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This study investigates the correlation between economic development and scientific knowledge production indicators in the BRICS countries from 2000 to 2020, highlighting the importance of human resources, natural resources, and innovation. Addressing a gap in the existing literature, this study aims to contribute significantly to understanding this relationship.

Employing a descriptive statistical approach, this study utilizes GDP and per capita income as economic indicators and scientific data from WoS and SCOPUS databases, focusing on scientific document production and citations per document.

The analysis reveals a strong correlation between economic development and scientific performance within the BRICS nations during the specified period. It emphasizes the interdependence of economic progress and scientific prowess, underscoring that they cannot be considered independently.

However, limitations exist, notably the reliance on specific databases that might not cover the entire scientific output and the inability to capture all factors influencing economic and scientific development.

Understanding this interdependence has crucial originality. Policymakers and stakeholders in BRICS countries can leverage these insights to prioritize investments in human capital development and scientific research. This approach can foster sustainable economic growth by reducing reliance on natural resources.

The purpose of this paper is to present findings related to an instrument for the self‐appraisal of scientists' research performance, and highlight the suitability of self‐appraisal instruments for members of the scientific community.

An examination of the literature on self‐appraisal and the measurement of scientific research is presented. The initial development of the instrument employed qualitative methods through interview and discussions with PhD‐qualified scientific researchers (n=13). A quantitative investigation of the usefulness of the instrument was then conducted on a sample of biological and chemical research scientists (n=270). Results were compared with an existing performance measure and examined for representative reliability.

Results suggest that the instrument may be a reliable measure of research performance when used in a non‐critical context.

While the instrument shows promise, further research is needed to examine aspects of inter‐rater reliability. Additional research is also needed to further examine relationships between it and other measures of research performance at the same level of analysis. While the usefulness and validity of this instrument at the “international level” has been examined, further research is needed to examine the relative validity and reliability of the instrument at the “institutional” and “national” levels.

The instrument provides a useful and cost‐effective tool for use in the performance appraisal process of research scientists, and for use in focusing discussion on performance for developmental purposes. It is also useful as a research tool for the timely and cost‐effective measurement of research performance at an institutional, national and international level.

The paper presents an original paper and pencil instrument for the appraisal of scientific research performance at an institutional, national, and international level.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

The purpose of this paper is to describe the development and validation of an instrument for measuring intellectual capital in the academic research context. The current research context describes a new paradigm of scientific production characterized by interdisciplinarity, heterogeneity and the intensification of the relations between the generators of knowledge. In this scenario, traditional measures of intellectual capital do not capture all the variables that make up the environment in which the research activities are carried out. This transformation of research processes suggests the need to bring theories of organizational behavior, more appropriate to an organizational context, to the study of scientific context. Thus, the paper contextualizes the intellectual capital approach, thereby explaining how the different attributes that build it influence scientific productivity and providing a measurement instrument to evaluate relative levels of intellectual capital in an academic research context.

The scale was designed through a double qualitative–quantitative scale development process. The literature on intellectual capital does not provide strong theoretical support for the definition of a specific set of items to be applied in the specific academic research context. Consequently, the scale constructs and observable variables were initially conceptualized through a Delphi panel. This initial set of indicators was empirically validated through a second quantitative stage to a sample of 1,798 Spanish academics. Given that no prior published studies have examined the construct validity of the proposed scale, and the proposed scale is not based on other previously validated scales, the authors used exploratory and confirmatory factor analysis to assess the internal consistency, using Cronbach’s α to determine reliability.

Drawing on the evidence obtained from a double qualitative–quantitative process, a scale consisting of 47 items was proposed to measure the three dimensions of intellectual capital, namely, the researcher’s human capital, as well as the nature of the social capital and organizational capital of the team in which the scholar is integrated. The process of identifying and validating indicators of intellectual capital allowed the authors to identify certain intangible elements that are key in the research process and that, therefore, determine scientific productivity. Thus, the proposed scale contributes by conceptualizing new variables that could be used to deepen and broaden the study of the determinants of research performance. The contextualization of intellectual capital approach can also help to assess the value of intangibles, offering an external reporting tool and making universities’ social contributions more visible to public and private stakeholders, justifying the efforts made by societies in the generation of academic knowledge.

The empirical analysis was carried out with an initial sample of 1,798 Spanish scholars. The validation of the scale should therefore be confirmed in different national contexts, with larger data sets. Likewise, the use of longitudinal data sets could help to study the effects of intellectual capital in academic research, thereby contributing to the ongoing debate on the determinants of research performance.

From a practical perspective, the instrument could be considered both as a management and an external reporting tool, providing a self-assessment instrument of the levels of intellectual capital. As a management tool, a specific measure of intellectual capital in an academic context could help to identify training needs, the implementation of practices that encourage the capability for building research networks and the development of reports with intellectual capital-related inputs for the justification of the resources received. At an institutional level, the proposed set of indicators also identifies the attributes of scholars linked to higher scientific performance, and the scale could be used as an instrument for selection processes in academic institutions, to develop practices related to the distribution of workload or the publication of intellectual capital indicators of its researchers in a healthy exercise of transparency.

Proper topic selection is an essential prerequisite for the success of research. To study this, this article proposes an important concerned factor of topic selection-topic popularity, to examine the relationship between topic selection and team performance.

The authors adopt extracted entities on the type of gene/protein, which are used as proxies as topics, to keep track of the development of topic popularity. The decision tree model is used to classify the ascending phase and descending phase of entity popularity based on the temporal trend of entity occurrence frequency. Through comparing various dimensions of team performance – academic performance, research funding, relationship between performance and funding and corresponding author's influence at different phases of topic popularity – the relationship between the selected phase of topic popularity and academic performance of research teams can be explored.

