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In temporary construction project organizations, general contractors need to strengthen control over subcontractors through such measures as supervision and coordination, and resource sharing. In the management process, the good implementation of relational contracts among the general contractor and subcontractors is affected by the quality of relationship between managers and followers. From the perspective of leader–member exchange (LMX) theory, this study explores the influence of LMX, which reflects the quality of relationship between superiors and subordinates, on relational contracts.

By combining the longitudinal influence mechanism and organizational background of relational contracts in project organizations, this study constructed a multi-level structural equation model. The hypothesis is tested based on data collected from 213 respondents.

The findings of this study show that LMX has a positive influence on relational contracts and organizational identification in construction project organizations. Organizational identification has a positive effect on relational contracts and plays a mediating role between LMX and relational contracts. Power distance plays a moderating role on the influence of LMX on organizational identification.

This study explores the influence of LMX on relational contracts from a new perspective, which can help establish a high-quality relation of the general contractor and subcontractors in project organizations and enriches the longitudinal study of relational contracts in project organizations.

This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.

At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.

The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.

This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

This paper aims to examine the relationship between executive pay disparity and the cost of debt.

The authors use a sample of syndicated bank loans granted to United States (US) listed firms from 1992 to 2014 and adopt the loan yield spread (Chief Executive Officer (CEO) pay slice) as the main proxy for the cost of debt (executive pay disparity). The authors also use the Heckman two-stage model to address the sample selection bias and the two-stage least squares and propensity score matching methods to control the potential endogeneity issues. To test different views about executive pay disparity, the authors adopt the cash-to-stock ratio to proxy for managerial risk-shifting incentives.

The authors find that the cost of debt is significantly higher for firms with larger executive pay disparity, which is robust to sample selection bias, endogeneity concerns, alternative measures and various controls. This positive relationship increases with the risk-shifting incentives of CEOs instead of other top executives, which supports the managerial power view, and is stronger for firms with higher levels of financial distress. The findings suggest that creditors view executive pay disparity are associated with higher credit risk and CEO entrenchment.

This paper reveals one “dark” side of executive pay disparity: it increases the cost of debt and identifies a significant role played by CEOs' risk-shifting incentives. The authors provide direct evidence of the relevance of pay differential to corporate credit analysis.

Online health question-and-answer (Q&A) forums have developed a new business model whereby listeners (peer patients) can pay to read health information derived from consultations between askers (focal patients) and answerers (physicians). However, research exploring the mechanism behind peer patients' purchase decisions and the specific nature of the information driving these decisions has remained limited. This study aims to develop a theoretical model for understanding how peer patients make such decisions based on limited information, i.e. the first question displayed in each focal patient-physician interaction record, considering argument quality (interrogative form and information details) and source credibility (patient experience of focal patients), including the contingent role of urgency.

The model was tested by text mining 1,960 consultation records from a popular Chinese online health Q&A forum on the Yilu App. These records involved interactions between focal patients and physicians and were purchased by 447,718 peer patients seeking health-related information until this research.

Patient experience embedded in focal patients' questions plays a significant role in inducing peer patients to purchase previous consultation records featuring exchanges between focal patients and physicians; in particular, increasingly detailed information is associated with a reduced probability of making a purchase. When focal patients demonstrate a high level of urgency, the effect of information details is weakened, while the interrogative form is strengthened.

The originality of this study lies in its exploration of the monetization mechanism forming the trilateral relationship between askers (focal patients), answerers (physicians) and listeners (peer patients) in the business model “paying to view others' answers” in the online health Q&A forum and the moderating role of urgency in explaining the mechanism of how first questions influence peer patients' purchasing behavior.

Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

The purpose of this study is to examine whether corporate tax avoidance behavior increases firm value in Chinese context. A large number of studies conduct their designs on the consumption that tax avoidance represents wealth transfer from government to enterprises and therefore enhances firm value. This study argues that, contrast to developed countries, tax avoidance does not necessarily add value to opaque Chinese firms relative to transparent counterparts due to higher agency costs.

Using a large sample of Chinese listed-firms data for the period 2001-2009 and fixed effects regression model, this study examines the relation between tax avoidance and firm value. A series of robustness checks are conducted to alleviate the concern of endogeneity.

