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In response to the 2008 financial crisis, the performance of the Chinese construction industry seems to be more successful, especially in total factor productivity growth (TFPG) and its contribution (TFPGC). Hence, the purpose of this paper is to investigate and reveal the potential successful lessons in this regard.

This study is conducted innovatively based on a special comparative analysis of TFPG and TFPGC between pre- and post-2008 financial crisis. Solow Residual Approach is used to measure TFPG and TFPGC for the period 2002–2016. Given that the crisis hit China at the end of 2008, the pre-2008 financial crisis period is from 2002 to 2008, and the post-2008 financial crisis period is limited to 2009–2016.

The results indicate that the industry indeed has better performance in promoting TFPG and TFPGC, TFP thus achieved significant accumulative growth before and after the crisis. However, from an evolutionary perspective, both TFPG and TFPGC presented an overall downward trend from before the crisis to after the crisis. Further, the game between the centrally planned economy and the market-oriented economy was identified and revealed as the essential reason behind the evolution of TFPG and TFPGC.

Some valuable lessons for policies and practices in promoting TFPG and TFPGC were summarized and learned from the Chinese experience, such as reducing administrative intervention and making the construction market play a decisive role.

This study provides some new empirical evidence to enrich the overall body of knowledge on growth theory, especially in promoting TFPG and TFPGC.

Since China's accession of the World Trade Organization (WTO), its construction industry has attained unprecedented growth. However, for the sources of this enormous growth, a controversy regarding the total factor productivity growth (TFPG) still remains in production practice and extant studies. In view of this, the purpose of this paper is to measure TFPG and to explore its sources in the industry post-WTO accession.

This study presents an innovative source analysis of TFPG. Stochastic frontier approach is adopted to measure TFPG and to explore its sources by decomposing TFPG into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC). Although China joined WTO in 2001, to provide an effective baseline, the study period is from 2000 to 2017.

The empirical results reveal that TFPG presented an overall downward evolutionary trend, but it still maintained a high growth post-WTO accession. From the perspective of decomposition, TP was the main source of TFPG. Furthermore, as a neglected source, interaction effects among TP, TEC, AEC and SEC have been demonstrated to have a significant influence on the cumulative TFPG.

To make the results be reliable, the authors discuss the empirical findings mainly by revealing the reasons behind the evolutions of TFPG and its sources. Based on these revealed reasons, government and policy makers can further refine and summarize some more detailed and targeted policy implications to improve TFPG.

By providing many empirical evidences to solve the aforesaid TFPG controversy, this paper, therefore, enriches the body of knowledge on growth theories, especially at the level of industrial economics.

Although research on prefabricated construction has gained increasing attention in recent years, limited efforts have been devoted to investigating safety issues in the off-site manufacture, especially workers’ behavioral intentions to work safely. Thus, research is needed to identify the motivational factors determining off-site construction workers’ safety behaviors. The purpose of this paper is to investigate workers’ safety behavior by examining the determinants of behavioral intention in the off-site manufacturing plants in China.

Theory of planned behavior (TPB) was modified and used in this study to explain how the elements in the hypothesized model interact. Data were collected using a questionnaire. Structural equation modeling technique with partial least-squares estimation was used to analyze the data collected.

The findings of this study indicated that workers’ tendency to engage in safety behavior is positively related to attitude toward the behavior, subjective norm, perceived behavioral control and behavioral habit, among which attitude toward the behavior and behavioral habit have most significant influences on safety behavior. This finding provides a better explanation on the pathways and the impacts of the crucial factors on the safety behaviors for the off-site manufacture.

The possible innovation of this research lies in its attempt to understand the antecedents of workers’ safety behavior in the off-site construction environment, which may make original contributions to construction safety research and practice. The findings of this study contribute to the body of knowledge in TPB. Corresponding countermeasures are put forward in order to improve workers’ safety behavior in off-site construction.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

This study aims to improve the calibration accquracy of the road condition sensor. A road condition sensor is widely used to detect water or ice deposits on the road to assess associated driving risks. Its quantitative calibration is central to the thickness measurement accuracy and conventionally performed using the single fitting method-based calibration method. One existing limitation is that the distribution characteristics of calibration data of different road conditions are diversified, which leads to the fitting error can not be minimized when using the conventional calibration method. Thus, the multiple fitting methods-based calibration method is developed to realize an optimal calibration for the road condition sensor.

A fitting method assignment for the calibration data of different road conditions was introduced to realize an optimal combination for fitting method and calibration data. In the experiments, the calibration methods were tested in the absence of measurement errors, then tested with calibration data, and finally, in the covering thickness measurement.

The comparison results indicate that compared with the conventional calibration method, the multiple fitting methods-based calibration method cuts the fitting error in the quantitative calibration by 13.3% and contributes to reducing the thickness measurement error by 8.11% for different road conditions.

