The COVID-19 pandemic highlighted vulnerabilities in global healthcare systems, emphasizing the need for innovation to enhance resilience, particularly in response to outbreaks and demographic changes like an ageing population. Many of these innovations originate from university research and are brought to market by health-tech spin-offs, creating significant societal value. In the Netherlands, academic entrepreneurial ecosystems play a crucial role in supporting these spin-offs, particularly in their early stages, through various facilitators. However, challenges unique to health-tech spin-offs, such as clinical regulations, often hinder their growth. Despite current efforts, facilitators lack comprehensive knowledge about the effectiveness of their support, leading to gaps that need addressing. Many publications exist about academic entrepreneurial frameworks however there remains a lack of knowledge about their applicability to Dutch academic entrepreneurial ecosystems. This study investigates how the three Dutch academic entrepreneurial ecosystems (Delft University of Technology, Leiden University Medical Centre and Erasmus Medical Centre) can design their academic entrepreneurial support systems to aid health-tech spin-offs effectively. Through a combination of literature analysis and interviews with facilitators and founders, the study identifies mismatches in support, including the need for tailored health-tech programs, clinical validation support, and efficient networking. It also explores the specialized support areas within Dutch ecosystems, such as TU Delft's focus on high-tech inventions, EMC's emphasis on medical devices, and LUMC's concentration on pharmaceutical spin-offs. The findings suggest that while each ecosystem has its strengths, there is a need for more targeted and effective support structures to meet the specific needs of health-tech spin-offs. The study offers practical implications for both facilitators and founders, providing a clearer understanding of how to optimize support within these ecosystems. Additionally, it contributes to the academic literature by offering insights into the unique challenges faced by health-tech spin-offs and the role of academic entrepreneurial ecosystems in addressing these challenges. Future research is recommended to further explore the effectiveness of tailored support solutions and the role of national policies in fostering the growth of health-tech spin-offs.

Background Issues:
Healthcare is essential for individuals and society, but faces challenges such as diseases, aging populations, financial burdens, and fair distribution of services. Regenerative medicine, which aims to replace or regenerate human cells, tissues, or organs, offers promising solutions but encounters difficulties in transitioning from research to clinical practice. This transition requires societal changes including political, regulatory, and public acceptance.

Regenerative Medicine in Japan:
Japan’s healthcare system is strained by an aging population and other health issues. To address this, Japan has implemented unique regulations over the past decade to promote regenerative medicine. These regulations allow hospitals to provide regenerative treatments without extensive scientific assessments and enable pharmaceutical companies to market products with conditional approval before full scientific validation. Although these measures aim to expedite access and industry growth, they also raise concerns about safety and standardization.

Research Question:
This research investigates how ethical considerations can be incorporated into the translation of regenerative medicine in Japan. Using an interdisciplinary approach, the study examines key processes and actors involved, ethical issues, and potential solutions through literature review, actor analysis, thematic analysis, conceptual synthesis, and an online questionnaire with supplementary interviews.

Results and Conclusion:
Key processes and influential actors in the translation of regenerative medicine in Japan were identified, including the Ministry of Health, Labour and Welfare (MHLW), the Ministry of Education, Culture, Sports, Science and Technology (MEXT), large hospitals, and universities/national research institutions. Current and potential ethical issues were explored, with suggested approaches derived from literature and comparative analysis with other countries.

Recommendations include:

MHLW should clearly define terms like “unproven,” “innovative,” “research,” and “treatment.”
Hospitals should refine their practices and guidelines, collaborating with MHLW and MEXT.
MHLW and MEXT should gather feedback from practitioners to update ethical guidelines.
Doctors and researchers should contribute to policy-making and practical guideline reflections.
Public discussion should be encouraged to gain societal acceptance and contribute to policy formation.

The research proposes a framework for incorporating ethical considerations into regenerative medicine in Japan, offering insights for policy refinement and further research in emerging technologies both in Japan and globally.

