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The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

Scientific collaboration is becoming a common pattern in the social organization of knowledge production. The paper tries to figure out the relationship between scientific collaboration team size and scientific output.

Based on ESI database from year 2009–2019, the paper describes changes of collaboration team size from one author to more than 10 authors in 22 disciplines. Kernel density estimation and multidimensional kernel density estimation method are used to calculate optimal collaboration team size and appropriate collaboration team size in 22 disciplines. As bandwidth is one of the major issues in construction of kernel density estimation, the paper uses five different algorithms to calculate bandwidth. The method with the lowest mean absolute percentage error is chosen. Robustness test is conducted based on different sets of data.

The results show that scientific collaboration becomes more widely and deeply. As time goes by, collaboration team size is becoming larger and larger. Natural science disciplines have larger collaboration team size and faster growth rate than social science disciplines. Considering both qualitative and quantitative measures, the paper proves the universality of optimal and appropriate scientific collaboration team size among 22 disciplines and calculates the specific number.

The paper tries to investigate the law of scientific collaboration team size variation and provide a full picture of evolution of collaboration team size among 22 disciplines in 10 years. The paper first applies distribution method to figure out the relationship between scientific collaboration team size and scientific output and provides optimal collaboration team size and appropriate collaboration team size.

The purpose of this paper is to evaluate green buildings from the angle of greenness and improve the evaluation system. And the matter-element extension method is used to evaluate the greenness of green buildings, in order to provide useful references for the evaluation system of green buildings.

First, this paper studies the aspects of safety and durability, health and comfort, living convenience, resource-saving, environmental liability and ecological quality, etc. For the first time, carbon emission is included in the evaluation system, 18 key evaluation indexes are determined by using the Delphi method, and the green building evaluation index system is established. Then, the combined weight method is proposed to determine the weight of each evaluation index, and the greenness evaluation model of green building is established with the matter-element extension method. Finally, taking Beijing Daxing International Airport as an example, the evaluation model of green building greenness was established to evaluate the building.

In this paper, the greenness evaluation model of green building established by the matter-element extension method solves the problem of incompatibility between qualitative and quantitative material elements in multi-factor evaluation. It makes the evaluation indexes more accurate and objective relative to the affiliation calculation of the evaluation set and improves the scientific, accuracy and reliability of the evaluation model.

In this paper, for the first time, carbon emission-related indicators are included in the green building evaluation system, which makes the evaluation system more perfect. In addition, a more scientific extension matter-element method is used to evaluate the greenness of green buildings, breaking the previous rough star evaluation method.

The metaverse offers great potential for creating a new educational environment with unique experiences. Currently, it has been integrated into many stages of education, including classroom study aids, clinical skill interaction and image training simulators, thanks to a new generation of Internet applications. This paper aims to provide a comprehensive systematic review using bibliometric analysis on the metaverse in health education and analyze the trends and patterns of research output within the field.

The paper conducts bibliometric analysis and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent review process. Specifically, this article identifies research questions, develops a data-collection strategy and establishes a screening approach that includes determining relevant keywords and applying inclusion and exclusion criteria.

A bibliometric analysis is conducted comprising 231 studies from 145 scientific journals to assess the trends, patterns and collaboration networks in research on the use of metaverse technology in health education. This paper provides insights into the research themes, publication trends and countries leading in this field, which can guide future research in this field.

The use of metaverse technology in health education has gained momentum in recent years. Despite this interest, comprehensive studies to review and analyze the existing literature on this topic systematically are lacking. In response, this paper provides a systematic review that explores the potential role of the metaverse in health education. By considering the current research, key trends, research hotspots and opportunities for future investigations are identified. The findings not only shed light on the current state of research but also offer guidance for advancing this exciting field.

The main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.

In this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.

The findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.

The findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.

