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This paper provides evidence on the impact of regulatory environment on financial reporting quality of transitional economies. This study compares the financial reporting quality of Hong Kong firms which are cross-listed in mainland China with those of Hong Kong firms cross-listed in China using specific earnings management metrics (earnings smoothing, timely loss recognition, value relevance and managing towards earnings targets) under pre- and post-IFRS regimes.

The financial reporting quality of Chinese A-share companies and Hong Kong listed companies are examined using earnings management measures. Using 2007 as base year, the study used a cumulative of −5 and +5 years of convergence experience which provide a total of 3,000 firm-year observations. In addition to regression analyses, we used the difference-in-difference analysis to check for the impact of regulatory environments on earnings management.

Through the lens of contingency theory, our results indicate that the adoption of the new substantially IFRS-convergent accounting standards in China results in better financial reporting quality evidenced by less earning management. The empirical results further shows that accounting data are more value relevant for Hong Kong listed firms, and that firms listed in China are more likely to engage in accrual-based earnings management than in real earnings management activities. We established that different earnings management practices that are seemingly tolerable in one country may not be tolerable in another due to level of differences in the regulatory environments.

The findings show that Hong Kong listed companies’ exhibit higher level of financial reporting quality than Chinese listed companies, which implies that the financial reporting quality under IFRS can be significantly different in regions with different institutional, economic and regulatory environments. The results imply that contingent factors such as country’s institutional structures, its extent of regulation and the strength of its investor protection environments impact on financial reporting quality particularly in transitional and emerging economies. As such, these factors need to be given appropriate considerations by financial reporting regulators and policy-makers interested in controlling earnings management practices among their corporations.

This study is a high impact study considering that China plays a significant role in today’s globalised economy. This study is unique as it the first, that we are aware of, to compare real earnings activities against accrual-based earnings management in pre- and post-IFRS adoption periods within the Chinese and Hong Kong financial reporting environments, distinguishing between cross-listed and non-cross-listed firms.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

This study scrutinised Shanghai junior high school teachers’ emotions and emotion management strategies in relation to teachers’ work settings and content. A mixed-methods approach was applied to collect data via field observations, interviews, and a quantitative survey. The aim of this study was two-fold. Firstly, it aimed to identify the typical work settings in which teachers experienced work-related emotions. Secondly, it aimed to reveal teachers’ priority work in school and how it affected teachers’ choices of emotion management strategies.

The data were analysed through the lens of emotional labour theories and professional agency theories. Findings showed that classroom teaching and the professional learning community activities were two typical settings in which the teachers experienced the most intensive emotions. Most Shanghai teachers managed their momentary emotions by either genuinely expressing their emotions that matched their roles and the scenario, or by purposely suppressing emotions to meet social and organisational expectations. Furthermore, most teachers adopted the long-term mood regulation strategy by aligning their emotions with long-term goal achievement in the future. As professional agents, the Shanghai teachers did not only manage their own emotions at work using these two strategies, but also managed students’ emotions as part of the moral education.

This chapter measures stock market liquidity with three low-frequency liquidity estimators, and investigates the long-term behaviour of commonality in liquidity on the Shanghai Stock Exchange (SSE) through principal component analyses and panel regressions. The findings provide strong evidence of liquidity co-movement on the SSE from its inception to the present. The extent of commonality in liquidity varies significantly over time. Remarkably, it surged in 2007, which corresponds to the onset of the subprime mortgage-triggered financial crisis; however, the subsequent behaviour is divergent among our different liquidity proxies.

This chapter presents a preliminary discussion of potential impacts of climate change on nomadic pastoralists on the Qinghai-Tibetan Plateau (QTP). Both climate model projections and observations suggest that (1) the QTP is becoming warmer and (2) precipitation is increasing. Evidence also suggests that (3) glaciers on the QTP are declining and (4) the permafrost is degrading. Nevertheless, little is known as to how climate change will affect nomadic pastoralists although environmental variability is likely to increase, which may again exacerbate production risks. Pastoral risk management strategies, such as mobility, may thus increase in importance. It is, however, difficult to translate changes in important climate measures like precipitation and temperature to effects on pastoralists and livestock since they mainly affect livestock indirectly via their effect on vegetation productivity. Consequently, to increase our understanding of climate change-related effects on pastoral adaptations, satellite-based measures directly linked to both vegetation characteristics and climatic variables should be utilized in future studies rather than, for example, overall changes in precipitation and temperature. Finally, official policies that constantly introduce reforms that reduce pastoral flexibility represent a far more significant threat for nomadic pastoralists on the QTP than climate change because they may result in the wholesale extinction of the pastoral culture.

This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.