The Impact of Value-Added Intellectual Capital on Corporate Performance: Cross-Sector Evidence

Abstract

This study explores the relationship between intellectual capital (IC) and the financial performance of 250 publicly traded companies in France, Germany, and Switzerland from 2009 to 2018, addressing the gaps in prior research regarding the differential impacts of IC components across countries and industries in Western and Central Europe. Using the Value-Added Intellectual Coefficient (VAIC™) approach, this study evaluates human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE). Panel regression analyses at the country and industry levels were conducted to assess their effects on financial metrics, such as return on equity (ROE), return on assets (ROA), and asset turnover ratio (ATO). The findings reveal a significant positive association between SCE, CEE, and firm performance, with CEE showing the most substantial effect, while HCE had a relatively weaker impact. Additionally, the study uncovers a trade-off between the accumulation of patents and trademarks and short-term financial performance, raising new considerations for intellectual property management. This research contributes to the literature by providing a nuanced understanding of how IC components influence financial outcomes across different contexts and offers practical insights for firms aiming to optimize structural capital and capital-employed strategies for improved financial performance while acknowledging the limitations regarding the sample of publicly traded firms.

1. Introduction

In today’s knowledge-driven global economy, measuring intellectual capital is crucial for understanding how intangible assets can drive a company’s economic performance. Most studies investigating the impact of intellectual capital (IC) on corporate performance show that intangible assets significantly affect competitiveness and value creation across various sectors (Subramaniam and Youndt 2005; Berezinets et al. 2016; Glova and Mrázková 2018; Elsten and Hill 2017; Dancaková et al. 2022). However, despite the well-established significance of IC, the complete resolution of its components remains elusive.

Over the past decades, the concept of IC has been shaped by a variety of definitions and approaches. IC is often viewed as a combination of knowledge-related assets that contribute to overall organizational innovativeness (Barrena-Martínez et al. 2020). It can also be described as intangible assets that enable a company to function (Brooking 1996). For some authors, IC represents the gap between the book and market value of a firm (Berzkalne and Zelgalve 2014; Rieg and Vanini 2015; Ousama et al. 2020). Additionally, intellectual capital is characterized as collective wisdom and energy, making its accurate measurement and management challenging (Stewart 1997). Despite variations in the explanation of IC among researchers, two essential dimensions of IC have been broadly accepted: human capital and structural capital. These two categories of IC have received considerable attention due to their potential positive effects on a firm’s financial performance (Sumedrea 2013; Janošević et al. 2013; Dalwai and Salehi 2021). While human capital is uniquely linked to a company’s employees and their mental capabilities, structural capital comprises various aspects associated with the internal and external processes of a business (Bontis et al. 2000). Structural capital encompasses various types of intangible assets, such as patents, research and development, trademarks, market strategy, customer relations, distribution channels, organizational structure, and other aspects of the business that support human capital and enable knowledge transfer within the enterprise (Bontis et al. 2000; Edvinsson and Sullivan 1996; Benevene et al. 2017). However, it should be noted that there are a number of authors who do not consider customer relations as part of structural capital, but rather as the third dimension of IC, relational capital, broadly defined as the value of a firm’s relationships with its customers, partners, and suppliers (Bontis et al. 2018; Xu and Li 2022; Andreeva et al. 2021; Ali and Anwar 2021).

This study aims to fill a gap by providing a comprehensive understanding of the relationship between intellectual capital (IC) assets and the financial performance of 240 publicly traded companies across different industries and three specific countries. Our focus is on examining how the unique dimensions of intellectual capital influence firm financial performance, while also capturing variations across different industries and countries. In pursuit of these goals, our study seeks to investigate two primary research questions:

RQ1: To what extent does intellectual capital (IC) affect performance measures across companies within specific countries?

RQ2: Are there noticeable variations between industries or countries that can be attributed to the unobservable endogenous and exogenous features of individual countries and industries?

To address these specific inquiries, we will conduct an analysis at both the country and industry levels. Our analysis will involve employing the Value-Added Intellectual Capital (VAIC^TM) model to examine data from publicly listed companies operating in five specific business sectors across three countries over a period of ten years. This approach allows us to delve deeper into the complex relationship between intellectual capital and organizational performance within specific contexts. Our study provides valuable insights into the importance of corporate intellectual capital (IC) assets in enhancing competitiveness and creating value within industry-specific and country-specific contexts. Moreover, it allows us to identify patterns and trends that influence how intellectual capital impacts performance outcomes.

This research paper is structured as follows: Section 2 provides the theoretical background of the study. Section 3 outlines the research design, data collection methods, and analytical approaches. Empirical results are presented in Section 4 and discussed in Section 5. Finally, Section 6 offers conclusions drawn from the findings.

2. Literature Review

The Value-Added Intellectual Coefficient (VAIC^TM), originally proposed by Pulic (2000), stands as one of the most widely recognized tools for evaluating corporate intellectual capital (IC). This methodology quantifies a company’s capacity to create value-added through the efficient utilization of three inputs, human capital, structural capital, and physical capital (CE), denoted as human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE), respectively. Specifically, the VAIC is calculated by summing the individual efficiencies of these three elements:

$$\mathrm{V}\mathrm{A}\mathrm{I}\mathrm{C}=\mathrm{H}\mathrm{C}\mathrm{E}+\mathrm{S}\mathrm{C}\mathrm{E}+\mathrm{C}\mathrm{E}\mathrm{E}$$

In academia, the VAIC^TM methodology, followed by the regression analysis, has been extensively utilized to examine the relationship between intangible assets and corporate performance across various industries.

In their study of Oman’s financial sector, Dalwai and Mohammadi (2020) explore the interplay between intellectual capital and corporate governance, noting that governance structures, such as board size and audit committee activities, significantly affect intellectual capital efficiency. Their findings highlight the importance of governance mechanisms in unlocking the potential of IC, particularly within industries like banking, where intangible assets play a central role in generating value. The study suggests that governance frameworks can either enhance or restrict a firm’s ability to leverage its intellectual resources, pointing to the need for stronger regulatory oversight to promote IC-driven value creation.

