In an adaptive reuse decision-making setting, there is usually an occurrence of conflicting beliefs, opinions, interests, and resources among relevant stakeholders. Knowing who these stakeholders are and why, through a collaborative approach, will allow stakeholders with diverse interests regarding adaptive reuse to come together and participate either directly or indirectly in any stage of the decision-making process. This paper examines the usefulness of collaborative rationality among stakeholders involved in an adaptive reuse decision-making process. The specific objectives include: to characterise the stakeholders involved in an adaptive reuse decision-making process; and; investigate how their collaborative rationality can be effectively integrated into the adaptive reuse decision-making process. After a review of existing literature, four typical categories of stakeholders involved in an adaptive reuse decision-making process were identified: i) investors; ii) producers; iii) regulators; and iv) users. Also, the effectiveness of collaboration among the diverse stakeholders of an adaptive reuse decision-making process was validated using a focus group workshop to incorporate transparency, common goal, ideal speech, and consistency into the process. These findings imply that the active collaboration among characterised adaptive reuse stakeholders is important to mitigate the risk of manipulation of an adaptive reuse decision-making process, and, for policy makers to understand better the expectations and needs of the public, thereby, enhancing consents for optimal adaptive reuse decisions.

在自适应重用决策环境中，相关利益相关者之间通常会发生相互冲突的信念，观点，利益和资源。了解这些利益相关者是谁以及为什么通过协作方法，将使对适应性重用具有不同兴趣的利益相关者聚集在一起，直接或间接地参与决策过程的任何阶段。本文研究了参与自适应重用决策过程的利益相关者之间协作合理性的有用性。具体目标包括：表征参与自适应重用决策过程的利益相关者；和; 研究如何将他们的协作合理性有效地整合到自适应重用决策过程中。回顾现有文献后，确定了参与自适应重用决策过程的四个典型利益相关者类别：i）投资者；ii）生产者；iii）监管机构；iv）用户。此外，使用焦点小组研讨会将适应性重用决策过程的不同利益相关者之间的协作有效性进行了验证，该研讨会将透明性，共同目标，理想的言论和一致性纳入了过程。这些发现表明，具有特征的适应性重用利益相关者之间的积极协作对于减轻操纵适应性重用决策过程的风险非常重要，并且对于决策者更好地理解公众的期望和需求，从而增强了对共识的同意。最佳的自适应重用决策。