First, topic popularity can impact team performance in the academic productivity and their research work's academic influence. Second, topic popularity can affect the quantity and amount of research funding received by teams. Third, topic popularity can impact the promotion effect of funding on team performance. Fourth, topic popularity can impact the influence of the corresponding author on team performance.

This is a new attempt to conduct team-oriented analysis on the relationship between topic selection and academic performance. Through understanding relationships amongst topic popularity, team performance and research funding, the study would be valuable for researchers and policy makers to conduct reasonable decision making on topic selection.

The purpose of this study is to investigate the mechanism of shared leadership on team members’ innovative behavior.

Paired questionnaires were collected from 89 scientific research teams in the Beijing-Tianjin-Hebei region of China at two-time points with a time lag of 4 months. Then multilevel structural equation model method was applied to analyze the multiple mediating effects.

This study finds that: the form of shared leadership in scientific research teams of universities; shared leadership has a positive impact on team members’ innovative behavior; there are multiple mediations in the relationship including synchronization and sequence of creative self-efficacy and achievement motivation.

According to the “stimulus-organism-response” model, this paper has constructed a multi-level theoretical model that shared leadership influences individual innovation behavior and reveals the “black box” from the perspective of psychological mechanism. It not only verifies that “can-do” shapes “willing to do” but also makes up for the gap of an empirical test of the effectiveness of shared leadership in scientific research teams of universities. Besides, the formal scale of shared leadership in the Chinese situation is revised, which can provide a reference for future empirical research on shared leadership. The research conclusions provide new ideas for improving the management of scientific research teams in universities.

Currently, the evaluation of scientific performance of universities is one of the important indicators in various ranking systems. One way to evaluate the academic performance of universities is to analyze the scientific documents of universities in reputable international databases. The purpose of this article is to analyze and evaluate the scientific publications of Alzahra University (Iran) as the top 100–200 universities during 1986–2019.

This study was performed using bibliometrics and visualization techniques. The Scopus database was used to collect data. Affiliation search and advanced search were used to retrieve the data. Excel, VOSviewer and CRExplorer software were used to analyze the data.

The results showed that the scientific publications and received citations by Alzahra University documents during the time have been upward. At the national level, it was the most scientific collaboration with researchers at the University of Tehran. Also at the international level, the most scientific collaboration has been with the United States, Canada and Germany. In total, 80% of scientific publications were published by 20% of authors. Also, 70% of the highly cited articles were published in journals with quartile 1. Finally, clustering results showed that Alzahra University's scientific publications are in five main categories, including “chemistry,” “physics,” “biology,” “psychology and educational sciences” and “accounting sciences, management, and computer science.”

This study could be a good model for evaluating the performance of scientific productions of universities and scientific institutions with bibliometrics and visualization approaches.

The purpose of this paper is to provide empirical evidence on the relationships between the dimensions of intellectual capital (IC) and the generation of knowledge in public universities.

An online survey was developed and administered in Colombia. A total of 209 researchers participated in the study. Data were collected through IC measurements concerning the research mission of the universities. Scientific publications from the respondents and the citations received were taken as proxies for the generation of knowledge. To test the hypotheses, structural equation modeling was used.

Hypotheses proposing a positive association between the dimensions of IC, namely, human capital, structural capital, and relational capital, and the generation of knowledge were tested. The findings highlight that human capital is indirectly and positively related to the generation of knowledge through relational capital, as well as through the path of structural capital-relational capital.

The study suggests that directors of research at universities could improve the results of this activity by analyzing and understanding the dimensions of IC that contribute to the development of scientific capacities and the generation of knowledge.

This is one of the first studies that has examined the interrelationships between the dimensions of IC at universities and the generation of knowledge.

The purpose of this paper is to define the scientific production and productivity, and to present the main indicators for the measurement of the scientific activity. The impact of the research is to be measured and analyzed through citation analysis.

This is an exploratory study made at the Transilvania University of Brasov to evaluate the research output of the faculty. The author analyzed the 2008 research performance as documented in its annual evaluation that states the number of papers, books, and research contracts. Using Publish or Perish, the author calculated the H‐index, G‐index, HC‐index and HI norm, of the 60 more‐productive professors. Correlation indicators are presented and the importance of open access tools and repositories for increasing the impact of scientific research is discussed.

The Publish or Perish software is an easy to use instrument for analyzing the impact of research. It calculates the impact of the researchers using the very visible resource, Google Scholar. Google Scholar has better indexing of proceedings and non‐English language material than ISI Web of Science but does not perform so well tracking citations from books and chapters of books.

Authors' impact analysis and citations are not an assessment tool in Romanian universities, but it is commonly accepted that increasing research impact through more citations is one qualitative indicator.

It was suggested to the faculty that all professors have to be analysed with the same indicator. Further, Google Scholar and the H‐index obtained using Publish or Perish offer tools for assessing scientific research in a university and for evaluating professors.

The results showed that an open access institutional repository would significantly add to the visibility of the university's scientific production.

The paper presents the methodology and the results of an exploratory study made at the Transilvania University of Brasov regarding the H‐Index of the academic staff. It analyzes the research performances achieved by Brasov academic community in 2008, as realised in its annual evaluation – number of papers, books, research contracts, etc. – by comparing the four indexes of those 60 professors with the best results.

A strong, useful theoretical foundation for performance assessment and prediction relies on four components: preliminary observation of a system, identification of key or dominating variables in the system, synthetic and vertical thinking, and successive refinement.