The multiple fitting methods-based calibration method has been successfully applied for quantitative calibration and shown to reduce calibration errors. The comparison between different calibration methods demonstrates the superior performance of the new calibration method.

This study aims to report the development of a provisional robotic cell for additive manufacturing (AM) of metallic parts. To this end, the paper discusses cross-disciplinary concepts related to the development of the robotic cell and the associated command and control system such as the Computer-Aided Design (CAD) interface, the slicing software and the path planning for the robot manipulator toward printing the selected workpiece. This study also reports the development of a virtual production cell that simulates the AM toolpath generated for the desired workpiece, the adaptation of the simulation environments to enable AM and the development of a user application to setup, command and control the AM processes. If a digital twin setup is efficiently built, with a good correlation between the simulation environment and the real systems, developers may explore this functionality to significantly reduce the development cycle, which can be very long in AM applications where metallurgic properties, part distortion and other properties need to be monitored and controlled.

To generate the robot manipulator path, several simulation programs were considered, resulting in different solutions to program and control the robot of choice [in this study, Kuka and Asea Brown Boveri (ABB) robots were considered]. By integrating the solutions from Slic3r, Inventor, Kuka.Sim, Kuka.Officelite, RobotStudio and Visual Studio software packages, this study aims to develop a functional simulation system capable of producing a given workpiece. For this purpose, a graphical user interface (GUI) was designed to provide the user with a higher level of control over the entire process toward simplifying the programming and implementation events.

The presented solutions are compatible with the simulation environments of specific robot manufacturers, namely, ABB and Kuka, meaning that the authors aim to align the developments with most of the currently realized AM processing cells. In the long-term, the authors aim to build an AM system that implements a produce-from-CAD strategy i.e. that can be commanded directly from the CAD package used to design the part the authors are interested in.

This study attempts to shed light on the industrial AM, a field that is being constantly evolved. Arguably, one of the most important aspects of an AM system is path planning for the AM operation, which must be independent of the robotic system used. This study depicts a generic implementation that can be used with several robot control systems. The paper demonstrates the principle with ABB and Kuka robots, exploiting in detail simulation environments that can be used to create digital twins of the real AM systems. This is very important in actual industrial setups, as a good correlation between the digital twins (simulation environment and real system) will enable developers to explore the AM system in not only a more efficient manner, greatly reducing the development cycle but also as a way to fully develop new solutions without stopping the real setup. In this research, a systematic review of robot systems through simulation environments was presented, aiming to emulate the logic that is, used in the production cell development, disregarding the system brand. The adopted digital twin strategy enables the authors to fully simulate, both operationally and functionality, the real AM system. For this purpose, different solutions were explored using robots from two different manufacturers and related simulation environments, illustrating a generic solution that is not bound to a certain brand.

Using specific programming tools, fully functional virtual production cells were conceived that can receive the instructions for the movements of the robot, using a transmission control protocol/internet protocol. Conversion of the CAD information into the robot path instructions for the robot was the main research question in this study. With the different simulation systems, a program that translates the CAD data into an acceptable format brings the robot closer to the automatic path planning based on CAD data. Both ABB and Kuka systems can access the CAD data, converting it to the correct robot instructions that are executed. Eventually, a functional and intuitive GUI application capable of commanding the simulation for the execution of the AM was implemented. The user can set the desired object and run a completely automatic AM process through the designated GUI. Comparing ABB simulation with the Kuka system, an important distinction can be found, namely, in the exportation of the programs. As the Kuka program runs with add-ons, the solution will not be exported while maintaining its functionality, whereas the ABB program can be integrated with a real controller because it is completely integrated with modules of the virtual controller.

To conclude, with the solutions exploited, this study reports a step forward into the development of a fully functional generic AM cell. The final objective is to implement an AM system that is, independent of any robot manufacturer brand and uses a produce-from-CAD strategy (c.f. digital manufacturing). In other words, the authors presented a system that is fully automatic, can be explored from a CAD package and, consequently, can be used by any CAD designer, without specific knowledge of robotics, materials and AM systems.

Recent legislation in Europe and North America encourages women’s participation in corporate boards based on the belief that gender-diversified boards contribute positively to firm performance and increased competitiveness. Contrary to the West, the women’s participation rate in business has been traditionally high in China. The purpose of this paper is to find out whether gender-diverse corporate boards of Chinese automotive firms perform better financially than gender-homogeneous boards.

By drawing on data from the Chinese Government and Bloomberg, the authors compare and analyze the differences in financial performance (return on equity, asset growth, sales growth) and risk behavior (debt risk, R&D expenditure) of Chinese automotive firms with and without women on their corporate board.

