Innovation diffusion research alternates between structured life cycle and evolutionary models and process perspectives that emphasize disruption, reversals, and contextual complexity. This thesis develops the Modular Non Linear Technology and Innovation Diffusion Model as an integrated analytical architecture that reconciles structural regularity with real world complexity.
The model consists of two tightly coupled components. The trajectory model is descriptive and answers how technology and innovation diffuse. It conceptualizes diffusion as an eight phase modular system consisting of Invention or Discovery, Innovation, Pilot, Adaptation, Acceleration, Stabilization, Decline, and Legacy. Phases are defined by functional purpose and empirically interpretable entry and exit conditions. By treating phases as modular states rather than as a fixed chronological sequence, diffusion histories are reconstructed as transition sequences instead of a single canonical path. This enables systematic representation of skipping, looping, regression, compression, overlap, and parallel progression within a bounded transition space. The navigation framework is explanatory and answers why diffusion unfolds along a specific pathway. It identifies and categorizes internal and external drivers and links them to transition sequences through a structured driver transition matrix. This framework clarifies how interacting technological, resource, market, institutional, organizational, and infrastructural conditions shape feasible pathways and constrain alternative trajectories.
To operationalize the model, a methodological framework was developed that integrates criteria driven phase reconstruction, transition space mapping, driver typology construction, and structured case validation. The architecture was subjected to breadth oriented micro validation across more than forty historical innovation and technology cases and depth validation through two longitudinal illustrative cases, namely mRNA COVID 19 vaccines and passenger airplanes. Across cases, diffusion histories mapped coherently onto the modular phase architecture without imposing artificial linearity. The bounded transition grammar proved sufficiently flexible to represent diverse diffusion patterns while preserving analytical comparability. Driver mappings provided systematic explanatory depth for pathway divergence, acceleration, regression, and stabilization.
The thesis contributes an integrated descriptive and explanatory language for analyzing innovation diffusion, enabling cross case comparison, cumulative theory building, and more structured reasoning for managerial and policy decision making.

Indonesia is facing a severe plastic waste crisis, generating nearly 9 million tons of plastic annually, of which only 11% is recycled. Sachet packaging alone contributes 16% of the country’s plastic waste, much of which leaks into rivers and oceans. Despite its convenience and affordability, plastic has become the dominant packaging format due to its protective qualities, low weight, and marketing advantages. However, recycling, which has been highly praised as the one of the best solutions to the plastic crisis, has proven largely ineffective in particularly for small multi-layered packaging. This creates an urgent need for alternative systems. Reusable Packaging Systems (RPS), designed for repeated use, represent a promising contender to disrupt the dominance of disposable plastics in Indonesia’s fast-moving consumer goods (FMCG) industry. To explore the potential adoption of RPS, this study investigated the factors influencing its implementation from the perspective of FMCG firms in Indonesia. A qualitative, three-stage methodology was applied:
• A systematic literature review (PRISMA framework) identifying 115 adoption factors across peer-reviewed journals;
• Two rounds of expert interviews with six industry practitioners in packaging, operations, and corporate affairs from both multinational and national FMCG firms operating in Indonesia, resulting in the identification of 48 factors. The integration of literature and industry insights resulted in the incorporation of 153 adoption sub-factors into eight adoption factor categories
• Multi-Criteria Decision Analysis (MCDA), which specifically employed the Best-Worst Method (BWM) to rank the consolidated factors.

The analysis revealed that Customer Social & Behavioral Drivers (CSBD) ranked as the most critical factor (score 0.18), underscoring that consumer demand, price sensitivity, and convenience decisively shape firms’ willingness to adopt reusable systems. Coercive Pressure (CP), which scored 0.17 was identified as the second most important factor, with experts stressing the need for stronger regulatory enforcement, the role of NGO advocacy, and global headquarters mandates. Technology Quality
(0.14) followed, reflecting concerns about durability, hygiene, and safety of reusable packaging. Other factors, such as Expected Benefits & Usability (EBU) and Operational Feasibility & Applicability (OFA), were rated moderately (0.12 each), while Financial Viability (0.11), Normative Pressure (0.10) ranked lower. Lastly, Partner & Collaborative Networks (PCN) ranked the lowest with a score of 0.07.

These results highlight that while cost and logistics matter, consumer acceptance and regulatory frameworks are the true tipping points for adoption. Experts agreed that Indonesian consumers remain highly price-sensitive, with sachets favored for affordability, and lack widespread environmental awareness. Without regulatory enforcement and supportive incentives, FMCG firms are unlikely to do the transition to RPS voluntarily. Conversely, successful adoption depends on coordinated action between various stakeholders. Government must enforce bans or provide subsidies, firms must ensure durable and hygienic RPS design, NGOs and media must amplify awareness, and waste banks, retailers and logistics partners can provide operational support.

