Problem statement: Stress in the work environment is a growing problem, pushed forward by changing societal standards associated with meritocracy and the omnipresence of technology and communication devices. Individuals and organizations are simultaneously becoming more aware and active in this domain of healthy workplaces. To make an actual improvement within the work environment to reduce stress, a stronger knowledge base is required than is currently present. In this research, the relation between the workplace and stress is further investigated. The main research question is: “How can insights in the relation between workplace and activity on employee stress be used to develop a real estate decision-making model?” Research method: By conduction an operational-empirical research, the research questions are investigated. The operations research develops a model that can be used by real estate managers in the process of forming accommodation strategies. The empirical research is needed to form the required input for the operational model. This input is the quantified correlations between stress and the work environment. To obtain the input, a method is developed based on structured observations, using a smart wearable device to get a bio-metric for stress through sensor data.
Goals and objectives: The goal of the research is to help employees reduce their stress within the work environment. To scope the goal, the research will focus on the knowledge base on the correlation between stress and the workplace. The objectives of the research are to provide a tool in the form of an operational model for Real Estate Managers to help reduce stress, by getting a better understanding of the relation between workplace and stress.
Key findings and conclusion: The developed method of using a smart ring in combination with a structured observation design resulted in a feasible method for doing stress measurments in the work environment. The outcomes of the stress measurements can be operationalized into a real estate decision-making model, but the usability of the model output is still to be reaserched. Current findings mainly support existing findings from literature, with a main focus on the importance of privacy and control as a factor in the forming of stress. Due to the non-random nature of employee workplace choices, it is not possible to expect findings on all possible workplace characteristics. Future research with an increased sample size or based on intervention testing with the developed method could improve the creation of insights in the relation of stress in the work environment.

Deciding on where to locate the company’s assets is one of the main issues of the corporate real estate management field. It should support aligning the organization’s real estate strategy to its organizational objectives. Understanding which information is needed by the decision-makers is crucial to make a proper judgement. However, the awareness of criteria range and their relative importance is lacking in the majority of cases. Although literature recognises a number of structures based on which the criteria should be determined, this knowledge is disperse. It is not fully exploited to enrich the process, due to the scarcity of proper expert tools. As the high level of expertise and the vast amount of knowledge is required, the companies have to rely solely on available human resources.

This thesis provides the development of an expert tool, gathering state-of-art knowledge of location criteria. It strives to improve the (re)location decision making in large companies. It is done by using a hybrid research method, combining the operational research with an empirical one. The determinants for use of certain location criteria and the criteria itself are identified in the literature and linked to each other in a computer program. Through gathering the input from stakeholders, the tool suggests a prioritised list of criteria that should be used in the process.

The expert tool is tested in a pilot study at a company in order to evaluate the effectiveness and attractiveness of the model. It is done with a series of tests and interviews in connection with a pilot case. In addition, the acceptance and trust in the system are challenged.

Overall the tool is evaluated positively. It improves the location decision-making process by increasing the stakeholders understanding of the problem, transparency level, as well as empowering the decision-makers to express their needs that could have been omitted when using a traditional approach. This leads to better assessment of location alternatives. Although the tool itself requires a number of improvements before being able to be used in practice, the idea behind it was highly valued by the users.

