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This chapter examines China’s corporate governance and accounting environment that shapes the adoption of internationally acceptable principles and standards. Specifically, it examines international influences, including supranational organizations; foreign investors and international accounting firms; domestic institutional influences, including the political system, economic system, legal system, and cultural system; and accounting infrastructure. China’s convergence is driven by desired efficiency of the corporate sector and legitimacy of participating in the global market. Influenced heavily by international forces in the context of globalization, corporate governance and accounting practices are increasingly becoming in line with internationally acceptable standards and codes. While convergence assists China in obtaining legitimacy, improving efficiency is likely to be adversely affected given that corporate governance and accounting in China operate in an environment that differs considerably from those of Anglo-American countries. An examination of the corporate governance and accounting environment in China suggests heavy government involvement within underdeveloped institutions. While the Chinese government has made impressive progress in developing the corporate governance and accounting environment for the market economy, China’s unique institutional setting is likely to affect how the imported concepts are interpreted and implemented.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

Purpose: The purpose of this study is to analyze whether information asymmetry (ASYM) plays a mediating role in the relationship between corporate disclosure and cost of equity capital (COEC) in emerging markets such as Indonesia.

Design/Methodology/Approach: This study is a quantitative study using secondary data obtained from listed manufacturing firms from 2015 to 2017. Purposive sampling was used to select 105 firms. The design of this study was causality research, and the analysis was performed through ordinary least squares (OLS) regression and path analysis.

Findings: The results show that the level of disclosure for corporate social responsibility (CSR), intellectual capital, and enterprise risk management (ERM) reduces the COEC by suppressing ASYM. This finding confirms the argument that managers can reduce their companies’ COEC by reducing ASYM through increased disclosure. These results are controlled by earnings quality (EQL) because that is most relevant to the COEC, as well as corporate size, leverage, and differences in institutional factors.

Originality/Value: This research is based on the central assumption that disclosure enhances the level of information while EQL remains the focus for investors. This research is also the first to study CSR disclosure, intellectual capital disclosure, and ERM disclosure together as a proxy for disclosure. The findings confirm that managers can reduce their companies’ agency conflict by increasing their level of disclosure. Managers can also reduce the COEC by reducing ASYM through increased disclosure. This also implies that increasing the level of disclosure will be effective in reducing the COEC for companies in emerging markets, such as Indonesia.

Necessary conditions represent the factors that cannot be compensated but must be present to aim the desired outcome; if a necessary condition is absent, the outcome will not exist. This logic of necessity causality differs from the conventional logic that has been evaluated by the methods drawing the lines “through the middle of the data” (e.g., regression and SEM). The authors argue that the empirical investigation of necessity causality has been largely ignored in hospitality and tourism literature although the notion of necessary causes for achieving certain outcomes is widespread throughout the studies. Thus, the authors introduce “necessary condition analysis” (NCA) as a suitable analytical method to identify necessary conditions in hospitality and tourism research. This chapter provides details on the underlying logic, key advantages, and an illustrative example of NCA. The chapter concludes by offering a few recommendations for future NCA applications in hospitality and tourism research.

This chapter discusses the current risk perception literature in the tourism field. The chapter critiques the literature and offers a solution through a more conceptual and operational definition of risk perceptions. Specifically, the inclusion of affective risk perceptions will be added to the literature via the risk-as-feelings hypothesis. Extension of the current literature will enhance research moving forward.

The chapter will provide a literature review, propose a conceptual model, and operationalize the risk perception variables.

The outcome of this chapter is to provide a conceptual model as a framework to address risk perception studies in tourism and hospitality in the future. The model will provide clear measurement scales to be tested.

This chapter gives a much needed theoretical and conceptual foundation to the study of risk perceptions in the travel and tourism literature.

Drawing upon resource-based view (RBV) and attribution theoretical lenses, this chapter provides a paradigm for examining the interplay among environmental investment towards green innovation, environmental disclosure as well as firm performance using the structural equation modelling (SEM) methodology. This chapter demonstrate a growing environmental awareness among stakeholders of the relevance of environmental performance to share value. It is also suggested that the mediating power of environmental disclosure between environmental investment and firm value as well as incremental goodwill is crucial. The findings of this chapter provide critical implications for several stakeholders that if environmental performance is hypothesised to affect the firm's value, companies may take proactive measures to avert potential environmental-related violations. Besides, investors may trade based on the evidence as to how firm value and its goodwill from acquisition will be affected by news of its environmental performance.

This chapter discussed the implementation of the digital twin (DT) idea into construction. Through the adoption of DTs into construction practices, construction professionals have been able to project an identical virtual concept of sections of the project execution right from the onset. In the introduction and discussing of its origin, the DT was further assessed about its applications in construction beneficial in enhancing project delivery. Other sections like barriers, drivers and benefits of the DT in construction summarised what this chapter represents in terms of discussing the new involvement of digital tools in construction execution, management and sustainability.

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.