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Travel bragging, driven by impression management, is common on social media. However, straightforward bragging can create negative perceptions. To mitigate this, tourists often turn to humblebragging, but its effectiveness is unclear. This study aims to examine whether humblebragging elicits more positive responses from viewers than straightforward bragging.

Drawing on social comparison theory and compensation theory, this paper developed a moderated mediation model to explore the impact of bragging type (bragging vs humblebragging) on viewer behavior. The model was validated through two scenario-based experiments.

The results reveal the double-sword effect of humblebragging: Humblebragging elicits stronger benign and malicious envy than bragging. Benign envy mediates the relationship between bragging type and consumption intention, while malicious envy mediates between bragging type and avoidance/gossip. Perceived deservingness moderates the effect of bragging type on envy and the mediation processes. When viewers perceive the poster’s advantage as deserving, humblebragging elicits more benign envy than bragging. When perceived as undeserving, humblebragging leads to more malicious envy.

This study is innovative in validating the double-edged sword effect of humblebragging and identifying perceived deservingness as a boundary condition.

This study investigates the heterogeneous impact of R&D capital market distortions (CMDs) on the quantity and quality of innovation in Chinese firms by exploring key pathways from a dual-arbitrage perspective.

Using data from Chinese A-share listed companies (2015–2021), we employ a multidimensional fixed effects model to validate the research hypotheses. Under the Systems of National Accounts (SNA, 2008) framework, we use the Bureau of Economic Analysis method to estimate enterprise R&D capital stock and the Cobb-Douglas production function to estimate R&D CMDs.

Results show that R&D CMDs drive firms toward strategic innovation, emphasizing quantity over quality. Policy arbitrage, including policy catering and rent-seeking, emerges as a pivotal mechanism under R&D CMDs, encouraging firms to prioritize quantity over quality in innovation. High-technology firms and those in the decline stage are more inclined to spearhead strategic innovation within the context of R&D CMDs.

These findings help policymakers promote high-quality innovation in Chinese enterprises by enhancing patent review mechanisms and shifting policies from quantity-driven to quality-oriented goals.

This study enriches the research on factor market distortions and innovation in emerging markets from the perspective of R&D CMDs, based on the “emerging + transitional” comprehensive framework. Unlike previous studies, which generally use enterprise R&D expenditure flow data, we apply the theory of R&D capitalization accounting to the micro-enterprise level under the SNA (2008) framework, enhancing the accuracy of R&D CMD estimations.

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

According to the resource-based theory, a firm’s unique resources and capabilities are the key to its competitive advantage. This paper aims to investigate the effect of top management team (TMT) stability, an important intangible resource of the firm, on the maturity mismatch between investment and financing of companies. Additionally, we explore the moderating effects of state ownership and institutional ownership in this context.

This study conducts an empirical analysis based on the ordinary least squares (OLS) model with a sample of Chinese companies listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2022.

The results show that TMT stability significantly mitigates the degree of maturity mismatch. Both state ownership and institutional ownership weaken the negative effect of TMT stability on maturity mismatch. Besides, alleviating financing constraints is a crucial pathway through which TMT stability influences maturity mismatch.

The findings help firms to effectively retain TMT talents and reduce the occurrence of maturity mismatch.

This paper not only helps to expand the research on the economic effects of TMT stability but also provides new ideas on how to alleviate the maturity mismatch of companies.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This study aims to understand how the facesheet size, orientation and core size influence the analytical failure mechanism mode of glass fibre reinforced polymer (GFRP)/polyvinyl chloride (PVC) sandwich structures subjected to three-point bending. The purpose of this study was to develop failure-mode map of GFRP/PVC sandwich structures. Sandwich structures with different facesheet and core thicknesses were used to develop the failure map.

The sandwich structure and facesheet were fabricated using a vacuum-assisted resin infusion method with core sizes of 10, 15 and 20 mm and facesheet thicknesses of 1.5 and 3 mm and were arranged in three different orientations: angle-ply, cross-ply and quasi-isotropic. The key failure modes that occur in sandwich structures were used to predict possible failures in the developed material. Analytical equations were used in MATLAB for each observed failure mode. The probable failure modes, namely, face yielding, core shear and indentation equations, were used to construct the failure maps and were compared with the experimental data.

The boundary of the two failure modes shifts with changes in the facesheet and core thicknesses. The theoretical stiffness of sandwich panels was higher than the experimental stiffness. Based on strength-to-weight ratio, specimens E10-4, A15-8 and E20-8 exhibited the best optimum values owing to their shorter distance to the boundary lines.

