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This study aims to identify an antecedent that hinders knowledge sharing, namely, the perceived climate of Cha-xu. Based on the social exchange perspective, the authors propose a theoretical model that links the perceived climate of Cha-xu to employee knowledge sharing. This model focuses on the mediating role of two types of trust (vertical and horizontal trust) and the moderating role of task interdependence in influencing the mediation.

Using a sample of 509 Chinese employees, this study carried out a survey on an online platform. This study developed a structural equation model and tested the moderated mediation hypothesis by using Mplus 8.0.

The results showed that two types of trust act as mediators in the relationship between the perceived climate of Cha-xu and knowledge-sharing processes. The mediating effect of horizontal trust is stronger. Most significantly, findings show that this mediated relationship is contingent on the level of task interdependence.

This paper provides evidence for distinguishing vertical trust and horizontal trust in the field of knowledge management. From a managerial perspective, this study identifies traditional cultural factors for hindering knowledge-sharing processes within Chinese organizations.

This chapter focuses on pedagogies of working with diversity centers on West-East reciprocal learning through a Reciprocal Learning Program in preservice teacher education between a Canadian university and a Chinese university. By presenting our initial analysis of fieldwork with our Teacher Education Reciprocal Learning Program participants through excerpts from newsletters, surveys, and interviews, we explore how participants from both China and Canada made sense of their learning from the other cultural and educational system through the Reciprocal Learning Program within broad educational, social, and cultural contexts. We argue that both global and multicultural dimensions are cultivated in reciprocal learning that infused the lived experiences of both Canadian and Chinese preservice teacher candidates. We discuss the pedagogic implications for working with diversity and believe that reciprocal learning can take place while working with people from different cultures with an attitude of mutual respect and appreciation and an appetite for learning in our increasingly interconnected world.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The purpose of this paper is to explore how a neo-liberal nationalist discourse of China imagines the spatial identity of the post-1997 Hong Kong with reference to Lost in Hong Kong, a new Chinese middle-class film in 2015 with successful box office sales.

Textual analysis with the aid of psychoanalysis, postcolonial studies and semiotics is used to interpret the meaning of the film in this study. The study also utilizes the previous literature reviews about the formation of the Chinese national identity to help analyze the distinct identity of the Chinese middle class today.

The discussion pinpoints how the new Chinese middle class as neo-liberal nationalists take Hong Kong as a “bizarre national redemptive space”. While Hong Kong is cinematically constructed as such a national other, this paper argues that the Hong Kong in question stands not for itself but in a form of “reverse hallucination” for pacifying the new Chinese middle class’ trauma under the rapid neo-liberalization of China in the 1990s.

This paper shows the new of formation of the Chinese nationalist’s discourse, especially the new Chinese middle-class discourse on Hong Kong after 1997.

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.

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 study examines the impact of China’s mixed-ownership reform on the innovation of non-state-owned acquirers, with a particular focus on the impact on firms with high financing constraints, low-quality accounting information or less tangible assets.

We use a proprietary dataset of firms listed on the Shanghai and Shenzhen Stock Exchanges to investigate the impact of mixed ownership reform on non-state-owned enterprise (non-SOE) innovation. We employ regression analysis to examine the association between mixed ownership reform and firm innovation.

The study finds that non-state-owned firms can improve innovation by acquiring equity in state-owned enterprises (SOEs) under the reform. Eased financing constraints, lowered financing costs, better access to tax incentives or government subsidies, lowered agency costs, better accounting information quality and more credit loans are underlying the impact. Additionally, cross-ownership connections amongst non-SOE executives and government intervention strengthen the impact, whilst regional marketisation weakens it.

This study adds to the literature on the association between mixed ownership reform and firm innovation by focussing on the conditions under which this impact is stronger. It also sheds light on the policy implications for SOE reforms in emerging economies.

This study aims to examine the impact of intellectual capital (IC) and its three components (human, structural and relational capital) on corporation performance in the Chinese transportation industry. In addition, this study also investigates auditor characteristics (both Big-N and non-Big-N auditors) as a moderating role to examine the relationship between IC and corporate performance.

The data include 398 firm-year observations of transportation companies listed on the Shanghai and Shenzhen Stock Exchange from 2011 to 2020. Value-added intellectual coefficient (VAIC) model and its modified version (MVAIC) are applied to measure IC efficiency. Finally, the fixed effects regression analysis is used to mitigate the endogeneity issue. To investigate the moderating effect of auditor characteristics, the authors divide the samples based on the clients audited by Big-4 and non-Big-4 firms.

This study reveals that IC can enhance firm performance in China’s transportation sector. Overall, findings indicate that on the whole, IC has a positive and significant impact on corporation profitability and productivity. Human capital and physical and financial assets (capital employed) play highly important roles, but structural capital has no significant impact. The authors also found that auditor characteristics play an important moderating role in the connection between IC and corporate performance. For example, the positive association between IC and corporate performance is more pronounced when Big-4 auditors audit client firms. At the same time, the authors found a negative relationship between IC and firm performance when non-Big-4 auditors audit client firms.

Managers must understand that several components of IC have a total effect on corporate financial performance. Therefore, managers can dedicate more resources to such components based on the performance outcomes to emphasize their business strategies.

This study is the first empirical analysis of the impact of IC and its components on corporation performance in the transportation sector in China, an emerging market. Previous studies mainly focus on developed countries’ high technology and financial industries sectors but the impact of IC in transportation industry largely remains unknown. Thus, the present findings contribute to IC literature by revealing several underlying mechanisms by which the components of IC help achieve good firm performance.

The purpose of this paper is to investigate the impact of intellectual capital (IC) and its components (human, structural, relational and innovation capitals) on financial performance (FP) at different life cycle stages.

The study uses the data from Chinese manufacturing listed companies during 2014–2018. The modified value added intellectual coefficient (MVAIC) model is employed as the measurement of IC efficiency. Finally, multiple regression analysis is used to test the research hypotheses.

This study shows that the impact of IC on FP is different across life cycle stages. Specifically, at the birth stage, human capital (HC), structural capital (SC) and innovation capital (INC) have a positive impact on FP. At the growth and mature stages, all IC components contribute to FP improvement. HC and SC play an important role at the revival stage, while only HC positively affects FP at the decline stage.

The findings may help corporate managers to make optimal strategies to improve FP by effective utilization of IC resources in the complex and competitive business environment. Meanwhile, companies can invest in the core elements of IC at different stages of development, so as to maximize the contribution of IC to company value.

This is among the few studies to explore the impact of IC on FP of manufacturing listed companies in the Chinese context from the perspective of life cycle. It also makes novel contributions in measuring IC by the MVAIC model with the inclusion of relational capital and INC that are largely neglected in previous research.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.