectors.

A further aim of this study was to decompose the competitiveness index and analyse the relevance of the individual components. Section 3.2 provides a detailed presentation of the ten pillars that define the competitiveness index. The prior sections of the study have statistically confirmed the significant positive correlation between competitiveness and value. However, the tests conducted have consistently utilised the composite index to measure competitiveness, leaving uncertainty regarding which of the ten pillars act as true value drivers. To investigate this problem, a stepwise regression analysis was conducted using SPSS software. Similarly to the original model, LogValue was used as the dependent variable, however, the explanatory variable COMPINDEX was replaced by ten variables that represent the scores of the individual pillars of competitiveness (HUMAN_CAPITAL, PRODUCT, DOMESTIC_MARKET, NETWORKS, INTERNATIONAL_MARKETS, ONLINE_PRESENCE, MARKETING, DECISION-MAKING, STRATEGY and TECHNOLOGY. The control variables NO_EMP, INDUSTRY and REGIOn were also preserved. Table 6 presents the finalised model obtained from the stepwise regression analysis.

The test showed that in addition to the number of employees, which accounts for 27.9% of the overall adjusted R² of 32.3%, the following four components of competitiveness could be included as predictors of the equity value: international markets, decision-making, domestic market and online presence. These variables were found to be significant at the 5% level and could contribute to the explanatory power of the model by 2.3%, 1.4%, 0.6% and 0.3%, respectively. All other independent variables lacked significance (for each of them, the p value exceeded 0.05) and therefore were excluded. The robustness of these results was confirmed by additional quantile regression tests, in which the same stepwise analysis was conducted on two subsamples divided by the median of LogValue. As in the initial test on the entire sample, international markets, online presence and decision-making were identified as significant explanatory variables of LogValue in the subset models. All independent variables that were excluded in the original stepwise test were also excluded in the subset analyses.

These findings hold crucial managerial implications. They emphasise that to increase equity value, managers should focus their efforts on improving the true value drivers, which, according to the test results, include international markets, decision-making, domestic market and online presence. Although the importance of the excluded factors, such as human capital, product, networks, technology, strategy and marketing, is undeniable, their impact on equity value could not be verified statistically.

Test results, especially the stepwise regression results presented in Table 6 reveal that most of the explanatory power can be attached to the firm’s size. This may be seen as a basis for stating that a higher company value is only related to a larger size, questioning the value-generating effect of competitiveness. However, the statistical data shown in Table 4 (which prove that COMPINDEX alone is a relevant explanatory variable on LogValue) refute such misinterpretations.

Finally, to provide support for the decision that the market approach was excluded from the investigation, additional tests were conducted. In these tests, the equity value was computed based on the industry-specific P/E ratio. In the course of these tests, COMPINDEX was found to be insignificant even at the 10% level, which may be taken as confirmation that the use of benchmarks from large firms is not suitable for the purpose of judging the equity value of SMEs.

This paper examines the relationship between the equity value and the resource-based competitiveness of businesses. While business valuation methods provide a framework for evaluating businesses based on financial data, they do not address how to increase the equity value for the business. In contrast, research on business competitiveness tends to focus on improving competencies, yet the relationship and impact on business value remain understudied. While prior studies have examined the role of intellectual and intangible assets in shaping firm performance and market value, especially in the context of listed companies and macroeconomic outcomes, no previous research has directly linked SME-level competitiveness, captured through a multidimensional framework, to equity valuation in privately held firms. This kind of investigation is of particular importance in the context of small businesses. The competitiveness of smaller businesses is an understudied field due to the lack of reliable data and the heterogeneity of the SME sector. With a unique GCP-based questionnaire and the associated financial data set, containing 1,023 Hungarian small businesses, we were able to examine the effect of competitiveness on equity value. The equity valuation was conducted in accordance with the IVS methodology.

