ABSTRACT
Contract violations are frequent due to the high uncertainty and complexity of construction projects. However, enforcement after a violation has received limited attention. This study distinguishes between three types of violations, i.e., letter violations, mutually agreed spirit violations, and unilaterally assumed spirit violations, based on the documentation and mutuality dimensions. By using the data collected from Chinese general contractors, this study concludes that compared with unilaterally assumed spirit violations, violations of high mutuality of obligations (the first two violations) will lead to more severe contractual and reputational enforcement while with high mutuality, whether the violated obligations are written in the contract or not (corresponding to the first two violations, respectively) does not significantly affect the severity of enforcement. The mediating effects of relational risk perception on the aforementioned effects are empirically supported. This study contributes to the enforcement literature by exploring the effects of the characteristics of violations, especially violations of undocumented elements of contracts, on enforcement and fills the gaps in the scarce literature on reputational enforcement and its antecedents. Project managers can benefit from this study by recognizing the application of reputational enforcement and making better alignment between different types of violations and enforcement. IEEE
ABSTRACT
Intense and frequent changes increase uncertainty and complexity in decision-making. The COVID-19 pandemic exacerbates this situation. Therefore, the decision-maker seeks to reduce risks and meet these challenges. The manuscript aims to identify cause-effect relationships between variables affecting countries and changes caused by the COVID-19 pandemic and propose an algorithm to facilitate decision-making by identifying forgotten effects. The authors use thematic analysis to synthesize the semi-systematic literature review findings. The applied research uses a quantitative approach through modeling and simulation. The results highlight that the pandemic effects are associated with causes such as health care, political and economic stability, social justice, and the level of corruption. Decision-makers must prioritize the management of these variables guided by science. The main contribution is to show an algorithm that identifies forgotten effects in pandemics' socio-economic and health management, preventing future crises. In addition, the study advances the frontier of knowledge by addressing identified gaps and contributes to academia and policy makers. The most critical limitation is the number of variables included in this research. Future investigations could include analyses on the impact of climate change and sustainable development of nations and country-specific studies on the forgotten effects of the COVID-19 pandemic. © 2022 Taylor & Francis Group, LLC.