Further examination of VAIC and its application comes from Ting et al. (2022), who analyzed its effect on corporate profitability in Vietnamese firms. Their research reinforces the positive influence of IC on profitability while emphasizing that the modified version of VAIC provides more precise insights into this relationship. Their findings highlight the complexity of IC’s role, with individual components, such as structural capital efficiency (SCE) and human capital efficiency (HCE), having varying impacts. That underscores the need for ongoing refinement of IC measurement models to predict profitability more accurately and assist firms in optimizing their intellectual resources.

Research by Bhattu-Babajee and Seetanah (2022) supports the conclusion that VAIC has a positive effect on firm performance but highlights its stronger impact over the long term. Their study of Mauritian companies shows that IC’s contribution to financial performance becomes more pronounced with time, suggesting that investments in intellectual capital yield cumulative benefits. Additionally, the researchers observed a self-reinforcing relationship between IC efficiency and financial performance, where higher levels of IC efficiency lead to improved financial outcomes, allowing firms to further invest in and leverage intellectual capital. This dynamic is crucial for long-term value creation, and their findings indicate the importance of a strategic focus on tangible and intangible assets.

In another study, Marzo and Bonnini (2023) address potential issues with the conventional application of VAIC, uncovering a hidden non-linearity between its components, SCE and HCE. Their research suggests that this non-linearity leads to misleading interpretations of VAIC’s relationship with firm performance. They argue for introducing interaction terms in regression models to capture VAIC’s effects more accurately. As such, scholars and practitioners are encouraged to reevaluate their reliance on VAIC in decision-making processes, particularly when assessing financial and market performance.

Gupta et al. (2023) offer further insights by investigating the relationship between IC and financial performance in India’s pharmaceutical industry using the VAIC and enhanced e-VAIC models. Their study reveals a positive relationship, with capital employed efficiency (CEE) emerging as the most influential factor. This emphasizes the continuing relevance of physical capital in driving firm performance, even in innovation-intensive industries. The addition of relational capital in the e-VAIC model improves its explanatory power, underlining the need for more comprehensive IC models encompassing a broader range of intangible resources.

Khurana and Sharma (2024) expand the discussion by linking VAIC to default risk. Their research, focusing on Indian firms, shows that higher VAIC scores are associated with a lower probability of default, indicating that firms with strong intellectual capital are better positioned to maintain financial stability. This study introduces an important dimension to the IC-performance relationship, suggesting that effective IC management enhances profitability and reduces financial risk, making it a critical element in risk management strategies.

These studies suggest that, while VAIC remains a valuable tool for assessing IC’s impact on firm performance, it requires further refinement to capture the complexity of the IC-performance relationship fully. The hidden non-linearity identified by Marzo and Bonnini (2023) and the modified VAIC models proposed by several researchers (Gupta et al. 2023; Ting et al. 2022) reflect the need for more sophisticated approaches. Moreover, industry-specific factors, such as those highlighted in the studies on governance and risk, must also be considered when assessing intellectual capital’s role in firm success. Overall, the body of literature points to intellectual capital as a key driver of financial performance and long-term organizational stability.

While many studies indicate a positive relationship between the components of the VAIC^TM methodology and the financial performance of companies, the findings are not unequivocal and often seem contradictory. For instance, Sumedrea (2013) analyzed 62 non-finance companies listed on the Romanian stock exchange and found that VAIC^TM positively affects return on assets (ROA) and return on equity (ROE). Similarly, Muhammad and Ismail (2009) demonstrated a positive impact of VAIC^TM index on ROA using data from 18 Malaysian finance companies. Results reported by Al-Musali and Ismail (2014) are consistent with other studies, as they used data from 11 banks in Saudi Arabia to confirm that VAIC^TM affects ROA and ROE positively. However, they also noted that human capital is more efficient in generating corporate value-added than financial and physical resources.

On the contrary, Mollah and Rouf (2022) found a positive association between human and capital employed efficiencies and bank performance in commercial banks in Bangladesh, but did not establish a clear relationship between structural capital efficiency and bank performance. Janošević et al. (2013) confirmed the positive impact of VAIC^TM on financial performance indicators in the construction sector, but found that ROE was more affected by the utilization of human and financial capital, while ROA was impacted by the efficient utilization of human and physical resources. In contrast, Bharathi Kamath (2008) analyzed a sample of 25 of the largest pharmaceutical companies in Iran and found that human and financial capital efficiency positively impacted ROA, while structural capital had a negative impact on it.

Nevertheless, despite the widespread adoption of the VAIC^TM, it is imperative to acknowledge several shortcomings inherent in its original methodology. Foremost among these limitations is how the model defines human and structural capital, a criticism underscored by Ståhle et al. (2011) and Andriessen (2004). Critics argue that the model’s conceptualization is somewhat detached from the essence of intangible assets, which constitute corporate IC. However, proponents contend that calculating corporate human and structural capital accurately remains contingent upon available data.

Marzo (2022) further critiques the VAIC^TM, highlighting its inconsistency with prevalent definitions of IC in the literature. According to Marzo (2022), the model does not directly measure the impact of IC; rather, it assesses IC efficiency in the value creation process. Additionally, an inherent weakness of the original model lies in its failure to account for potential interactions between HCE, SCE, and CEE, thereby impacting performance indicators.

Moreover, detractors argue that the VAIC^TM is not suitable for valuing companies with negative revenues, as negative value-added may distort the efficiency sub-indices, rendering analysis interpretation challenging (Chu et al. 2011). Another area for improvement of the original VAIC^TM model is its inability to elucidate how intangible assets contribute to a company value-added.

In the relevant literature, diverse modifications have been employed by researchers to integrate various aspects of intellectual capital into analyses concerning the impact of the VAIC^TM coefficient on market value or corporate performance metrics. For instance, Nazari and Herremans (2007) expanded the concept of structural capital by incorporating customer and innovation capital, integrating marketing costs and R&D expenditures into the VAIC^TM coefficient model. Similarly, Chang and Hsieh (2011) incorporated R&D expenditure and patents as proxies for corporate innovation, alongside partial coefficients of the VAIC^TM index, in regression analysis models. Clarke et al. (2011) not only considered R&D expenditures in regression analysis, but also explored the influence of interactions between individual components of the aggregate VAIC^TM index. Vishnu and Gupta (2014) adjusted the original definition of structural capital by equating it with investments in R&D, and they further augmented the model by incorporating customer capital as an additional partial index in computing the VAIC^TM coefficient. Additionally, Xu and Wang (2018) accounted for the effects of innovation and customer capital as control variables in their regression models, following in line with previous researchers. Furthermore, Bayraktaroglu et al. (2019) introduced a modification to the VAIC^TM model by directly considering the impact of customer capital, represented by marketing costs and R&D investments, on corporate value added. Similarly to Clarke et al. (2011), the authors of this study investigated the influence of mutual interactions among different intellectual capital components on selected financial performance indicators and market value through regression analysis. Nevertheless, these adjustments do not appear to substantially alter the overall effect of IC components on firm performance. While existing studies generally report a positive influence of VAIC^TM on accounting performance indicators, it is noteworthy that the direct effects of individual IC components may vary (Janošević et al. 2013; Al-Musali and Ismail 2014).

