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In the digital economy, digital capability has become an important dynamic capability of enterprises and plays an essential role in enhancing firm resilience. This study aims to investigate the relationships among digital capability, knowledge search, coopetition behavior and firm resilience based on knowledge-based view and resource-based view.

This study uses the hierarchical regression and bootstrapping methods to test the theoretical framework and research hypotheses. The survey data were collected from 241 Chinese enterprises.

Digital capability has significantly positive effects on knowledge search and firm resilience. Knowledge search positively affects firm resilience and partially mediates the relationship between digital capability and firm resilience. Coopetition behavior weakens the relationship between digital capability and knowledge search, and the mediating effect of knowledge search in the relationship between digital capability and firm resilience. The moderating effect of coopetition behavior on the relationship between digital capability and firm resilience is insignificant.

This study clarifies the effect of digital capability on firm resilience and uncovers the “black box” from digital capability to firm resilience. In addition, this research enriches the literature on digital capability and firm resilience and expands the application of knowledge-based view and resource-based view in the digital context.

On the basis of labor economics theories, this study examines how adjustment in human capital accounts for labor cost stickiness.

This study makes use of employee education level as a measure of the quality of human capital and relies on data from Chinese public firms to conduct the empirical test. This study focuses on two important components of labor cost changes: one corresponding to the adjustment in the number of employees (capacity adjustment) and another corresponding to the adjustment in the mix of employee education levels (quality adjustment).

This study reveals that labor cost changes driven by the adjustment of employee education level are sticky. This stickiness cannot be explained by the standard adjustment cost theory. This further shows that firms that actively adjust their employee quality during downturns experience improved future performance. The findings are robust to alternative measures and specifications.

This study provides new evidence for and insights into the cost behavior literature. Previous studies treat input resources in a homogenous way and focus on the effect of capacity adjustment. This study considers the heterogeneity of resources and examines three dimensions of salary cost adjustment: capacity, structure, and unit cost. In line with the economic theory of sticky costs proposed by Banker et al. (2013a), the study’s evidence sheds light on the additional underlying economic mechanisms driving cost stickiness behavior. Specifically, managers asymmetrically adjust both employee structure and average salaries, in addition to employee number. This study also adds to the existing knowledge of the consequences of managers' actions regarding cost behavior.

The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection. This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.

First, to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion, this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity. Second, for the CSPLayer of the PAFPN, a weight gain-based normalization-based attention module (NAM) is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians. Finally, the authors experimentally determined the optimal values for the confidence loss function.

The experimental results show that, compared with the original YOLOX algorithm, the AP of the improved algorithm increased by 2.90%, the Recall increased by 3.57%, and F1 increased by 2% on the pedestrian dataset.

The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.

The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.

This study examines the determinants of medical crowdfunding performance. Drawing on signaling theory, the authors investigate how funding-related signals (funding goal and duration), story-related signals (text length, text sentiment, and use of first-person pronouns), and donor-related signals (donor identity disclosure) affect medical crowdfunding performance.

This study analyzed the data of 754 medical crowdfunding projects collected from the Qingsongchou platform in China to test the proposed model.

The empirical findings reveal that both funding goal and funding duration exhibit a U-shaped relationship with crowdfunding performance. Additionally, the authors find evidence that story text length and donor identity disclosure are positively related to crowdfunding performance, whereas the use of first-person pronouns is negatively related to crowdfunding performance.

This study extends the understanding of the determinants of medical crowdfunding performance through the signaling theory. Specifically, this study provides new insights into the roles of funding goal and funding duration in predicting medical crowdfunding performance and identifies several new predictors of crowdfunding performance, including the use of first-person pronouns in project story text and donor identity disclosure.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

As convenience and anonymity, people with mental illness are increasingly willing to communicate and share information through social media platforms to receive emotional and spiritual support. The purpose of this paper is to identify the degree of depression based on people's behavioral patterns and discussion content on the Internet.

