Stakeholder orientation and organizational performance in an emerging market

There has been research that studies Chinese firms’ stakeholder orientation but fails to identify Chinese firms’ specific stakeholder groups. In addition, little research in this line has been conducted so far to reflect recent Chinese constitutional transition. This study seeks to fill these gaps. It extends previous studies assuming that a fixed set of stakeholders is suitable for firms in different countries context, and identifies Chinese firms’ key stakeholder groups by adopting the descriptive approach of stakeholder theory. Based on this identification, the authors further examine how these stakeholder orientations influence organizational performance and how they interact. Interviews with managers from 107 firms show that customer, employee, shareholder, supplier, and competitors are perceived as Chinese firms’ most important stakeholders; empirical studies using data collected from 307 Chinese firms reveal that orientations towards these stakeholders enhance organizational performance. Moreover, there are synergy effects existing among customer orientation, supplier orientation, and competitor orientation, and between customer orientation and competitor orientation, while shareholder orientation has significant hindering effects upon competitor orientation as a reflection of recent institutional changes taking place in China

Discovering Discriminative Geometric Features with Self-Supervised Attention for Vehicle Re-Identification and Beyond

In the literature of vehicle re-identification (ReID), intensive manual labels such as landmarks, critical parts or semantic segmentation masks are often required to improve the performance. Such extra information helps to detect locally geometric features as a part of representation learning for vehicles. In contrast, in this paper, we aim to address the challenge of {\em automatically} learning to detect geometric features as landmarks {\em with no extra labels}. To the best of our knowledge, we are the {\em first} to successfully learn discriminative geometric features for vehicle ReID based on self-supervised attention. Specifically, we implement an end-to-end trainable deep network architecture consisting of three branches: (1) a global branch as backbone for image feature extraction, (2) an attentional branch for producing attention masks, and (3) a self-supervised branch for regularizing the attention learning with rotated images to locate geometric features. %Our network design naturally leads to an end-to-end multi-task joint optimization. We conduct comprehensive experiments on three benchmark datasets for vehicle ReID, \ie VeRi-776, CityFlow-ReID, and VehicleID, and demonstrate our state-of-the-art performance. %of our approach with the capability of capturing informative vehicle parts with no corresponding manual labels. We also show the good generalization of our approach in other ReID tasks such as person ReID and multi-target multi-camera (MTMC) vehicle tracking. {\em Our demo code is attached in the supplementary file.

Innovative Disaggregate Modelling Frameworks for Predicting Tourists' behaviours

As both data and computational access have grown, disaggregate modelling has been gradually growing its popularity in the domain of travel demand modelling and tourism decision modelling. One of the major benefits of disaggregate model is that the actual behaviour of individuals can be observed and captured when a model is developed in a bottom-up way, as opposed to a top-down fashion. In addition, to this behavioural characteristic, disaggregate models are more policy sensitive as the impact of a small change in the market on people’s decisions can be observed and estimated. Tourism decision-making process is commonly perceived as a multifaceted and complex problem. Tourists are usually required to make decisions on a series of behavioural choices including destination, time of travel, transport modes, travel party, Length of stay (LOS) and expenditure, etc. Many tourism researchers demonstrated that these decisions are interrelated in nature. Such interrelations indicate that an intended tourism policy or marketing strategy would have its unintended consequences. Therefore, the objective of this thesis is to explore, introduce and develop model frameworks with highly disaggregate system of models for jointly simulating multiple attributes activities. In the first core chapter (CHAPTER 3), the aim is to develop a hazard-based discrete-continuous model for understanding tourists’ decision making on time of year for travel (seasonal variation) and associated length of stay (LOS) with regard to different travel modes. The model results, which is tested with data from select years between 1999-2018, including years of significant exogenous shocks, show mixed evidence of stability and changes in the parameters. Building on this evidence, this chapter concludes with underlying temporal choice behaviours of tourists that may be of relevance during- and post-COVID19 environment. In the second core chapter (CHAPTER 4), the aim is to extend the previous work to incorporate three tourism decisions by endogenizing transport modes, where a fully nested Archimedean copula structure is adopted for modelling practice. To illustrate its application, the modelling results are used to build a simulated COVID-19 pandemic scenario according to the social distancing restrictions within New South Wales, Australia, and a three-dimensional elasticity analysis for trip destinations is performed. The findings suggest the model provides nuanced insights into simulating tourist behaviours and appraisal of transport policies aimed at tourism recovery or/and development. In the third chapter (CHAPTER 5), the aim is to calibrate a trivariate Archimedean copulas for activity participation, transport modes and accommodation selection while accounting for the correlation between the unobserved heterogeneity of each individual model. Based on the findings of this chapter, we identify the major participants of Australia’s aboriginal activities are senior groups. However, in order to inherit and develop Australia’s indigenous cultural tradition, potential collaborations can be built up among stakeholders such as government, schools/universities, travel agents, bus/coach rental corporates and hotels. Overall, this thesis advances the study and applications of joint models in the domain of multiple tourism modelling. Results of this work provide valuable insights into the interrelations of different tourism decisions, understanding of which can greatly assist in developing more tailored and oriented policies or marketing strategies towards tourists

