Search results

The study aims to present an insight into the research landscape of Library and Information Science (LIS) by India using a bibliometric visualization tool. The study analyses the research growth and trends, highly cited articles, productive publication titles, institutional and country collaboration.

The data were downloaded from the Web of Science Core Collection for a period of 20 years and analysed through VOSviewer, a data visualization software.

The results indicate that the overall annual contributions are increasing, although with uneven and slow growth from 2001 to 2014. However, the highest contributions and impact is witnessed over the past few years. All the top 10 cited papers are related to the area of information processing and management. The visualization technique made it clear that the area of research has made a transition from traditional concepts of library and information to novel ones involving big data, machine learning, altmetrics, etc. Also, the Indian Institute of Technology System, the Council of Scientific and Industrial Research and the Indian Institute of Management System have made the highest contributions. Furthermore, India shares maximum collaborations with the USA, followed by England and China.

The findings of this study would help readers to gain understanding about the contribution of India for the development of the LIS. It would also help researchers to identify the hotspots and left out areas of research in the Indian context that require further investigation, thus would help in policy decisions and future research. Furthermore, researchers will be sensitized about the network visualizations that can also help them to get connected with the peers. The study can also help the journals to recognize the trending topics, which will provide the researchers with the opportunities to work on the same. Funding agencies can also be benefitted by the findings of the current study as they will be informed about the research areas which need to be funded.

There are not many research studies that highlight the research trends in the area of LIS from India and visualize the collaboration among institutions and countries. The study tries to showcase the research trends and collaborative frameworks in the field of LIS in terms of network visualization.

This study intends to analyze the trend of walkability research over two years (2021–2022) in several aspects, including the research problem, gap and aim underlying walkability issues, research setting, the scale of analysis, the type of data, the data collection tool, the analysis method and contribution.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is employed to conduct a systematic literature review from three leading databases including Web of Science (WoS), Scopus and Sage.

From the review, it can be concluded that the research focuses on two aspects, namely, the built environment and people. The studies in this field are conducted at different scales of the built environment, from macroscale to microscale, including region, city, neighborhood and street-level studies that are conducted around the world including Asia, Europe and America which receive high attention from researchers. Meanwhile, the study of walkability in Australia and Africa gains less attention. It is revealed that there are four factors of walkability including land use and accessibility for macroscale which cover region, city and neighborhood, while street network and pedestrian infrastructure and facility are mainly analyzed for street-level. Most of the studies widely utilize a quantitative approach for analyzing physical walkability factors in a measurement-based approach by using certain tools/software, and a perception-based approach by using questionnaires.

The findings can assist to keep up with the rapid pace of scientific publications and help accelerate the understanding of particular ideas in the field of walkability for the planning and design practice and policy that can be used by researchers/academia, urban designers, urban planners, architects, engineers and policymakers. Also, this study hopefully could assist further research direction for the future and could add value to a more advanced way of understanding the research on walkability.

This research provides a robust framework for walkability trend analysis and a comprehensive understanding of the main ideas from the existing research on walkability worldwide.

This study aims to measure the global research landscape of the National Institute of Pharmaceutical Education and Research (NIPER) of India on a set of quantitative and qualitative metrics in terms of research output toward exploring research trends and give an overview of collaborative practices by researchers of NIPERs.

The present study has selected the Scopus database as a tool to retrieve potential publications of studied NIPERs during the last 12 years (2010–2021). NIPER-Mohali, NIPER-Hyderabad, NIPER-Ahmedabad, NIPER-Guwahati and NIPER-Kolkata have been selected for the study. The study has adopted a comprehensive search strategy to extract 3,926 publications data. VOS viewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The global scientific research output of NIPERs accrued 3,926 publications with an average of 327 publications per year. The retrieved publications fetched a total of 67,772 citations with an average citation impact of 17.26. There observed a steady growth of publications from 168 to 509 registered with an average growth rate of 18.44%. The mean relative growth rate and doubling time of research output are 0.26 and 2.94. The authorship patterns explore collaborative trends as most of the publications were published by multiple authors (99.39%). NIPERs have expanded their outreach to collaborate with the USA, Malaysia, Saudi Arabia, Australia and the UK to collaborate on research and regulatory reforms exhibits in the USA as a major contributor.

