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This paper aims to map the creation and evolution of centering resonance analysis (CRA). This method was an innovative approach developed to conduct textual content analysis in a semi-automatic, theory-informed and analytically rigorous way. Nevertheless, despite its robust procedures to analyze documents and interviews, CRA is still broadly unknown and scarcely used in management research.

To track CRA’s development, the roadmapping approach was properly adapted. The traditional time-based multi-layered map format was customized to depict, graphically, the results obtained from a systematic literature review of the main CRA publications.

In total, 19 papers were reviewed, from the method’s introduction in 2002 to its last tracked methodological development. In all, 26 types of CRA analysis were identified and grouped in five categories. The most innovative procedures in each group were discussed and exemplified. Finally, a CRA methodological roadmap was presented, including a layered typology of the publications, in terms of their focus and innovativeness; the number of analysis conducted in each publication; references for further CRA development; a segmentation and description of the main publication periods; main turning points; citation-based relationships; and four possible future scenarios for CRA as a method.

This paper offers a unique and comprehensive review of CRA’s development, favoring its broader use in management research. In addition, it develops an adapted version of the roadmapping approach, customized for mapping methodological innovations over time.

Relying on mergers and acquisition transaction-level data set and adopting a more region-specific approach with a focus on industry-region pairs, this paper aims to examine how cross-border acquisitions (CBAs) have an effect, in terms of technological spillover and collaboration, on European regional clusters.

Adopting an industry-region pair approach, this study is based on a quantitative analysis of regional clusters belonging to 262 European regions and 25 patenting industries. Different thresholds of industrial specialization are used to identify clustering industries within a region. Invention performance at the regional cluster level is defined through two sets of different measurements to assess the impact of CBAs on invention quantity performance and internal and external technological collaboration.

The results reveal that CBAs have a positive and significant impact on the number of patents as well as the number of internal and external technological collaborations and that this effect is persistent over time. Furthermore, through exploring the interindustry technological spillover effect of CBAs registered in the same region of a cluster but outside the cluster itself, the authors found that CBAs in a regional cluster are inclined to produce technological spillovers within the cluster but no significant effects in the other industries of the region.

This paper is an attempt to empirically explore CBAs and technological spillover in European regional clusters. Therefore, it contributes to the debate, thanks to the use of an industry-region pair approach.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The purpose of this paper is make a significant contribution to the supply chain knowledge system through research on modern supply chain system in China, providing guidance for theoretical research such as methodology of dynamic resource allocation and application of innovative small- and middle-sized service system in the supply chain.

The paper uses structural analysis of Chinese competitive advantage, and it applies comparative analysis of supply chain models in China, the USA and Japan through the factor disintegration of trading environment.

China’s supply chain model has virtual scale and virtual capabilities. The relationship with suppliers is more dynamic. The requirements for resolving uncertainty are higher. Business transfer is more frequent.

Researchers are encouraged to propose the specific supply chain models in China further with the game theory, auction theory, etc.

It provides advice for government policy making and gives Chinese enterprises guidance to improve operation management.

This paper specifically analyzes characteristics of China supply chain and gives enlightenment for supply chain innovation.

Challenging, novel, and educational experiences are critical contributors to effective leader and follower development, in part, because such developmental experiences enable people to reconceptualize their perceptions about the traits and characteristics of effective leaders, or their implicit leadership theory (ILT). To understand how ILTs develop throughout developmental experiences, we examine the ILTs of 276 undergraduate students at the beginning and end of their first year of college. Using an open-ended ILT response format, a unique ILT trait of integrity emerged along with the dominant ILT traits of sensitivity, charisma, and dedication identified in previous literature. Overcoming limitations of the factor-based ILT approach, we investigate the development of ILT constellations, allowing a holistic picture of ILTs and how they throughout a developmental experience. Based on cluster analyses, four constellations of ILT themes emerged. Participants shifted to different ILT constellations after a year in college, moving from a non-descript, varied constellation toward constellations with clearly defined, dominant themes. Exploratory analysis revealed that completion of leadership coursework partially explains the shift towards more descript ILT constellations. Results have implications for new methods of future research using the ILTs constellation perspective and the practice of undergraduate leadership education in facilitating leader development among college students.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

This study aims to provide a comprehensive review and bibliometric analysis of the literature in the field of science, technology, engineering and mathematics (STEM) education over the past 15 years, with a specific focus on global distribution and research trends.