This thesis concludes that accelerating RPS adoption in Indonesia’s FMCG sector requires aligning consumer behavior change with regulation and quality assurance. The study contributes a decision-analytic framework for stakeholders that identifies the relative importance of adoption factors. For policymakers, it emphasizes the need for clear regulations and incentives, and for firms, it highlights the importance of consumer-driven strategies and product quality.

The emergence of 6G networks introduces novel use cases such as holographic communication and autonomous driving. To meet these ambitious requirements, 6G is expected to adopt decentralised architectures that enhance performance by bringing computation closer to the user. However, this shift also introduces new security challenges, as the increased distribution of network components makes it harder to detect threats, prevent unauthorised access, and respond effectively to vulnerabilities. In this context, Security-as-a-Service (SECaaS) presents a promising solution by allowing enterprises to outsource their security operations to specialised providers. Yet, while most existing research focuses on SECaaS within centralised cloud environments, little is known about how it must evolve to address the unique demands of decentralised 6G networks. This thesis investigates how network decentralisation influences value co-creation among SECaaS stakeholders and explores its impact on the antecedents of customer trust. Drawing on ten qualitative interviews with experts from the telecom, infrastructure, and security sectors, the study applies the Value Network Configuration framework and the Integrative Model of Organisational Trust.

Findings reveal that decentralised 6G networks enable more distributed value co-creation, potentially even independent of the Mobile Network Operator. However, these deployments require enhanced stakeholder coordination to ensure secure service delivery meets customer expectations. While decentralisation does not significantly alter the core antecedents of customer trust, it can indirectly enhance relational trust through greater proximity, customer intimacy and contextual understanding. The study outlines key responsibilities across four stakeholder groups, identifies emerging trust-related factors such as regulatory compliance and geopolitical influences, and offers strategic recommendations for firms developing viable SECaaS business models in next-generation mobile networks.

The increasing demand for Electrical and Electronic Equipment (EEE) and the subsequent rise in e-waste has become a pressing environmental concern. Traditional linear economic models in the EEE manufacturing sector, which emphasize a "take-make-dispose" approach, are not sustainable and contribute to significant environmental degradation. This thesis explores the integration of circular economy (CE) principles with digital twin technology to address the environmental challenges posed by the EEE sector. Through an extensive literature review and expert interviews, this research develops and validates a framework for integrating digital twins into CBMs, aiming to enhance resource efficiency and reduce waste across the product lifecycle. While promising, challenges related to data governance and organizational alignment remain. The Digital Twin Nexus Circular Business Models framework offers strategies to improve sustainability in the EEE sector, aiding manufacturers in transitioning from linear to circular business practices.

The 21st century has seen a significant rise in clean technologies driven by urgent environmental challenges alongside transformative digital innovations shaping global interactions. Despite these significant events, cleantech startups face substantial challenges and missed opportunities in effectively integrating digital technologies into their sustainable business strategies. To fully harness the potential of digital innovations, addressing the trade-offs and synergies cleantech startups encountered between digital and sustainable strategic practices is crucial, as is understanding the underlying causes of their strategic choices. Despite the rising interest of academia in sustainable and digital entrepreneurship, academic research fails to identify these trade-offs and synergies within cleantech startups.
As a result, the main objective of this research is to explore and identify trade-offs and synergies that arise when entrepreneurs follow a sustainable and digital strategy simultaneously within cleantech startups and how they are influenced by their stakeholders, resources, and external environment. By examining the influences of resources, stakeholders, and external pressures, the research seeks to understand entrepreneurs' strategic decision-making process and the implementation of digital technologies within a complex environment. The main research question is defined as follows:

“How do Dutch cleantech startups navigate the synergies and trade-offs between digital and sustainable practices, and what are the underlying dynamics shaping these interactions?”

A systematic literature review mapped the existing academic perspectives on the strategic practices of Dutch cleantech startups, revealing the research gaps. The study employs the resource-based view theory and is complemented by the institutional theory as the foundation of the research. The research uses the concepts of digital and sustainability strategic orientation to explain how their practices are influenced and define the resources and external incentives as moderators. Semi-structured interviews were conducted with nine Dutch cleantech startups in various industries, such as agriculture, shipping and renewable energies. A qualitative content analysis was conducted to explore the trade-offs and ynergies, resources and incentives, followed by a stakeholders analysis, interview comparison and relational analysis to research the underlying dynamics. The findings show that the startups navigate complex trade-offs between sustainable and digital practices, balancing investment in digital tools for profitability and scalability with commitments to sustainable R&D.
Pressures from financial investors prioritise digital scaling and profitability over sustainable impact. Startups prioritise external sustainability impacts over internal sustainable practices and technical talent acquisition over workforce equality. Synergies emerge from leveraging technologies like AI, machine learning, dashboards, and blockchain to enhance product quality, validation, credibility, operational efficiency, and regulatory compliance, supported by collaborations with universities and incubators. Stricter sustainable regulations secure funding and stimulate market demand, facilitating the integration of digital and sustainable strategies. Moderating dynamics are financial resources, influencing the balance between profit-driven practices and sustainable investments. Technical team knowledge enhances digital capabilities, and social networks with incubators and R&D institutions foster collaboration synergies despite challenges in data exchange due to limited digital infrastructure. Normative pressures from customers' expectations and coercive pressures from investors and regulators further shape strategic priorities, emphasising compliance with technical standards. Further research is recommended to evaluate the impact of various digital technologies in enhancing synergies and trade-offs. Furthermore, the direct relationship between resources and external incentives warrants further exploration to understand their influence on strategic practices. Additionally, addressing investor influence, including distinctions between impact investors and traditional venture capitalists, could be helpful. Broader studies with diverse samples from different regions and countries and longitudinal studies are recommended to explore the long-term effects of digital and sustainable orientations and practices.

