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To investigate the consequence of COVID-19 pandemic on the financial reporting and disclosure (FRD) practices, the study has been conducted. Moreover, this paper highlights the significance of FRD practices in any emergency period and its relevance with legitimacy theory in Bangladesh Perspective.

The COVID-19 pandemic has adverse impact on business. Hence, all the business activities have been categorized into five major aspects which are financial factors, business operations, business contracts, business value and stakeholders. These five major activities have been considered as independent variable. By analyzing various policy recommendations and guidelines of global and local accounting bodies, a structured questionnaire was developed in association with related IAS and IFRSs. Then, it was distributed among the accounting professionals of Bangladesh who are currently engaged in financial statement preparation and auditing services. Finally, data was analyzed through structural equation modeling (SEM) to test the hypothetical relationship between dependent variable and independent variable.

This study finds that financial factors, business contracts and stakeholders have significant relationship with the financial reporting and disclosure practices during the COVID-19 pandemic period. However, business operation and business value have no significant relationship with financial reporting and disclosure practices.

This study tries to analyze why and how firms should disclose essential information (both financial and non-financial) to the financial statement users during the COVID-19 pandemic. This study can be used as benchmark to issue a separate policy or standard for reporting any kind of adverse event in the financial reporting and disclosure practices.

To our best knowledge, we believe that this is first kind of study undertaken to investigate the consequence of COVID-19 pandemic on the FRD practices in the context of Bangladesh. This study is kind of exploratory in nature. Hence, future studies can explore industry-based financial reporting and disclosure practice in any pandemic period.

Managing projects is an important part of operations management, but many projects fail. This study focuses on attribution processes of such disruption from the underrepresented perspective of the project manager. The authors consider two types of causes: the more frequently researched environmental uncertainty (i.e. uncontrollable events) and the scarcely researched uncertainty imposed by non-collaborative project sponsors (i.e. other-controllable events).

The authors test conceptual arguments grounded in attribution theory and the notion of psychological contracts in a scenario-based experiment among 325 practicing project managers.

The findings indicate that non-collaborative project sponsors negatively affect project managers' motivation, whereas uncontrollable disruptions leave hope to achieve positive future outcomes. This latter effect is further strengthened when project managers have an internal attribution style. They tend to blame the disruption on themselves and generally feel in control of achieving success even if they are not.

These socio-psychological insights nuance the economic idea that uncertainty reduces motivation per se in the context of project disruption appraisal. The authors contribute to the behavioral project management literature and general attribution theory and help guide the allocation of resources during the recovery of failed projects.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.