This study examines the research landscape of Lean Six-Sigma (LSS) applications in hospitals for the period of the last decade (2011–2020) to derive answers to the research questions RQ 1: What are the current publication trends for the application of LSS in hospitals concerning document type, Journal (Source), active authors and country-wise publications and their comparison in the two most reputed scientific databases, i.e. Scopus and Web of Science (WoS), RQ2: What are the clusters based on the authors and keywords? RQ3: What are the research trends and author's productivity in LSS applications in Hospitals? RQ4: What are the future research areas?

This article compares these two databases (Scopus and WoS) based on publication pattern, document type, active authors and co-citation analysis. This article analyzes the core sources, author's productivity, globally cited articles, word growth analysis, thematic map and world collaboration map on the WoS and Scopus dataset. The software used are Vosviewer, Biblioshiny (R Package for Bibliometric) and M.S. Excel.

The application of LSS in hospitals is a niche theme. In the WoS database International Journal of Lean Six-Sigma and in Scopus database International Journal of Health Care Quality Assurance are the most relevant sources publishing research articles in this field. The USA has the highest scientific production in this field. Among the authors, Antony J is the most active author in this area, with the highest contribution over the years.

This study fills the literature gap by mapping the field of LSS in hospitals.

The current study focuses on many risk categories that have emerged in the digital ecosystem of the financial technology industry, which has dramatically changed traditional financial systems as a result of innovations in financial technology.

The Web of Science Core Collection database was used to find a data set of 719 pertinent papers on the subject encompassing the year 2015–2023. The sample procedure was carried out utilising the PRISMA approach. The keywords were first gathered relating to technological risks in banking sectors and after confirming the keywords, the authors performed the search by the “topic” which covers “title” in the search bar. On February 15, 2023, the Web of Science database was searched using the terms “Cyber security risk OR data theft OR financial crimes OR financial stability risk OR operational risk OR default risk OR money laundering OR financial terrorism AND FinTech AND banking sector”. Two-step approach is applied in this study. First, descriptive analysis is applied using RStudio to highlight prominent authors, countries and affiliations. Furthermore, relationship among authors, countries and keywords is shown by using three fields plot. Second, using VOSviewer, co-occurrence of keyword analysis is used to determine the most influential themes.

The findings show that 2,611 documents have been published from 2016 to 2023. Year 2021 is the most productive year in terms of number of publications. The results also show that WANG XC is tied for the position of most prolific contributing author. In a similar vein, the United States leads the world in publication output. Furthermore, Southwestern University of Finance and Economics in China is leading the list with 15 articles. The results from the co-occurrence of keywords reveal that “default risk”, “operational risk”, “money laundering”, “credit risk”, “corporate governance”, “systematic risk”, “financial stability risk”, “risk management” and “crises” are the frequently keywords.

The results of this study are beneficial to academia and industry in order to advance their current understanding of FinTech and associated concerns. This work expands the understanding of the technology hazards facing the banking industry from a broad perspective.

In order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.

Through the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.

It effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.

Existing research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.

The purpose of this study is to analyze and map the terrain of human resource management (HRM) in higher education (HE) contexts, with the aim of uncovering potential gaps within the existing knowledge base.

This study employs a unique, in-depth bibliometric analysis of 945 publications from the Web of Science database over the past 40 years between 1981 and 2022.

Based on the bibliometric analysis, the authors retrospectively examined the dominant research themes in HRM within the HE sphere, tracing their evolution over time. Four central clusters emerged: the theoretical foundations of HRM, strategic HRM, organizational culture and human capital development. Furthermore, the authors pinpointed critical research gaps and proposed areas for future inquiry, such as the impact of HRM on productivity, leadership dynamics, sustainable growth development, international staffing strategies and knowledge transfer mechanisms.

This study demonstrates how academics can use bibliometric techniques to systemize literature, expose potential gaps and suggest fruitful lines of inquiry in the field of investigation. The findings of this study can also help improve the decision-making processes of managers and human resource professionals.

This study provides readers with a systematic understanding of the development of HRM in HE settings and presents forward-looking perspectives, highlighting future research possibilities. Moreover, it validates the significance of bibliometric analysis as an efficient technique for discovering gaps in the existing literature.