3. Research Methodology

In our study, we formulate hypotheses based on established theoretical frameworks linking intellectual capital to corporate performance. The Value-Added Intellectual Coefficient (VAIC™) model decomposes intellectual capital into three key components: human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE), each of which is theorized to contribute to firm performance in distinct ways.

H1. 

Human capital efficiency (HCE) positively affects corporate performance indicators, such as return on equity (ROE), return on assets (ROA), and turnover of assets (ATO).

The theoretical basis for this hypothesis stems from the resource-based view (RBV) of the firm, which posits that human capital is a unique and valuable resource that can drive competitive advantage (Barney 1991). Human capital, including employee skills, knowledge, and innovation, directly influences a firm’s productivity and adaptability. In this context, HCE reflects the efficient utilization of these intangible assets, essential for enhancing profitability and operational efficiency. This suggests that higher human capital efficiency will lead to improved financial outcomes such as ROE, ROA, and ATO, as firms with well-developed human capital are more likely to generate superior returns on both equity and assets.

H2. 

Structural capital efficiency (SCE) positively affects corporate performance indicators, such as return on equity (ROE), return on assets (ROA), and turnover of assets (ATO).

The theory behind SCE’s impact is grounded in organizational learning theory and knowledge management literature, which highlight the importance of a firm’s internal structures, processes, and systems in leveraging knowledge (Nonaka and Takeuchi 1995). Structural capital includes elements like organizational culture, information systems, and intellectual property, which facilitate efficient business operations. The more effectively these structures support human capital, the better firms can convert knowledge into value creation, improving their performance. Therefore, firms with high SCE should experience greater ROE, ROA, and ATO due to the optimized use of knowledge and processes.

H3. 

Capital employed efficiency (CEE) positively affects corporate performance indicators, such as return on equity (ROE), return on assets (ROA), and turnover of assets (ATO).

The capital employed efficiency (CEE) hypothesis draws on traditional economic theory and the theory of capital structure, which suggests that effective utilization of physical and financial capital leads to better firm performance. CEE reflects the ability of firms to generate profits by efficiently managing their tangible assets and financial resources. Firms with high CEE are likely to improve their financial ratios, such as ROE, ROA, and ATO, because they are better at converting capital investments into financial returns.

By grounding each hypothesis in established theoretical frameworks, the connections between VAIC™ sub-components and corporate performance indicators become clearer and more robust. These theories provide the foundation for expecting a positive relationship between the intellectual capital sub-coefficients and the firm’s financial outcomes.

We analyzed data from the “Orbis” database, which provides comprehensive financial and business information on European public and private companies. To ensure the robustness and validity of our analysis, a meticulous approach was adopted in selecting and refining the dataset from the “Orbis” database, which offers extensive financial and business data on European public and private companies. Our initial dataset included 4687 enterprises from 18 European countries, covering ten years from 2009 to 2018. To maintain data accuracy and relevance, we implemented a series of stringent criteria for sample selection.

Inclusion Criteria: (a) Geographical Focus: We specifically selected companies based in Germany, France, and Switzerland due to their historical significance in innovation and their high volume of patent and trademark applications, which provided a relevant context for our study on intellectual capital. (b) Financial Stability: To ensure the reliability of financial performance measures, we included only companies with positive Earnings Before Interest and Taxes (EBIT). This exclusion was crucial as firms with negative EBIT, indicating a lack of profitability, could distort the analysis of intellectual capital’s impact on financial performance.

Exclusion Criteria: (a) Data Quality: Records with incomplete or inaccurate financial information were excluded to ensure the integrity of the analysis is maintained. This filtering step was necessary to ensure that the dataset accurately reflected the financial status and operational performance of the companies. (b) Sector Representation: Companies from sectors with minimal representation were consolidated into an “Other services” group to maintain a robust dataset while preserving observation counts. However, this sector was not analyzed separately due to its low individual representation.

After applying these criteria, the dataset was refined to include 240 companies with complete and accurate data spanning the period from 2009 to 2018. The final dataset was organized into a balanced panel, yielding 2400 data points. The largest share of the dataset comprises French companies (84.43%), followed by German companies (11.90%), and Swiss companies (3.70%). The sectoral distribution was also categorized according to NACE Rev.2 classifications, with sectors varying in representation from 36.90% to 13.10%.

These rigorous selection and filtering processes were essential for ensuring that the final dataset provided a reliable foundation for examining the impact of intellectual capital on financial performance. Future research could build upon this work by exploring additional countries or sectors to further validate and extend these findings. The variables used in our analysis are presented in

Table 1. Variables used in analysis. Source: own elaboration.

Dependent Variables
ROE	Return on equity = Net income/shareholders’ equity.
ROA	Return on assets = Net income/total assets.
ATO	Assets turnover = Total revenue/total assets.
Independent Variables
HCE	Human capital efficiency = Value added/Employee costs (Pulic (2000)).
SCE	Structural capital efficiency = (Value added less employee costs)/Value added (Pulic (2000)).
CEE	Capital employed efficiency = Value added/(Total assets less current liabilities) (Pulic (2000)).
Control Variables
LEV	Leverage = Long-term debt/ Total assets (Ozkan et al. (2017)).
SIZE	Firm size = Natural logarithm of number of employees (Wang (2011)).
Dummy Variables
R&D	Dummy variable, which assumes a value of 0 or 1, signaling the absence or presence of R&D expenditures for the respective year.
PATRADE	Dummy variable, which assumes a value of 0 or 1, signaling the absence or presence of patents and trademarks for the respective year.