The present study is the first effort to evaluate the global research productivity of NIPERs and assess the current research trends on a set of quantitative and qualitative metrics to provide some insights into the complex dynamics of research productivity. The study’s outcome may help to identify the current research progress of NIPERs at the global level.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

This research aimed to examine the impact of input, process, output, productivity and outcome variables on university research performance and the indicators that represent them in order to improve academic quality and contribute to government policy.

The quantitative approach was used through a survey method that obtained samples using questionnaires from 150 leaders of research institutions and continued analysis using the structural equation modeling-partial least square (SEM-PLS) to test the developed model.

Except for the relationship between process and productivity variables, all variable relationships had a positive and significant effect. Furthermore, the input, process, output, productivity and outcome variables each include seven, twelve, four and ten indicators.

This study has several ramifications because it provides a clear policy input and advances science. As a prelude to developing research performance assessment tools that take into account variances in a tertiary institution, this research aids in the implementation of national policies for assessing research performance in postsecondary institutions.

To improve the accuracy of the information acquired, we conducted a survey among the heads of research units at various higher-ranking Indonesian universities, taking into consideration their skill and experience in leading research organizations and conducting research. Other than that, our belief in the originality of our manuscript is strengthened by the way we applied systems theory to construct a performance evaluation model that examines each contribution made by each system aspect.

High-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.

This paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.

The findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.

This study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.

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.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

Although universities have extensive research and initiatives in place that align with the United Nations’ Sustainable Development Goals (SDGs), there is still a significant gap in documenting and assessing these efforts. This paper aims to discuss how academic libraries can apply their information management skills and open-access platforms, to facilitate the discoverability and retrieval of evidence on SDGs.

Introduced by a brief literature review on the role of libraries in contributing to the SDGs in general, the authors draw on their personal experiences as metadata specialists, participating in a project aimed at linking their university’s research output to the SDGs. A case study, from the University of Pretoria’s Veterinary Science Library, is used as an example to demonstrate the benefits of resourceful metadata in organising, communicating and raising awareness about the SDGs in the field of veterinary science.

Through practical examples and recommended workflows, this paper illustrates that metadata specialists are perfectly positioned to apply their information management skills and library platforms to facilitate the discoverability and retrieval of evidence on SDGs.

Although there are increasing reports on the contributions of libraries to support the successful implementation of the SDGs, limited information exists on the role of metadata specialists, as well as those with a practical focus.

This study aims to analyze the effectiveness and efficiency of research-based community service programs in Indonesia that used government funds in 2017–2021.

The design of this research is a quantitative research method using a data envelopment analysis to evaluate 370 leading universities in Indonesia. Furthermore, six analytical models were considered to compare effectiveness and efficiency between universities. It involved two resource (budget and staff academic involved), three output (intellectual property, prototype and publication) and three outcome variables (economic impact, social impact and capacity building).

The findings showed that several universities are considered necessary, with great potential to increase output and outcome efficiency in community involvement. The study mapped and divided the position of 370 universities for additional information. The effectiveness aspect provides another perspective in assessing the performance of tertiary institutions in Indonesia and can be an option for evaluating research performance to improve the quality of output.

The authors use data from research and community service management information systems used, both the resources used and the results. Efficiency and effectiveness of 370 universities were compared in this study, including comparing their position on the previous assessment with the assessment of the results of this study. Approach to the concept of Mandl et al. (2008) regarding the relationship between input, output and outcome as the main component of the indicators, the authors apply to analyze efficiency and effectiveness.