Context Digitalisation enables students to study virtually everywhere. However, demand for study space on the campus remains high and is even increasing. Additionally, students place higher demands on the quality and availability of facilities (Valks, Arkesteijn, den Heijer, & Van de Putte,  2016, p. 15). However, recent research on Dutch campuses (Campus NL, 2016) shows several problems were identified regarding the alignment of campus space. (1) The unpredictable nature of demand for campus space makes it difficult to align demand with supply. (2) Another reason for not being able to comply with campus space demand is a result of problems in finding a suitable study space. Objective Preference Function Modelling already proved its potential in designing accommodation strategies. Furthermore, there is the need to involve a greater number of stakeholders and a need to improve the usability of the modelling technique. A smart tool presents opportunities to improve the user involvement in the management of design accomodation. As a result of the scientific gap, this thesis will explore these opportunities by developing a smart tool which provides information on study space while simultaneously generating information to support campus management.MethodsFor this thesis an engineering design process is used. Literature research is used in this research to gain understanding of the design problem and its users. With this knowledge, a smart tool will be developed along a iterative sequence of prototype evaluations to support the design process. Design user involvement will be established by two prototype evaluations evaluations with the use of interviews. For the evaluation of the proposed smart tool an assisted approach is utilized. In this evaluation, the data collection methods Task load index and Post-Experience interview are used. A Task Load Index (TLX) measures cognitive workload by assessing how much mental effort a user expends whilst using a prototype or deployed system. While individual Post-Experience interviews are a quick and inexpensive way to obtain subjective feedback from users.ResultsAs a result of the first user interviews it was established that users had difficulty with understanding how the system works and which values needed to be entered. Therefore, the primary focus for version 2 was finding ways to improve understandability and usability of the system. One implementation of this, was to improve navigation by having all the input fields on the same tab in excel. Therefore, User forms were used, allowing to have a better overview of the system by preventing the need to switch between tabs. The database structure represent the storage (back end) of information which is needed to operate the proposed Smart Tool. To visualize this an Entity Relationship (ER) model has been constructed in MySQL workbench. The ER model shows all the tables relevant to the proposed Smart Tool. A wireframe model is constructed with use of the program Balsamiq. A wireframe is chosen in this stage of the design because it does not distract users with commenting on stylistic issues (i.e. colour schemes or transitions). Balsamiq allows users to interact with the screens, allowing them to get an idea of the workflow and how to navigate the mobile application.It was made clear that using Preference function modelling is not designed for users on this scale but rather in making complex decisions and by generating alternatives. The purest form of the method is not desired from a user perspective as it was shown users need considerable (mental) effort to determine their preference values. However, if these values only need to be input once the strain on the user is significantly reduced and acceptable. Users clearly showed that once they became familiar with the system they were able to more easily adapt their preferences. This indicates that there is a level of intuitiveness in the use of the smart tool.ConclusionIt can be argued that hypothesis is confirmed and that the described problem can be solved by providing information about study spaces with the use of smart tools. The findings from the interviews suggest that the proposed smart tool can potentially add much value for both the user and campus management. The users generally reacted very positive to the concept of the smart tool. This shows that the smart tool will have significant value in supporting study space findability when executed correctly. To achieve this, usability of the smart tool is crucial for ultimate use. As this research comprises a design problem, it is important to realize that design problems can be solved in numerous ways, each leading to various results. Therefore, this can be seen as one of the possible ways a smart tool is able to support users in finding a suitable study space. 

The Dutch government has issued a legislation that follows the Paris Agreement of 2016. This legislation means that all office buildings in 2023 must have minimum energy label C while the ambition in 2030 is to have energy label A and in 2050 zero energy buildings. It is estimated that currently more than half of the office buildings in the Netherlands must take measures to meet this upcoming obligation before 2023. This research helps the building owner with the decision making in which measures they should invest. Office building owners want to know whether investments can be made from a broader sustainability perspective within the value areas People, Planet and Profit that are also financially justified. Therefore a model is designed which will help the building owners in the decision making process. The objective of the model is to enable mathematical operations on different elements. By using the model, office building owners consider their own organizational interests and objectives within the dimensions People, Planet & Profit. This is done in a so-called weighting process of the different dimensions and their criteria. This will lead that informed decisions can be made on the sustainable development of the building which fits the best between the value areas People, Planet and Profit given the objectives of the organization or client.

Zoetermeer is facing two challenges currently. The first is a housing program which is defined only at a superficial level. Secondly, the municipality has expressed the ambition to also take into account the migration of households, but has no data on which dwellings facilitate migrations the best. This research thus set out to first define the migrations chains which are found in Zoetermeer and then subsequently use this information in designing a suiting housing program.

The outcome of the Markov chain theory, which was used to determine residential migration chain length, showed that a number of dwellings facilitated migration the most. These were the more expensive dwellings in general and middle segment dwellings for households residing for social rental apartments specifically. Second, to construct the housing program, detailing on the living environments and dwelling models was defined and combined with ambition of the city.

Around this information an LP decision-making model was built which could use these constraints and find the optimal solution, if one existed. Ten different housing programs were designed, each with a different aim, and the results were assessed. Out of the ten programs, one seemed the most appropriate overall, as it achieved reasonable values in a number of important variables, including number of migrations, spread and value of newly added dwellings and area requirement. A surprising find was however that one of the programs, which in advance was seen as an extreme, proved to fit the ambitions of Zoetermeer in terms of increasing the city’s monetary value and attracting a new type of resident with new dwelling types.

Lastly, a program was designed with the aim of combining all benefits of the other programs into one. This program showed the results of the most overall appropriate program as well as adding a lot of value and the new types of dwellings. To conclude, this research found that the link between the two challenges could be made relatively easily and that the results of the model were very well received after even the first iteration. It should however be mentioned that the input of the model turned out to be very influential and that the municipality had difficulty in defining this input throughout this research as a shift in focus had recently occurred, which resulted in a change of thinking from quantitatively to qualitatively defining goals.

Finally, the two models designed proven to work for Zoetermeer, but were also designed to be used by any other party desiring to either determine migrations patterns, design a housing program or doing a combination of the two.