.

To provide an overview of the data utilized in our analysis, we present a summary of descriptive statistics in

Table 2. Descriptive statistics. Source: own elaboration.

Variable	Mean	SD	Min	1Q	Median	3Q	Max
ROE	0.07	0.25	−6.30	0.04	0.09	0.14	0.91
ROA	0.04	0.06	−0.33	0.01	0.04	0.06	0.41
ATO	0.95	0.52	0.00	0.61	0.88	1.19	4.63
HCE	1.76	1.22	0.29	1.23	1.45	1.78	14.13
SCE	0.32	0.23	−2.43	0.19	0.31	0.44	0.93
CEE	0.73	0.82	0.00	0.34	0.52	0.83	9.94
SIZE	7.93	2.16	0.69	6.30	0.72	9.40	13.07
LEV	0.14	0.12	0.00	0.04	0.12	0.21	0.62

. The following table presents the descriptive statistics of the final dataset for the period ranging from 2009 to 2018. The mean values for ROE and ROA are 0.07 and 0.04, respectively, with standard deviations of 0.25 and 0.06, respectively. The average ATO value is 0.95, indicating potential challenges in efficient revenue generation from total assets. Pulic (2008), in his publication, categorized company performance based on VAIC^TM index coefficients. He stated that an HCE between 1.44 and 1.62 indicates a good performance, and the same applies to SCE values between 0.31 and 0.38. Indeed, the mean value of human capital efficiency (HCE) at 1.76 indicates that, on average, companies were proficient in generating value-added through the effective utilization of their human capital. This suggests that the firms in the sample were successful in leveraging the skills and expertise of their employees to enhance their overall performance.

Similarly. the average value of structural capital efficiency (SCE) at 0.32 signifies that, on average, companies were moderately effective in harnessing the value embedded within their structural capital. While not as high as HCE, this value still suggests a reasonable level of efficiency in leveraging structural assets, such as patents, Trademarks, and organizational processes to create value. Moreover, the mean of capital employed efficiency (CEE) at 0.73 indicates that, on average, companies were reasonably efficient in utilizing their capital employed to generate value. This suggests that the firms in the sample were effective in deploying their financial and physical resources to drive performance and create value for stakeholders. Overall, the values of individual VAIC^TM components highlight the importance of both human and structural capital in contributing to the value creation process within companies, alongside the effective utilization of capital employed.

Based on the data described in the previous section, we analyzed and discussed the impact of intangible assets quantified using the VAIC^TM methodology, alongside firm-specific characteristics, on firm performance. Our analysis was enhanced by employing a panel data regression model with fixed effects (p-value < 0.000 in each tested model based on the Hausman specification test). We started with a linear regression model, then adjusted it for the panel data structure and tested each model with diagnostic tools to ensure it was appropriately fitted. The basic model for our regression analysis takes the following form:

$$y_{it}={x^{\prime }}_{it}\beta +z^{\prime }it\alpha +{\epsilon }_{it}$$

where ${x^{\prime }}_{it}$ consists of K regressors without a constant term; $z^{\prime }it\alpha$ includes individual effects or heterogeneity and incorporates a constant term along with individual or group-specific variables, which may remain constant over time t, whether observed or unobserved. According to Greene (2002), the fixed effects model encapsulates all observable effects and treats $z^{\prime }it\alpha$ as a group-specific constant term within the regression modeling framework. Greene (2002) emphasizes that “fixed” in this context pertains to the lack of variation over time in the unobserved constant term $z^{\prime }it$ and does not imply that these effects are deterministic rather than stochastic. In panel data models, such as the one described above, ${\epsilon }_{it}$ represents idiosyncratic error terms. It is worth noting that serial correlation among these idiosyncratic errors is a common occurrence in panel data analysis, as highlighted by Wooldridge (2009). Like Vishnu and Gupta (2014), Bayraktaroglu et al. (2019), Nadeem et al. (2019), and Singla (2020), we adjusted the basic regression model by incorporating R&D expenditures and patents, among other variables. Additionally, following scholars such as Clarke et al. (2011), Bayraktaroglu et al. (2019), and Tiwari (2022), we included interactions among VAIC^TM components in the model. Our final model takes the following form:

$$\begin{gathered} Y=\alpha +{\beta }_{1}HCE+{\beta }_{2}SCE+{\beta }_{3}CEE+{\beta }_{4}HCExSCE+{\beta }_{5}SCExCEE+{\beta }_{6}LEV \\ +{\beta }_{7}SIZE+{\beta }_{8}R\&D+{\beta }_{9}PATRADE+\epsilon \end{gathered}$$

where $Y$ represents the dependent variable (ROE, ROA, and ATO), and $\alpha$ and ${\beta }_1$ through ${\beta }_9$ represent the estimated parameters of the model, with $\epsilon$ denoting the idiosyncratic error term.

To check for individual and time effects in each model, we used the F-test and Honda test (Lagrange multiplier test):

ROE Model
F-test	F = 2.8703. df1 = 242. df2 = 2188. p-value < 0.000;
Honda test	normal = 13.14. p-value < 0.000;
ROA Model
F-test	F = 8.6884. df1 = 242. df2 = 2188. p-value < 0.000;
Honda test:	normal = 43.327; p-value < 0.000;
ATO Model
F-test	F = 82.618. df1 = 242. df2 = 2188. p-value < 0.000;
Honda test:	normal = 90.543. p-value < 0.000

The resulting models were subjected to various diagnostic tests to verify if they met the linear model assumptions for the panel data structure. These tests revealed the presence of serial correlation, cross-sectional dependence, and heteroskedasticity in the residuals of each of the three models; however, the data were stationary across all models. According to Croissant and Millo (2008), the effects of heteroskedasticity and serial correlation in panel models can be mitigated by applying a standard clustering errors estimation method, as described by Arellano (1987). This approach was implemented for all the panel models in our study. It is necessary to note that we also attempted to incorporate the lagged values of our variables into the models. However, the outcomes of the models did not yield any economically interpretable conclusions. Therefore, we opted to use solely the variables within the same time intervals.

Our panel data analysis was conducted using R Studio and the “plm” package.

4. Results

In the country-level analysis presented in

Table 3. Panel regression results—a country level analysis for Germany (DE), France (FR), and Switzerland (CH). Source: own elaboration.