As anticipated, the competitiveness index score exhibited a positive and statistically significant influence on the equity value, thereby indicating a direct relationship between the inputs and the outputs of the business. The results indicated that the size of the business constituted another crucial determinant of equity value. While industry control variables were not significant, the model demonstrated superior performance when applied to the retail and service sectors relative to the manufacturing sub-sample. This separation revealed that industry-specific examinations yielded more accurate results.

We divided the competitiveness index into ten components and examined their effect on the equity value. Four of the ten components were found to be significant: international markets, decision-making, the domestic market and online presence. The international markets and the domestic market pillars capture the overall market size of the business. Higher presence in international markets and wider coverage of the domestic market increase the equity value of the business. This can be further strengthened by being present on the online sphere. The decision-making pillar reflects the utilisation of financial reporting techniques and the prevalence of participatory decision-making, as opposed to a more centralised approach. These findings do not imply that the other components are not important; however, their effect on equity can be captured by the key four components.

Our findings have important implications for policy makers concerned with SME development, competitiveness and valuation. While broad support mechanisms are often discussed, only specific aspects of competitiveness, such as internationalisation, market access, digital presence and the quality of managerial decision-making, show clear links to higher firm equity values. This highlights the need for more targeted, evidence-based policy approaches. First, internationalisation should be a top priority. SMEs active in foreign markets exhibit higher equity values. Policies that reduce export barriers, for example targeted promotion programmes or subsidised trade fair access, are more effective than general subsidies. Second, domestic market development also drives value. Supporting SMEs in accessing and responding to local demand through reduced internal barriers or clustering initiatives is particularly important in emerging economies. Third, digital presence matters. SMEs benefit financially from digitalisation, highlighting the need for programmes that support website development and online visibility. Fourth, stronger decision-making capabilities boost firm value. This underscores the importance of managerial capabilities, management training, financial literacy and the use of analytical tools. From a valuation standpoint, intangible assets and capabilities play a real role in firm value. Evolving valuation standards to include indicators such as digital presence or international reach would improve transparency and reduce information gaps between SMEs and investors.

Despite the robustness of the methodology and the empirical depth of the analysis, this study has certain limitations that should be acknowledged. First, the data are based on a cross-sectional sample of Hungarian SMEs, which does not allow for tracking changes in competitiveness or equity value over time. Longitudinal data could provide additional insights into causal relationships and temporal dynamics. Second, while the competitiveness index captures a wide spectrum of RBV-based factors, the measurement is based on self-reported survey data. Although the survey was conducted with methodological rigor, subjective bias and perception-based distortion may still affect the results. Third, the study focuses exclusively on Hungarian SMEs. Although the methodological framework is applicable to other emerging economies, country-specific institutional, cultural and economic factors may limit the generalisability of the findings. Fourth, due to the lack of reliable market data for similar SMEs, the market-based valuation approach was excluded from the final equity value calculation. While this choice is theoretically and empirically justified, it nonetheless restricts the comparative use of market multiples, especially in an international context. Future research could address these limitations by employing panel data, expanding the scope to include other Central and Eastern European countries and exploring additional qualitative aspects of SME competitiveness and strategic behaviour.

Additionally, further investigation could consider how improved reporting standards and integrated non-financial disclosures may enhance the transparency and valuation accuracy of SMEs with strong intangible asset bases. Future studies could also investigate sector-specific dynamics by exploring how different configurations of competitiveness pillars affect equity valuation across industries. Furthermore, future research could explore how emerging themes, such as digital transformation, ESG considerations and the long-term effects of the COVID-19 pandemic, interact with SME competitiveness and influence equity valuation. These topics, although beyond the current study’s empirical scope, represent timely and highly relevant avenues for extending the present framework in broader strategic and financial contexts.

The data that support the findings of this study are available from the corresponding author upon request.

The research was conducted in accordance with the Code of Ethics of the Faculty of Business and Economics at the University of Pécs, in alignment with Hungarian higher education and social science research ethics standards.

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