Vacancy expansion in alpha-Ti under tensile loads at different strain rates with MD simulation

In order to analyze the effect of strain rate under tensile load on microcrack growth in Alpha Titanium, molecular dynamics simulation was used to analyze the results of atomic location, dislocation distribution, lattice phase transition, potential energy distribution and volume strain distribution. It was found that the cracks gradually evolved into holes after unstable propagation, and the holes were occupied by clusters on both sides of the material in the later stage under the necking of the material. The higher the tensile strain rate, the earlier the crack initiation and the larger the evolution of the through-hole. When the same strain value is reached, the lattice transformation ratio is higher under high strain rate loading. HCP is transformed into amorphous structure, BCC lattice type and a small amount of FCC type. Moreover, the larger the strain rate, the less the compatible deformation ability of the lattice is, and the more twins are produced. In addition, it is found that there are volumetric strain wave emission and diffusion in the model at the moment of void birth, and voids play a role in dividing the energy absorption region. Dislocation emission occurs at the crack tip and energy competition exists between dislocation and crack propagation

The research of polishing nozzle quality based on discrete element method

In order to get further study for the effect of abrasive grains to the wall of the workpiece during polishing process, a new method of discrete element that carry out the numerical simulation with DEM is put forward, and the visual calculation is performed for the abrasive grain movement in the nozzle. The interaction of particles-particles or particles-workpiece wall during the polishing process and the tracks of single grain in the workpiece are analyzed by observing the distribution of abrasive grain in the workpiece at different time. The surface removal mechanism of abrasive grains to the workpiece material is discussed by analyzing the collision process of particles to the workpiece wall. The wear level of the abrasive grains to the inner surface of the workpiece is studied through the force of abrasive grain to the workpiece wall consumption, and finally explore the cutting effect of particles to workpiece wall. As a consequence, the abrasive flow processing experiment is carried out. The surface roughness of the large hole and small hole of the nozzle are detected by stylus measurement. The conclusion shows that the surface roughness for the large hole and the small hole before the experiment is1.741 μm and 1.201 μm, its 0.801 μm, 0.651 μm after it. Further roughness tests are performed on the surface of the pores by means of a grating surface measuring instrument. The result indicates that the surface roughness reduces from 2.67 μm to 0.697 μm, 0.728 μm, 0.782 μm. Apparently, the surface roughness of the hole is sharply reduced, which has a smooth and flat inner surface, the effectiveness and reliability of the abrasive flow are verified

Acoustic test facility at Tianjin spacecraft AIT center of China

The China Academy of Space Technology (CAST) designs and builds a new world-class Assembly, Integration and Test Center (AITC) at Tianjin, China. Modal, vibration and acoustic test facilities are built to support the future environmental testing needs of China space station program. The Reverberation Acoustic Test Facility (RATF) is about 4000 m3 in volume and can achieved an empty chamber acoustic overall sound pressure level (OASPL) higher than 156 dB. It is the largest acoustic facility in Asia. Beijing Institute of spacecraft Environment Engineering (BISEE) started its design and construction work from 2012 and put it into use at October of 2015. The RATF will provide an efficient support for environment testing needs of China and world’s space program in the future