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Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

This paper aims to analyse the impact of innovation on the productivity of firms in India using the data from the World Enterprise Survey. This paper first classifies three different types of innovation measures then further analyses their relation with the productivity of the firms.

The methodology used for this study has incorporated the structural Crépon-Douget-Mairesse (CDM) model wherein productivity is measured using both the innovation inputs and the innovation outputs. Three main equations have been used to quantify this relation includes the knowledge intensity function, innovation function and the productivity equation.

Findings indicate that decision to invest in research and development (R&D) is influenced negatively by financial obstacles and trade obstacles and positively influenced by telecommunication obstacles, government obstacles and the size of the firm in India. Similarly, financial obstacles and the size of the firm are affecting the firm’s research expenditure per employee. Also, financial obstacles seem to hinder the research intensity and larger firms seem to have higher research intensity. The size of the firm contributes significantly to product innovation. However, R&D spending seems to be negatively related to the innovation outcome. The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms. However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner.

The findings relating to productivity shows neither product nor process innovation outputs, independently are not contributing significantly to the productivity of firms (which has been measured by sales per worker is impacted by the capital and the labour inputs). However, product and process innovation, together serve as innovation outputs is a significant contributor to firm productivity. On the other hand, organisational innovation contributes significantly to the productivity of the firms in a negative manner. The reason could be due to the fact that the definition of organisational innovation incorporates both dissolutions and mergers.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

This paper intends to explore the measure for aligning the goals of researchers toward achieving organizational R&D targets. The paper also explores the significance and ordering of R&D outputs and the factors that influence generation of R&D outputs, from the perspective of researchers working in the Indian public sector organizations.

Data were collected in five Indian R&D laboratories, and weighted average method Spearman correlation coefficient and rank regression were utilized for the analysis.

The findings indicated that various groups of researchers prefer to target different R&D outputs and not all the factors are considered as equally significant in influencing the generation of R&D outputs. Further, the R&D organization should include preferred real factors while policy making for achieving collaborative efforts toward fulfilling organizational objectives. The set of selected R&D outputs and influencing factors were also ordered according to the average rankings given by the researchers.

The findings can help R&D managers to identify the expectations of the researchers and include their preferences in R&D Planning. The study could be extended to a larger dataset of researchers working in other government as well as private R&D organizations.

Hardly any studies were found that explored the preferences of researchers with respect to R&D outputs and influencing factors with respect to the Indian public sector R&D laboratories.

The objective of the study is to analyse the impact of research and development investment on the firm performance of “small” scale firms vis-a-vis “medium”-scale firms.

The dataset comprised of a balanced panel of 486 research and development conducting Indian manufacturing small and medium enterprises, constructed for the period of 2006–2017. Fixed Effects, Random Effects Model and Hausmann test were used to analyse the determinants of firm performance in manufacturing small and medium enterprises in India.

It was found that from firms’ research and development (R&D) investments in terms of performance could be attained if simultaneously internationalisation and higher capital intensity could be achieved.

Managers could pay specific attention to the antecedents of firm performance and calibrate their R&D investment, internationalisation efforts and capital intensity simultaneously to achieve higher growth and productivity. For policymakers, the results provide an insight into how the firms in both categories could be differently incentivised, such that resources are better utilised.

The study analysed the determinants of firm performance in small and medium-sized firms at a disaggregate level as well as at a sectoral level using fixed effects, random effects and lagged effects to arrive at novel results, which have important implications for their competitiveness.

This study is designed to examine the impact of favorable working environment (FWE) and R&D on manufacturing productivity of labor intensive industries. More specifically, the purpose of this study is to generate quantitative evidence of the effect of FWE and R&D‐based manufacturing process on outputs and productivity.

Convenience sampling method has been used to conduct this study. This method provides the opportunity for selecting those manufacturing industries that are convenient to get access for collecting relevant information. Three categories of labor intensive manufacturing industries such as category A, B and C have been chosen to perform this research. Industrial category A represents the manufacturing operations which are based on skill of labor. Category B is a group of industries which provides the FWE the ability to utilize the potential of skill in the manufacturing process. However, category C is a specialized group of industries and its manufacturing process is dependent on R&D. The operating data of inputs cost and the revenue of corresponding outputs have been gathered from audited documents of the relevant sample industries and the data have been analyzed by using standard statistical techniques in order to establish the relationship between dependent and independent variables.

It is found that the industrial category B has spent about 1 percent of revenue on FWE and gained 9.5 percent higher productivity compare to industrial category A. However, the result has shown that the expenditure on FWE is positively associated with productivity (r<0.5). Whereas, the study has revealed that industrial category C has spent about 1.5 percent of revenue on R&D activities for improving manufacturing process and gained 20 higher productivity compare to industrial category A. Nevertheless, the expenditure on R&D is strongly correlated with productivity (r>0.7). The study concludes that FWE as proxy of job satisfaction of workforce and R&D on manufacturing process are value‐added inputs for labor intensive industries and it is positively associated with manufacturing productivity.

