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Value-added intellectual coefficient (VAIC) is extensively used as a measure of intellectual capital (IC), but it is criticized for not capturing the totality of IC. Therefore, this study aims to analyse critiques of the original VAIC and proposes a modified VAIC by adding missing IC components and adjusting for exogenous factors. The study uses a modified VAIC model to investigate the relationship between IC, firm performance (FP) and market value (MV) for US non-financial firms.

This study employed fundamental data of US non-financial firms listed on the NYSE and NASDAQ from 1980 to 2019. A final sample consisted of 6,019 firms and 62,686 firm-year observations.

The results provide a significant positive effect of aggregate and components of modified VAIC on FP and MV. Moreover, results validate the modified VAIC model and find that the modified VAIC explains changes in shareholders' MV. In addition, findings indicate that modified VAIC serves as an additional intangible factor to explain firms' capital structure decisions.

The findings have important implications for management, owners, researchers and investors.

The modified VAIC model differs from the original VAIC model in four ways: first, it corrects the measurement of structural capital efficiency (SCE) following the accounting principle. Second, it replaces SCE with innovation capital efficiency (InVCE) and relational capital efficiency (RCE) to account for missing components of information of structural capital (SC). Third, the modified VAIC model adjusts for exogenous factors like business cycles and cross-industry variations. Finally, with the addition of InVCE and RCE as components of SCE, innovation capital (InVC) and relational capital (RC) are added to the calculation of value-added (VA) as components of IC.

The purpose of this study is to analyse the validity of the value added intellectual coefficient (VAIC) method as an indicator of intellectual capital.

The paper describes VAIC through its calculation formulae and aims to establish what exactly it is that the method measures. It also looks in detail at how intellectual capital is understood in the method, and discusses its conceptual confusions. Furthermore, the paper tests the hypothesis according to which VAIC correlates with a company's stock market value, and reflects the contradictory results of earlier studies.

The analyses show, first, that VAIC indicates the efficiency of the company's labour and capital investments, and has nothing to do with intellectual capital. Furthermore, the calculation method uses overlapping variables and has other serious validity problems. Second, the results do not lend support to the hypothesis that VAIC correlates with a company's stock market value. The main reasons behind the lack of consistency in earlier VAIC results lie in the confusion of capitalized and cash flow entities in the calculation of structural capital and in the misuse of intellectual capital concepts.

The analyses show that VAIC is an invalid measure of intellectual capital.

The result is important since the method has been widely used in micro and macro level analyses, but this is the first time it has been put to rigorous scientific analysis.

This study proposes a comparison between Value Added Intellectual Coefficient (VAIC) and one of the most important performance evaluation methods, the Economic Value Added (EVA), starting from a re-interpretation of the VAIC.

The empirical data were gathered from AMADEUS Bureau van Dijk and consist of 2,596 companies operating in Northern Italy, from six different economic sectors, observed for the year 2011. A correlation analysis was carried out in order to highlight whether there is a relationship between the two concepts of VAIC and EVA.

Results show that EVA and VAIC have no significant relationships; as a matter of fact, EVA is based on financial theory, whereas VAIC is focalised on the assessment of Intellectual Capital Efficiency (ICE).

Managers could be misled due to the fact that they often make decisions by taking into account only financial indicators such as EBIT, EVA, etc. Although methods like EVA have improved modern accounting systems, they do not take into account information linked to ICE. Therefore, these two perspectives can be useful in a context in which firms' performances are measured through multi-criteria methodologies (i.e. Balanced scorecard).

The proposal describes the differences between VAIC and EVA considering these two concepts as not contrasting. In fact, in order to better measure firms' performances, it could be useful to consider VAIC and EVA as an integrated vision in order to develop multi-criteria evaluation systems, rather than consider them separately.

The purpose of this paper is to propose an extended and modified value-added (VA) intellectual coefficient (VAIC) model, which includes intellectual capital (IC) components which were missing in the original VAIC approach. The proposed model has been used to explore the relationship between IC and firm performance for Turkish manufacturing firms on a more detailed level.

Multiple regression analysis has been employed to identify the IC components, which predict the performance of the firm and the moderating effect of some IC components on IC components–firm performance relationship. Data are required to calculate the IC components, and firm performance variables have been obtained from the financial reports of the Turkish manufacturing firms for the period 2003–2013.