	ROE				ROA				ATO
	ROE_Model	DE	FR	CH	ROA_Model	DE	FR	CH	ATO_Model	DE	FR	CH
HCE	0.020	−0.048	0.021	0.051	0.001	−0.013	0.001	0.038	−0.007	−0.061	−0.007	0.932 *
	(0.012)	(0.063)	(0.012)	(0.553)	(0.002)	(0.025)	(0.002)	(0.080)	(0.020)	(0.182)	(0.020)	(0.375)
SCE	0.126 *	0.471	0.117	0.141	0.065 ***	0.092	0.059 ***	0.062	−0.696 ***	0.103	−0.673 ***	−2.642 ***
	(0.059)	(0.269)	(0.061)	(0.999)	(0.014)	(0.068)	(0.013)	(0.112)	(0.160)	(0.716)	(0.160)	(0.585)
CEE	0.155 **	0.235 ***	0.150 ***	0.641	0.015	0.048	0.014	−0.085	0.287 ***	0.629	0.275 ***	3.205 ***
	(0.053)	(0.068)	(0.054)	(1.216)	(0.011)	(0.094)	(0.011)	(0.150)	(0.075)	(0.418)	(0.075)	(0.640)
LEV	−0.091	−0.148	−0.103	0.179 *	−0.089 ***	−0.085 *	−0.080 ***	−0.037 *	−0.489 *	−0.963 ***	−0.562 *	0.597 *
	(0.070)	(0.195)	(0.078)	(0.080)	(0.018)	(0.042)	(0.019)	(0.018)	(0.228)	(0.282)	(0.256)	(0.236)
SIZE	0.005	0.005	0.007	−0.023 *	0.000	−0.002	0.001	−0.002	−0.002	0.011	0.003	0.008
	(0.003)	(0.008)	(0.004)	(0.011)	(0.001)	(0.003)	(0.001)	(0.001)	(0.012)	(0.031)	(0.013)	(0.012)
R&D	−0.004		−0.027		0.005		−0.006		−0.064		−0.093
	(0.012)		(0.014)		(0.004)		(0.004)		(0.052)		(0.054)
PATRADE	−0.037 *	−0.001	−0.042 *		−0.003	−0.001	−0.002		0.029	0.122	0.019
	(0.018)	(0.014)	(0.019)		(0.004)	(0.009)	(0.004)		(0.065)	(0.126)	(0.068)
HCExCEE	−0.182 **	−0.180 *	−0.178 **	−0.765	−0.018	−0.063	−0.016	0.101	−0.078	−0.037	−0.072	−2.424 ***
	(0.064)	(0.079)	(0.065)	(1.355)	(0.012)	(0.096)	(0.012)	(0.148)	(0.073)	(0.459)	(0.073)	(0.626)
SCExCEE	1.319 ***	0.986 ***	1.324 ***	2.925	0.298 ***	0.641 ***	0.269 ***	0.242	1.629 ***	0.622	1.564 ***	6.720 ***
	(0.277)	(0.300)	(0.298)	(2.770)	(0.044)	(0.183)	(0.041)	(0.217)	(0.354)	(1.449)	(0.372)	(0.871)
Clustering of standard errors:	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes
Number of observations:	2440	290	2060	90	2440	290	2060	90	2440	290	2060	90
R2	0.266	0.583	0.249	0.735	0.490	0.782	0.456	0.932	0.437	0.516	0.437	0.944
Adjusted R2	0.261	0.557	0.242	0.677	0.487	0.769	0.451	0.917	0.433	0.486	0.432	0.931

Note: Model represents aggregated results for the entire dataset spanning 240 companies over a 10-years period. The stars denote level of significance. If the p-value is less than 0.05 (*); if the p-value is less than 0.01 (**); and if the p-value is less than 0.001 (***).

, no statistically significant positive impact of human capital efficiency (HCE) on firms’ performance indicators, including return on equity (ROE), return on assets (ROA), and productivity (ATO), was observed. We did not uncover a direct correlation between human capital and financial performance in France and Germany. However, HCE was found to positively influence the financial performance of Swiss companies. Conversely, a positive association was identified between structural capital efficiency (SCE) and performance indicators, such as ROE and ROA. This indicates that the companies were able to adjust their business models during the reference period, using assets within their corporate structure effectively, resulting in improved profitability measures. However, the hypothesis of a statistically significant positive effect of SCE on ATO was not confirmed.

Our results also reveal a positive effect of capital employed efficiency (CEE) on ROE and ATO, but no significant effect on ROA was established. In general, our results align with the findings of Firer and Williams (2003), Chen et al. (2005), and Bayraktaroglu et al. (2019), who suggested that VAIC^TM components are positively associated with firm performance. However, it should be noted that the effect of individual IC components may vary depending on regional or industry-specific factors.

To mitigate the acknowledged limitations of the VAIC^TM method, we incorporated the interaction between human and employed efficiency (HCExSCE) and the interaction between structural and capital employed efficiency (SCExCEE) into our country-level models. While the interaction between structural capital and capital employed efficiencies exhibited a statistically significant positive effect on all three corporate performance indicators, human capital and capital employed efficiencies negatively impacted these indicators. The analyzed companies can only achieve higher profits and generate positive cash flows through the efficient utilization of corporate structural capital alongside financial resources. This phenomenon can be explained by the essential role that corporate structural capital plays in enhancing overall efficiency and competitiveness. Investments in enterprise infrastructure, software solutions, market strategies, and customer relationships are all components of structural capital that contribute to the long-term sustainability and growth of a company. Additionally, our findings suggest that an increase in the number of patents and trademarks may decrease performance in terms of ROE, ROA, and ATO.

An industry-level analysis revealed significant differences between business sectors in our dataset, visible in

Table 4. Panel regression results—an industry level analysis for France (FR). Source: own elaboration.

