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This study investigated the relationship of strengths-based leadership with nurses’ turnover intention and the mediating roles of job crafting and work fatigue in the relationship.

Data comprising 318 valid participants from three hospitals in Beijing were gathered at two points in time, spaced by a two-month interval. Structural equation modeling with a bootstrapping analysis was applied to test hypotheses.

This study found that strengths-based leadership negatively relates to nurses’ turnover intention, and job crafting and work fatigue mediate the relationship of strengths-based leadership with turnover intention, respectively.

The findings of this study highlight the importance of strengths-based leadership in decreasing nurses’ turnover intention and reveal two potential mechanisms through which strengths-based leadership is related to nurses’ turnover intention. In order to retain nursing staff better, nurse leaders should execute more strengths-based leadership behaviors and make more efforts to promote nurses’ job crafting and to reduce nurses’ experience of work fatigue.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Common institutional ownership is a phenomenon that has extended throughout the capital markets in recent years and has a significant impact on business strategy decisions. The study intends to investigate the effect of common institutional ownership on corporate over-financialization and potential functioning mechanisms.

Using panel data from Chinese-listed companies over the period of 2003–2021, the authors conduct regression models which controlled year-, industry- and regional fixed effects to explore the impact of common institutional ownership on corporate over-financialization.

This study concludes that corporate over-financialization may be prevented via common institutional ownership. The mechanism test suggests that common institutional ownership inhibits corporate over-financialization by improving internal control quality and enhancing financial flexibility. Besides, heterogeneity analysis shows that the inhibiting effect of common institutional ownership on corporate over-financialization is more pronounced in stability-oriented institutional investors and high financing constraints firms.

This paper makes a valuable contribution to the current studies on effective strategies to prevent enterprises from becoming overly financialized by recognizing common institutional ownership. Furthermore, this paper adds to the research on common institutional ownership’s economic consequences. Finally, this study provides management implications for how to optimize corporate governance structures, curb the financialization of entities in practice and promote the development of the real economy.

According to extensive analysis, employee agility is influenced by teamwork, coordination and the organizational environment. However, less consideration has been given to the role of work stressors (challenge, hindrance) in influencing employee agility. To address this research gap, this study sheds light on how the use of enterprise social media (ESM) for social and work purposes influences employee agility through work stressors.

This research also explores how ESM visibility enhances the interaction between work stressors and employee agility by using primary data obtained from Chinese workers. A total of 377 entries were analyzed using AMOS 24.10 tools. All the hypotheses were tested using structural equation modeling (SEM).

The findings revealed that ESM use (social and work) negatively impacts challenge and hindrance work stressors. The results also reflect that challenge stressors have a significant impact on employee agility, whereas hindrance stressors are negatively related to it. Furthermore, the outcome also indicated that increased ESM visibility reinforces the connection between challenge stressors and employee agility. However, ESM visibility did not indicate a significant moderating impact on the link between hindrance stressors and employee agility.

This study describes how ESM usage effects agility of stressed employees. This research also explores how ESM visibility improves the interaction between work stressors and employee agility. The study results contribute to growing research on social media and employee agility and suggest several points of guidance for managers.

Little is known about how religious beliefs can motivate consumers to behave more pro-environmentally. Drawn on an integrated model of the theory of planned behavior, the norm activation model and the self-determination theory, this study aims to explore the effects of religious beliefs (especially, karmic beliefs (KB) and beliefs in a just world (BJW)) on consumers' pro-environmental behavior.

A sample of 736 consumers recruited from the eight most populous cities in Vietnam using the mall-intercept survey approach and structural equation modeling (SEM) was utilized to test the hypothesized model and hypotheses.

The findings indicate that KB and BJW can increase consumers' green intrinsic motivation, which subsequently encourages them to engage in pro-environmental consumption. Moreover, awareness of consequences (AOC) and ascription of responsibility (AOR) serially indirectly inspire consumers' sustainable consumption through serial mediators, including personal norms (PN), attitudes toward green products and green purchase intention.

Based on the findings, some theoretical and managerial implications for pro-environmental consumption are provided.

The study offers fresh perspectives on the role of religious beliefs in pro-environmental research. Additionally, this study sheds new light on the marketing literature by integrating the theory of planned behavior (TPB) and norm activation model (NAM) with self-determination theory (SDT) to explore the underlying mechanisms and effects of psychological components on consumers' pro-environmental behaviors.

This study aims to explore the impact of tourists’ perceptions of two rural destination attractiveness dimensions on tourists’ environmentally responsible behavioral intentions (ERBI). Further, the mediating effects of tourists’ green self-identity on the relationship between the perception of rural destination attractiveness and ERBI are investigated.

This study collected survey data from 188 tourists who had visiting experiences in rural attractions located in the Guangdong Province of China. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed hypotheses.

The results found that rural destination specialty fresh food attractiveness perceived by tourists was positively associated with their ERBI. Moreover, tourists’ green self-identity positively mediated the perception of rural destination attractiveness and ERBI.

This study explains how the tourists’ perceptions of two rural destination attractiveness dimensions influence their ERBI. By exploring the mediating role of tourists’ green self-identity, this study also emphasizes the transforming mechanism from tourists’ perceived experience to their ERBI. The study provides insights into nature-based tourism destination management and sustainability practices.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.