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This work aims to propose an alternative method of human capital calculation for research laboratories of public university, taking into account some drawbacks of the methods currently applied in this field.

This method is implemented via a linear program extracted from Data Envelopment Analysis based on slack movement. This is the formulation of Copper et al. (2000), which is used as the starting point for developing the proposed method through important transformations.

The proposed method is supported by an illustration related to a Moroccan public university. This illustration showed that 57 per cent of the laboratories and all the research activities that they perform are in deficit with respect to target scores.

The proposed method has technical limitations related to scores equal to 1 and to variables when those are numerous. To solve them, it is possible to use peer benchmarking system for the first limitation, and methods of regrouping the variables when those are numerous for the second limitation. Equally, the proposed method does not associate slack with important factors like governance and the impact analysis of research on innovation, competitiveness, and societal aspects. Likewise, it does not use the slack to measure individual efficiency at the same laboratory. Future research can fill these gaps.

This work allows making appropriate budgetary and research policy within university, through budgeting process and management control by using raw and adjusted target values as well as actual ones. Also, the highlighting of the excessive slacks leads the university to take actions to reduce them, according to the most loss-making research activities.

The proposed method is original, since it fills a deficit in terms of human capital target values calculation and of the slack movement concept in relation to the efficient frontier. Additionally, it transforms the Data Envelopment Analysis program into a program that eliminates the slacks linked to the inputs, the radial movement related to the outputs and treats only the outputs and slacks related to these outputs.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

Low levels of human capital in rural China are rooted in the poor schooling outcomes of elementary school students. The purpose of this paper is to provide insight into the distribution of academic performance in rural China and identify vulnerable groups.

The authors draw on a data set of 25,892 observations constructed from 11 school-level surveys spanning nine provinces and one municipality in China conducted from 2013 to 2015.

The authors find that the distribution of academic performance is uneven across provinces and subgroups. In general, male students, Han, living in richer counties, living with their parents and studying in rural public schools do better academically than female students, non-Han, living in poorer counties, left behind and studying in private migrant schools in cities.

Using the results of this study, policymakers should be able to better target investments into rural education focusing on at risk subpopulations.

With limited data sources, the research on the academic performance of students in rural China is largely absent. The findings of this study help to fill the gaps in the literature base.

Although the impact of human capital on productivity has long been discussed in prior studies, empirical evidence for African firms remains limited. The existing few studies have focussed on one type of human capital in isolation and failed to explore the distinct role of different types of human capital on productivity. The aim of this study is to examine the extent to which various typologies of human capital – schooling, on-the-job training (OJT) and slack time –, both in isolation and as a combination, contribute to the productivity of African firms.

To this end, a cross-sectional firm-level data set from 13 African countries was used. To unravel the casual relationship, propensity score matching (PSM) and multinomial endogenous switching treatment regression (MESTR) techniques were employed.

Results indicate that all typologies of human capital – schooling, slack time and OJT – have a significant and positive impact on firms' productivity. The findings of the study further point out that the highest payoff, in terms of increased productivity, is achieved when various typologies of human capital are used in combination, rather than in isolation, in the production process.

The policy implications are that productivity of African firms can be improved by increasing the general level of schooling; encouraging firm-sponsored OJT; and giving employees time to develop new ideas.

The present study provides important insights into the distinct role of different types of human capital on productivity. In addition, it provides empirical evidence for a region where empirical evidence is scant.

The article describes the constraints of the current system for accounting for the value and importance of an organization’s human capital. It describes the Human Capital Capability Scorecard (HCCS), which allows for better valuation of human capital, and examines the implementation of that HCCS system inside diverse organizations, and analyzes the relationship between human capital indices and organizational outcomes. The article describes the composition of five human capital indices, and examines their relationship to key organizational outcomes in a manufacturing firm, public school district, and consortium of banks and finds that human capital indices are positively related to a variety of organizational outcomes, including sales office effectiveness, student achievement test scores, and summary financial measures such as net income per employee. Future research should focus on the application of standard measures of human capital across additional organizations, and seek to more fully explore the effects of specific human capital items. The article suggests that organizations should devote significantly more attention to measuring their human capital in a way that recognizes its value, and that reporting such information publicly will help publicly traded organizations to avoid the too‐common short‐term focus on quarterly earnings. New quantitative evidence is provided to executives about the importance of human capital and methods for human capital and people‐related factors to be more appropriately measured, valued, and reported by organizations are suggeted.

This paper aims to study the effect of family socioeconomic status (SES) on academic and labor market outcomes.