	ROE				ROA				ATO
	FR_C	FR_J	FR_K	FR_M	FR_C	FR_J	FR_K	FR_M	FR_C	FR_J	FR_K	FR_M
HCE	0.005	0.198	−0.004	0.121	0.024	−0.012	0.003	−0.007	1.661	−0.117 **	−0.028	0.126
	(0.042)	(0.169)	(0.008)	(0.108)	(0.023)	(0.026)	(0.003)	(0.008)	(1.318)	(0.043)	(0.050)	(0.077)
SCE	0.127	−0.108	0.023	−0.409	−0.006	0.041	0.041 *	0.090 **	−1.720	−0.136	−0.472	−1.524 ***
	(0.128)	(0.197)	(0.067)	(0.403)	(0.024)	(0.048)	(0.017)	(0.031)	(1.062)	(0.151)	(0.245)	(0.375)
CEE	0.283 *	0.493	−0.139	0.174	0.139	−0.013	0.026	0.001	4.248	0.263 ***	0.259	0.233
	(0.136)	(0.398)	(0.121)	(0.218)	(0.097)	(0.053)	(0.032)	(0.018)	(2.650)	(0.059)	(0.524)	(0.166)
LEV	0.018	−0.522	−0.014	−0.169	−0.030	−0.075	−0.072	−0.098 ***	−1.289	−0.341	−0.162	−0.379
	(0.056)	(0.445)	(0.190)	(0.118)	(0.032)	(0.056)	(0.041)	(0.030)	(1.536)	(0.177)	(0.562)	(0.196)
SIZE	−0.002	0.015	0.000	0.011	−0.003	0.002	0.000	0.001	−0.031	0.012	0.021	0.004
	(0.004)	(0.014)	(0.007)	(0.008)	(0.003)	(0.002)	(0.001)	(0.002)	(0.035)	(0.008)	(0.033)	(0.016)
R&D	0.007	−0.025	0.005	−0.066 **	(0.000	−0.021	0.002	−0.015 *	−0.219	−0.069 *	−0.136	−0.009
	(0.008)	(0.041)	(0.033)	(0.024)	0.004)	(0.012)	(0.008)	(0.007)	(0.167)	(0.033)	(0.143)	(0.056)
PATRADE	−0.023 *	−0.053	−0.090 **	−0.080	(0.001	0.005	−0.016 *	−0.002	0.279	0.024	0.057	−0.136
	(0.011)	(0.072)	(0.030)	(0.070)	(0.008)	(0.018)	(0.008)	(0.007)	(0.300)	(0.063)	(0.122)	(0.141)
HCExCEE	−0.373 *	−0.487	0.116	−0.448	−0.208 *	0.015	−0.030	−0.011	−3.891	0.083	−0.061	−0.203
	(0.151)	(0.396)	(0.120)	(0.348)	(0.101)	(0.053)	(0.033)	(0.021)	(2.564)	(0.061)	(0.534)	(0.175)
SCExCEE	1.598 ***	1.749 *	1.199 ***	2.832	0.868 ***	0.222 *	0.341 ***	0.324 ***	5.134 *	0.579 ***	1.717	2.553 ***
	(0.335)	(0.698)	(0.199)	(1.470)	(0.175)	(0.099)	(0.058)	(0.069)	(2.405)	(0.156)	(0.986)	(0.758)
Clustering of standard errors:	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes
Number of observations:	290	300	290	740	290	300	290	740	290	300	290	740
R2	0.744	0.252	0.459	0.293	0.720	0.485	0.519	0.433	0.169	0.893	0.574	0.557
Adjusted R2	0.727	0.204	0.423	0.275	0.702	0.452	0.487	0.419	0.114	0.886	0.546	0.546

Note: The stars denote level of significance. If the p-value is less than 0.05 (*); if the p-value is less than 0.01 (**); and if the p-value is less than 0.001 (***).

for French companies and

Table 5. Panel regression results—an industry level analysis for Germany (DE). Source: own elaboration.

	ROE				ROA			ATO
	DE_C	DE_J	DE_M	DE_C	DE_J	M	DE_C	DE_J	DE_M
HCE	0.334	0.075 ***	−1.057 **	0.127 *	0.052 ***	−0.248	0.297 *	0.491 ***	−1.956
	(0.229)	(0.012)	(0.361)	(0.055)	(0.007)	(0.157)	(0.135)	(0.072)	(1.454)
SCE	−0.426	0.034	2.532 ***	−0.334	0.008	0.611 *	−0.840 *	−2.104 ***	4.064
	(0.575)	(0.098)	(0.664)	(0.185)	(0.063)	(0.283)	(0.365)	(0.332)	(2.860)
CEE	0.383	0.713 ***	−1.217	0.164	0.442 ***	−0.342	1.687 ***	0.856 *	−2.945
	(0.368)	(0.136)	(0.637)	(0.113)	(0.074)	(0.249)	(0.416)	(0.362)	(2.244)
LEV	−0.704 *	−0.255	0.176	−0.163 *	−0.157 *	−0.083 **	−0.736 *	0.345 ***	−1.508 ***
	(0.311)	(0.129)	(0.109)	(0.071)	(0.063)	(0.029)	(0.329)	(0.056)	(0.401)
SIZE	0.014	0.017 ***	−0.012	−0.003	0.009 ***	−0.002	0.024	−0.020 ***	0.030
	(0.011)	(0.004)	(0.007)	(0.004)	(0.002)	(0.003)	(0.021)	(0.004)	(0.029)
R&D
PATRADE			0.024			0.017			0.241 **
			(0.016)			(0.015)			(0.091)
HCExCEE	−0.758 *	−0.746 ***	1.416 *	−0.369 **	−0.450 ***	0.372	−0.990 ***	−0.358	3.948
	(0.346)	(0.126)	(0.693)	(0.121)	(0.068)	(0.261)	(0.214)	(0.327)	(2.420)
SCExCEE	2.695 **	1.718 ***	−1.982	1.715 ***	1.019 ***	−0.271	3.116 ***	2.009 ***	−7.705
	(0.876)	(0.077)	(1.269)	(0.519)	(0.049)	(0.470)	(0.777)	(0.418)	(4.624)
Clustering of standard errors:
Number of observations:	120	40	100	120	40	100	120	40	100
R2	0.547	0.947	0.835	0.772	0.949	0.892	0.711	0.906	0.843
Adjusted R2	0.477	0.911	0.801	0.737	0.913	0.870	0.666	0.842	0.811

Note: The stars denote level of significance. If the p-value is less than 0.05 (*); if the p-value is less than 0.01 (**); and if the p-value is less than 0.001 (***).

for German companies. Interestingly, HCE positively affects the performance measures of German companies in manufacturing (C) and the information and communication sector (J). Conversely, the ATO measure is negatively affected by HCE in the case of French companies operating in sector J at the 0.01 significance level. This finding aligns with the study by Xu and Liu (2020), where the authors demonstrated that HCE positively impacts ROA and ROE, while it is associated negatively with ATO in the Korean manufacturing industry. In our study, the negative association between HCE and ATO may be explained by the fact that the way in which French companies use their human capital in sector J is not sufficient to deploy corporate assets to produce higher revenues efficiently. Additionally, we found a negative impact of HCE on ROA in the case of German companies from the professional, scientific, and technical services (M) sector.

