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Since the early 2000s, China has been actively promoting school collaboration to narrow down educational gaps between schools to achieve inclusive and quality education for all. Among the reforms, building education groups has become one of the most widely adopted approaches for school improvement. This chapter aims at visualizing a hybrid model of school collaboration formulated by both hierarchist and egalitarian approaches in the Chengdu City of China. It starts with policy review on the construction of education groups for education improvement in China to interpret how social cohesion and regulation are constructed at policy level to promote school collaboration. Through data collected from education groups in District A of Chengdu, it then provides an analysis of the practice of improving education quality through promoting education groups among public schools in this province. The study reveals top-down policy initiatives, bottom-up school autonomy, and a shared responsibility for constructing quality education for all are key factors which enabled education groups to contribute to school improvement. The research also reveals how a lack of policy coordination and limited shared value and trust within education groups have become barriers to this reform. It concludes by discussing possible solutions for further promoting a sustainable school collaboration based on experiences of some ongoing practices at school level.

Although preference for sons has been documented among parents in developing countries, it is an open question whether and to what extent intra-household resource allocation is influenced by family sex composition. This study investigates the effects of sex composition on intra-household resource allocation based on the collective household model of Dunbar, Lewbel, and Pendakur (2013). I extend their model to estimate the influences on a household member’s resource share by observing how budget shares of a private assignable good vary not only with total expenditure and family size, but also with family sex composition. Using data from the 2005 Iranian Household Income and Expenditure Survey, I find that family composition significantly affects intra-household resource allocation in Iranian rural areas. Specifically, rural parents assign 1.6–1.9 percentage points more resources toward their sons. These resources are essentially coming at the expense of mothers. In all-boy families, mothers get 2.8–3.6 percentage points fewer resources than they do in all-girl families. These effects are more pronounced among farmer families than nonfarmer families. However, I find no significant role for gender composition in intra-household resource allocation in urban areas.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This study seeks to find an interactive effect between ethical leadership (EL) and corporate governance (CG) variables and investigate whether they would affect employee organizational citizenship behavior (EOCB) in a Malaysian organizational setting. The collected data from the 300 accounting/finance department employees were analyzed using Statistical Package for Social Sciences (SPSS) and Partial Least Square–Structural Equation Modeling (PLS-SEM; SmartPLS 3.0). Several primary results confirmed a coherent significant relationship between EL and ethical climate (EC), EL and EOCB, EL and CG, and CG and organizational success. Theoretically, it implies a more enhanced EOCB literature on how it can be infused in an organization. It also offers valuable knowledge by providing organizations with several insights concerning the improvement of EOCB, enabling the organization to achieve its desired success and, more importantly, how the findings could contribute directly and indirectly to emerging markets in terms of their industrial and financial performance.

Using initial public offering (IPO) involuntary delisting data, this chapter examines whether and how motivated institutional investors affect the survivability of IPO firms. The empirical evidence shows that the likelihood of future delisting is much lower for IPOs with more motivated institutional investors. This impact is more pronounced for firms with higher information asymmetry. The motivated institutional investors also facilitate better post-IPO operating performance. The results are consistent with the prediction of the limited attention theory.

This chapter investigates the effect of the geographical distance between institutional investors and firms on managers' financial misconduct. The evidence shows that the likelihood of committing financial misconduct by management is positively associated with distance. The distance effect is more prominent for firms with higher information asymmetry and more dedicated institutional investors. In line with the balance between risk-taking and benefit extraction from misconduct, the severity of financial misconduct is higher for firms closer to their institutional investors. Results show that geographical proximity can significantly reduce the cost of information production and facilitate monitoring through access to soft information.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.