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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 study aims to examine the connection between scholars' research performance and the multidisciplinary nature of their collaborative research. Furthermore, in response to mixed results regarding the effects of multidisciplinarity on research performance, this study explores how human resource management (HRM) practices may moderate this link.

The authors built a model based on the theoretical arguments and empirical evidence found in the review of diversity and HRM literature. The authors also performed a quantitative study based on a sample of scholars in the field of management. Different econometric estimations were used to test the proposed model.

The results of this empirical analysis suggest that multidisciplinary research has a non-linear effect on research performance. Certain HRM practices, such as development and collaboration, moderated the curvilinear relationship between multidisciplinarity and performance, displacing the optimum to allow higher performance at higher levels of multidisciplinary research.

The paper provides advances on previous works studying the curvilinear relationship between multidisciplinarity and the researchers' performance, confirming that multidisciplinarity is beneficial up to a threshold beyond which these benefits are attenuated. In addition, the findings shed light on important issues related to team-oriented HRM practices associated with the outcomes of multidisciplinary research.

The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time.

The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer.

The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme.

The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.

The purpose of this paper is to acknowledge that there are bibliometric differences between Social Sciences and Humanities (SSH) vs Science, Technology, Engineering and Mathematics (STEM). It is not so that either SSH or STEM has the right way of doing research or working as a scholarly community. Accordingly, research evaluation is not done properly in one framework based on either a method from SSH or STEM. However, performing research evaluation in two separate frameworks also has disadvantages. One way of scholarly practice may be favored unintentionally in evaluations and in research profiling, which is necessary for job and grant applications.

In the case study, the authors propose a tool where it may be possible, on one hand, to evaluate across disciplines and on the other hand to keep the multifaceted perspective on the disciplines. Case data describe professors at an SSH and a STEM department at Aalborg University. Ten partial indicators are compiled to build a performance web – a multidimensional description – and a one-dimensional ranking of professors at the two departments. The partial indicators are selected in a way that they should cover a broad variety of scholarly practice and differences in data availability.

A tool which can be used both for a one-dimensional ranking of researchers and for a multidimensional description is described in the paper.

Limitations of the study are that panel-based evaluation is left out and that the number of partial indicators is set to 10.

The paper describes a new tool that may be an inspiration for practitioners in research analytics.

The purpose of this study is to identify papers that have produced the most significant impact on research on strategic alliances in the biotechnology industry. The authors attempt to illustrate the thematic evolution of its intellectual structure through 616 papers published between 1992 and 2021.

The present research methodology relies on three distinct techniques, implemented using SciMat software: (1) bibliometric techniques, (2) scientific map analysis and (3) content analysis of research documents from the Web of Science (WoS). In this manner, the authors analyse the intellectual structure of the field of strategic alliances in the biotechnology industry, tracking its evolution over a period of three decades.

The study emphasises the relevance of “innovation” as a key theme and identifies several potential areas for future research, which could serve as a foundation for further investigations.

This study represents a novel contribution to the literature as it is the first to use the SciMat tool to analyse strategic alliances in the biotechnology industry. This research reveals that while strategic alliances have been assessed extensively across various industries, some topics, such as the types and formation of alliances, have not been specifically studied in the biotechnology industry. These areas as well as the barriers and variables influencing the formation of alliances offer promising avenues for future research in this field.

本研究旨在確定對關於生物科技產業內的策略聯盟的探究產生極其顯著影響的學術論文。我們擬透過探討於1992年至2021年期間發表的616篇學術論文，去闡明策略聯盟的知識結構的主題演變。

研究依賴三個不同的技術來進行，並以SciMat 可視化軟件來做具體實施的工作。這三個技術為、(一) 文獻計量技術;(二) 科學製圖分析;和 (三) 就取自 Web of Science 的學術文章而進行的內容分析。我們採用這研究法，對在生物科技產業內的策略聯盟的知識結構進行分析，俾能對有關的知識結構的主題演變進行一個涵蓋三十載的跟蹤調查。

研究強調了創新，並視之為主要的主題的重要相關概念;研究亦確定了一些今後可供研究的潛在領域，這或許會成為進一步研究的基礎。

由於本研究是首個研究、使用SciMat這工具去分析在生物科技產業內的策略聯盟，故就有關的文獻而言，它給予新穎的貢獻。研究結果顯示，雖然策略聯盟已在各個不同的產業裡被廣泛評價，但在生物科技產業裡，一些如聯盟的種類和形成方式等的課題仍未得到適切的研究。這些課題，以及影響著聯盟的形成的障礙和變數，會為這領域內今後的學術研究、提供光明的途徑。

This paper examines how the degree of happiness affects corporate risk-taking and the moderating influence of family ownership of firms on this relationship.

The authors use an international sample of 17,654 firm-year observations from 24 countries around the world from 2008 to 2016.

Using the happiness index from the World Happiness Report developed by the United Nations Sustainable Development Solutions Network, the authors show that a country's overall happiness is negatively correlated with risk-taking behavior by firms. The findings are robust to an alternative measure of risk-taking by firms. Further analyses document that the negative influence of happiness on firm risk-taking is more pronounced for family-owned firms.

The paper is consistent with the notion that happier people are likely to be more risk-averse in making financial decisions, which, in turn, reduces corporate risk-taking.

This study contributes to the broad literature on the determinants of corporate risk-taking and the growing literature on the role of sentiment on investment decisions. The authors contribute to the current debate about family-owned firms by demonstrating that the presence of family trust strengthens the negative influence of happiness on corporate risk-taking, a topic that has been unexplored in previous studies.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

The purpose of this paper is to critically analyze the influence of the algorithm used on scholarly search engines (Garfield’s algorithm) and propose metrics to improve it so that science could be based on a more democratic way.

This paper used a snow-ball approach to collect data that allowed identifying the history and the logic behind the Garfield’s algorithm. It follows on excerpting the foundation of existing algorithm and databases of major scholarly search engine. It concluded proposing new metrics so as to surpass restraints and to democratize the scientific discourse.

This paper finds that the studied algorithm currently biases the scientific discourse toward a narrow perspective, while it should take into consideration several researchers’ characteristics. It proposes the substitution of the h-index by the number of times the scholar’s most cited work has been cited. Finally, it proposes that works in languages different than English should be included.

The broad comprehension of any phenomena should be based on multiple perspectives; therefore, the inclusion of diverse metrics will extend the scientific discourse.

The improvement of the existing algorithm will increase the chances of contact among different cultures, which stimulate rapid progress on the development of knowledge.

The value of this paper resides in demonstrating that the algorithm used in scholarly search engines biases the development of science. If updated as proposed here, science will be unbiased and bias aware.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.