The individual impacts of SCE and CEE on performance measures vary significantly across different business sectors and countries. SCE demonstrates a positive and significant association with ROA in French sectors M and K, but it negatively affects ATO in German sectors J and C, as well as in French sector M. On the other hand, SCE positively influences ATO in companies within French sector M. Interestingly, the efficient utilization of corporate capital and financial resources (CEE) appears to exert the most pronounced influence on the performance of French and German companies compared to other VAICTM components. Moreover, the findings presented in this study indicate that CEE has a positive impact on ROA in German sector J and ROE in French sectors J and C. Similarly, it shows a positive and significant relationship with ATO in German sectors J and C, as well as in French sector J. It is worth noting that CEE has the strongest impact on performance indicators among the VAIC^TM components, as discussed previously.

To address the limitations of the original VAIC model, we integrated interactions between human capital efficiency and invested capital (HCExSCE) and between structural capital efficiency and invested capital (SCExCEE) into our analysis. Our findings reveal a negative trend, where the interaction of human capital efficiency and invested capital adversely affects company performance indicators in our dataset. Specifically, the HCExCEE variable significantly impacts return on equity. In contrast, the interaction of structural capital efficiency and invested capital positively influences all three performance indicators. This suggests that effective utilization of structural capital alongside financial resources enhances profitability and cash flow generation for firms.

Additionally, we investigated whether research and development activities (R&D and PATRADE dummy variables) positively impact performance indicators in our dataset. However, our results indicate no statistically significant positive influence of these variables, except for a negative impact of the PATRADE variable on ROE at a 5% significance level.

5. Discussion

Based on the findings of our analysis, we partially confirmed the initial hypotheses proposed for this study. Similar to findings by Clarke et al. (2011), Guo et al. (2012), Xu and Liu (2020), and Li et al. (2021), we partially confirmed the hypotheses regarding the positive impact of VAICTM components on company performance. Our analysis revealed that intellectual capital significantly impacts financial performance indicators, such as return on equity (ROE), return on assets (ROA), and asset turnover (ATO). Estimations from complex and partial models showed that the impact of VAIC^TM index components varies across countries and industry sectors. Numerous other studies assessed financial performance based on the VAIC^TM index (Pal and Soriya 2012; Rossi and Celenza 2014; Poh et al. 2018; Haris et al. 2019), but only a few focused on the empirical research of VAIC component impacts on Western and Central Europe. Sardo and Serrasqueiro (2017) analyzed a dataset of 1052 European firms, including those from Germany and France. They took a holistic approach without separately analyzing the impact of individual VAIC^TM components on ROA in each country. Their findings indicated a statistically significant positive effect of the individual components of the VAIC index on firms’ asset profitability from 2004 to 2015. Since French and German firms were part of the analyzed dataset, the study did not identify specific trends in how the IC components influenced the chosen performance indicators in Germany and France. In contrast to Sardo and Serrasqueiro (2017), our comprehensive model did not confirm hypothesis H1, regarding the positive impact of human capital efficiency on ROE, ROA, and ATO. However, hypothesis H2, concerning the positive impact of structural capital efficiency on ROE and ROA, was partially confirmed. The efficiency of capital employed positively affected ROE and ATO, partially confirming the hypothesis H3. These findings are inconsistent with those of Dženopoljac et al. (2016), who empirically demonstrated that SCE and CEE negatively impact ROE and ROA in the Serbian ICT industry. Moreover, our analysis unveiled that the effectiveness of companies in leveraging their overall financial capital (capital employed) had the most substantial impact on their performance. Particularly notable was the variable CEE, which exerted the strongest impact on performance indicators compared to other components of the VAIC index. This result aligns with the study conducted by Li et al. (2021), where the authors analyzed the effect of VAIC^TM components on performance measures of the 100 innovative companies indexed by Forbes in 2016. They showed that CEE has the greatest impact on performance measures in terms of ROA and ROE.

An interesting finding emerges when considering the interaction between HCE and CEE, which negatively affects performance measures in both ROE and ROA models in German business sectors J and C. Conversely, the interaction between SCE and CEE is positively and significantly related to all three performance measures in German business sectors J and C, as well as in all French companies. These results align with the findings of Bayraktaroglu et al. (2019). Importantly, the effect of the SCE and CEE interaction surpasses the individual effects of the SCE and CEE variables, respectively, on the performance measures of French and German companies.

In our analysis, the control variables (LEV and SIZE) indicate that increasing corporate debt significantly reduces profitability and asset turnover indicators, consistent with the findings from previous studies, like Xu and Wang’s (2018). The impact of firm size did not prove significant in our models, except for the ATO variable. However, we consistently observed a negative impact of the SIZE variable across all three performance indicators. This suggests that hiring additional employees may not necessarily increase company profits within the observed period under unchanged conditions.

To explore how innovative activities impact the financial performance of companies, we analyzed dummy variables representing research and development (R&D) expenditures and the number of patents. However, we did not find empirical evidence supporting a positive influence of R&D spending on performance indicators. Specifically, in French firms’ manufacturing (C) and information and communication (M) sectors, we observed a negative trend where investments in R&D appeared to temporarily reduce profitability. This could be attributed to the initial substantial financial outlay for R&D projects, leading to a short-term decline in profits, with benefits from these projects potentially appearing in later periods.

