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The main purpose of this study is to empirically investigate the impact of intellectual capital efficiency on US multinational software companies' performance from 2012 to 2016 by applying data envelopment analysis (DEA).

It adopts a new slacks-based measure (SBM) to obtain a more accurate performance estimation and rank between companies. Regression analysis is used to test the overall IC and each of its elements (Human Capital, Innovation Capital, Process Capital and Customer Capital).

The univariate result shows that multinational companies are more efficient than non-multinational companies. However, the regression result shows that multinationality can hardly explain the firm efficiency of software firms. Another interesting finding is that intellectual capital has a positive and significant impact on software firm performance in the US human capital influences firm efficiency directly. However, when human capital is combined with the other elements of IC, the contribution of human capital becomes less significant. This is because people may think that innovation capital, process capital and customer capital can replace human capital, but it is not. In short, human capital may affect firm efficiency through other elements of IC (innovation capital, process capital and customer capital) as it is the base of other elements.

The results show that multinational companies have higher efficiency scores than non-multinational companies. In addition, Intellectual capital has a positive and significant impact on software firm performance in the US human capital influences firm efficiency directly. However, when human capital is combined with the other elements of IC, the contribution of human capital becomes less significant. This is because people may think that innovation capital, process capital and customer capital can replace human capital, but it is not. In short, human capital may affect firm efficiency through other elements of IC (innovation capital, process capital and customer capital) as it is the base of other elements.

Overall, the study highlights the needs of having intellectual capital and its elements (Human Capital, Innovation Capital, Process Capital and Customer Capital) to increase firm efficiency.

First, the authors use a more comprehensive elements of IC, which are human capital, innovation capital, process capital and customer capital for a better IC measurement. Second, this study makes the first attempt using the DSBM model via DEA to examine the operating efficiency of US multinational software firms.

The purpose of this paper is to apply Value Added Intellectual Coefficient (VAIC™) of Pulic to compare quoted information technology and communication companies on the Istanbul Stock Exchange (ISE), in terms of intellectual capital efficiency. This study also examines VAIC™, and its components' impact on company performance.

Multiple regression analysis was employed to identify the variables that significantly contribute to the company performance. Data required to calculate VAIC™ and its components were obtained from the 2005‐2007 annual reports and balance sheets of the companies.

As a whole, all the companies had a relatively higher human capital efficiency than structural and capital efficiencies. In 2007, Turkcell was the most efficient company based on VAIC™ assessment, while Link Bilgisayar and Plastikkart were the least efficient companies. Additionally, the results of the study revealed that factors such as human capital efficiency, firm leverage, and firm size, predicted profitability well. Among them, human capital efficiency had the highest impact. In addition, capital employed efficiency was found to be a significant predictor of both productivity and return on equity, and the only determinant of market valuation was the firm size.

This study allowed ITC companies to benchmark themselves according to the intellectual capital efficiencies and develop strategies to enhance their company's performance.

This study is the first that measures intellectual capital performance and its impact on the company performance of the quoted information technology and communication companies on the ISE.

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.

The purpose of this paper is to empirically investigate the impact of economic policy uncertainty on firms' labor investment decision, which includes labor investment level and efficiency, especially human capital allocation.

This paper uses Economic Policy Uncertainty Index for China and Chinese A-share listed firms in the period 2002–2016 to constructs a sample of 20,779 firm-year observations and applies the methods of pooled OLS regressions to do an empirical study.

This paper finds that firms' labor investment is negatively correlated with economic policy uncertainty. And firms' labor investment efficiency (and overinvestment in labor) is positively (negatively) correlated with economic policy uncertainty, which is more significant for non-SOEs and firms with less government intervention. Further, the positive relation between economic policy uncertainty and labor investment efficiency is more significant for labor-intensive firms, firms in competitive industry, firms in developed labor market and firms under strong labor law protection. In addition, economic policy uncertainty induces firms to make adjustment on human capital structure and allocate more employees with high human capital, which eventually helps firms achieve higher total factor productivity.

The study of this paper indicates that the government needs to consider economic policies' impact on firms when introducing and changing policies and guide firms to improve human capital allocation under different internal and external conditions to finally realize the optimal allocation of social resources.

This paper studies the influence of external economic policy environment on firms' labor investment decision, which lacks adequate attention in the literature and indicates that under economic policy uncertainty, firms actively decrease labor demand and increase labor investment efficiency by optimizing human capital allocation.

As an emerging socio-technical paradigm, high-speed railways profoundly change individuals' lifestyle and allow for the shift toward a green transportation. Digital technologies open an opportunity window for the development of enterprises. This study aims to clarify the impact of firm digitalization on the innovation efficiency of the Chinese high-speed rail industry. In addition, human capital is the important non-physical capital of enterprises. The authors also elucidate the moderating role of human capital on the above relationship.

Based on the data of Chinese high-speed railway listed companies from 2015 to 2021, this study explores the impact of digital transformation on the innovation efficiency, and further clarifies the boundary role of human capital with two-way fixed effect regression models.

