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The purpose of this paper is to examine whether and how underpricing is associated with board structure and corporate ownership among firms conducting initial public offerings (IPOs) in the Indonesian equity market.

To capture the most recent development, the sample comprises 101 firms conducting IPOs in Indonesia's primary equity market in the period of 2003‐2011. The explanatory variables consist of board size, board independence, ownership concentration, and institutional ownership. In further analysis, the authors perform regressions considering three types of the controlling shareholder, namely families, foreign entities, and the government.

Providing some support for signaling theory, it is found that board independence is positively related to the level of underpricing. Further, this study provides evidence that the level of underpricing is negatively associated with both board size and institutional ownership, indicating that these two governance mechanisms play important roles in mitigating information asymmetry between the issuer and potential investors. Ownership concentration is insignificant in explaining the first‐day returns. When the type of corporate control is taken into account, it is revealed that government‐controlled companies tend to experience higher underpricing.

This paper contributes to the IPO underpricing literature since the influence of corporate governance mechanisms on initial returns is relatively under‐researched, particularly within the context of emerging markets.

This study aims to analyze the policies and strategies used by governmental organizations to address the impacts of climate change in informal neighborhoods, kampungs, such as Bukit Duri and Melayu in Jakarta, Indonesia, focusing on canal and river flooding mitigation and infrastructure development. The research examines the displacement of residents due to the demolition of informal settlements along riverbanks, the role of different governmental organizations and the implications of these policies on affected communities.

This study uses the theoretical framework of environmental injustice to evaluate the strategies used by the Indonesian Government to address climate change adaptation in Jakarta, with a specific focus on the problem of flooding and its impact on displacement. By analyzing the history and outcomes of flood mitigation policies, this paper assesses the government’s strategies related to infrastructure, evacuation and socialization. In doing so, the study examines the social impact of these policies on affected communities. Furthermore, social listening and media analysis of Twitter data and various news outlets are conducted to gain insights into the living conditions and experiences of displaced residents in two public housing projects.

The study revealed the challenges faced by the government in implementing policies for climate change adaptation and flood mitigation in Jakarta, including a lack of community engagement with residents of the Kampungs in the decision-making process for relocation. Despite government efforts and providing low-cost apartments (rusuwana), the analysis sheds light on the various forms of injustice that result from the government’s approach to climate change adaptation in Jakarta.

This study examines social justice issues in Jakarta’s informal neighborhoods and explores locally driven efforts vs government-mandated policies for managing natural hazards and adapting to climate change.

The use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV scheduling considering loading/unloading and collision-avoidance constraints simultaneously as these issues have only been considered separately in the previous literature.

This paper proposes a novel model for dual-RGV scheduling with two-sided loading/unloading operations and collision-avoidance constraints. To solve the proposed problem, a hybrid harmony search algorithm (HHSA) is developed. To enhance its performance, a descent-based local search with eight move operators is introduced.

A group of problem instances at different scales are optimized with the proposed algorithm and the results are compared with those of two other high-performance methods. The results demonstrate that the proposed method can efficiently solve realistically sized cases of dual multi-capacity RGV scheduling problems in AS/RSs.

For the first time in the research on dual multi-capacity RGV scheduling in an AS/RS, two-sided loading/unloading operations and collision avoidance constraints are simultaneously considered. Furthermore, a mathematical model for minimizing the makespan is developed and the HHSA is developed to determine solutions.