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This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Government subsidies in China are aimed at boosting the import of strategic resources. Yet, they are not wholly beneficial, and so this brief looks at whether subsidies help gain competitive advantage.

The briefing saves busy executives, strategists, and researchers’ hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

In today's electronic package development cycle, activities are managed by multiple participants in the supply chain, which might have different quality and reliability impacts to the end product. As a result, the reliability risk is much higher for companies who do not have insight into and/or control over the products received. The purpose of this paper is to show how design‐for‐reliability (DFR) approaches will come into play to manage the risk.

In this paper, DFR approaches for package development will be discussed from the perspective of the original equipment manufacturers (OEMs). DFR practices through the package development cycle will be described based on key development modules. A case study for flip chip ball gris array package development using an advanced Cu/Low‐k silicon technology will be presented. Key measures to help control the quality and improve the reliability will be presented.

The proposed methodology significantly improves component and package reliability through the engagement in design, manufacturing, assessment and system evaluation.

The paper discusses the research results and the proposed DFR methodology will be helpful for fabless design houses, electronics manufacturing service (EMS) partners in the supply chain, and OEMs to manage the reliability of the products.

The purpose of this study is to examine different paths to overcoming the liability of foreignness. Based on the eclectic paradigm, the authors construct a theoretical framework comprising enterprise nature, location choice, entry mode and internationalization strategy.

The paper uses fuzzy-set qualitative comparative analysis (fsQCA) method to test the framework with data covering 120 multinational Chinese subsidiaries in 34 host in 2019.

The results show that liability of foreignness (LOF) is multiple concurrency, equifinality and asymmetry. When investing in Belt and Road (B&R) countries, non-SEOs can weaken LOF by applying the greenfield mode and resource-seeking strategy, other MNEs can implement a market- or resource-seeking strategy via cross-border M&A to reduce LOF. But when investing in non-B&R countries with a strategic asset-seeking strategy, the LOF is increased. The B&R initiative can reduce the LOF effectively.

The authors construct a general framework to explain the paths of overcoming LOF by bridging the OLI with LOF and introduce fsQCA method into the field of LOF to make up for the shortcoming of existing test method by explaining the influence of more than three factors on LOF.

This paper aims to study largely recent aspects of entrepreneurship and innovation in China. It synthesizes the research in the current special issue (SI) of Multinational Business Review (MBR) on this topic. In addition, this paper differs from other work on this topic in examining entrepreneurship and innovation from a more global standpoint with relevant international effects.

The paper provides an overview of the literature on entrepreneurship, innovation and key related topics such as firm and economic growth, as well as linking this research to related international works. It also summarizes the papers of the SI.

The authors’ analysis suggests that the study of entrepreneurship and innovation should be placed in the context of a country’s economic development and institutional environment as well as the firm internationalization trajectories and business models. In addition, the authors believe that a good understanding of economic growth in a transition economy like China (which is a key goal of China’s recent emphasis on innovation) is facilitated by understanding the comparative advantages and disadvantages of an economy with respect to the global innovation system.

The authors’ study explores the local-global and parent-subsidiary connectivity and co-evolution of firm strategies and the institutional environment in entrepreneurship and innovation in emerging and transition economies. The authors summarize and synthesize the papers in this SI to provide the results as well as some directions for future research in the domain of entrepreneurship, innovation and new venture creation, which is believed to be a key engine of economic growth in the coming years.

Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover.

The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter.

Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination.

The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL.

The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process.

Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.

Although there has been much research on government support for export in China and other emerging economies, considerably less attention has been given to government subsidy-related importing activity in China. This study aims to propose that the government subsidies as the source of financial resources produce a significant increase of imports, as the firms are more likely to engage actively in importing technology-related products which are conducive for China’s future innovation. However, state ownership in firms negatively moderates this relationship and holds back technology imports. Improved formal regulatory institutions do not help to improve but rather weaken this relationship.

To investigate how government policy affects imports of strategic resources in China, all of the listed firms on Chinese stock markets (from 2008 to 2014) have been selected, the firms that are engaged in exporting and importing activities. The data from the China Stock Market & Accounting Research database have been selected and merged with those of the General Administration Customs in China. A panel analysis has been done with several robustiness tests.

First, the study indicates that government subsidies are a driving force for the development of importing activities. Second, it finds conflicts of interests between government subsidies and state ownership of a firm, as increased ownership will weaken and even negate the positive effect of a government policy, thus negatively affecting the national competitiveness in the long run. Third, it is important to take into account the issue on different levels of institutional development, even allowing for the fact that a nationwide government policy is applied to the firms located in all corners of the country.

The authors suggested a regional difference in regulatory development but did not find the proposed direction. In their future study, the authors will validate and generalize this intriguing substitutional effect. They expect the results will help the government to ensure that it can fulfill a policy (e.g. regulation) down to every gross-roots organization so the development of regulatory infrastructure will help the firm to obtain and accumulate strategic resources through increased imports of them. Another direction of their future study will explore how government policy will prompt the firms to increase their spending so that they can possess plenty of “stamina” for their future development.

Different levels of institutional development exist in China even allowing for the fact that a nationwide government policy should be applied to all firms within the territory. This certainly has impacts on technology imports and thus creates difficulties for firms located in the western parts of China about which the government is particularly concerned. The government needs to ensure that its policies (laws and regulations) can be fulfilled down to every gross-roots organization so that the development of regulatory infrastructure can be inclusive and pervasive, given its influence on technology importation and indigenization.

Both of the theoretical and empirical work centered on policy initiatives and particularly government subsidies in emerging economies that significantly influence imports of strategic resources, a means with which the firm is better able to maintain and develop its competitive advantages, particularly in an economy with institutional void. Relatedly, the results on a causal relationship help envision a transcending trajectory of China’s economy, suggesting that businesspeople should capitalize on the policy advantage so that they are better able to sustain their long-term development. The results also present implications for policymakers to encourage and support strategic move toward such import endeavors.

The leakage problem caused by machining error, assembly error, wearing and thermal deformation has been the main factor hindering the development of scroll compressor. This paper aims to investigate the lubrication characteristics of radial clearance and further optimize the radial clearance, which can reduce the leakage in the tangential direction of the working chamber.

This paper establishes a model of radial clearance oil film lubrication in scroll compressor. And, the method to solve the Reynolds and energy equations is presented, as well as the dimensionless and discretization by finite element difference method. To verify the established model, performance experiment of scroll compressor for electric vehicle air conditioning system is also carried out.

Based on the presented model, the temperature field and distribution of the oil film in the radial clearance are analyzed. And the influence of the structural parameter on the radial clearance is further discussed. The optimum radial clearance could be achieved at β = 40°–42°, where the orbiting scroll is in the state of rotary balance. And, the simulation results coincide well with the experimental results.

This work provides an effective model to evaluate the lubrication characteristics of radial clearance in scroll compressor, which can provide guidance for the design of scroll compressor.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.