In today’s climate of the increasing use of fake news for which deepfakes may be used, and their widespread distribution through social media, the challenge of distinguishing reality from manipulation is growing. The development of deepfake detection technologies (DFDTs) is therefore crucial. The number of deepfake detection companies (DFDCs) is growing rapidly, changing the competitive landscape. Literature review shows that little is known about strategic opportunities for DFDCs. Therefore, the aim of this research is to provide business strategies, for DFDCs in the emerging DFDT market, in order to maintain or gain a competitive advantage. The knowledge gap lies in the fact that research is done into business strategies for emerging technologies, however, not specifically for DFDTs. To generate concrete and valuable results, the focus is on the Netherlands...

90% out of all startups fail, which is the cause of several reasons like lack of funds, lack of market need and bad management among other things. Digitalization can help startups to solve these problems, by performing processes more efficient than when they are performed manually.

The objective of this research is to build a model to estimate the average influence of digitalization on the success of a startup in all phases during their lifecycle among other established factors, according to their own input. Moreover, this research will investigate the current use of digitalization at industrial startups and how digitalization can help industrial startups to accelerate their innovations. Additionally, this will result in a few examples of how digitalization is used today at startups and a number of recommendations for further research.

This research focuses on industrial startups that are located in the Netherlands, because the Netherlands is a leading high tech country with a world class technical university and science hub and for the reason that similar research has been done in several other countries, only no research has been found on the impact of digitalization (on industrial startups) in the Netherlands. This research will answer the following main research question and sub-questions:

Main RQ: How can digitalization help industrial startups to accelerate their innovations?

SQ1. What are the obstacles that industrial startups in the Netherlands run into during the startup and transition phase?

SQ2. How do startups evaluate their digitalization strategy?

Employees from eight startups have been interviewed during qualitative exploratory expert interviews. These eight startups are divided in two groups. The first group will entail five startups that are currently in the early stage startup phase and the second group will entail three startups that are currently in the scale up phase. During the analysis of the data, the startups (and their data) in the first group are compared with each other. After this, the startups (and their data) in the second group are compared with each other and at last, (the startups in) both groups are compared with each other.

The different obstacles from startups resulted from different research methods. The obstacles that were found during a literature review are: a lack of funds, lack of market need, lack of experience, bad management, premature scaling and a strong competition. From the interview with the investment director of YES!Delft the following obstacles resulted: lack of long term vision, producing everything in-house, going to the market too late, not separating main and side issues & not clearing obstacles in the near future before they run into them. The startups came up with some similar obstacles, but also different ones, like finding (new) people, sales and/or customer acquisition, cybersecurity, lack of funds, big geographical distances, strict/heavy legislation, finding suitable (scalable) software programs, maintaining high quality standards, long negotiation times with customers and decisions of widening/narrowing the product portfolio.

All startups stated that digitalization is very important (one even called it a key success factor), however only three startups could give some kind of definition of what it is exactly and only two startups have a digital roadmap. Even though several startups stated that they would recommend to other startups to start as early as possible with digitalization, they all stated that digitalization is the least important in the first two phases of a startup compared to the last two phases.

The examples of applications that startups mentioned, range from the more simple examples like online meetings and 3D modelling software, to the more advance examples like an ERP system, MES system, machine learning models and newly created API’s. With the help of these applications of digitalization, startups can save time and money in the long run.

During this research it became clear that digitalization can accelerate the innovations of industrial startups, but it is not the most important factor and cannot carry a startup on its own. Digitalization is a tool to get somewhere and not a goal on itself.

The contribution of this research to the literature is a conceptual model that has been used during this research to measure the influence of digitalization (among other variables) on the success of industrial startups in the Netherlands. The practical contribution of this research for startups is to create awareness among startups about the influence of digitalization, the fact that startups can read about the obstacles that they could encounter and some possible solutions for these obstacles as well. Companies that offer applications of digitalization can use this research as orientation for the creation of tailormade digitalization solutions for startups.

Recommendations for further research are: to dive deeper into the phenomenon digitalization, to investigate why some startups say that digitalization needs to be used early, but then contradict themselves with filling in the conceptual model, the influence of digitalization at startups in other sectors and the influence of digitalization among larger corporations.