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National policy makers in India have realized that the research and development (R&D) performance of India has not been up to the benchmark. This paper aims to identify and develop the structural cause and effect relationship among various factors that affect the performance of national R&D organizations in India.

A questionnaire‐based survey, interpretive structural modelling and structural equation modelling, has been applied in this paper. In this paper, ten factors have been identified such as clear R&D vision and strategic directions; top management commitment (TMC); customer focus and market orientation; organization culture, etc.

Clear R&D vision and strategic directions are the major drivers for the performance of national R&D organizations. Statistically, TMC; organization culture and human resource focus; R&D project management skills; and customer focus and market orientation have emerged as the critical factors for improving performance of national R&D organizations in India.

This model can be validated for various other national R&D organizations in India as well as in other countries.

This model provides a comprehensive structural cause and effect relationship among various success factors to the policy makers and provides the direction to for designing and implementing appropriate policies in order to enhance the performance of national R&D organizations.

The paper not only provided a cause and effect relationship among various factors, but also a magnitude of relationship.

The purpose of this paper is to reveal and justify influential factors of dynamic capabilities on research organizations’ R&D and innovation performance.

Adoption of seminal D. Teece’s (1997) concept of dynamic capabilities and operationalized matrix of key performance indicators in the area of R&D and innovation allowed the construction of the strategic management model for research organizations, consequently tested by methods of statistical analysis.

The empirical findings reveal that there exists positive influence of the dynamic capabilities on research organizations’ R&D and innovation performance. Explicitly, sensing, seizing and re-configuring dimensions of dynamic capabilities have positive impact on R&D and innovation results; consequently, the peculiarities of their inter-dependencies are identified.

Delivered research is based on the investigation of Lithuanian research organizations’ dynamic capabilities and their impact on their R&D and innovation performance. Therefore, further research could be extended to foreign countries.

The model on management of research organization’s dynamic capabilities with the aim for better R&D and innovation performance is conceptualized and specified hereinafter. In the course of the research, constructed toolkit to eventually measure research organization’s R&D and innovation performance or use it as the set of key performance indicators in the benchmarking exercise is suggested.

The paper is one of the first to suggest novel application of dynamic capabilities’ view within the domain of research organizations.

This paper aims to examine the challenges experienced by Russian research and development (R&D) organisations in international technology collaboration in the global innovation arena.

A total of 33 Russian R&D organisations were selected from a sample of 138 potential participating organisations from the Central, Nord‐Western and Volga Federal Districts of Russia to take part in a qualitative interview study. Organisations were selected based on their responses to a questionnaire which measured the extent and nature of their experience in international technology collaborations. Participants were interviewed about their experiences of engaging in international technology collaboration under two different modes of collaboration: short term customer‐supplier relationships vs longer‐term strategic alliances. The semi‐structured interviews focused on 15 different issues which had been derived from the previously published literature on international technology collaboration and a thematic analysis was conducted on the resultant data.

The analysis indicates which of the issues reported as problematic in the literature were pertinent to the Russian organisations in the sample under each mode of collaboration. The findings also provide some evidence that Russian R&D organisations have made progress in the transition from the command to the market economy and are adjusting to the new environment, albeit gradually in some cases.

The paper presents interpretive, qualitative findings, which were analysed from a Russian perspective in three out of seven Federal Districts of Russia. The research sample does not include non‐Russian counterparts and the analysis is restricted to those variables which have previously been identified as exerting an influence over international technology collaborations.

The study reveals a broad range of insights into the types of issues which warrant close managerial attention from both Russian managers and their international partners in engaging in international technology collaborations with contemporary Russian R&D organisations.

The research suggests that different sets of challenges emerge for organisations engaged in different modes of international collaboration and provides insight into the unique context of Russia, challenging some of the previously published analyses of the influence of Russian business and managerial practices on the innovativeness of contemporary Russian organisations.

Deriving a measure of efficiency of public-funded organizations (primarily not-for-profit organizations) and ranking these efficiency measures have been major subjects of debate and discussion. The purpose of this paper is to evaluate the relative performances of public-funded research and development (R&D) organizations functioning across multiple countries working on similar research streams. The authors use multiple measures of inputs and outputs for this purpose.

The authors use the data envelopment analysis (DEA) as the primary methodology of analysis The keywords highlighting the major research areas in the field of non-metrology, conducted by National Physical Laboratory (NPL), India, were utilized to select the global comparators working on similar research streams. These global comparators were three R&D organizations located in the USA and one each located in Germany and Japan. The relative efficiencies of the organizations were assessed with the following output variables – external cash flow, and the numbers of technologies transferred, publications and patents; and the following input variables – amount of grants received from the parent body, and the number of scientific personnel working in these public R&D organizations. The authors follow the output-oriented measure of efficiency at constant return to scale and variable return to scale, along with scale efficiencies.