There is significant evidence that firms with women on the board perform better across all three categories, with the exception of return on equity, for which they found no significant differences among the analyzed firms.

While women’s participation in corporate boards in China is low, the results of this study suggest to policy makers and firms alike to implement measures that support gender-diversified boards in order to take advantage of their potential to increase corporate performance.

So far, the performance of corporate boards of countries with a traditionally high share of female participation in the workforce has rarely been analyzed. Research focusing on the Chinese automotive industry is new and underrepresented, although China is the largest automotive market worldwide and a key industry of the domestic economy. This investigation contributes to the literature stream on board diversity in as well as to industry-related studies. With the example of the Chinese automotive industry, it provides empirical evidence of better performance of firms with gender-diversified boards within the categories tested.

This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with their resulting hydraulic permeability. The focus of the investigation is to define a method that generates a valid geometric and topologic representation from a computational modelling point of view for explosion-generated fractures in rocks. In particular, extraction of geometries from experimentally validated Eulerian smoothed particle hydrodynamics (ESPH) approach, to avoid restrictions for image-based computational methods.

Three-dimensional stabilized ESPH solution is required to model explosively created fracture networks, and the accuracy of developed ESPH is qualitatively and quantitatively examined against experimental observations for both peak detonation pressures and crack density estimations. SPH simulation domain is segmented to void and solid spaces using a graphical user interface, and the void space of blasted rocks is represented by a regular lattice of spherical pores connected by cylindrical throats. Results produced by the RCPNMs are compared to three pore network extraction algorithms. Thereby, once the accuracy of RCPNMs is confirmed, the absolute permeability of fracture networks is calculated.

The results obtained with RCPNMs method were compared with three pore network extraction algorithms and computational fluid dynamics method, achieving a more computational efficiency regarding to CPU cost and a better geometry and topology relationship identification, in all the cases studied. Furthermore, a reliable topology data that does not have image-based pore network limitations, and the effect of topological disorder on the computed absolute permeability is minor. However, further research is necessary to improve the interpretation of real pore systems for explosively created fracture networks.

Although only laboratory cylindrical rock specimens were tested in the computational examples, the developed approaches are applicable for field scale and complex pore network grids with arbitrary shapes.

It is often desirable to develop an integrated computational method for hydraulic conductivity of explosively created fracture networks which segmentation of fracture networks is not restricted to X-ray images, particularly when topologic and geometric modellings are the crucial parts. This research study provides insight to the reliable computational methods and pore network extraction algorithm selection processes, as well as defining a practical framework for generating reliable topological and geometrical data in a Eulerian SPH setting.

This case is suitable for undergraduates, MBA students and students from business administration departments in the teaching of human resources management and performance management.

Luodian Electric Power Construction Corporation Group (LEPCC Group) is a state owned enterprise transformed from a construction unit of Luopu Power Supply Bureau (LPSB), a governmental organization in charge of all the electricity supply in Luopu City. The general manager of LEPCC, Gu Ming tried to set up a modern market-oriented management system for LEPCC. Unfortunately the problems that had accumulated in the past two decades during which LEPCC was a governmental organization made his reforms very difficult. The first headache for Gu Ming was the performance appraisal reform in LEPCC. The existing performance appraisal system seemed to have at least three problems in practice: unclear appraisal objectives, an improper assessment system, a different appraisal standard for similar positions. What should Gu Ming do to build a proper performance appraisal system to help the fast-growing LEPCC Group to make LEPCC a competitive market-oriented player?

The first objective of this case is to enable students to understand that the issues of working performance are issues of people first, rather than issues of the management system. If the management focuses on the system instead of on the staff of the company to design the performance management system, the system will be fruitless and inefficient. The second objective is to cultivate students' capability to apply the basic theories of human resource management and the knowledge of performance appraisal in case analysis and practical management. This case, seemingly about performance appraisal, is in fact about the organizational structure and processes of the organization. Reform should start with organizational analysis, job analysis and job descriptions. Only when all these have been done correctly, can the performance management system be designed more reasonably, scientifically and efficiently.

Teaching notes are available; please consult your librarian for access.

This paper aims to sketch the emerging notion of auto‐adaptive software when applied to e‐learning software.

The study and the implementation of the auto‐adaptive architecture are based on the operational framework “ActiveTutor” that is used for teaching the topic of computer science programming in first‐grade university courses.

The paper describes an adaptive heuristic dedicated to the display of knowledge entities which is based on the dynamic balancing of four behavioral criteria for the low‐level agents (cooperation, pedagogy, audacity, and sensibility).

The heuristic may be used in all multi‐agent systems where load balancing problems appear and where an auto‐adaptive behavior is important. Originality/value –The paper presents an auto‐adaptive algorithm.