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The author argues and explains that the indigenous Eastern epistemological frame of yin-yang balancing can be taken as a unique system of thinking toward a meta-perspective. It is not only deeply rooted in the indigenous Eastern culture traditions, but also bears salient global implications, especially in the domain of paradox management. The purpose and contribution of this chapter are twofold: (1) to explain the unique and salient features of yin-yang balancing (the “either/and” system to reframe paradox into duality as partially conflicting and partially complementary, both spatially and temporarily) as compared with the Western logic systems (the “either/or” and “both/or” or “both/and” systems); and (2) to explore the global implications of the “either/and” system for future paradox research, including the three unique themes of overlap between opposites with the “seed” of one opposite inside the other; threshold from the contingent balance between partial separation and partial integration in line with specific contexts through three operating mechanisms, and knot for the special role of third-party to shift paradox from a dyadic level to a triadic and even a multiplex level.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

This chapter examines the relationship between directors and officers (D&O) liability insurance and stock-price synchronicity by testing competing corporate governance-related monitoring and moral hazard-related agency conflict hypotheses. Testing a sample of stocks listed on the Taiwan Stock Exchange and the Taipei Exchange for 2008–2020, the empirical results of this study indicate that D&O insurance in Taiwan is negatively correlated to stock-price synchronicity. This negative relation is robust to a battery of tests, including those of fixed-effects regression models, alternative sample periods, alternative synchronicity measures, and alternative insurance measures. Further evidence indicates that this negative relationship is more pronounced among firms with greater agency problems, especially during periods of high market uncertainty. Overall, these findings support the corporate governance-related monitoring hypothesis, which posits that firms with greater D&O insurance are likelier to be characterized by better governance structures and information transparency. Additionally, their stock prices are more likely to reflect firm-specific information in a timely and precise manner, and they are more likely to have lower synchronicity with the industry and market.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.