The performance of NPL, India under multiple dimensions has been evaluated relative to its global comparators – the National Institute for Materials Science, Japan; the National Renewable Energy Laboratory, USA; Fritz Haber Institute of the Max Planck Society, Germany; the National Centre for Atmospheric Research, USA; and the Oak Ridge National Laboratory, USA. The study indicates suggested measures and a set of targets to achieve the best possible performance for NPL and other R&D organizations. In most cases of efficient local but not so efficient global efficiency scores indicate that, on an average, the actual scale of production has diverged from the most productive scale size.

The approach highlights the utilization of the DEA methodology for relative R&D performance assessment of global comparators. The discriminatory analysis has brought into sharp focus the dichotomy between local efficiency and global efficiency scores of these units and issues of scale size and regional disparities. The outcome of this approach is dependent upon correct selection of input and output variables and data availability.

The study results have profound implications for the management of public R&D institutions across nations working on similar-focused research streams, but functioning within different societal, economic, and political contexts.

The present work, being perhaps one of the few multinational studies of relative performance assessment of pubic-funded R&D organizations working on similar research streams, signifies the relevance of such an approach in the field of R&D/innovation management. This has opened up new avenues for further research in this area.

The purpose of this paper is to evaluate and compare the performance of national R&D organizations in India in terms of their relative efficiencies using a multiple output measurement criteria incorporating the quality of the output measures.

An integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) has been used.

The relative efficiency for national R&D organizations is obtained not only based on quantity of output, but also on the basis of quality of the output and provides more comprehensive and realistic results to the decision makers in identifying the benchmark national R&D organizations and inefficient organizations.

Owing to difficulty in approaching all the departments, the application of the model is limited to a small group of Indian national R&D organizations. This model can be extended for various departments/agencies heading various national R&D organizations in India as well as in other countries according to their requirement and suitability.

This model provides a more comprehensive method to the policy makers for evaluating the performance of national R&D organizations in terms of their relative efficiencies also incorporating the quality of the output measures. The results provide the indicators to the policy makers for redesigning the R&D processes in the inefficient national R&D organizations.

In the context of R&D, an integrated DEA‐AHP model is applied for the first time to evaluate the performance of national R&D organizations in terms of their relative efficiency obtained not only on the quantity of the output taken, but also on the quality of the output.

This issue is a selected bibliography covering the subject of leadership.

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 illustrate the relationship between total quality management (TQM) and innovation and the way each impacts on the performance of agricultural research and development (R&D) organisations.

A gap score survey instrument based on the balanced scorecard approach, 25 most commonly reported TQM practices and two types of innovation were considered for data collection. Structural equation modelling was used for the analysis of the relationships between the surveyed constructs.

A positive relationship between TQM, innovation and performance was observed. A hypothesised model depicting the complex relationships between the investigated constructs was developed.

The model, which also predicts total effects of various organisational practices on performance, provides an opening for developing a TQM-innovation-performance framework for agricultural R&D organisations.

The survey instrument presents a novel approach for assessment of R&D policies and practices through determination of gap scores.

Presents an illustrative example of a benchmarking exercise for non‐corporate R&D organizations. Drawing from a global study of 60 R&D organizations from around the world, the benchmarking practice for identification of processes, practices and best practices is examined. Focuses primarily on R&D‐industry linkage – a nagging problem, particularly relevant for non‐corporate R&D organizations. Processes like project selection, human resource development and knowledge delivery systems are functionally related to R&D‐industry linkage and therefore constitute the main body of the benchmarking exercise. It has been argued that the basic organizational principle behind successful R&D is partnership between R&D organizations and users of the research results. Practices under each of the selected processes, therefore, have to be examined in the light of this basic organizational principle. Argues that, to achieve this, actionable accuracy is more important than quantitative indicators for identification of best practices.

This paper aims to understand whether there are different styles of organisation and management of industrial R&D activities. Recent cases of re‐organisation of R&D activities within firms suggest that: R&D activities tend to be carried out in different units of the same organisation and often in different locations, and there seem to be inter‐industry differences among R&D processes. This paper attempts to explore whether and how a certain managerial/organisational style relates to the type of the R&D process (which varies from industry to industry) and to the different nature of the R&D activities. It also identifies the key characteristics of the R&D organisation and the strategic management of technology, associated with each style. The analysis is based upon an empirical study of 19 multinationals operating in various industrial sectors. A classification of the types of R&D process is adopted and the key characteristics of R&D management and organisation in each R&D type are described and discussed.