The authors used a rich data set of administrative records for test scores, individual background and adult earnings of a cohort of agents, covering a period spanning the agents' upper-secondary education and their early years in the labor market.

The authors find that students with the highest SES obtained a 1.5 standard deviations higher score in the college admission test than students who had the same academic outcomes in the eighth grade test but belong to the lowest SES. Similarly, among students that obtained the same scores in the college admission test, those with the highest SES earned monthly wages 0.7 standard deviations higher than those with the lowest SES.

The findings highlight that family socioeconomic background continues to influence outcomes during individuals’ upper secondary education and early years in the labor market.

The purpose of this paper is to suggest and empirically test a model that explains employee capabilities from the knowledge‐based perspective. In this model, human capital management practices are employed as a moderator variable.

A valid research instrument was utilized to conduct a survey of 14,769 current employees of a major North American financial services institution. The model was tested by using the partial least squares (PLS) structural equation modeling technique. A thorough analysis of the role of moderator was carried out.

Findings provide support for the proposed model and show that employee capabilities depend on his or her training and development as well as job satisfaction levels. Job satisfaction in turn is affected by training and development, pay satisfaction, supervisor satisfaction, and job insecurity. These relationships are moderated by employee perceptions of human capital management practices. The model exhibits the highest predictive power when the employee perceptions of human capital management practices are also high.

With respect to a moderator analysis, no interaction effects of human capital management policies and other constructs were discovered, and the moderator was referred to as a homologizer that modifies the strength of the relationships among constructs through an error term. It was discovered that PLS and moderated multiple regression (MMR) produced very similar structural relationships when a moderator was employed.

The findings may be utilized by knowledge management, organizational behavior, and human resources practitioners interested in the development of strong employee capabilities.

This paper represents one of the first documented attempts to utilize human capital management practices as a moderator in organizational models.

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 effectiveness of any government interventions to support small firms is always a concern in achieving improvements in enterprise performances. The purpose of this paper is to evaluate thoroughly the impact of micro and small enterprises’ (MSEs’) support programs on core intermediate and final outcomes of interest.

The impact evaluation employs a non-parametric matching procedure for parametric outcome analysis using the propensity score matching (PSM) method. Aiming at a doubly robust evaluation process, the study applies parametric analyses than non-parametric permutation-based tests to investigate the causal effects of the public intervention.

The study reveals that the public intervention encouraged MSEs to develop innovative business practices and improve their human capital development process. Moreover, the intervention had a positive effect in expanding employment opportunities in urban areas. Contrariwise, the study shows that support beneficiaries are not at an advantage in investment intensity. The lower level of investment intensity on fixed capital resulted inefficiency among the recipients. Moreover, the intervention did not have an effect on changing the net-asset over time for the recipients.

This study implies that the support programs need to be dynamic and also targets on creating innovative high-growth MSEs.

This paper is fairly original and provides policy makers and MSE promoters/facilitators evidence-based information on the effectiveness of the support services, with looking at firm-level analysis.

The purpose of this study is to further develop an emerging thread of quantitative research that grounds investment in school infrastructure in a unified theoretical framework of investment in human, social, and physical capital.

To answer the research question, what is the impact of investment in human, social, and physical capital on student achievement, the author uses canonical analysis, a multivariate statistical approach that allows for multiple independent and dependent variables. The null hypothesis is selected given the limited body of research on this question, and the state is selected as the unit of analysis. Level of student poverty is added as a control variable given an extensive body of research that supports its negative impact on achievement. Descriptive statistics are generated as well as a Pearson product moment correlation matrix to diagnose and address potential issues of multicollinearity and simultaneity. Three national databases are used: United States Census Bureau, US Department of Education's National Assessment of Educational Progress test score data, and the US Department of Education's Common Core of Data. Years analyzed are 2003, 2005, and 2007.

The results of the canonical analysis are robust, statistically significant, and consistent over time. Investment in human, social, and physical capital accounts for between 55.8 and 77.2 percent of the variation in student achievement in fourth and eighth grade Reading and Mathematics. Investment in human capital is consistently the largest influence on student achievement followed by social and physical capital. The null hypothesis is rejected.

This study advances the use of theory in explaining the impact of investment in school infrastructure on student achievement, a feature which distinguishes it from much previous research in this domain. The use of a theory also addresses a major weakness of traditional, atheoretical education production function research. In addition, utilization of canonical analysis rather than multiple regression to operationalize the theoretical model and analyze the data represents an advance in research design and statistical analysis for this type of research question.