This graduation project explores the barriers to the implementation of change in the municipality of Amsterdam. The change initiative is a new contract for the public procurement, named the collaboration agreement, after one of the two main criteria, collaboration and innovation. As a result of this thesis the 'detection, action, reflection' toolkit was developed to create awareness, in the team responsible for implementing the collaboration agreement, of the complexity of implementing radical change.

The key barriers to the implementation of the collaboration agreement were the 'lack of conviction of the need to change' in the project team and the 'lack of a compelling case' in the implementation team. An effective change vision is essential to successful implementation and the occurrence of these barriers can be linked to the fact that this element is missing. The 5-step approach shows the team, which steps are needed to create a change vision by being aware of the complexity of radical change, creating a sense of urgency, build a guiding coalition, form a strategic vision and enlist a volunteering army. This toolkit supports the first step of the 5-step approach.

Artificial Intelligence (AI) has permeated every part of our world. It discovers new molecules, recommends what to watch and informs many business decisions everyday. There it has also become part of hiring, where recruiters are in need of more efficiency. The digital age has caused the amount of applications per position to skyrocket, while organisations have noticed that in the last decades an increasing amount of their value generation is directly connected to their people. Vendors market AI systems as intelligent workers that can help human resources (HR) departments find the best people efficiently. Different systems help with analytics, writing, assessments or video interviews, where participants answers questions from an AI system on their computer.

With the implementation of AI systems, there are often ethical problems involved. Biases are often hidden in the data or algorithm, that may cause people to be unfairly treated by an AI system. But AI interviews have another ethical problem that has gotten relatively little attention: autonomy over self-representation.

During an interview, you always want to show your best side and focus on your strengths and best experiences. You know better than anyone else what you can do and therefore it is important that you are able to represent yourself. But AI interviews interfere with that self-representation because they make assumptions on what you mean before they present that information to a recruiter. Also, by nature AI systems can only work directly with quantitive data, so how are you sure that your meaning of ‘teamwork’ was properly processed?

This project uses a novel approach of Value Sensitive Design in combination with
a different framework for generative prototypes to find a solution to this problem. Generative prototypes focus on generating hypotheses to further understanding. Here they were used with provocation in multiple iterations to elicit the values that people have about self-representation in hiring.

Those findings were synthesised into a new process that helps applicants maintain autonomy over self-representation through conveying feedback so they understand how well they are doing, through steering the interpretation of their answers and by keeping regular interviews to ensure the right nuance still arrives at the recruiter. This process was evaluated with another generative prototype, which informed the final three design requirements for AI interview systems in hiring: integrating feedback into the interview, managing expectations and assumptions and building in options for escalation.

In this way, the first steps are made for designing better AI systems that respect the autonomy over self-representation of applicants.

The maritime sector will have to deal with various challenges in the time to come, like sustainability and competitiveness. To deal with these challenges, there are several opportunities to improve through the use of data and technology. The first part of this report examines the developments taking place in both the global maritime sector and the Dutch seaports. In general, we see that major changes are afoot within the maritime sector. Data is starting to play an increasingly important role in decision-making. With data, new insights can be generated and specific parts where quick wins can be made can be tackled. By optimising processes, efficiency can be increased, allowing more to be achieved with the same effort. This helps the sector to stay competitive. The sector is facing declining interest from employees and emerging local production, making transport a less important factor. To remain competitive, processes are being automated and cooperation between different companies is increasing. This allows innovations to be applied more widely and ultimately achieve more. The more important role of data also leads to risks concerning security and privacy. Soon, the sector will have to learn to deal with these risks.

KPN is a large, Dutch telecommunication company that serves a large part of the Dutch consumer and business market and is one of three companies with a Dutch mobile network. As people’s perception of mobile communications changes, KPN is seeing a decline in revenues from consumers and is having to introduce innovations despite receiving little additional revenue in return. As more companies can deliver connectivity, KPN is focusing on delivering extra services to companies on top of their connectivity. This way, KPN tries to create the financial space to keep their network up to date and to add new functionalities. In addition, KPN distinguishes itself within the Dutch market by operating in a climate-neutral way. KPN is now using the knowledge it has gained to help other companies make their businesses more sustainable. KPN believes that the use of technology can ensure that companies can operate in a climate-neutral way without compromising their business.

