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The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

The purpose of this paper is to empirically investigate the effect of lean manufacturing on productivity changes and to identify the root sources of productivity changes. Furthermore, the authors explore the moderating effects of research and development (R&D) to examine the relationship between lean manufacturing and productivity changes.

This paper employs the propensity score matching (PSM) model combined with the difference-in-difference (DID) estimation to overcome the selectivity bias. The Malmquist productivity index is used to capture productivity changes. By analyzing 671 Chinese manufacturing listed firms from 2009 to 2014, the moderating effects of R&D on the relationship between lean manufacturing and productivity changes are measured.

The results reveal that lean manufacturing implementation has non-significant effects on productivity changes in principle, while a detailed analysis indicates that lean manufacturing could improve scale efficiency significantly. While engaged in R&D could significantly improve the efficiency of technological changes for lean manufacturing implementation firms, there exist negative effects on pure technical efficiency.

This research only covers manufacturing listed firms in China. Further studies should extend the generalizability of the findings.

This study helps managers to identify the important role of R&D on the relationship between lean manufacturing and productivity changes and provides insights into how to improve the lean manufacturing performance.

This paper appears to be one of the earliest studies on the relationship between lean manufacturing and productivity changes by applying the PSM combined with DID estimation in Chinese manufacturing environment.

The paper examines the impact of export activity on productivity and how this effect is moderated by R&D investment and foreign ownership.

A time-lag effect is taken into account when examining the proposed model. Data are collected from the Annual Industrial Survey of the National Bureau of Statistics of China. A dataset containing 117,340 firms across the sample period (2001–2007) are used to test the hypotheses.

The results indicate that while R&D investment plays a significant role in strengthening the positive effect of export activity on a firm's productivity, foreign ownership surprisingly has a negative moderating role.

Scholarly interest in the links between export activity and productivity is on the rise. However, the bulk of research has been focused on understanding the effects of export activity on productivity at the country or industry level. Little has been done at the firm level. Another gap in the literature is that the mechanism through which the impact of export activity can be leveraged to enhance the firm's productivity has been largely ignored. To address these issues, the study adopts the learning-by-exporting theory to examine the relationship between export and productivity at the firm-level and how R&D investment and foreign ownership may explain how learning can be leveraged to enhance the firm's productivity. Finally, these relationships are examined in the context of firms from an emerging market, China, which is especially relevant for the learning-by-exporting argument used in this study.

The effect of global integration of businesses and interchange of ideas and technology through internet has many facets. Increase in economic recessions and decrease in research funding has forced both private and public sector research organizations to introduce measures of self-sustainability. Enhancing research and development (R&D) productivity of researchers can be one of those measures. The purpose of this paper is to attempt toward identifying and analyzing those factors of productivity that may be related to the manpower in R&D.

Based upon the theoretical background and the nature of data available, this empirical study has been carried out as a case study for a public sector research laboratory. The extent of involvement in research projects have been analyzed with respect to the R&D outputs generated by the researchers, which if found to be positively related, may be focussed upon, for increasing productivity of manpower in R&D. Several other determinants of R&D productivity were identified from the literature review and were analyzed in association with the “involvement.” “Robust Regression” technique was used for the statistical analysis.

It was found that the R&D productivity of researchers has a positive correlation with their extent of involvement in the R&D projects.

The result may help in creation of the policies for enhancing organizational self-sustainability.

Several prior studies have been conducted with different determinants of R&D productivity but hardly any studies were found considering “extent of manpower involvement in research projects.” This study can be useful for public sector research organizations to relate the findings with their endeavors of enhancing R&D productivity.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

The purpose of this paper is to analyze the effects of public investment in agricultural R&D and extension on broadacre farming productivity in Australia.

An autoregressive integrated moving average (ARIMA) regression model is applied to estimate the effects of public investment in agricultural R&D and extension on Australian braodacre productivity.

The study reveals that public investment in agricultural R&D and extension has contributed almost two-thirds of average annual broadacre productivity growth between 1952-1953 and 2006-2007, the average internal rate of return to public investment in agricultural R&D and extension was 28.4 and 47.5 per cent a year, respectively, and overseas spill-ins is an important source of domestic agricultural productivity growth.