The empirical results indicate that digital transformation has a positive impact on the innovation efficiency of the Chinese high-speed railway enterprises. Furthermore, human capital significantly enhances the above relationship. In addition, digital transformation fosters the innovation efficiency of small- and medium-sized enterprises and private-owned enterprises, but the correlation coefficients between digital transformation and the innovation efficiency of large enterprises and state-owned enterprises are not significant.

This is one of the earliest studies to explore how digital technologies shape R&D activities. From the perspective of relative efficiency, this study evaluates the effectiveness of digital transformation and provides empirical evidence for the formulation and implementation of corporate digital strategies. Moreover, this study links human capital with digital transformation and identifies condition factors that affect the effectiveness of digital transformation, thereby supplementing existing knowledge.

The purpose of this paper is to investigate publicly traded restaurant companies and food & beverage companies from 2014 to 2019 in Taiwan to explore their human capital efficiency.

According to the theoretical framework of human capital in micro and macro perspectives, the empirical model is built with two stages; the first stage is to examine the perspective of micro-level human capital theory through determining whether knowledge ability and working experience (proxies for micro-level human capital) can efficiently convert to employee-level output such as salary. The second stage is to test macro-level human capital theory through checking whether company inputs such as salary expenses and benefits expenditures can be efficiently transferred into enterprise annual revenues.

The results of this research reveal that the average efficiency score of stage 1 is 73.6% while that of stage 2 is 75.1%; this indicates that micro-level human capital has more room to improve than macro-level human capital. Meanwhile, the findings also demonstrate that there is negative relationship between efficiency score from stage 1 and turnover rate; this implies that companies with higher micro-human capital have lower turnover rates. Furthermore, there is significantly positive relationship between a company's efficiency score from stage 2 and its return on equity (ROE).

This study contributes to both academia and industry. From a theoretical perspective, the theory of strategic human resources management is applied through the methodology of production theory to examine human capital management efficiency in the restaurant and food and beverage industry. From a practical perspective, this study identifies the factors that assist the restaurant or food and beverage industry retain employees and gain a solid workforce, because manpower is the core resource for an industry and a country to grow sustainably.

This research paper examines the information content and managerial incentives for labour cost voluntary disclosures for a sample of United States publicly traded companies. We focus on labour productivity and managerial efficiency in labour usage and argue that these human capital indicators could provide valuable information to capital market participants seeking human resource‐type of performance measures and signals. Labour productivity and efficiency indicators are estimated following a production function approach and are included in logistic regressions to help explain and predict labour cost voluntary disclosure decisions. We find that labour productivity and managerial efficiency in labour use indicators are generally different between disclosing and non‐disclosing firms, and that proprietary information costs and political cost proxies are significantly related to labour costs voluntary disclosure, consistent with previous literature. These empirical results corroborate the ‘proprietary information’ hypothesis of voluntary disclosure where the strategic costs of disclosure outweigh the signaling benefit from disclosing human capital information.

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 paper aims to assess human capital efficiency's impact on commercial banks' credit risk in six GCC member countries.

The study employs quarterly balanced panel data of banks between 2014 and 2019. The authors use three different constructs of credit risk, namely the probability of default which is a forward-looking quantification, a book value-based infection ratio and independent opinion of credit ratings, to assess the relationship with human capital efficiency. Different macro and firm-specific control variables are introduced, including a dummy for technological innovation and a GARCH-based measure of oil price volatility.

The findings of this study reveal that human capital efficiency is negatively related to the credit risk profile and banks with higher human capital efficiency tend to have lower credit risk. These results remained robust across the three definitions of credit risk used in this study.

This study is unique in exploring the impact of human capital efficiency on credit risk because credit risk is not only a central determinant of bank performance but also can trigger a systemic panic. Therefore, it is vital to assess its relationship with human capital efficiency. The different constructs of credit risk are innovative with reference to human capital. Lastly, using EVA as a measure of value addition in the context of human capital efficiency is a methodological contribution.

For the developing countries involving in the Belt and Road Initiative (BRI) with China as the main source of foreign development investment (FDI) and development as the top priority, it appears to attract more and more attention on how to make the best use of China’s outward foreign development investment. However, the contradictory evidence in the previous studies of FDI spillover effect and the remarkable time-lag feature of spillovers motivate us to analyze the mechanism of FDI spillover effect. The paper aims to discuss this issue.

The mechanism of FDI spillovers and the unavoidable lag effect in this process are empirically analyzed. Based on the panel data from the Belt and Road developing countries (BRDCs) and China’s direct investments (CDIs) from 2003 to 2017, the authors establish a panel vector autoregressive model, employing impulse response function and variance decomposition analysis, together with Granger causality test.

Results suggest a dynamic interactive causality mechanism. First, CDI promotes the economic growth of BRDCs through technical efficiency, human capital and institutional transition with combined lags of five, nine and eight years. Second, improvements in the technical efficiency and institutional quality promote economic growth by facilitating the human capital with integrated delays of six and eight years. Third, China’s investment directly affects the economic growth of BRDCs, with a time lag of six years. The average time lag is about eight years.

Based on the analysis on the mechanism and time lag of FDI spillovers, the authors have shown that many previous articles using one-year lagged FDI to examine the spillover effect have systematic biases, which contributes to the research on the FDI spillover mechanism. It provides new views for host countries on how to make more effective use of FDI, especially for BRDCs using CDIs.