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Public-private partnerships (PPPs) have become a significant global phenomenon and governments are utilizing them more frequently to deliver projects that satisfy increasing societal demands in infrastructure sectors such as highways. Compared to traditional project delivery approaches, PPPs are long-term contracts between the public and the private sectors, where the private sector is engaged in more project tasks and accepts more risks. However, due to their long-term and complex nature, PPP contracts face many issues. Consequently, each project's contract becomes vital to project success because it: allocates risks, governs project relationships, and can align parties' interests. This dissertation examined 21 project contracts in the US highway PPP market to investigate risk allocation; contract designs and risk sharing mechanisms; and revenue risk guarantees. Using a content analysis framework, the allocation of 31 risks associated with highway PPPs was determined. These risks were mostly transferred to the private sector or shared between public and private parties, and project context had a significant influence on risk allocation. Assessment of contract designs indicated that the public sector imposes extensive monitoring and retains a majority of the decision rights to preclude opportunistic actions by the private sector; further, risk sharing mechanisms were complex and largely dependent on resolution during project implementation, which likely increases ex post transaction costs. Finally, revenue guarantees, commonly structured as standard options to mitigate revenue risk, were redesigned to incorporate exotic option features; quantitative analysis revealed that exotic structures can better serve chief PPP stakeholders' interests through increased robustness and flexibility. ; Ph. D.

This book constitutes the refereed proceedings of the 13th International Conference on Software Business, ICSOB 2022, which was held during November 8-11, 2022 in Bolzano, Italy. The special theme of ICSOB 2022 was "Software for Digital Transformation". The 19 full papers together with 6 short papers presented were carefully reviewed and selected from 53 submissions. The conference is covering different aspects of Digital Transformation, Software Startups, Software Ecosystems, Software Processes, Platform Economy, Software Sustainability, and People and Process Analytics.

Software Sustainability -- Elements of Sustainability for Public Sector Software – Mosaic Enterprise Architecture, Macroservices, and Low-Code -- How could we have known? Anticipating Sustainability Effects of a Software Product -- Software Sustainability: Academic Understanding and Industry Perceptions -- Engineering and Management -- Tailored Design Thinking Approach - A Shortcut for Agile Teams -- Deliberative Technical Debt Management: An Action Research Study -- Blockchain Governance: A Dynamic View -- Framework for creating relevant, accessible, and adoptable KPI models in an Industrial Setting -- Risk exposure and management in software development – a survey of multiple software startups -- Software Startups -- Supporting Entrepreneurship with Hackathons - A Study on Startup Founders Attending Hackathons -- User Experience Practices in Early-Stage Software Startups - An Exploratory Study -- A Study of Factors on Women from the Tech Sector and Entrepreneurship -- Making Internal Software Startups Work: How to Innovate Like a Venture Builder -- Software Platforms and Ecosystems -- The Economic Anatomy of Paid Crowdsourcing -- Power Relations within an Open Source Software Ecosystem -- When Player Communities Revolt Against the Developer: A Study of Pokémon GO and Diablo Immortal -- Continuous Improvement -- Design Principles for a Crowd-based Prototype Validation Platform -- What are critical success factors of DevOps projects? A systematic literature review -- Towards Federated Learning: A Case Study in the Telecommunication Domain. .

1. Six pillars of modern entrepreneurial theory and how to use them -- 2. Pivoting in Software Startups -- 3. Yes, we can! Building a capable initial team -- 4. The Perception and Management of Technical Debt in Software Startups -- 5. An analytical framework for planning a Minimum Viable Products -- 6. Software Startup ESSENCE – How Should Software Startups Work? -- 7. Startup Metrics that Tech Entrepreneurs need to know -- 8. Early-stage software startups: main challenges and possible answers -- 9. The Roles of Incubators, Accelerators, Co-working Spaces, Mentors, and Events in the Startup Development Process -- 10. Fostering open innovation in coworking spaces – A study of Norwegian startups -- 11. The maturity of startup ecosystems – The cases of New York, Tel Aviv and San Paolo -- 12. Thailand's Tech Startup Ecosystem -- 13. Software Startup Education - A Transition From Theory to Practice -- 14. Teaching "through" Entrepreneurship: an Experience Report -- 15. Lean Internal Startups: Challenges and Lessons Learned -- 16. Software Startup Education: Gamifying Growth Hacking -- 17. Key influencing factors in early-stage Norwegian hardware startups– A trilateral model of speed, resource and quality -- 18. The rise and fall of a database-as-a-service Latvian unicorn -- 19. Triggers of Business Success of IT Startup Owners in Russia -- 20. Brazilian startups and the current Software Engineering challenges - The Case of Tecnopuc.

Intro -- Preface -- Organization -- Contents -- Keynote Addresses -- From Efficiency to Effectiveness: Delivering Business Value Through Software -- 1 Introduction -- 2 Background -- 3 Challenges -- 4 From Efficiency to Effectiveness -- 5 Conclusion -- References -- The Rise of Software Startup Research: An Insider's View -- 1 Software ``Eats'' the World, Startups Change the World -- 2 What Is a Software Startup, Exactly? -- 3 Evolution of Software Startup Research -- 4 The Role of a Purposeful Research Network -- 5 Concluding Remarks -- References -- There's No Business Like Software Business: Trends in Software Intensive Business Research -- 1 Introduction to This Keynote Paper -- 2 Software Product Management -- 3 Software Ecosystems -- 4 Continuous X, Agile, and Technical Debt -- 5 Software Startups -- 6 Software Intensive Business Hype Cycle -- 7 Discussion and Conclusions -- References -- Software Ecosystems -- A SECO Meta-model -- 1 Introduction -- 2 Related Work in Classification and Meta-models -- 3 Research Method -- 3.1 Step 1: Systematic Literature Review -- 3.2 Step 2: Meta-model Relevant Entity Selection -- 3.3 Step 3: Model Development -- 3.4 Step 4: Expert Review -- 4 Results and Analysis -- 4.1 Step 1: Paper Selection -- 4.2 Step 2: Entity Selection and Theme Construction -- 4.3 The SECO Meta-model -- 4.4 Expert Review -- 5 Discussion and Conclusion -- 5.1 Limitations -- 5.2 Conclusion -- 5.3 Future Work -- References -- Towards an Understanding of iIoT Ecosystem Evolution - MindSphere Case Study -- Abstract -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Discussion of Key Insights, Limitations and Outlook -- References -- Identifying Architecture Attributes in the Context of Software Ecosystems Based on a Mapping Study -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Software Ecosystems -- 2.2 Platform Maintenance.

There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems. ; peerReviewed

Ethical concerns related to Artificial Intelligence (AI) equipped systems are prompting demands for ethical AI from all directions. As a response, in recent years public bodies, governments, and companies have rushed to provide guidelines and principles for how AI-based systems are designed and used ethically. We have learned, however, that high-level principles and ethical guidelines cannot be easily converted into actionable advice for industrial organizations that develop AI-based information systems. Maturity models are commonly used in software and systems development companies as a roadmap for improving the performance. We argue that they could also be applied in the context of developing ethically aligned AI systems. In this paper, we propose a maturity model for AI ethics and explain how it can be devised by using a Design Science Research approach. ; peerReviewed