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The purpose of this paper is to examine whether the political connections of listed firms in China affect how the market reacts to cases of financial misrepresentation investigated by the regulatory authorities.

The authors use an event study method and the financial misrepresentation events in China stock markets as research setting and empirically test the association between market reactions to the announcement of financial misrepresentations and the presence of political connections.

The results show that on average, there is no significant market reaction to financial misrepresentation for politically connected firms. In contrast, however, there is a significantly negative market reaction for non-connected firms, which suggests that investors do not punish politically connected firms for financial misrepresentation. The authors argue that politically connected companies use the altered financial information to gain legitimacy and obtain benefits from the government. Consistent with the argument, the authors find that in the years after they disclose their financial misrepresentation, firms with political connections are more likely to increase their bank loans than firms without political connections.

The authors provide a new explanation for the low-earnings quality of politically connected firms.

This paper aims to propose an integrated framework combining the cost-reduction rationale and the institution-leveraging rationale to explain how institutional distance, both formal and informal, influences emerging multinational enterprises (EMNEs)’ foreign direct investment (FDI) location choice. This paper also explores the moderating role of EMNEs’ FDI experience and strategic intent on value chain positioning as a reflection of firm heterogeneities, on the link between institutional distance and location choice.

This paper tests the hypotheses based on a firm-level longitudinal data set of FDI by Chinese EMNEs. The unique data are manually collected from Chinese companies listed on Shenzhen and Shanghai Stock Exchanges, composed of 250 FDI entries of 122 manufacturing firms from 2006 to 2010. The conditional logit model is used to estimate the proposed main effect and moderating effect.

Cultural distance does not deter Chinese EMNEs’ entrance in general, but firms investing in low value-added manufacturing subsidiaries are more likely to choose culturally similar countries than those investing in high value-added subsidiaries such as in upstream R&D and downstream marketing. Formal institutional distance with positive direction promotes Chinese EMNEs’ entrance, and this effect is enhanced when firms have less FDI experience and have the strategic intent to invest in high value-added subsidiaries.

This paper contributes to the current literature by identifying a holistic view of the institutional influences on FDI location choice of EMNEs and revealing how firm-level heterogeneities, particularly FDI experience and strategic intent of subsidiary value chain positioning, shape the boundary conditions of the institutional effects in different ways.

The purpose of this paper is to find the influence of employees’ work values on their creative performance and test the role of knowledge sharing among them.

This work surveyed 387 employees in six companies across three cities to test the research hypothesis model.

The findings reveal that comfort and security (comfort) work values have a significant negative impact on the creative performance, whereas competence and growth (competence) work values and status and independence (status) work values have a significant positive impact on creative performance. Knowledge sharing plays a mediating role between work values and creative performance.

This study reveals the influence mechanism of work values on creative performance from a new perspective and confirms the differing effects of different types of work values on creative performance.

Studies on China suggest that institutional environment plays a significant role in business activities; however, the issue of how firms attend to institutional environment is largely under-explored. This paper responds to the oversight by examining the potential ways in which firms can demonstrate heterogeneity in terms of vigilance to government policy. Drawing from the attention-based view of firms and the institutional logic perspective, the authors aim to propose that firms with market logic or non-market logic will show difference in vulnerability to policy change. Further, firm ownership type and policy-leveraging capability would moderate the relationship between institutional logic and attention to policy environment.

The empirical background of this study is based on Chinese pharmaceutical firms. The new reform on health-care system launched by Chinese government in 2009 provides a fertile context to observe firms’ attention to government policy. The hypotheses are tested by using data of 145 Chinese pharmaceutical public firms from 2009 to 2013.

The results generally support the hypotheses: market logic has a positive effect on attention to policy, whereas non-market logic has a negative effect. The impact of market logic is weakened when firms have a higher policy-leveraging capability (in terms of getting government subsidies); the non-market logic effect is strengthened both when firms are state-owned enterprises and have a higher policy-leveraging capability.

Instead of focusing on how institutional environment have an influence on firm behavior as previous studies do, this paper examines the interaction between institution and firms by exploring how firms pay attention to government policy. Under the context of China, this study sheds light on how institutional logic plays a role in determining cognitive resource allocation of firms.

Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy.

First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency.

The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process.

This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation.

In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation.

Point set calibration is a building block in the field of intelligent manufacturing.

The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.

Examines the fifteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

We take a state-stewardship view on corporate governance and executive compensation in economies with strong political involvement, where state-appointed managers act as responsible “stewards” rather than “agents” of the state.

We test this view on China and find that Chinese managers are remunerated not for maximizing equity value but for increasing the value of state-owned assets.

Managerial compensation depends on political connections and prestige, and on the firms’ contribution to political goals. These effects were attenuated since the market-oriented governance reform.

Economic reform without reforming the human resources policies at the executive level enables the autocratic state to exert political power on corporate decision making, so as to ensure that firms’ business activities fulfill the state’s political objectives.

As a powerful social elite, the state-steward managers in China have the same interests as the state (the government), namely extracting rents that should adhere to the nation (which stands for the society at large or the collective private citizens).

As China has been a communist country with a single ruling party for decades, the ideas of socialism still have a strong impact on how companies are run. The legitimacy of the elite’s privileged rights over private sectors is central to our question.

Chinese executive compensation stimulates not only the maximization of shareholder value but also the preservation of the state’s interests.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.