In this study, a failure map was used to predict the possible failure modes for different GFRP facesheet orientations and thicknesses and PVC core thickness sandwich structures. Little is known about the prediction of the failure modes of unidirectional GFRP arranged in different orientations and thicknesses and PVC core thicknesses for sandwich structures. Few studies have used failure mode maps with unidirectional GFRP oriented in angle-ply, cross-ply and quasi-isotropic directions as a facesheet for sandwich structures compared to bidirectional mats. This study can serve as a guide for the correct selection of materials during the design process of sandwich structures.

This paper takes the gig workers in Chinese delivery platform as the research object and adopts a questionnaire survey to explore the complex influence of job gamification perception on the job involvement of gig workers, via the mediating role of cognitive assessment and moderating role of overwork, in order to provide research and data support for the development of platform gamification.

The study conducted a three-wave online questionnaire survey to obtain 300 final samples from Chinese delivery platforms. Hypotheses were tested hierarchical regression and bootstrap methods.

Drawing on cognitive appraisal theory, we observed an inverted U-shaped relationship of gig workers between job gamification perception and job involvement. The mediating role of cognitive assessment and moderating role of overwork were also considered. Both challenge and threat assessment mediate the relationship between job gamification perception and job involvement. Direct effect of job gamification perception on job involvement and indirect effect of job gamification perception on cognitive assessment are moderated by overwork.

In the past, the research on job gamification mostly focused on the traditional forms of employment, but this study focuses on the new forms of employment and from the perspective of individual self-perception, explores the influence of job gamification perception on the job involvement of gig workers in Chinese delivery platform and investigated the dialectical role of job gamification perception. The findings enrich the literature and theoretical research on job gamification perception and job involvement and provide new references and perspectives for management practice.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Automatic anatomical therapeutic chemical (ATC) classification is progressing at a rapid pace because of its potential in drug development. Predicting an unknown compound's therapeutic and chemical characteristics in terms of how it affects multiple organs and physiological systems makes automatic ATC classification a vital yet challenging multilabel problem. The aim of this paper is to experimentally derive an ensemble of different feature descriptors and classifiers for ATC classification that outperforms the state-of-the-art.

The proposed method is an ensemble generated by the fusion of neural networks (i.e. a tabular model and long short-term memory networks (LSTM)) and multilabel classifiers based on multiple linear regression (hMuLab). All classifiers are trained on three sets of descriptors. Features extracted from the trained LSTMs are also fed into hMuLab. Evaluations of ensembles are compared on a benchmark data set of 3883 ATC-coded pharmaceuticals taken from KEGG, a publicly available drug databank.

Experiments demonstrate the power of the authors’ best ensemble, EnsATC, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. The MATLAB source code of the authors’ system is freely available to the public at https://github.com/LorisNanni/Neural-networks-for-anatomical-therapeutic-chemical-ATC-classification.

This study demonstrates the power of extracting LSTM features and combining them with ATC descriptors in ensembles for ATC classification.

The aim of this study is to investigate the horizontal displacement effects of foundation pit excavation on adjacent metro stations and shield tunnel composite structures. It seeks to develop a theoretical calculation method capable of accurately assessing these engineering impacts, aiming to provide practical assistance for engineering applications.

This study introduces a model for shield tunnel segments incorporating rotation and misalignment, considering the constraints of metro stations. It establishes a displacement model for tunnel-station combinations during foundation pit excavation, deriving a formula for calculating station-proximal tunnel horizontal displacements. The method's accuracy is validated against field data from three engineering cases. The research also explores variations in tunnel displacement, inter-ring shear force, misalignment and rotation angle under different spatial relationships between pits, tunnels and stations.

This study models uneven deformation between stations and tunnels due to bending stiffness and shear constraints. It enhances the misalignment model with station-induced shear effects and introduces coefficients for their mutual interaction. Results show varied responses based on pit-station-tunnel positioning: minimal displacement near pit edges (coefficients around 0.1) and significant effects near pit centers (coefficients from 0.4 to 0.5). “Whip effect” from station constraints affects tunnel displacement, shear force, misalignment and rotation, with fluctuations decreasing with distance from excavation areas.

This study demonstrates significant originality and value. It introduces a novel displacement model for tunnel-station combinations considering station constraints, addressing theoretical calculations of horizontal displacement effects from foundation pit excavation on metro stations and shield tunnel structures. Through validation with field data and parameter studies, the concept of influence coefficients is proposed, offering insights into variations in structural responses under different spatial relationships. This research provides crucial technical support and decision-making guidance for optimizing designs and facilitating practical construction in similar engineering projects.