A clear university management vision for sustainability in the campus built-environment by 2030, coupled with continual encouragement to bring about an impact for a better society, and unparalleled access to un- or partly demonstrated innovative technologies at universities, provide the ideal environment to use campus as a living laboratory. However, changing the university campus into a testing and demonstration platform of new technology poses a particular challenge to Campus Real Estate (CRE) management units. New technology and innovation are by nature undemonstrated over the longer term, new and risky when compared to business-as-usual, and stakeholders in the process of implementation do not have clear performance indicators for innovation implementation. Innovation implementation decisions on campus is therefore an inter-disciplinary balancing act. 

In this study, two approaches to identify the decision making criteria and decision points for innovation assessment and implementation were used. A multi-criterion decision making process, using an expert model and verification approach (Chorus, Ten Broeke, et al.,  2021) was used to develop a transparent model highlighting decision-making preferences for innovation implementation on campus. This process focusses on capturing and reiterating expert decisions for numerous innovations. However, due to the unique nature of innovations, the benefits of an expert model might be limited. We therefore also used the Preference-based Accommodation Strategy (PAS) method (Arkesteijn, 2019) to identify decision making criteria. The value of the PAS method is twofold, first, in its essential consideration of the broader requirements from four stakeholder perspectives in real estate management (den Heijer, 2011) which is especially important in inter-disciplinary decision making; second, in its systematic compilation of preference scores along the identified decision criteria. 

Initial indications are that innovations with clear added value in sustainability, education and research, which are simple, aligned to current systems and relatively easily embedded in current regulation, as well as representing low financial, construction and operational risks are preferred. However, both research approaches go beyond identification of the decision-making criteria by plotting the acceptable levels of alignment, benefit and risk as informed by a broad panel of stakeholders. The result is a first, clear view on the elements and preference points used in the balancing act which is necessary to facilitate a safe, thriving technologically advanced and innovative campus. 

Finally the two decision making models are assessed on their ease of use, attractiveness and effectiveness, elements identified by Visser (2016) for assessment of decision support tools.