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SOX Section 404 requires that public companies evaluate and have their auditors attest to the effectiveness of their internal control over financial reporting (ICFR). These companies compare their ICFR effectiveness to the Internal Control Frameworks issued by Committee of the Sponsoring Organizations of the Treadway Commission (COSO). This paper aims to examine whether the implementation of the 2013 Control Framework has a positive impact on the information environment of U.S. public companies.

The study sample comprises firms from the S&P 1500 index and the Russell 2000 index firms that filed their annual reports after December 15, 2014. This paper uses bid-ask spread as a primary measure of information asymmetry, while controlling for the simultaneous effects of the new COSO framework on trading volumes and price volatility.

This paper finds a significant reduction in bid-ask spreads – a proxy for an improved information environment – among our sample firms following the adoption of the 2013 Control Framework, leading us to conclude that the 2013 Control Framework represents a substantial improvement.

This study specifically examines the impact of control frameworks on the information environment under SOX 404. Future research could explore other economic consequences associated with the adoption of the new COSO Framework. Additionally, it would be valuable to investigate whether the Cadbury model, which also qualifies as a “suitable” control framework under the SEC rules for ICFR assessments, produces similar or different outcomes. Future studies could also analyze the implementation details across all five components concerning the three types of objectives.

The findings will provide valuable insights for policymakers on the effectiveness of the COSO 2013 Framework in enhancing internal control reporting.

The findings will also contribute to improving the information environment in the capital markets by guiding policymakers and regulators in assessing the effectiveness of the new COSO framework.

While extensive research has focused on the consequences of accounting and related internal control disclosures, there has been limited examination of how the underlying internal control benchmarks affect the quality and reliability of ICFR assessments and disclosures. This research aims to address this gap.

The purpose of this research is to address the challenges of selecting optimal drones for disaster response operations under uncertainties. Traditional static (deterministic) models often fail to capture the complexities and uncertainties of disaster scenarios. This study aims to develop a more resilient and adaptable decision-making framework by integrating the best-worst method (BWM) with stratified multi-criteria decision-making (SMCDM), focusing on various uncertainty scenarios such as weather conditions, communication challenges and navigation and control issues.

The methodology involves identifying seven essential criteria for drone evaluation, guided by contingency theory. The BWM derives optimal weights for each criterion by comparing the best and worst alternatives. The SMCDM incorporates different uncertainty scenarios into the decision-making process. Sensitivity analysis assesses the robustness of decisions under various criterion weightings and operational scenarios. This integrated approach is demonstrated through a practical application to the Kerala flood scenario.

The integrated stratified BWM method proves to be highly effective in adapting to different uncertainty scenarios, enabling decision-makers to consistently identify the optimal drone for disaster response. The method’s ability to account for uncertain conditions such as weather, communication challenges and navigation issues ensures that the optimal drone is selected based on the situation at hand.

The methodology fills critical gaps in the literature by offering a comprehensive model that incorporates various scenarios and criteria for optimal drone selection. However, there are certain limitations. The reliance on expert opinions for criterion weightings introduces subjectivity, potentially affecting the generalizability of the results. In addition, the study’s focus on a single case, the Kerala floods, limits its applicability to other geographic contexts. Integrating real-time data analytics into the decision-making process could also enhance the model’s adaptability to evolving conditions and improve its practical relevance.

This research offers a practical, adaptable framework for selecting optimal drones in disaster scenarios. By integrating BWM with SMCDM, the methodology ensures decision-makers can account for real-time uncertainties, such as weather or communication disruptions, to make more informed choices. This leads to better resource allocation and more efficient disaster response operations, ultimately enhancing the speed and effectiveness of relief efforts in various contexts. The method’s ability to adjust based on scenario-specific factors ensures that drones are optimally deployed according to the unique demands of each disaster.

By incorporating SMCDM, the proposed methodology assists decision-makers in appropriately choosing drones based on their characteristics crucial for specific scenarios, thereby enhancing the efficiency and effectiveness of relief operations.

This study presents a unique integration of the BWM with SMCDM, creating a dynamic framework for drone selection that addresses the challenges posed by uncertain disaster environments. Unlike traditional methods, this approach allows decision-makers to adjust criteria based on evolving disaster conditions, resulting in more reliable and responsive drone deployment. The method bridges the gap in existing literature by offering a comprehensive tool for disaster response, providing new insights and practical applications for optimizing drone operations in complex, real-world scenarios.

Knowledge loss and retention are common phenomena not only for organizations but also for interorganizational projects, where multiple organizations are involved. This paper sets out to understand why knowledge loss occurs and how to retain knowledge, particularly in the context of interorganizational projects. For this purpose, the Orange Line Metro Rail Transit System in Lahore, the Bus Rapid Transit in Peshawar and the Green Line Metrobus in Karachi, all in Pakistan, were examined.

A multi-case study approach is employed in this paper. Empirical data were collected through semi-structured interviews and archival documents. To analyze the data, we used a three-step thematization procedure, which included data condensation, data presentation and conclusion.

The findings present the determinants of knowledge loss, including high time pressure, memory decay, lack of sharing of personal knowledge and tenuous relationships between salary and experience. For knowledge retention, the findings provide evidence of the transformation of the working environment, externalization, job shadowing, the hiring and rehiring individuals and the provision of incentives.

By examining knowledge loss and retention in interorganizational projects, this article contributes to the literature on knowledge-based theory.