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The extensive use of mobile money (MM) has been widely recognized as a digital engine of socioeconomic development in sub-Saharan Africa (SSA). This paper aims to focus on the effects of MM use and stockouts on informal microenterprise performance and investigate whether MM use mitigates the relationship between stockouts and firm performance.

This study utilizes firm-level data from the latest World Bank Informal Sector Enterprise Surveys across six SSA countries. We employ instrumental variable-adjusted and propensity score-weighted regressions to investigate the buffering effect of MM use.

We find a significantly positive effect of MM use and a significantly negative impact of stockouts on informal microenterprise performance. Importantly, we establish that MM use attenuates the negative impact of stockouts on firm performance. We further document that the attenuating effect of MM use is more profound for firms using MM for transactions with supply chain partners, located in communities with high MM use rates, and operating in the retail industry.

Our research generates important managerial and policy implications. Future policies should capitalize on MM to foster an effective financial ecosystem in which informal microenterprises can survive and grow, thereby deepening their contributions to sustainable development.

Whereas the business benefits of MM among small, medium and large firms are well-documented, the role of MM use on informal microenterprise performance is less understood. This study fills the research gap in the literature by focusing on the influence of MM use on the relationships between informal microenterprise operations and performance.

This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities.

This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks.

The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models.

This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

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.

The main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.

This paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.

Firstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.

A composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

This study aims to investigate the impact of technological turbulence on entrepreneurial orientation (EO) in Chinese e-commerce enterprises. It also examines the mediating roles of business ties and political ties, and the moderating effect of transaction uncertainty on these relationships.

A sample of 173 Chinese e-commerce enterprises was analyzed using survey data. Structural equation modeling was employed to test the proposed hypotheses, including the direct effects of technological turbulence on EO, the mediating roles of business and political ties, and the moderating effect of transaction uncertainty.

The results indicate a positive correlation between technological turbulence and EO. Business ties mediate the relationship between technological turbulence and EO, while political ties do not. Transaction uncertainty negatively moderates the relationship between business ties and EO but does not significantly affect the relationship between political ties and EO. Additionally, EO positively impacts market performance.

This study extends the understanding of how external environmental factors, such as technological turbulence, influence EO in the context of Chinese e-commerce. It highlights the differential roles of business and political ties and provides insights into the moderating effects of transaction uncertainty. The findings offer practical implications for e-commerce firms seeking to enhance their entrepreneurial capabilities in turbulent environments.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

The aim of this study is to establish the nonlinear dynamics equations of roller bearings with surface faults on outer raceway, inner raceway and rolling elements to analyze the dynamic characteristics of the double row self-aligning roller bearings, and provided theoretical basis for bearing fault diagnosis and life prediction.

First, based on the momentum theorem, the formulas for quantitative calculation of impact load were established, when roller was in contact with the fault of the inner or outer raceway. Then, the fault position piecewise functions and the load-carrying zone piecewise functions were established. Based on these, the nonlinear dynamic equations of double row self-aligning roller bearings are established, and Matlab is used to simulate the faulty bearings at different positions, sizes and rotational speeds. Finally, the vibration test of the fault bearings are completed, and the correctness of the nonlinear dynamic equations of the rolling bearing are verified.

The simulation and test results show that: the impact load increased with the increasing rotate speed and fault size, and the larger the fault size, the longer the impact load existed and the shorter vice versa.

The nonlinear dynamic equation of double row self-aligning roller bearings is established, which provides a theoretical basis for bearing faults diagnosis and fatigue life prediction.

This study aims to examine the risk-resistant role of environmental, social and governance (ESG) performance in the capital market, focusing on an organizational standpoint. Furthermore, it aims to offer management decision advice to companies seeking protection against stock market risks. Conclusions obtained through this research have the potential to enrich the economic consequences of ESG performance, provide practical implications for enhancing corporate ESG performance, improving corporate information quality and stabilizing capital market development.

Based on the data of Chinese A-share listed companies from 2009 to 2020, this study examines the risk-resistant function of ESG performance in the capital market. The impact of ESG performance on management behavior is analyzed from the perspective of organizational management and the three mechanisms of pre-event, during the event and post-event.

This paper demonstrates that companies that effectively implement ESG practices are capable of effectively mitigating risks associated with stock price crashes. Heterogeneity analysis reveals that the inhibitory effect of ESG performance on stock price crash risk is more pronounced in nonstate-owned enterprises and enterprises with higher levels of marketization. After controlling for issues such as endogeneity, the conclusions of this paper are still valid. The mechanism analysis indicates that ESG performance reduces the risk of stock price crash through three paths of organizational management: pre-event, during the event and post-event. That is, ESG performance plays the role of restraining managers’ opportunistic behavior, reducing information asymmetry and boosting investor sentiment.

This paper provides new insights into the relationship between ESG performance and stock price crash risk from an organizational management perspective. This study establishes three impact mechanisms (governance effect, information effect and insurance effect), offering a theoretical basis for strategic corporate decisions of risk management. Additionally, it comprehensively examines the contextual differences in the role of ESG performance, shedding light on the specific domains where ESG practices are influential. These findings offer valuable insights for promoting stable development in the capital market and fostering the healthy growth of the real economy.

The purpose of this study is to develop a transient wheel–rail rolling contact model to primarily investigate the rail damage under wet condition when the train passes through the welded joints.

The impact force induced by welded joints is obtained through vehicle–track coupling dynamics. The normal and tangential wheel–rail contact pressures were solved by elastohydrodynamic lubrication (EHL) theory and simplified third-body layer theory, respectively. Then, the obtained tangential pressure and normal pressure were applied to the finite element model as moving loads, simulating cyclic loading. Finally, the shakedown map and critical plane method were used to predict rolling contact fatigue (RCF) and the initiation of fatigue cracks.

The results indicate that RCF will occur and fatigue cracks are more prone to appear on the subsurface of the rail, specifically around 2.7 mm below the rail surface in the vicinity of the welded joint and its heat-affected zone.

The cosimulation of numerical model and finite element model was implemented. The influence of surface roughness and fluids was considered. In this model, the normal and tangential wheel–rail contact pressure, the stress and strain and the rail fatigue cracks were obtained under a rail-welded joint excitation.