Information Technology [IT] is increasingly being integrated into the modern society. Novel IT solutions are Cloud based. Cloud solutions from Microsoft Azure and Amazon AWS are examples of services which offer the benefits of cloud computing. For IT providers, leveraging novel IT artefacts may require the development of a new service model. IT services enable new service delivery models and have the ability to disrupt whole industries. Spotify, Airbnb, and Uber are examples of successful businesses using IT driven service models. The academic community researches aspects of IT governance, frameworks, architecture, and business model innovation, management and organisational theories in order to develop a more successful IT service delivery model. The heterogeneous body of these academic theories addresses different parts of the service model development. However, current IT service delivery models lack sufficient support for contextualisation. In a case study at Dutch IT services provider KPN, Agent Based modelling is used to develop future scenario’s for the IT services organisation. With a strong focus on the organisation and the agents in the organisation, the Agent Based modelling approach OperA is applied. By Using semi-structured interviews to collect data throughout the services delivery chain, a future scenario with the IT broker as a new role was developed. Consequently, roles facilitating legacy platforms are expected to discontinue. From the future scenario, a roll-out plan using three phases is developed using the social structure of the organisation model. The environments of the OperA model enable the research to focus on the social structure of IT services delivery and the analysis of interactions between roles. The OperA model is a sufficient model for strategic decision making in IT services delivery. Further work could generate tools to fully automated the validation of the OperA model and connect it to real life phenomena with real time adaption to changes.

Our routines play an important role in a wide range of social challenges such as climate change, disease outbreaks and coordinating the staff and patients of a hospital. Studying these systems via agent-based simulations (ABS) enables researchers to gain insight into complex aspects of these challenges such as human interaction, learning, heterogeneity, feedback loops and emergence. Current agent frameworks do not integrate social and psychological evidence on human routines: humans make habitual decisions, interconnect these habits throughout the day and use these interconnected habits as a blueprint for social interaction. This thesis provides the domain-independent SoPrA (Social Practice Agent) framework that integrates theories on social practices to support the simulation of human routines. Social practice theory is a socio-cognitive theory applicable to model human routines as the theory aims to describe our ‘daily doings and sayings’. The first part of the thesis identifies the aspects of social practice theory that are relevant for agent-based simulation, distils requirements from the literature, reviews current agent models and provides the SoPrA framework that satisfies said requirements. The second part describes applications of SoPrA on the value-alignment problem in AI, identifying social bottlenecks in hospitals and comparing theories on how habits break. This results in an agent framework with a clear relation to current evidence and, due to its modularity and focus on domain-independence, is usable for a wide range of ABS studies that involve human routines. As such, SoPrA is relevant for scientific work in (1) ABS by enabling a new way to know, explore and improve the world, grounded in evidence on human routines; (2) in multi-agent systems by enabling agents that understand and interact with human routines; and (3) in the social sciences by crystallizing theories on human routines and enabling exploration of these theories via simulation. Furthermore, this thesis shows the societal relevance of SoPrA for understanding and improving the role of routines in AI safety, emergency rooms, commuting behaviour and consumption behaviour.@en

Over the last 20 years, the Chinese corporations have accumulated essential technological competencies such as those in the consumer goods manufacturing and information technologies. Therefore, Chinese multinational enterprises (MNEs) have high global ambitions to expand their reach into the foreign markets as an important role in contributing to the rising economic power of China. They are establishing greenfield subsidiaries from the ground up to penetrate into foreign markets. On the other hand, service offerings and service solutions are becoming the popular options for the consumption of our daily life. It has a huge potential for company to increase the customer engagement and “lock in” the customers. The term “servitization” is going viral nowadays which refers to an organizational transformation of companies from being a product-centered organization to being a service provider for customers. However, given the more and more integrated world economy, the servitization difficulties that are faced by companies, especially Chinese MNEs, are even more complex due to the changing environment and the involvement of different stakeholders in a global environment. This problem of servitization, especially for the greenfield subsidiaries of the Chinese MNEs, gets even difficult to be handled. This research explains the reasons of why the Chinese MNE greenfield subsidiaries are facing many obstacles. By looking into the characteristics of the organization, this research suggested that the performance of the servitization is correlated to the organizational features. Using agent-based model OperA to study the servitization in the organization is served as an exploration of a new approach to study the servitization. The insights that are explored from this approach is able to assist the future studies of other research problems regarding the servitization. On the other hand, people in the industries of different countries could be aware of the challenges of the globalization of Chinese MNEs, as well as the issues that they are facing. Understanding their problems will be helpful to establish better business cooperation and networking connections with the Chinese MNEs.