This study contributes to the growing body of research on the relationship between Value-Added Intellectual Coefficient (VAIC™) components and firm performance by providing nuanced insights into how these components interact across different countries and industries. The results reveal that, while certain aspects of VAIC™ positively influence financial performance indicators, such as return on equity (ROE), return on assets (ROA), and asset turnover (ATO), the effects are not uniform across all contexts. This contrasts with earlier studies that primarily offered broad, generalized findings. This analysis confirms that structural capital efficiency (SCE) and capital employed efficiency (CEE) positively impact ROE and ATO, with CEE having the most pronounced effect on performance measures. This finding extends the work of Firer and Williams (2003), Chen et al. (2005), and Bayraktaroglu et al. (2019), who established a positive association between VAIC™ components and firm performance. However, unlike these studies, our research highlights the differential effects of VAIC™ components across countries and sectors, a nuance not fully addressed in previous literature. For instance, while SCE positively impacts ROE and ROA, it does not significantly affect ATO, diverging from the broader positive associations reported by Sardo and Serrasqueiro (2017). This indicates that the effectiveness of structural capital may be contingent on specific industry contexts and geographical factors.

The study’s results challenge some previous findings, particularly those of Dženopoljac et al. (2016), who reported the negative impacts of SCE and CEE on ROE and ROA in the Serbian ICT industry. In contrast, our study finds that CEE, in particular, has a substantial positive effect on performance indicators, aligning with Li et al. (2021), who observed similar results for innovative firms. This discrepancy underscores the importance of contextual factors in determining the impact of intellectual capital components, suggesting that the relationship between VAIC™ components and performance is more complex than previously understood. The study also extends existing research by exploring the interactions between VAIC™ components. Specifically, the positive interaction between SCE and CEE on all performance indicators is a significant addition to the literature. This finding corroborates Bayraktaroglu et al. (2019), who noted that the combined effect of these components can enhance firm performance more effectively than their contributions. On the other hand, the negative impact of the interaction between human capital efficiency (HCE) and CEE on performance measures in certain sectors, such as German business sectors J and C, presents a novel insight into how different intellectual capital types can influence financial outcomes.

The investigation into the impact of research and development (R&D) activities reveals that, contrary to some expectations, R&D expenditures do not consistently improve financial performance in the short term. This is consistent with the findings of Xu and Liu (2020), who observed a temporary reduction in profitability due to substantial initial investments in R&D. This suggests that, while R&D can be crucial for long-term growth, its immediate impact on profitability may not be as pronounced, highlighting the need for a strategic balance between investment and short-term financial performance.

The findings of this study offer several actionable recommendations for practitioners aiming to leverage intellectual capital for enhanced financial performance. First, companies should focus on optimizing their structural capital investments, as our study highlights that the efficient use of structural capital, such as enterprise infrastructure, software solutions, and market strategies, can significantly improve financial performance. Firms should regularly assess their structural capital to ensure that it aligns with their strategic objectives and contributes effectively to their financial outcomes. Second, businesses should strategically manage the interaction between structural capital and capital employed to maximize profitability. This entails integrating financial resources with well-developed structural capital to enhance overall efficiency and competitiveness. For example, firms could invest in advanced technologies and systems that complement their financial investments, improving operational effectiveness and higher returns.

Third, companies should be cautious when acquiring and managing patents and trademarks. While these assets can offer competitive advantages, their management should be carefully planned to avoid potential complexities and costs that might negatively impact short-term performance. Firms should evaluate the cost–benefit ratio of intellectual property investments and consider how these assets fit into their broader business strategy. Finally, given the varying impacts of intellectual capital components across different industries and regions, companies should tailor their intellectual capital strategies to their specific sector and geographic location. Customizing approaches based on industry-specific dynamics and regional economic conditions can enhance the relevance and effectiveness of intellectual capital investments.

In summary, our study provides valuable insights into how intellectual capital can be effectively utilized to boost financial performance. By focusing on optimizing structural capital, strategically managing the interaction between capital components, and carefully handling intellectual property investments, companies can enhance their financial outcomes and maintain a competitive edge in their respective markets.

6. Conclusions

This study investigates the possible impact of intellectual capital components on ROE, ROA, and ATO. The strength of our work lies in a comprehensive analysis based on the cross-country comparison of publicly traded companies. Together, our results suggest that it is essential for companies to develop those intellectual capital components, which contribute the most to financial performance. It should be noted that IC components do not create value independently; rather, their impact is magnified when combined with other assets. Moreover, the individual effect of IC components may differ between business sectors. Therefore, understanding how the interactions between human, physical, and financial resources impact the profitability of companies is crucial. Surprisingly, human capital did not prove to be the most important driving force behind the economic performance of companies in terms of ROE, ROA, and ATO; instead, the interaction between SCE and CEE did. Our research showed that the efficient combination of physical and financial resources in the form of structural capital and capital employed may have the greatest impact on the financial performance and productivity of companies across different business sectors and economies. Our findings also suggest that patents and trademarks are valuable assets for protecting intellectual property and gaining competitive advantages. However, their acquisition and management can introduce complexities and costs that may temporarily impact accounting performance measures. In conclusion, the final effect of individual intellectual capital (IC) components expressed through the VAICTM index may be mixed, depending on country and industry-specific attributes.

From an academic and scientific standpoint, these findings contribute valuable empirical research applied to companies in Germany, France, and Switzerland, highlighting differences across countries and industries.

Despite these contributions, our study is not without limitations. First, selection bias may affect the generalizability of our findings. Our dataset is limited to publicly traded companies in specific countries, which might not represent the broader business landscape, including privately held firms and companies in other regions. Future research could address this by including a more diverse sample of public and private firms across a wider range of countries to enhance the generalizability of the findings.

Second, generalizability concerns arise from the focus on specific industries and countries. The results may not apply to firms operating in different sectors or regions with varying economic conditions. Future studies should consider a more comprehensive range of industries and geographical areas to overcome this limitation, allowing for a broader understanding of how intellectual capital components impact financial performance in diverse contexts.

Third, measurement issues related to the accuracy and reliability of intellectual capital metrics could influence the results. Variations in how intellectual capital is measured and reported across companies might lead to inconsistencies in the data. Future research could improve measurement accuracy by standardizing intellectual capital reporting practices and incorporating longitudinal data to assess the impact of these components over time.

In summary, while the study provides valuable empirical insights into the role of intellectual capital components in enhancing financial performance, addressing these limitations in future research will offer a more robust and comprehensive understanding of the impact of intellectual capital across different contexts and conditions.