According to the results for Turkish manufacturing sector innovation capital efficiency has a moderating effect on the relationship between structural capital efficiency (SCE) and profitability, meaning, depending on an increase in R&D expenses, the effect of SCE on profitability also increases. On the other hand, it has been found that innovation capital efficiency has a direct impact on firms’ productivity. The results also showed that IC efficiency components have a moderating role on the relationship between capital employed efficiency and profitability.

There might be a time lag until the effect of R&D investments can be observed in firms’ performance. However, this lagged impact of innovation capital and also other IC components on future firm performance has not been investigated due to concerns related to sample size.

The proposed model differs from the original VAIC model in three ways: it, namely, includes two additional IC components, customer capital (CC) and innovation capital. It explores the moderating effect of innovation capital on structural capital–firm performance relationship and the moderating effect of IC components on employed capital–firm performance relationship. As the last difference, it proposes an alteration in the VA calculation due to newly added IC components, CC and innovation capital.

The aim of this paper is to investigate the relationship between corporate performance and Value Added Intellectual Coefficient (VAICTM) on the one hand, and the relationship between the variations in market value and the variations in VAIC on the other hand.

Starting from the VAIC model, 23 Italian listed companies were examined with the aim of investigating the relationship between VAIC and the performance of the firms in the sample. The analysis was divided into two stages. In the first stage, eight models of linear regression were estimated to verify the presence of a positive and statistically significant relationship between M/BV and VAIC and between accounting performance indicators (ROE, ROI, ROS) and the VAIC. In the second stage, six other models were tested, considering as an independent variable the variations in VAIC and the variations in profitability indicators.

The outcomes of the application stress the importance of VAIC in the explanation of the variations in MV and its role as “additional coefficient” in the analysis of equity performance.

This methodology highlights some very interesting aspects. In particular, whereas the relationship between M/BV and VAIC and between profitability indicators (ROI, ROE, ROS) and VAIC is statistically insignificant, the subsequent analysis highlights the importance of VAIC as a variable capable of increasing the explanatory power of the regression in a cross-sectional perspective.

The purpose of this paper is to empirically assess the impact of value-added intellectual capital (VAIC) on the financial performance (FP) of companies in Mauritius.

The research uses a dynamic panel vector error correction model (PVECM) which simultaneously allows for endogeneity and causality issues among the variables used.

The results show that VAIC enhances corporate FP, with a reported lower effect in the short run as compared to the long run. Other important determinants of firm’s performance are asset turnover, capital turnover and firm’s size. Leverage, on the other hand, is observed to be performance reducing in nature. FP of the companies is also a significant determinant of VAIC, implying reverse causal effects exist between the two variables of interest, namely, VAIC and FP.

The study can be enhanced by doing an industry-specific comparison of the impact of VAIC on FP for more insights.

It is recommended that managers pay more attention to the role of firms’ stock of tangible and intangible assets, as this has a positive impact on firms’ FP. Also, the results may help to increase awareness of the importance of effective intellectual capital (IC) management within an organization. More so, as demonstrated by Ståhle et al. (2011), VAIC indicates the efficiency of the company’s labor and capital investments within firms in Mauritius. This study may, therefore, enable Mauritian firms to measure their IC efficiency and develop policies to promote and improve upon their intellectual potential to enhance firm’s performance.

The main theoretical contribution of this paper relates to the assessment and conceptualization of the bi-directional relationship between VAIC and FP. It confirmed that there are self-reinforcing feedback effects between VAIC and FP. Methodologically speaking, this paper investigates the VAIC–FP nexus in a dynamic setting using a dynamic panel data framework, namely, a PVECM which also allows for additional insights into the short- and long-run effects.

This study aims to investigate the intellectual capital–financial performance relationship using two models, namely the conventional Value-Added Intellectual Coefficient (VAIC) model and the adjusted Value-Added Intellectual Coefficient (A-VAIC) model.

This study is designed as a quantitative research focusing on the relationship between intellectual capital and financial performance of the banking industry in Indonesia. As many as 114 data are derived from the publicly listed banks on the Indonesia Stock Exchange for the period of 2012–2017. The multiple regression analysis is employed to test the hypotheses studied.