Neonatal care in the Netherlands is under significant strain due to severe bed capacity and staffing shortages, particularly in the southwestern region, where 36% of hospitals have closed neonatal beds. The current neonatal care system, structured into three escalating levels of specialization—neonatal medium care, high care, and Neonatal Intensive Care Units (NICUs)—struggles to meet demand, resulting in high occupancy rates and frequent patient transfers. This study developed a discrete-event simulation model to analyze and address bed capacity shortages within these staffing limitations. The model, based on regional perinatal birth data, evaluated the impact of various interventions on bed occupancy and transfer rates across different care levels.

Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints.

In recent years, there has been a significant increase in the number of depression cases. However, only 9% of depression patients in Indonesia receive appropriate treatment. The rapid advancement of technology highlights the potential for telemedicine to close this gap. Unfortunately, the adoption of telemedicine in Indonesia remains challenging, with a small amount of the population having used this technology.

Several factors impact the adoption of telemedicine among its users. The adoption of this technology, including by healthcare professionals, will depend on these factors. To understand these factors, the adoption of telemedicine for depression care among healthcare professionals in Indonesia is explored in this study.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is employed as the theoretical foundation. Performance expectancy and effort expectancy are used to find the answer to the main research question. Performance expectancy refers to the degree to which one expects a system to help them raise their job performance, while effort expectancy refers to the degree to which a system is easy to use. Eleven interviewees were interviewed for this research, of whom eight are psychologists, two are psychiatrists, and one is an academic.

In terms of performance expectancy, there are nine key factors that could contribute to the adoption of telemedicine for depression care. In terms of effort expectancy, it was found that five key factors influence healthcare professionals’ perceived ease of use towards telemedicine. As a result, it was found through deeper analysis that education and technology are two additional contributors to the UTAUT framework in telemedicine for depression care. Additionally, the findings show that current regulations are still inadequate to provide comprehensive rules related to telemedicine practices in Indonesia.

This research has several limitations. It focuses exclusively on depression care in Indonesia as a country and not specific to certain locations. Bias can arise from the small number of interviewees in this research. Future research should consider the location, gender, age, and experience of interviewees to provide more comprehensive results. Another research can be employed to ensure the validity and generalization of the suggested framework in similar cases.

The implications of this research are intended for policymakers, academic institutions, and technology providers. To ensure the proper functioning of telemedicine, policymakers supported by academic institutions should incorporate telemedicine courses into the curriculum and establish formal regulations of telemedicine in the country. For technology providers, they must ensure that telemedicine functionalities meet the needs of healthcare professionals in order for them to perform their duties.

Telemedicine has the potential to significantly improve healthcare delivery in low-resource settings (LRS), yet its adoption remains low despite its benefits. This study explores the organizational factors of telemedicine adoption in LRS using qualitative research methods and adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The research identifies key barriers and facilitators, and a modified framework is proposed to better address the adoption dynamics. The study provides valuable insights into how telemedicine adoption can be examined among healthcare professionals in LRS, highlighting the need for further validation of the proposed framework.

During the COVID-19 pandemic, many elective surgeries had to be rescheduled as resources suchas beds and ventilators were reallocated, causing significant delays in patient care. To avoid such disruptions in the future effective resource management and planning approaches for elective surgery are essential. Optimisation is a commonly used method to enhance elective operating theatre scheduling, it is a technique used for mathematical modeling. It can integrate various criteria to maximise benefits and minimise costs within specified constraints. However, optimisation alone often falls short in addressing the complexities and required flexibility of operating theatre scheduling. This is where simulation can build upon these limitations. Simulation techniques can model system behaviour by replicating real-world processes, introduce uncertainty, and evaluate responses to different policies, helping to identify bottlenecks and improve system efficiency. Sequencing optimisation and simulation can test theoretically sound solutions under real-world uncertainties and complexities. This leads to the following research question:

“How can Discrete Event Simulation evaluate optimisation-generated Master Surgery Schedules for operating theatres?”

The research question focuses on a Master Surgery Schedule, which coordinates different surgical specialities, sharing OTs and pre- and post-surgery resources. While this approach increases resource utilisation, it also adds complexity by needing to accommodate the varying requirements of each speciality. The research aims to evaluate different Master Surgery Schedule using Discrete Event Simulation to contribute to the optimisation model.

