Search results

This study aims to explore the spatio-temporal dynamic characteristics and influencing factors of the coordination degree of the three systems of digital economy, energy and human habitat in Western China and to provide academic research support for promoting coordinated and sustainable development in similar regions of the world.

Based on system theory and sustainable development theory, this study primarily uses the coupled coordination degree model to assess the degree of coordination between the three systems.

The findings of this study indicate that: The three systems’ overall coordination is low. The distribution of the degree of coordination has spatial differences and its coefficient of variation is small. The probability of the coordination type changing for the better is greater than that of the opposite, and neighboring provinces interact with one another. The old-age dependence ratio, the resident population’s urbanization rate and public budget expenditure have the strongest gray association with the degree of coordination.

This study’s findings will be valuable for policymakers in developing policies to promote the coordinated and sustainable growth of the region’s digital economy, energy and human habitat. Additionally, the findings will aid in facilitating regional exchanges and cooperation to enhance the level of sustainable development.

This study’s findings will contribute to increased social interest in coordinating sustainable growth in the digital economy, energy and human habitat.

This study examines the digital economy, energy and human habitat within the same framework and investigates spatial spillover effects using spatial Markov chains.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

This paper aims to understand how quantum leaders influence employee work behavior through effective tasks.

In this study, 516 questionnaires were collected using the interval data method to explore the triggering mechanisms and paths of emerging quantum leadership on constructive deviance.

The findings indicate that quantum leadership promotes constructive deviance through facilitating recovery experience (affective path), job crafting (task path) and the chained mediation path between the two. Additionally, the moderating effect of openness to experience strengthens the pathways between quantum leadership and recovery experience, and between quantum leadership and job crafting.

This study focuses closely on the mechanism of leadership behavior on employees, neglecting the psychological state and behavior of the leader as a key resource element in the work environment. Quantum leadership emphasizes value-bound characteristics, so the role played by quantum leaders may vary in different cultures and values.

First, this study calls for the organizational management focusing on the advantages of quantum leadership thinking and its positive effects in practice. Second, the mediating mechanisms of recovery experience and job crafting provide insights into how quantum leadership can be used to enhance constructive deviance. Third, this study elucidates how individual responses to organizational environment and leadership style vary in management practices. Our study helps managers better understand how individual characteristics, such as openness to experience, influence managerial behavior.

This study enriches the qualitative research on emerging “quantum” perspectives of leadership, expands the mechanism of employee constructive deviance and highlights the need for organizations to take measures that encourage constructive deviance by their employees, as this can lead to high-quality and long-term growth.

Based on conservation of resources theory, authors revealed the mechanisms by which quantum leadership influences employees’ constructive deviance, confirming the mediating role of recovery experience and job crafting as well as the moderating role of openness to employee experience. We explored the moderating mechanisms of the individual trait of openness to experience in the quantum leadership-to-job crafting and the recovery experience-to-job crafting.

In recent years, the incidence of cardiovascular disease has continued to rise, and early screening and prevention are especially critical. Phonocardiography (PCG) and electrocardiography (ECG), as simple, cost-effective and non-invasive tests, are important tools for clinical analysis. However, it is difficult to fully reflect the complexity of the cardiovascular system using PCG or ECG tests alone. Combining the multimodal signals of PCG and ECG can provide complementary information to improve the detection accuracy. Therefore, the purpose of this paper is to propose a multimodal signal classification method based on continuous wavelet transform and improved ResNet18.

The classification method is based on the ResNet18 backbone, and the ResNet18 network is improved by embedding the global grouped coordinate attention mechanism module and the improved bidirectional feature pyramid network. Firstly, a data acquisition system was built using a MEMS-integrated PCG-ECG sensor to construct a private data set. Second is the time-frequency transformation of PCG and ECG synchronized signals on public and private data sets using continuous wavelet transform. Finally, the time-frequency images are categorized.

The global grouped coordinate attention mechanism and bidirectional feature pyramid network modules proposed in this paper significantly enhance the model’s performance. On public data sets, the method achieves precision, sensitivity, specificity, accuracy and F1 score of 97.96%, 98.51%, 97.58%, 98.08% and 98.23%, respectively, which represent improvements of 3.54%, 3.92%, 4.18%, 4.03% and 3.72% compared to ResNet18. Additionally, it demonstrates a clear advantage over existing mainstream algorithms. On private data sets, the method’s five metrics are 98.15%, 98.76%, 98.08%, 98.42% and 98.45%, further validating the model’s generalization ability.

The method proposed in this paper not only improves the accuracy and efficiency of the test but also provides an effective solution for early screening and prevention of cardiovascular diseases.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

With the advancement of technology, microlearning has emerged as a promising method to improve the efficacy of teaching and learning. This study aims to investigate the document types, volume, growth trajectory, geographic contribution, coauthor relationships, prominent authors, research groups, influential documents and publication outlets in the microlearning literature.

We adapt the PRISMA guidelines to assess the eligibility of 297 Scopus-indexed documents from 2002 to 2021. Each was manually labeled by educational level. Descriptive statistics and science mapping were conducted to highlight relevant objects and their patterns in the knowledge base.

This study confirms the increasing trend of microlearning publications over the last two decades, with conference papers dominating the microlearning literature (178 documents, 59.86%). Despite global contributions, a concentrated effort from scholars in 15 countries (22.39%) yielded 68.8% of all documents, while the remaining papers were dispersed across 52 other nations (77.61%). Another significant finding is that most documents pertain to three educational level categories: lifelong learning, higher education and all educational levels. In addition, this research highlights six key themes in the microlearning domain, encompassing (1) Design and evaluation of mobile learning, (2) Microlearning adaptation in MOOCs, (3) Language teaching and learning, (4) Workflow of a microlearning system, (5) Microlearning content design, (6) Health competence and health behaviors. Other aspects analyzed in this study include the most prominent authors, research groups, documents and references.