In general, the result confirms that intellectual capital affects financial performance. Although not all hypotheses of the study are supported by either the VAIC model or the A-VAIC model, the results provide a deeper and new insight on how each component of intellectual capital efficiency (human capital, structural capital, capital employed, innovation capital) relates to financial performance (return on asset, return on equity, asset turnover, price to book ratio). The results also justify that further improvements in measuring intellectual capital are still needed in the future.

This study limits its generalization since the sample is only in the Indonesian banking industry. Notwithstanding the limitation, the results imply that the Indonesian banking managers need to be aware of intellectual capital management because of its strategic role in enhancing financial performance.

This study contributes to the intellectual capital literature by providing empirical evidence on the use of both models, namely the conventional VAIC and the A-VAIC in the Indonesian banking industry research setting which is never been studied before.

This study has the social implication to the enhancement of the quality life of the society. The higher the quality of intellectual capital in the banking firms, the better the banks serve the needs of the community.

This study contributes to the IC literature by providing empirical research on the use of the VAIC model and the A-VAIC model in the Indonesian banking industry.

This study aims to examine the effect intellectual capital (IC) has on firm performance of Australian companies.

Quantitative data are collected for Australian companies listed between 2004 and 2008. IC is measured using Pulic's value added intellectual coefficient (VAIC) and its components (human, structural and capital employed efficiencies (HCE, SCE, CEE)). Direct and moderating relationships between VAIC, HCE, SCE, and CEE and four measures of performance are statistically analysed.

The results suggest that there is a direct relationship between VAIC and performance of Australian publicly listed firms, particularly with CEE and to a lesser extent with HCE. A positive relationship between HCE and SCE in the prior year and performance in the current year is also found. However evidence also suggests the possibility of an alternative moderating relationship between the IC components of HCE and SCE with physical and financial capital (CEE) which impacts on firm performance.

There are some missing data and some transgression of the assumptions of OLS regression.

This paper presents the first study of the IC relationship with firm performance in Australia. Inconclusive results from prior studies in developing countries suggested the need for a study from a developed country such as Australia. The paper is also the first to investigate whether IC moderates the relationship between CEE and firm performance.

The purpose of this paper is to study the relationship between intellectual capital (IC) and performance of pharmaceutical firms in India. The secondary objective is to propose and test modified models of Value Added Intellectual Coefficient (VAIC™) method.

Data on 22 large pharmaceutical firms collected for empirical investigation. Return on assets and return on sales are performance variables. IC and its components – human capital, structural capital and relational capital (RC), are predictor variables. Three extended and modified VAIC™ models (e-VAIC™) are proposed. Multiple regression technique is applied on pooled data to draw inferences.

Results show instances of positive relationship between IC and performance variables. RC, the new variable, does not demonstrate statistically significant relationship with performance variables.

Due to inadequate reporting of IC and its components, availability of data on various proxies is difficult. The new models proposed in this paper can be a template for future research and model development.

VAIC™ model, the proposed models (e-VAIC™) and the result analysis can be useful for evaluation and value creation purposes.

Previous researchers use original VAIC™ model. This paper modifies and extends the model in accordance with contemporary description and typology of IC. Inclusion of RC as a variable in VAIC™ model and use of new proxies for components of IC are the novelties of this paper.

This paper aims to address empirical analyses of the association of the VAIC and its components with firms’ market and financial performance, demonstrating that the ill definition of variables and constructs is responsible for a non-linearity concealed in the VAIC formula between two of its components (the Structural Capital Efficiency and the Human Capital Efficiency).

Through a conceptual analysis the paper identifies and formalises the non-linearity concealed in the VAIC formula and clarifies the relevant issues through an empirical analysis of a sample of Italian listed companies.

The paper finds that the non-linearity hidden in the VAIC formula should lead scholars to completely revise the ways they test the association of the VAIC and the market and financial performance of the firm. Useful insights are also provided for scholars interested in investigating the role of human capital, for those involved in analysing the interrelations among capitals through the introduction of interaction terms in their regression models and for researchers proposing modified versions of the VAIC. Practitioners could benefit from the paper as the non-linearity here discovered leads to a substantial of the decision-making based on the VAIC.

The paper offers new insights into analyses using the VAIC as it uncovers a non-linearity hidden in the VAIC, which has hitherto not been reported in the literature. The existence of this non-linearity has substantive implications for previous and future research in this domain.