Policy implications: the findings suggest that increasing public investment in agricultural R&D and extension and maintaining agricultural R&D policy stability are equally important to have a sustained long-term agricultural productivity growth, and maintaining an open trade and investment regime is important to benefit from foreign knowledge spillovers which is especially important for developing countries.

This paper contributes to the existing literature by employing more sophisticated econometric techniques with an extended data set for the period from 1952-1953 to 2006-2007. The study separates the contribution of public R&D investment and the extension investment, and also takes into account the contribution of overseas public investment on the TFP growth in the Australian broadacre sector.

The purpose of this paper is to empirically study the impact of product patent regime on the productivity of different categories such as ownership, R&D, size and product-wise of Indian pharmaceutical firms using non-parametric data envelopment analysis.

The present study has applied Ray and Desli’s Malmquist productivity index and its decomposition to measure total factor productivity (TFP) change, pure technical efficiency change, scale efficiency change and technical change under variable returns to scale (VRS) technology assumption for 141 Indian pharmaceutical firms during 2000-2001 to 2014-2015.

The study found the negligible impact of product patent regime on productivity. The technological change has played a positive role in the growth of productivity, whereas technical efficiency change depicts the judicious utilization of resources for improving performance. From the results, it is found that R&D intensive firms depict better stability in the TFP than the non-R&D firms. However, Granger causality between R&D and productivity found no relationship. Productivity is more directly affected by investment in fixed assets rather than in R&D, which focusses on incremental value additions in a largely branded/plain generic product market. In case of ownership, private foreign firms found to have registered progress in TFP while others have recorded marginal regress, which probably could be attributed to the superior marketing and management skills of the foreign firms, besides possessing proprietary technology. Both small and large firms have shown positive growth in the new regime as compared to the pre-patent regime. These small firms are able to compete with large firms because of their up-gradation of the technological base by improving access to better foreign technology. TFP growth for all the firms can be attributed to improvement in technology, and innovation in terms of high capital-output ratio. Further, the paper tried to identify the determinants of productivity from panel random effect regression, and it is found that export intensity, age and the new patent regime have negative and significant relationship with productivity, whereas other variables such as R&D, ownership, size and capital imports are insignificant. In the end, the results of sensitivity analysis have confirmed the validity of the selected variables.

The results suggest that Indian pharmaceutical firms need substantive improvement in TFP by improving managerial and scale efficiency. Indian pharmaceutical industry (IPI) needs to improve productivity across the network and drive cost excellence initiatives across the spend base through operational excellence and digital initiatives. The results of this paper can be applied in framing policies for future growth and improvement in the productivity of IPI.

The paper aims to make several new contributions to the existing literature. Most of the research papers only analysed TFP of the industry as a whole and detailed firm-wise analysis is needed to capture the true impact at a unit level. This study has analysed the impact of different categories such as ownership, R&D, size and product-wise, and determinants of productivity. The study has used a broader time period and larger panel data to predict the better picture.

The purpose of this paper is to examine which value capture strategies are efficient in the invention stage and commercialization stage for small and medium enterprises (SMEs).

According to the characteristic of four primary value capture strategies, the authors categorized the formal-oriented strategy (patent), informal-oriented strategies (secrecy, lead-time), mix strategies (both formal and informal strategies), and whether to invest in complementary assets. In order to assess R & D productivity based on value capture strategy, this paper applied the two-stage data envelopment analysis for data collected from Korea Innovation Survey 2010. The sample was made up of 640 SMEs in manufacturing industry.

The results suggest that informal strategy (secrecy, lead-time) is efficient in the invention stage. Furthermore, mixed use of formal (patent) and informal strategies results in higher productivity in the commercialization stage. Lastly, the results suggest that productivity may vary depending on the investment in complementary assets. Whereas the investment in complementary assets may reduce R & D productivity at the invention stage, it may increase the productivity at the subsequent commercialization stage.

This paper did not consider external conditions such as institutional environment, industry characteristics and complexity of technology, as possible factors affecting their firm performance that need future studies.

For practitioners, the results seem to stress that should be good at combining value capture strategies in order to capture financial performance from the invention.

This study is a novel attempt to analyze R & D productivity in terms of each value capture strategy throughout the two stages: invention stage; commercialization stage.

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.