This study collected 1,718 documents from the Web of Science (WOS) database and analyzed their timeline distribution, geographical distribution, research topics, subject areas, learning stages and citation burst using a bibliometric approach with VOSviewer and Citespace.

Results indicated that: overall, STEM education has increasingly gained scholarly attention and is developing diversely by emphasizing interdisciplinary, cross-domain and regional collaboration. In terms of global collaboration, a collaborative network with the USA in the center is gradually expanding to a global scope. In terms of research themes, four key topics can be outlined including educational equity, pedagogy, empirical effects and career development. Social, cultural and economic factors influence the way STEM education is implemented across different countries. The developed Western countries highlighted educational equity and disciplinary integration, while the developing countries tend to focus more on pedagogical practices. As for research trends, eastern countries are emphasizing humanistic leadership and cultural integration in STEM education; in terms of teachers’ professional development, teachers’ abilities of interdisciplinary integration, technology adoption and pedagogy application are of the greatest importance. With regards to pedagogy, the main focus is for developing students’ higher-order abilities. In terms of education equity, issues of gender and ethnicity were still the hottest topics, while the unbalanced development of STEM education across regions needs further research.

This study provides a global landscape of STEM education along the timeline, which illustrates the yearly progressive development of STEM education and indicates the future trends.

Tourism on wellness is a rapidly expanding segment of the travel industry; nevertheless, it is still in its infancy, and more study research is needed to develop a scientific foundation for health and wellness tourism. The study uses bibliometric indicators like as citations to determine the field structure on wellness tourism from 1998 to 2021, and the VOSviewer software to map the significant trends in wellness tourism (WT) area, to examine the present situation.

To reach this objective, the theme of “wellness tourism” was searched in the “Scopus” database, and bibliometrics data on the publications were obtained. In total, 414 papers were found during the initial search, which was then narrowed according to the criteria. Using this strategy, the author discovered 386 records, and after removing 4 duplicates and 1 irrelevant document, the refining produced 381 related documents. The most-cited papers, significant authors, co-citation of references, sources and authors were all investigated for the publications related to WT.

According to this report, research into wellness tourism has increased in recent years. The authors discovered two papers with over 238 “Scopus” citations and a total of 10 studies with 1414 citations. According to Scopus, the document Napier et al. receives 36.5% of citations each year. There were a total of 804 authors who published about WT between 1998 and 2021, with Smith, M, Voigt, c, and Puczkó, L being the most-cited reference authors in the subject. Han h. has the highest index of 56 of all the authors.

The “Scopus” database was used for bibliometric analysis, although the VOSviewer was used exclusively. This considered as a first study to utilize a bibliometric method to address this research gap, identifying the tools, journals, and, most crucially, conceptual subdomains like spa, yoga, therapy, spirituality and trekking that will be significant in future research.

Using a bibliometric analytic approach, this article looked at papers on wellness tourism published between 1998 and 2021. Thus, its goal is to learn more about wellness tourism and to enlighten wellness tourism scholars on the field's structure.

This paper aims to identify if entrepreneurship as a career option is related to job satisfaction and family satisfaction by comparing entrepreneurs with non-entrepreneurs. To do so, a tool was used to measure job satisfaction, family satisfaction and the existence of interrole conflicts.

Cluster analysis and linear regression models were used to test the hypotheses.

Results indicate that there is a positive relationship and that entrepreneurs have higher rates of job satisfaction than non-entrepreneurs. Contrary to what the literature suggests, this study found that entrepreneurs have a higher rate of family satisfaction than non-entrepreneurs, and that there is no difference between men’s and women’s levels of satisfaction.

Among the limitations of the research, the authors cite the fact that there was no randomness in the designation of the respondents and the fact that the answers were obtained by self-report. Also, the research was cross-sectional, making it impossible to compare answers between the same individuals at different points of time.

Results can help individuals decide whether to start a business and to assist development agencies in the creation of incentive policies.

As an academic implication, results contribute to the literature on this topic by relating entrepreneurship with the quality of life and not only with financial variables. Yet, it is one of the first studies on this topic in Brazil.