Based on the previous studies on depression, the severity of depression is divided into four categories: no significant depressive symptoms, mild MDD, moderate MDD and severe MDD, and defined each of them. Next, in order to automatically identify the severity, the authors proposed social media digital cues to identify the severity of depression, which include textual lexical features, depressive language features and social behavioral features. Finally, the authors evaluate a system that is developed based on social media digital cues in the experiment using social media data.

The social media digital cues including textual lexical features, depressive language features and social behavioral features (F1, F2 and F3) is the relatively best one to classify four different levels of depression.

This paper innovatively proposes a social media data-based framework (SMDF) to identify and predict different degrees of depression through social media digital cues and evaluates the accuracy of the detection through social media data, providing useful attempts for the identification and intervention of depression.

This study aims to explore how specific cues with new manifestations (i.e. herding message and price discount information) and customer cognitive style influence attention allocation and purchase intention.

To empirically validate the research hypotheses, an eye-tracking experiment with a 2 × 2 × 2 mixed design was conducted on a sample of 44 participants recruited from a university in China. Repeated measures analysis of variance was employed for data analysis.

The results show that herding message and price discount information play different roles in viewers' attention and have an interactive effect on attention. Moreover, individual cognitive styles moderate the impact of herding message on attention allocation. Still, two cues positively affect customer purchase intention.

This study guides future research by applying cue utilization theory to investigate the effects of two cues in live streaming. Findings offer practical implications for how live streaming cues affect viewers' attention allocation and purchase intention.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

This paper sets out to solve a common and crucial fundamental theoretical problem of gray incidence cluster analysis: to [X]={X|ρ(X,Y)≥1−ε0} constitute an approximate classification, it must first be proven that [X]={X|ρ(X,Y)=1} constitutes a rigorous classification.

This paper does not study the concrete expressions of various incidence degrees but rather the perfect correlation essence of such incidence degrees, that is, sufficient and necessary conditions.

For any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree, it is proven that the perfect correlation relation is an equivalence relation. The set composed of all sequences Y that are equivalent to sequences X is studied, that is, the equivalence class of X. The structure and mutual relations of these equivalence classes are discussed, and the topological homeomorphism concept of incidence degree is introduced. The conclusion is obtained that the equivalence classes of the two incidence degrees must be the same when the topological homeomorphism is obtained.

In this paper, only the perfect correlation relation of any order difference incidence degree, the similarity incidence degree, the direct proportion incidence degree, the parallel incidence degree and the nearness incidence degree are studied as equivalent relations.

Not only are the research results of several incidence degrees involved in this paper original but also many other effective incidence degrees have not done this basic research, so this paper opens up a research direction with theoretical significance.

Contemporary focus on infections and deaths in the event of pandemics may distract health institutions and medical practitioners from the psychosocial consequences of the outbreak in individuals. In light of the devastation, persistency and scarcity of pandemics, it is imperative to delve into individuals' psychological state and self-preservation instincts when confronted with the environmental danger arising from pandemic conditions and the environmental restrictions being imposed.

Guided by the self-preservation theory, the authors advance a research model to elucidate the moderated mediation effect of secondary traumatic stress on an individual's reactions when faced with environmental danger and restriction. The authors also consider the moderating influence of environmental restriction and media use diversity. The authors subsequently validated the research model via a survey with 2,016 respondents in China. The authors employed PLS-SEM to analyze the data and assess the hypothesized paths.

Analytical results revealed that secondary traumatic stress fully mediated the impact of environmental danger on external reliance but suppresses the mediating effects on internal reliance. The authors further confirmed that environmental restriction moderated the relationship between environmental danger and reliance. Furthermore, the authors attest to the moderating influence of media use diversity on the relationship between secondary traumatic stress and external reliance.

This study not only extends the theoretical lens of self-preservation to public health emergencies but also yields practical guidelines for coping with pandemics. Insights from this study can be harnessed to aid populations worldwide in coping and recovering from pandemics.