The authors find that tax avoidance behavior increases agency costs and reduces firm value. The authors further find that information transparency interacts with corporate tax avoidance, moderating the relation between tax avoidance and firm value. Investors in China react negatively to corporate tax avoidance behavior, but this negative reaction could be mitigated by information transparency. The results are robust to a series of alternative treatments, including varied measures, first-order differential approach and 2SLS.

The results suggest that tax avoidance does not necessarily increase firm value, part of gains are encroached by self-serving managers. Moreover, investors in China downplay the significance of tax avoidance, although corporate information transparency could soften their negative tone.

The purpose of this paper is to analyze the impact of reference effects on online purchase intention (OPI) of agricultural products in B2C context and to examine how consumers’ food safety consciousness (FSC) moderates that impact.

An empirical survey was used to test the hypotheses. Data were collected from a total of 297 online consumers in China. A structural equation modeling is utilized to assess the relationships proposed in the research model.

The findings of this study show that reference effects have a significant impact on OPI of agricultural products. Both perceived value (PV) and perceived risk (PR) play a mediating role in the relations between reference effects and OPI, but the mediating effect of the PV is found to be significantly greater than that of the PR. Consumers’ FSC significantly and positively moderates the impact of reference effects on OPI, meaning that the more attention consumers pay to food safety, the greater the impact of reference effects on OPI will become.

First, this study mainly analyzes the positive impact of reference effects on OPI. Future research could discuss the negative impact of reference effects and compare the differences between them. Second, this study only takes the PV and PR as mediators into the research model. Future research could consider adding trust, attitude, and other variables and further explore and clarify the influencing mechanism between reference effects and OPI. Third, this study examines the moderating role of consumers’ FSC but does not fully discuss the moderating role of product categories. Further research could compare the influence of reference effects among multiple product categories.

This study provides valuable insights for agricultural enterprises and online vendors that reference effects are one of the most important factors to influence OPI. It suggests to agricultural enterprises and online vendors that reference effects can be used as a new instrument to influence consumers’ online purchase decisions.

This study for the first time defines reference effects in an online setting and introduces the perspective of reference effects to establish a theoretical model to explain consumers’ OPI of agricultural products. The study reveals the influencing mechanism of reference effects on OPI and thus enriches the theory of online purchase behavior.

The purpose of this paper is to construct a frequency-domain framework to study the asymmetric spillover effects of international economic policy uncertainty on China’s stock market industry indexes.

This paper follows the time domain spillover model, asymmetric spillover model and frequency domain spillover model, which not only studies the degree of spillover in time domain but also studies the persistence of spillover effect in frequency domain.

It is found that China’s economic policy uncertainty plays a dominant role in the spillover effect on the stock market, while the global and US economic policy uncertainty is relatively weak. By decomposing realized volatility into quantified asymmetric risks of “good” volatility and “bad” volatility, it is concluded that economic policy uncertainty has a greater impact on stock downside risk than upside risk. For different time periods, the sensitivity of long-term and short-term spillover economic policy impact is different. Among them, asymmetric high-frequency spillover in the stock market is more easily observed, which provides certain reference significance for the stability of the financial market.

The originality aims at extending the traditional research paradigm of “time domain” to the research perspective of “frequency domain.” This study uses the more advanced models to analyze various factors from the static and dynamic levels, with a view to obtain reliable and robust research conclusions.

Tour social network data that are heterogeneous contain not only the quantitative structured evaluation data, but also the qualitative non-structured data. This is a big data scenario. How to evaluate tour online review and then recommend to potential tourists quickly and accurately are important parts of social responsibility of tour companies. The purpose of this paper is to propose a new method for evaluating tour online review based on grey 2-tuple linguistic.

The phenomenon of “poor information” exists in some big data scenario. According to social responsibility, grey 2-tuple linguistic evaluation model for tour online review is proposed.

Tour social networks contain data that are valuable to each individual on tourism industry's value chain. Grey 2-tuple linguistic evaluation model can be used for evaluating tour online reviews. This is a systems thinking method that takes social responsibility into account.

Due to the complex links among reviewers in social network, network mining approaches and models are expected to be added to this research in the near future.

Grey 2-tuple linguistic evaluation method can contribute to the future research on evaluating a variety of tour social network comment data in the real world.

A new evaluation method for making evaluation and recommendations based on tour social network comment information is proposed.