Search results

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

This paper aims to characterize the surface film formed on Alloys 800 and 690 in chloride and thiosulfate-containing solution at 300°C.

Alloy 800 and 690 were immersed in chloride and thiosulfate-containing solution at 300°C up to five days, and then the surface film was analyzed by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and energy dispersive X-ray spectrometers (EDX).

Through static immersion experiments in a high-temperature and high-pressure water environment, the alloy samples covered by surface film after five days of immersion were obtained. The morphology of the surface film was characterized at both horizontal and cross-sectional scales using SEM and focused ion beam-TEM techniques. It was observed that due to the influence of the quartz lining, the surface film primarily exhibited a bilayered structure. The first layer contained a significant amount of SiO₂, with a higher content of metal hydroxides compared to metal oxides. The second layer was predominantly composed of Fe, Ni and Cr, with a higher content of metal oxides compared to metal hydroxides.

The results showed that the materials of the lining of the autoclave could significantly influence the film composition of the tested material, which should be paid attention when analyzing the corrosion mechanism at high temperature.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model and the introduction of engagement theory and the meaning transfer model, this article uses the 2022 Beijing Winter Olympics mascot “Bing Dwen Dwen” as the research object to empirically analyze the effects and mechanisms of the mascot's attributes on preference, event engagement, sponsorship enterprise trust and sponsorship enterprise attitude, ultimately constructing a sponsorship effectiveness model.

The survey method was used to examine 238 respondents' emotions and attitudes towards companies participating in sponsoring Olympic mascots.

The study found that the main attributes of the mascot include visual and emotional factors, both of which have a positive impact on preference, with emotional factors having a greater influence than visual factors. Visual and emotional factors indirectly affect engagement through preference. Preference and engagement play a completely mediating role in the effect of mascot attributes on sponsorship enterprise trust and sponsorship enterprise attitude.

This study provides practical recommendations for managers to achieve marketing success in sports sponsorship through mascots.

This paper provides a measurement tool for the study of mascot attributes and important support for subsequent research in sponsorship marketing.

This study aims to investigate the association between executives with foreign backgrounds and the audit fees paid by the Chinese-listed firms over the period from 2010 to 2020.

To examine the association between executives’ foreign experience and audit fees, this study constructs the following empirical model: Lnfee_i,t = β₀ + β₁Foreign background_i,t + ∑β_j Control_i,t + YearFE + IndFE + ε_i,t (1).

This study finds that auditors charge higher fees for firms hiring more executives with foreign backgrounds. The results are robust to a battery of robustness checks, including fixed effects, alternative measures of independent variable, controlling for other characteristics of executives and auditors and entropy balancing method.

This study sheds light on how executives’ foreign backgrounds affect audit fees, enriching the literature on executive heterogeneity and audit fees and providing important implications for audit practitioners.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Coastal zone ecological restoration project is of great significance to alleviate marine ecological degradation. Evaluating the effect of coastal ecological restoration projects and identifying the obstacle factors affecting their restoration level can provide an empirical basis for future Marine ecological restoration projects.

However, due to the initial stage of coastal zone ecological restoration projects, the actual monitoring data of coastal zone ecological restoration is relatively lacking. Based on the CRITIC-TOPSIS (combination of CRITIC method and TOPSIS method) method, combined with the subjective perception of the public and the actual data of the restoration project, this paper proposes an evaluation method of the coastal zone ecological restoration effect to obtain the specific implementation effect of the coastal zone ecological restoration project. The main obstacle factors affecting the evaluation of coastal ecological restoration effect are identified by using the obstacle degree model.

This paper conducted an empirical study on the restoration of sandy shoreline and coastal wetland in Qinhuangdao city. Based on the data of restoration projects and the subjective perception of ecological restoration by the public in Qinhuangdao city, the research results showed that the coastal zone ecological restoration effect of Qinhuangdao city was general. The quality of the restoration project and the public perception have an important influence on the evaluation of the restoration effect. Improving the quality of the restoration project, strengthening the public's participation in ecological restoration and allowing the public to better participate in the ecological restoration of the coastal zone can improve the effect of ecological restoration of the coastal zone in an all-round way.

The research results of this paper have a guiding role in the ecological restoration of coastal cities in the future, and also have a demonstration and reference role for the assessment of the effect of ecological restoration of coastal zones.

Excessive borrowing significantly contributes to pushing businesses towards default and their transition into zombie enterprises. Despite government efforts to implement deleveraging policies and guide bank credit flows, it’s essential to delve into the internal dynamics that steer the borrowing behavior of these zombie enterprises at a micro level. To gain a comprehensive understanding of the issue, this study focuses on examining the incentives that drive corporate executives of zombie enterprises to consistently engage in large-scale borrowing from banks.

In this study, panel data analysis is utilized, incorporating firm-, industry- and year-fixed effects. Drawing from data pertaining to listed companies in China spanning from 2007 to 2020, we employ a one-by-one identification method to pinpoint zombie enterprises. Ultimately, a total of 2,533 samples of zombie enterprises were obtained.

The results indicate that as bank loans to zombie enterprises increase, executive monetary compensation decreases, while on-the-job consumption by executives increases, and they are less likely to be forced into rotation. Mechanism testing reveals that corporate performance partially mediates the relationship between bank loans and executive monetary compensation, but this mediation is ineffective for on-the-job consumption and job rotation. Further investigation suggests that the property rights nature of central enterprises and modified audit opinions can exacerbate the adverse impact of bank loans on the monetary compensation of zombie corporate executives, without significantly affecting on-the-job consumption or job rotation. Conversely, executive power does not enhance the positive effects of bank loans on monetary compensation or on-the-job consumption, but it diminishes the negative impact of bank loans on the forced rotation of zombie executives.

These results indicate that while bank loans may have a negative impact on corporate value, they function as safeguards for the positions and interests of executives. As a result, bank loans serve as incentives for executives of zombie enterprises.

This study holds theoretical significance as it explores the motivations behind non-performing loans in high-borrowing enterprises, sheds light on corporate governance challenges encountered by zombie enterprises and provides policy insights aimed at addressing the underlying causes of persistent non-performing loans in high-borrowing enterprises, including zombie enterprises.

Competitiveness is an important personality trait that has been studied in various disciplines and has been shown to predict critical work outcomes at the individual level. Despite this, the role of competitiveness in groups and teams has received scant attention amongst organizational researchers. Aiming to promote future research on the role of competitiveness as both an adaptive and maladaptive trait – particularly in the context of work – the authors review competitiveness and its effects on individual and team stress and Well-Being, giving special attention to the processes of cohesion and conflict and situational moderators. The authors illustrate a dynamic multilevel model of individual and team difference factors, competitive processes, and individual and team outcomes to highlight competitiveness as a consequential occupational stressor. Furthermore, the authors discuss the feedback loops that inform the different factors, highlight important avenues for future research, and offer practical solutions for managers to reduce unhealthy competition.