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In this chapter, we will review the history, deregulation, policy reforms, and airline consolidations and mergers of the Chinese airline industry. The measurement of airline competition in China’s domestic market will also be discussed. Although air deregulation is still ongoing, the Chinese airline industry has become a market-driven business subject to some mild regulations. Then, we will review the impressive development of the high-speed rail (HSR) network in China and its effects on the domestic civil aviation market. In general, previous studies have found that the introduction of HSR services has a significant negative impact on airfare and air travel demand in China. The rapidly expanding network of HSR has important policy implications for Chinese airlines.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

This chapter reviews three main issues in the interactions between air transport and high-speed rail (HSR) in China, namely the interaction between low-cost carriers (LCCs) and HSR, HSR speed effect on airlines, and airline–HSR integration. Studies on these three aspects of airline–HSR interactions have yet been well reviewed, and our chapter aims to fill in this gap. In this chapter, we comprehensively survey literature on the topics, especially studies on Chinese markets that have recently witnessed major HSR developments (and have planned further large-scale HSR expansion in the coming years). Our review shows that, first, compared to full-service carriers, LCCs face fiercer competition from HSR. However, the expansion of HSR network in China can be better coordinated with LCC development. Second, HSR speed exerts two countervailing effects on airline demand and price (the “travel-time” effect and “safety” effect, respectively). Specifically, an HSR speed reduction can have a positive effect on airlines due to longer HSR travel time, but a negative effect on airlines due to improved perception on HSR safety. Third, airline–HSR integration can be implemented through cooperation between airlines and HSR operators and through co-location of airports and HSR stations and can have important implications for intermodal transport and social welfare.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

This chapter outlines the philosophic underpinnings of the self-management paradigm developed over the past three decades by China’s Haier Group, a global leader in white goods. The successful transformation of Haier from a small resource-poor firm to a dominant global giant is often attributed to the self-management culture established in the company by its legendary leader Zhang Ruimin. This management paradigm is a function of the humbleness displayed by Mr. Zhang Ruimin and rooted in his strong belief in the traditional Chinese philosophy of I-Ching and Daoism. We show how the hexagram of Qian (“qian”: humbleness, modesty) from I-Ching is linked to Mr. Zhang’s humble approach and analyze how the six parts of the hexagram of Qian are related to the six development stages of the Haier Group. These insights are used to give some thoughts to the leadership challenge associated with the creation of a dynamic and responsive global organization.

With significant changes in the aviation industry, various airport–airline arrangements have been formed to achieve alternative objectives. However, no consensus has been reached on such arrangements’ economic effects and the associated optimal public policy. This chapter aims to provide an interpretive review of the common types of airport–airline arrangements, the different modeling approaches used and key conclusions reached by recent studies. Our review suggests that airport–airline arrangements can take diverse forms and have been widely used in the industry. They may allow the airport and its airlines to internalize demand externality, increase traffic volume, reduce airport investment risks and costs, promote capacity investment, enhance service quality, or simply are a response to the competition from other airport–airline chains. On the other hand, such vertical arrangements, especially for those exclusively between airports and selected airlines, could lead to collusive outcomes at the expenses of non-participating organizations. The effects of such arrangements are also significantly influenced by the contract type, market structure and bargaining power between the airport and airline sectors. While case by case investigations are often needed for important economic decisions, we recommend policy-makers to promote competition in the airline and airport segments whenever possible, and demand more transparency or regulatory reporting of such arrangements. Policy debates and economic studies should be carried out first, before intrusive regulations are introduced.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.