Artificial intelligence is a technology trend that potentially far exceeds the possibilities of all current state technologies, in a sense that these technologies have self-learning abilities. Organizations that manage to adopt and diffuse these technologies may effectively reach a competitive edge over their competitors faster. However, for large organizations such as traditional banks the question arises how to facilitate this transformation to effectively adopt AI technologies. Despite the fact that AI technologies are still in a relatively premature stage of development, traditional banks in the Netherlands are currently exploring its potential. In this exploratory study we interviewed 19 experts in the field of AI and Banking to identify all relevant challenges that arise when traditional banks adopt AI technologies. Additionally, we explored the importance of top management tech-executive roles as a possible solution to address certain challenges. As a result we have found that traditional banks have no common view or approach on how to adopt AI technologies enterprise-wide effectively. Therefore, the appointment of a new Chief AI Officer role may prove to be a valuable solution to address some of these challenges. However, there is still a mixed view on what this roles’ position, responsibilities and other managerial characteristics should be.

This dissertation is motivated by the need, in today’s globalist world, for a precise way to enable governments, organisations and other regulatory bodies to evaluate the constraints they place on themselves and others. An organisation’s modus operandi is enacting and fulfilling contracts between itself and its participants. Yet, organisational contracts should respect external laws, such as those setting out data privacy rights and liberties. Contracts can only be enacted by following contract law processes, which often require bilateral agreement and consideration. Governments need to legislate whilst understanding today’s context of national and international governance hierarchy where law makers shun isolationism and seek to influence one another. Governments should avoid punishment by respecting constraints from international treaties and human rights charters. Governments can only enact legislation by following their own, pre-existing, law making procedures. In other words, institutions, such as laws and contracts are designed and enacted under constraints.@en

The purpose of this empirical study is to gain insight in how Autonomous Weapons are perceived by the general public and the military and which moral values they consider important when Autonomous Weapons are deployed in the near future. The Value-Sensitive Design method is used to structure the study. This thesis proposes a design for a Moral Machine for Autonomous Weapons as part of the technical investigation phase of the study.

Businesses have leveraged Artificial Intelligence (AI) into many of their operational activities such as marketing, sales, and finance for its speed and cost-effectiveness. Lately, AI has also found applications in organizational recruitment processes. Unlike the conventional rule-based systems, present-day AI systems learn from data patterns—supported by the growing volumes of (big) data and increasing computing capacity—and make decisions independently without any human interventions. Thus, the perception that AI is fact-oriented and unbiased has led to this change in organizational recruitment practices. Though recent studies have shown that AI decisions could be unfair, scientific research on the fairness of AI recruitment systems is limited. This research fills this gap by designing a conceptual model to assist top-level HR managers in assessing the fairness of AI recruitment tools while drawing from information systems and responsible innovation literature. Guided by Design Science Research (DSR), the development of the model entailed three cycles of research, i.e., relevance cycle (which focused on design environment), rigor cycle (which focused on the existing knowledge base), and design cycle (which focused on development and evaluation). The design environment was explored by reviewing the literature on fairness in recruitment and algorithmic biases. Understanding both the recruitment fairness and potential causes of unfairness in AI helped to define the goal of the conceptual model. The design cycle was informed by the design principles for responsible AI, namely Accountability, Responsibility, and Transparency (ART), and General Data Protection Regulation (GDPR). The model presents seven dimensions which translate the principles to design requirements to assess the fairness of AI recruitment system. They are: (1)Justification; (2)Explanation; (3)Anticipation; (4)Reflexiveness; (5)Inclusion; (6)Responsiveness; and (7)Auditablity. The model also ties these concepts with specific criteria of conventional recruitment fairness such as consistency, interpersonal fairness, job-relatedness, and statistical parity. Finally, the completeness of the model was evaluated by discussing its alignment with other frameworks that had similar objective and utility of the model was validated by collecting feedback from the intended users. This thesis project makes several scientific and practical contributions. The research discusses the potential risks of using AI in the context of HR recruitment systems thereby contributes to the limited literature available in this respect. By using the DSR methodology for building the assessment model, this research serves as a case for DSR methodology in designing a non-IS artifact. Furthermore, the thesis has unified scattered studies in recruitment justice to provide a comprehensive overview of the characteristics of a fair recruitment system. Building on the theoretical contributions, the study has developed an assessment model to assist top-level HR managers in assessing the fairness of an AI recruitment tool. Employing this assessment tool can have positive effects on a business organization and society by eradicating the unfairness or bias that AI recruitment tools can bring into the organization. It would also raise awareness regarding the risks of AI. Given that the GDPR (article 35) mandate organizations to take responsibility in assessing the impact while introducing automated processing in new contexts or purposes, the assessment model designed in this study supports these regulations.