By deploying KPN’s capabilities within the insights gained from the analysis, the KPN Fleet ecosystem was designed. Within this ecosystem, KPN offers a platform to all kinds of maritime stakeholders, enabling them to make optimal use of the opportunities offered by data and new technologies. By involving all stakeholders in an ecosystem, every one of them can help each other get the most out of their processes and innovations. Data can be shared safely and quickly, allowing all stakeholders involved to use this data to optimise their processes or services. This further increases the quality of all services delivered within the ecosystem, which ultimately benefits all stakeholders involved. It also offers KPN the opportunity to use its capabilities and thus add extra value within the maritime sector. Within this report, more details about KPN Fleet are highlighted. In addition, a roadmap is presented to help KPN implement KPN Fleet and expand it in the future.

KPN Fleet helps KPN to expand their position in the maritime sector and increase the value they add to society. This allows them to remain relevant in the future and generate more revenue. In addition, KPN Fleet is fully in line with its vision of using technology to help other companies become more sustainable while improving business. Besides, it could help generate knowledge or experience for KPN to apply in other sectors they are active in, like automation or data spaces. In doing so, KPN can strengthen their strategic advantage in multiple sectors. Furthermore, the concept includes the development to collaborate with various companies that is seen in the market. So, it both fits KPN as the customers it is serving.

The thesis addresses the implementation challenges of Machine Learning (ML) for merchandisers in the scenario of digitalization of retailing, and proposes a product-service design as the solution. The digitalization of retailing is defined as an on-going process to integrate Internet-connected digital technologies into interfaces between retailers and consumers. The researcher collaborates with Bloomreach, who provides an ML-powered merchandising tool called Bloomreach Search & Merchandising (brSM), and uses the context as an example of digitalization of retailing with ML. brSM helps merchandisers to improve the search and category experiences by optimizing the ranking of products, improving search results and curating recommendations on e-commerce platforms. The project presents a comprehensive analysis of the product, service, and merchandiser. In the product analysis, it is suggested that brSM doesn’t facilitate the interaction between the merchandisers and algorithms. Due to the knowledge gap, merchandisers have difficulties to align the expectation of the product at the beginning. Furthermore, the product doesn’t provide proactive feedback that improves the supervision of the user. In the service analysis, the misalignment of the internal feature communication leads to the confusing implementation service for the merchandisers. Specifically, the internal workflow and communication during the new feature introduction are confusing internally and externally. In the merchandiser analysis, it identifies two personas of merchandisers during the implementation of ML due to different business contexts and product characteristics. It thus is suggested to provide customized implementation supports according to their different needs.To address these challenges, the design solution aims to improve the (new) feature communication by adopting a use-case oriented approach for merchandisers and internal stakeholders with supportive tools. Based on the implementation framework of service design, the solution will be addressed on three levels, experience, service and strategy. At the experience level, brXtrategy family, supportive tools that provide merchandising inspirations, is introduced. It provides customized implementation information according to merchandisers’ business context. Also, it simplifies the product information by the adopting use-case oriented approach, which provides example-based explanations. Moreover, it improves the interaction between the merchandisers and algorithms by an interactive education tool and proactive notification of algorithmic performance. The front-stage and back-stage services are illustrated by the user journey map and service blueprint, which specify methods to improve the intra-company collaboration and the customer services in the critical moments like new feature introduction, onboard, and re-training. On the strategy front, a roadmap and a transitional workflow are introduced to facilitate the product strategy and the solution implementation. The workflow, called Use-case oriented development workflow, bridges the gaps of product/merchandiser understandings between the field teams and the R&D teams during feature developments. With comprehensive research and three aspects of the design solution, the thesis contributes to the company and academic domain. It contributes to a better understanding of merchandisers in the process of digitalization of retailing. Also, the solution improves brSM’s services and facilitates the implementation of ML. Last but not least, it demonstrates a design approach that designers can perform that improves ML-powered products.