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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.

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.

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.

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.

During the centennial anniversary of Xinhai Revolution in 2011, the Chinese People’s Political Consultative Conference and the State Administration of Radio, Film, and Television supported the production of 1911 for celebrating such an important event that lead to the rise of the Republic of China in the contemporary Chinese history. This paper aims to reflect upon this film in relation to China’s propagation of “Greater China” for the Empire-building project.

By scrutinizing the film text and following the strait controversies over the film, this paper demonstrates how the Chinese Communist agents employed the coproduction model with Hong Kong for globalizing a cinematic discourse of Greater China in part of their Empire-building project.

The study challenges how contemporary Chinese history is ideologically and politically manipulated for advancing the Chinese Communist propaganda over Taiwan. The overall objective is to reflect upon the longstanding historical divergences that stand on the current geopolitical envision and strategy of China for reunification.

This paper provides an interdisciplinary reflection upon the intricate post-Cold War politics in part of the contemporary Chinese cinema under the China–Hong Kong coproduction model. The findings advance novel and timely insights into China’s current envision and strategy for reunification.

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.

The existing literature has established the importance of industry concentration in explaining firm performance and information environments. However, little is known about whether and how industry concentration affects investors’ ability to anticipate future earnings. This paper aims to investigate this query by identifying and testing two channels, product market power and intra-industry information transfer, through which industry concentration affects the informativeness of stock returns about future earnings.

The paper measures the informativeness of stock returns about future earnings by the future earnings response coefficient (FERC)). This study estimates the FERC using a firm-level sample from 38 economies.

The authors find that industry concentration significantly enhances investors’ ability to predict future earnings. Further tests show that both product market power and intra-industry information transfer contribute to explaining the positive association between industry concentration and the FERC, with the former playing a more salient role. Finally, the authors show that a country’s effective competition law attenuates the positive impact of industry concentration on the FERC by weakening the economic impact of the two underlying channels.

This study contributes to the growing literature on the price-leading-earnings relation, industry concentration and international corporate governance.

The case study demonstrates that China and the Kingdom of Saudi Arabia’s (KSA) energy transformation strategies share many similarities and enjoy a great degree of complementarity. This complementarity is not limited to the territory of the two nations, but has become a successful vehicle for joint Sino-KSA investments in the Arab peninsula as well as in the Middle East and North Africa. Leading to a new paradigm that runs along the lines of China’s staggering Belt and Road Initiative, the convergence of policies represents a new reality due to its potentiality to influence not only the energy sectors of the largest Arab’s economy and world’s most powerful one, but also over their partners.

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.