This paper presents three original case studies on labor intensive manufacturing industries. This study has addressed an important issue of labor intensive manufacturing industries and generated quantitative evidence of the impact of FWE and R&D activities on productivity. These issues have been well researched in developed and many developing countries in capital‐intensive industries, but no dedicated study is available that has addressed this issue from the perspective of the highly labor intensive industries such as the garment industry. The findings of this research would enrich the present knowledge stock of manufacturing systems. Eventually, the findings would be the basis for further research on manufacturing process for enhancing performance. Based on this concept, this study would be valuable to policy makers, academics and government agencies.

The objectives of this study are to investigate the role of wages, skills development and R&D on the productivity of Thai manufacturing firms, using data from the 2017 Industrial Census of Thailand.

The paper uses two-stage least squares (2SLS) to examine the role of wages, skills development and R&D, as well as other vital factors, impacting productivity as measured by labour productivity and total factor productivity.

Thai manufacturing firms' technology in aggregate exhibits decreasing returns to scale. Increasing wages and skills development promote the labour productivity and total factor productivity (TFP) of Thai manufacturers. R&D is also shown to be vital in promoting the labour productivity and TFP of large firms, but not small firms. Foreign direct investment (FDI) and government support can significantly increase large and medium-sized firms' labour productivity and TFP. Financially constrained firms tend to perform more productively. However, older firms, larger firms, labour supply shortages and political instability adversely affect labour productivity and TFP.

Upskilling and improving HRD policies could move Thailand towards a knowledge-based and high-income country in the future. Intellectual property protection should be strengthened to boost the country's R&D. The government should consider lifting restrictions on FDI to encourage international openness. The Thai Board of Investment’s promotion should target Thai manufacturing firms and FDI.

This study is the first to examine in detail the role of wages, skills development and R&D on the productivity of Thai firms based on the 2017 Thailand Industrial Census.

This paper aims to examine the contribution of physical capital, skilled labor, research and development (R&D), and imports to the productivity of ten Canadian food and beverage manufacturing sectors.

A Cobb‐Douglas production function is estimated to discern the relative contribution of physical versus R&D knowledge capital in fostering productivity growth. The paper uses a balanced panel dataset and is based on pooled cross‐sectional time series data for ten food and beverage manufacturing industries over the period 1994‐2005. The methodological framework adopted in this paper is a cross‐sectionally correlated and time‐wise autoregressive model. Data employed in this paper are from Statistics Canada's annual survey of manufacturing industries and Canadian Socio‐economic Information Management computerized database.

The results show that food manufacturing productivity for ten food and beverage sectors is more responsive to physical capital than R&D knowledge capital. Some of the other determinants of labor productivity in food manufacturing included the contribution from university‐educated workers.

The paper is useful to both policy makers and academics in the research fields of R&D and productivity. It provides some interesting insights into the contribution of physical and knowledge capital to food manufacturing industry productivity.

The purpose of this study is to address the impact of three issues: degree of skills, favorable working environment and R&D on manufacturing productivity of labor‐intensive industries.

Convenience sampling method has been used to conduct this study. Three categories of labor‐intensive manufacturing industries (A, B and C) have been chosen to perform this research. Industrial category A represents the manufacturing operations which are based on skill of labor. Category B is a group of industries which provides a favorable working environment to utilize the potential of skill in manufacturing process. Category C is a specialized group of industries and its manufacturing process is dependent on R&D. Input–output data for manufacturing operations of the sample industries have been analyzed by using standard statistical techniques to establish the relationship between dependent and independent variables.

The degree of skill and productivity is positively correlated, but not strongly (r<0.5). The study found that productivity is positively (r>0.5) associated with favorable working environment. However, a significantly positive correlation (r>0.7) is found between R&D expenditure and productivity. The study concludes that a higher degree of skills, favorable working environment and R&D are important inputs to a labor‐intensive manufacturing process, which is positively associated with productivity.

Manufacturing productivity is an important parameter of industrial growth, and this paper addresses this issue. The current work addresses the garments sector, i.e. a part of the labor‐intensive industries. Though this work is focused on only one part of this sector; the findings of this study have significant policy implications. The results would be useful for manufacturing business executives and policy makers.

This study addresses the important issue of productivity of labor‐intensive manufacturing industries, and generates quantitative evidence of the impact of degree of skills, favorable working environment and R&D on productivity. The generated information enriches the present knowledge stock of manufacturing systems. The findings could be the basis for further academic research on manufacturing systems.