The finding represents all topics at various educational levels to offer a comprehensive view of the knowledge base.

There is an emerging paradigm with reference to marketing communication, i.e. inclusive marketing communication (IMC). This calls for brands to be sincere with their customers and give due recognition and representation to underrepresented groups, communities and individuals. In this constantly evolving and globalized world, consumers have become more sensitive towards authentic portrayals of inclusivity and diversity by brands while choosing one brand over another.

A mixed method approach was used whereas in the first study, a quantitative method was used. Standardized questionnaire was used for collecting data through a time-lagged method. While the second study adopted the qualitative method. Semi-structured interviews were performed which lasted for 30–45 min for most of the participants.

It was found that IMC has a positive and noticeable effect on brand attitude (BA) and brand love (BL) when mediated by social identity (SI). While stigmatization was found to positively and significantly moderate the relationship between IMC and SI. Moreover, it was also found from both the studies that if a brand fails to strengthen the SI of an individual it doesn’t affect the BA as well as BL of customers.

Very few studies have incorporated a quantitative method of study while studying IMC. Furthermore, a collective study on different aspects of inclusivity like ethnicity, sexual orientation, disability, physical attractiveness, etc., has not been explored yet. Scattered studies on aspects like skin tone, body size, religion, etc., have been explored individually in prior studies.

This paper aims to examine the effects of economic policy uncertainty (EPU) on accounting conservatism in Korean firms. An increase in EPU could widen information asymmetry between insiders and outsiders, to the detriment of a firm’s investment decisions, stock price and cost of capital. This paper hypothesizes that Korean firms are likely to decrease accounting conservatism during high EPU due to inefficient institutional structure and weak corporate governance, together with the vulnerability of the Korean economy to exogenous shocks.

This study measures a firm’s level of conservatism using three accrual-based models proposed by Ball and Shivakumar (2006): 1) the cash flow model (CF model), 2) the Dechow and Dichev model (DD model) and 3) the Jones model. As a robustness test, this paper uses C-score model as an alternative measure of accounting conservatism. The data set used in this study is a total of 23,109 firm-year observations during the sample period from 2000 to 2018.

The test results show that an increase in EPU adversely affects Korean firms’ accounting conservatism, and that this adverse impact is more pronounced in financially distressed and non-manufacturing firms. This study’s findings highlight the importance of institutional structure during a period of high EPU, which can create incentives for either improving or deteriorating reporting quality.

This study adds new evidence to extant literature on the effects of EPU on managers’ choice of accounting policies and demonstrates that managers in emerging markets may have different incentives to cope with country-specific EPU fluctuations.

Increasingly, brands are using social media to attract consumers. However, it is not clear how social media activities influence consumers. To address this, the study examines the role of social media marketing (SMM) activities on consumer-based brand equity and continued usage intentions. The study also examines the moderating role of education.

The study utilized consumer-based brand equity (CBBE) and stimulus organism response (SOR) theories to develop a conceptual model. SMM activities were used as stimulus, whereas continued usage intentions were used as response. Also, the study used elements of CBBE as organisms. A survey-based questionnaire approach was used to collect data from 326 consumers. The collected data were analysed using structural equation modelling.

Results indicate that SMM activities are associated with elements of CBBE. Also, elements of CBBE shape continued usage intentions of consumers. The results of the multigroup analysis suggest the differences among the three groups with respect to their education level. The study’s findings enable managers to understand CBBE and continued usage behaviour of their customers and provide guidelines to manage their SMM activities.

The study findings are first to empirically test the effects of SMM activities on CBBE and continued usage behaviour. The results show that the stimulus strongly shapes CBBE and their continued intention to purchase brands. The findings contribute to the literature on SMM activities, CBBE and offer novel managerial implications.

This study aims to explore and address critical aspects of IoT adoption within supply chains by identifying the key enablers and barriers, examining the drivers and challenges, and assessing the benefits and drawbacks associated with IoT implementation. Additionally, the study aims to provide insights into how organizations can leverage IoT to enhance supply chain performance across economic, operational, social, and environmental dimensions.

This study employed a systematic literature review strategy to identify relevant research published between 2009 and 2024. Using the Scopus database, an initial search yielded 369 publications. Preliminary selection criteria were then applied to narrow down the articles for further detailed evaluation. This process led to an in-depth review of 123 publications, from which data were meticulously extracted, organized into tables and analyzed. Finally, the authors developed a categorization system based on the scope and temporal aspects of IoT implementation.

This study developed a comprehensive model that identified six themes influencing IoT implementation in supply chains: pre-implementation enablers and barriers, drivers and challenges during implementation, and post-implementation benefits and drawbacks. Recommendations were mapped into four performance dimensions: environmental, social, operational, and economic. The analysis showed that IoT can enhance supply chain risk management, process integration, sustainability, collaboration, resilience, and performance measurement. Key technologies aiding IoT implementation include blockchain, radio frequency identification (RFID), enterprise resource planning (ERP), and cloud computing.

This study is one of the few to conduct a comprehensive review of widely cited papers on IoT adoption in supply chains. It systematically summarizes the enablers, barriers, drivers, challenges, benefits, and drawbacks associated with such adoption. Furthermore, it proposes a distinctive model designed to assist organizations in successfully implementing IoT technologies, thereby filling a critical gap in the existing literature.