A study of Chinese cross-border M&A integration

Chinese cross-border mergers and acquisitions (M&As), particularly post M&A integration, is still a relatively poorly understood phenomenon. The extant literature on the topic has attempted to examine the factors that can explain the integration outcome of Chinese companies’ cross-border M&As (CBMA). Indeed, some China-specific frameworks, or Chinese CBMA integration frameworks, have been proposed to better understand the phenomenon. These include the so-called light-touch integration framework and courtship-marriage-birth framework. These conceptualization of Chinese CBMA have provided some answers to questions related to integration outcome and have shown the role of both culture and strategy. While insightful, they still have certain lacunae. In particular, this extant Chinese literature of integration outcome is somewhat narrow (e.g., focus on ‘integration mode’), ignores strategic review as part of strategic factors, ignores cross-cultural psychology as part of cultural factors, and considers only one aspect of mediating factors (through intermediaries). Therefore, in my thesis I offer a conceptual framework for Chinese cross-border M&A integration that not only draws from these past attempts at conceptualizing the phenomenon under study but builds and extends them to include the above mentioned factors. It groups direct factors as strategic influences and cultural influences, with indirect factors captured as mediating factors. Strategic influences consist of strategic fit (synergy potential and industry complementarity), strategic intent, and strategic review. The latter being my contribution to understanding Chinese CBMA integration as far as ‘strategic role’ impinges on integration outcome. Cultural influences are made up of cross-cultural differences, cultural specific dimensions, and cross-cultural thinking and decision-making. The latter, as a cross-cultural psychological factor, is my contribution from a cultural standpoint. Mediators or mediating factors are seen as comprising of intermediaries, communication and language, and learning and time. The notion of the role communication/language as mediating outcomes is somewhat new in the study of Chinese CBMA integration, as is time. This thesis then tests this framework adopting a multi-case study approach first and then a single case study approach. The former approach uses secondary archival sources on four high profile cases, namely, Geely’s acquisition of Volvo in 2010, SAIC’s acquisition of Ssangyong in 2004, Lenovo’s acquisition of IBM’s PC unit in 2005, and TCL merger with Thomson’s TV business in 2004. The latter approach uses primary source of information through data collected via an in-depth interview of a single case, Geely (CEO of said company to be precise). Both sets of qualitative evidence provide valuable insights on the phenomenon under study and about my conceptual framework (its validation or falsification). The evidence is overall supportive of the factors under study, with first-hand and second-hand evidence complementing each other and also offering different insights. For example, the second-hand case findings document the significant role of external advisors and professionals (intermediaries), but the first-hand case findings de-emphasize their role to that of a supporting act, though relevant (e.g., Geely’s CEO highlighted that ultimate decisions and strategizing still rest with the company). In addition, first-hand evidence shows the many facets of integration, not revealed by the second-hand data. This includes emphasis on how to measure integration through key performance indicators and the need to look beyond short-term measures and that measures can change due to the market environment and is contextual (especially culturally)

Analyze Factors Influencing Drivers' Cell Phone Online Ride-hailing Software Using While driving: A Case Study in China

The road safety of traffic is greatly affected by the driving performance of online ride-hailing, which has become an increasingly popular travel option for many people. Little attention has been paid to the fact that the use of cell phone online ride-hailing software by drivers to accept orders while driving is one of the causes of traffic accidents involving online ride-hailing. This paper, adopting the extended theory of planned behavior, investigates the factors that factors influencing the behavior of Chinese online ride-hailing drivers cell phone ride-hailing software usage to accept orders while driving. Results showed that attitudes, subjective norms, and perceived behavioral control have a significant and positive effect on behavioral intentions. Behavioral intention is most strongly influenced by attitude. There is no direct and significant impact of group norms on behavioral intention. Nonetheless, group norms exert a substantial and beneficial influence on attitude, subjective norms, and perceived behavioral control. This study has discovered, through a mediating effect test, that attitude, subjective norm, and perceived behavioral control play a mediating and moderating role in the impact of group norm on behavioral intention. These findings can offer theoretical guidance to relevant departments in developing effective measures for promoting safe driving among online ride-hailing drivers.Comment: 17 pages,7 tables and 2 figure

FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning

Due to the limited availability of data, existing few-shot learning methods trained from scratch fail to achieve satisfactory performance. In contrast, large-scale pre-trained models such as CLIP demonstrate remarkable few-shot and zero-shot capabilities. To enhance the performance of pre-trained models for downstream tasks, fine-tuning the model on downstream data is frequently necessary. However, fine-tuning the pre-trained model leads to a decrease in its generalizability in the presence of distribution shift, while the limited number of samples in few-shot learning makes the model highly susceptible to overfitting. Consequently, existing methods for fine-tuning few-shot learning primarily focus on fine-tuning the model's classification head or introducing additional structure. In this paper, we introduce a fine-tuning approach termed Feature Discrimination Alignment (FD-Align). Our method aims to bolster the model's generalizability by preserving the consistency of spurious features across the fine-tuning process. Extensive experimental results validate the efficacy of our approach for both ID and OOD tasks. Once fine-tuned, the model can seamlessly integrate with existing methods, leading to performance improvements. Our code can be found in https://github.com/skingorz/FD-Align.Comment: Accepted by NeurIPS 202

Improving Tolerance Control On Modular Construction Project With 3D Laser Scanning and Bim: A Case Study of Removable Floodwall Project

Quality control is essential to a successful modular construction project and should be enhanced throughout the project from design to construction and installation. The current methods for analyzing the assembly quality of a removable floodwall heavily rely on manual inspection and contact-type measurements, which are time-consuming and costly. This study presents a systematic and practical approach to improve quality control of the prefabricated modular construction projects by integrating building information modeling (BIM) with three-dimensional (3D) laser scanning technology. The study starts with a thorough literature review of current quality control methods in modular construction. Firstly, the critical quality control procedure for the modular construction structure and components should be identified. Secondly, the dimensions of the structure and components in a BIM model is considered as quality tolerance control benchmarking. Thirdly, the point cloud data is captured with 3D laser scanning, which is used to create the as-built model for the constructed structure. Fourthly, data analysis and field validation are carried out by matching the point cloud data with the as-built model and the BIM model. Finally, the study employs the data of a removable floodwall project to validate the level of technical feasibility and accuracy of the presented methods. This method improved the efficiency and accuracy of modular construction quality control. It established a preliminary foundation for using BIM and laser scanning to conduct quality control in removable floodwall installation. The results indicated that the proposed integration of BIM and 3D laser scanning has great potential to improve the quality control of a modular construction project

A Deep Belief Network Based Model for Urban Haze Prediction

In order to improve the accuracy of urban haze prediction, a novel deep belief network (DBN)-based model was proposed. Firstly, data pertaining to both air quality and the environment (e.g. meteorology) data was monitored and collected. The primary haze influencing elements were discovered by analyzing the correlations between each of the meteorological factors and haze. Secondly, a DBN combined with multilayer restricted Boltzmann machines and a single-layer back propagation network was applied. Thirdly, the meteorological data predictions were carried out by using a competitive adaptive-reweighed method. A stable model was established by big-data training and its accuracy was verified by experiments. Results demonstrate that the pollution haze occurs in accordance with regular laws, and is greatly affected by wind direction, atmospheric pressure, and seasons. The correlation coefficient (CC) between the actual haze value and the prediction of the proposed model is 0.8, and the mean absolute error (MAE) is 26 μg/m3. Compared with the traditional prediction algorithms, the CC is improved by 18 % on average, while the MAE is reduced by 15.7 μg/m3. The proposed method has a good prospect to predict haze and investigate the main causes of it. This study provides data support for urban haze prevention and governance

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202