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The purpose of this paper is to study the governance effect of total environmental target constraint policy (TETC) on border polluted areas. China’s administrative border areas are “pollution havens.” The paper analyzes how TETC affects the pollution situation in border areas. The TETC policy sets overall emission reduction targets for each province, and the achievement of these targets is closely related to the promotion of local officials. So, can TETC provide pollution control incentives for enterprises and governments? This deserves further research. By evaluating China’s overall environmental target policies, we can obtain directions for optimizing policy design.

The paper uses the difference-in-differences model to evaluate the impact of TETC on air pollution in boundary areas. Industrial enterprises located at the provincial border are in the treatment group, while industrial enterprises located within the province are in the control group. We also use group regression to analyze the policy impact effects caused by differences in urban resource endowment, types of corporate pollution and marketization levels. Finally, we constructed a spatial difference-in-differences model to analyze the distance heterogeneity of policy impacts.

TETC significantly reduces the SO2 emissions of enterprises in province borders areas compared with intra-provincial enterprises. TETC has reduced the proportion of highly polluting enterprises at province borders. Incentives for promoting officials have prompted local governments to increase environmental law enforcement and environmental protection investment, and the pollution emissions of border enterprises have been effectively alleviated. The heterogeneity analysis shows that TETC significantly restrains the pollution emissions with high pollution types, non-resource-based cities and enterprises with low market levels.

As a developing country, China has generated a large amount of pollution during the process of industrialization. The government has taken a series of measures to reduce pollution emissions. The TETC is a typical policy with high policy constraints. Although this article tries to control the impact of some important air pollution policies as much as possible, it cannot control the impact of all other policies. So, the impact effects we empirically obtained may include some other policy factors, but these effects are relatively small and do not affect the overall regression results.

This paper has theoretical reference significance for optimizing the design of environmental policies, reducing the “principal-agent” problem of environmental governance and improving pollution control at province borders.

The existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.

In this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.

Theoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.

The proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

The purpose of this paper is to investigate the effects of celebrity’s poses on consumer attitudes towards the endorsement advertisement by drawing from perceptual fluency hypotheses.

Study 1 used a single-factor, two-condition (distinctive pose and casual pose) between-subject design. Both Study 2a and Study 2b employed a single-factor, two-condition (distinctive pose, casual pose) between-subject design and tested the mediator of pose matchiness. Study 3 employed a 2 (pose condition: distinctive, casual)×2 (cognitive capacity: no load, load) between-subject design to test the moderator. All data were sourced from more than 600 respondents in China.

Study 1 illustrated that the existence of a distinctive pose can lead to higher consumer attitudes regarding advertising stimuli and the endorsed brands as well as more positive behavioural intentions towards endorsed products. Study 2a and Study 2b replicated such finding and demonstrated that the feeling of pose matchiness mediates the relationship between celebrities’ pose and endorsement outcomes. Study 3 further revealed that the cognitive capacity moderates such a relationship, that is, that the effect of a distinctive pose is stronger (lesser) when audiences’ cognitive capacity is loaded (not loaded).

Research efforts to date examining the nature of celebrity advertisement have been limited to celebrity’s faces and facial expressions. Little investigation in the marketing domain has considered the consequences of celebrities’ poses. This study takes the first step in revealing the positive effect of distinctive celebrity poses in product endorsement.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

There is growing interest among marketers in advertising and promoting their brands by adopting an online celebrity endorsement strategy. However, how online celebrities build their own brand equity and how online celebrity brand equity impacts fans’ purchase behavior have not been extensively researched in the extant literature. This paper aims to explore the factors that contribute to online celebrity branding and improving fans’ purchase intentions.

A survey and an experiment were conducted among consumers from the mainland of China. A total of 12 hypotheses were proposed to exam how self-congruity and virtual interactivity impact online celebrity branding and to explore the moderating role of perceived quality and product type.

This paper reveals that customers’ perceived self-congruity with online celebrities’ image and virtual interactivity positively impact the brand equity of online celebrities. Additionally, compared with virtual interactivity, the effect of customer perceived self-congruity on a brand is more significant. The brand equity of online celebrities thereby drives followers’ purchase intentions and the perceived quality of products positively moderates this relationship.

The research conclusions provide managerial implications for marketing practitioners for how to use human brands on social media platforms in the web 2.0 era and ultimately enhance consumer purchase intentions.

While in common English-language parlance speaking of “online celebrities” encourages the conflation of new forms of famousness with existing discourses on mass media stardom and fandom, the Mandarin Chinese term wanghong, a shorthand term for wangluo hongren (literally “person popular on the internet”), frames the enticing shores of online celebrity through the peculiar lexical domain of a grassroots popularity. The figure of the wanghong has in recent years accompanied the development of social media platforms in China, becoming a profitable profession, an inspirational role model, a morally condemnable by-product of internet economies, and in general a widely debated social phenomenon among local users. Drawing on interviews with more and less successful local online celebrities and discussions with their audiences, this chapter offers an up-to-date portrayal of the various forms of wanghong currently vying for attention on Chinese social media platforms, illustrating how popularity is crafted along with narratives of professionalism and economic aspirations intimately connected to the sociotechnical contexts of contemporary China.