Overall, the use of the Discrete Event Simulation model provided detailed insights into the
performance of various schedules. Testing these schedules under different types of uncertainty
demonstrated their robustness, as the behaviour remained consistent across scenarios. By
comparing the schedules side by side, the simulation model effectively evaluated their
effectiveness, ensuring the intended purposes were served and identifying areas for improvement.

The research specifically considers Discrete Event Simulation as this method can queue patients for different resources and have them move throughout the system based on decision rules. It is also a standard applied method for modelling healthcare systems. Both Discrete Event Simulation and optimisation are standard methods for improving OT scheduling. Even sequencing the methods has been proven to contribute to OT scheduling. However, applying this approach to a Master Surgery Schedule is new and introduces additional complexity related to the shared OTs and pre- and postsurgery resources.

To explore this research question, a case study was conducted at Sophia Children’s Hospital in
Rotterdam. Previous research developed the Master Surgery Schedule using an optimization
model, resulting in four different schedules. These schedules either balanced ward leveling and operating theatre utilization equally or prioritized ward leveling. They also varied in computational requirements, as the model updated the bed availability either every 15 minutes or every hour. This research categorizes 18 different surgical departments into 50 groups based on their surgery duration and length of stay, assigning time slots to these groups in the schedules.

This research uses these groupings to fit different distributions for the input variables of length of stay and surgery duration. Five different types of distributions were tested for each group, and the best-fitting distribution was assigned. Additionally, previous research provides a schedule for ward capacity and establishes decision rules for patient management. In collaboration with the hospital, the probability of Intensive Care assignment and the various decision rules were validated.

The sensitivity analysis revealed a slight overestimation of surgery duration compared to the test data, but this did not significantly impact outcomes. The model is more vulnerable to ward capacity and length of stay, which were then selected for scenario analysis. The schedule prioritising ward levelling and checking availability every 15 minutes outperformed others, effectively addressing ward unavailability, the primary bottleneck. However, increasing capacity improves the number of successful surgeries but also leads to more cancellations due to operating theatre unavailability and increased overtime occurrences. This highlighted a trade-off between operating theatre utilisation and other Key Performance Indicators.

The sensitivity analysis revealed a slight overestimation of surgery duration compared to the test data, but this did not significantly impact the overall outcomes. The model was found to be more sensitive to ward capacity and length of stay, which were then selected for scenario analysis. Among the different schedules, the one prioritizing ward leveling and checking bed availability every 15 minutes outperformed the others, effectively addressing ward unavailability, which was identified as the primary bottleneck. However, while increasing ward capacity improved the number of successful surgeries, it also resulted in more cancellations due to operating theatre unavailability and increased overtime occurrences. This finding highlighted a trade-off between operating theatre utilization and other Key Performance Indicators.

In line with the literature, the research reveals that striving for increased operating theatre
utilisation puts excessive pressure on other resources. Ward unavailability is identified as the main reason for surgery cancellations, indicating that wards require even greater focus. An increase in ward capacity had the best outcomes during the scenario analysis, showing that this is the biggest bottleneck in the system.

The simulation model identified new parameters for the optimisation model and highlighted
weaknesses in the system to be improved upon. Future research should explore ward capacity in greater detail and develop better methods for sharing resources across different operating theatres and wards to reduce the differences in utilisation. Additionally, further research could investigate other causes of surgery cancellations and refine the definitions of surgery groups

Amidst ongoing climate change, communities worldwide face increasing frequency and severity of floods, which cause extensive destruction and pose significant health threats. Post-flood effects include drowning, injuries, and both communicable and non-communicable diseases, varying by context. This study addresses a critical knowledge gap by exploring the complex relationship between flood disasters, healthcare systems, and communicable diseases. It aims to understand how socio-economic, institutional, and geographical factors interact with health outcomes following floods.

The study highlights vulnerabilities in countries like Peru, Sri Lanka, and Pakistan, where past floods have significantly impacted communicable disease incidence. It evaluates healthcare system resilience by analyzing vulnerability and resilience factors that determine a country’s capacity to manage health impacts from floods. Vulnerability is influenced by socio-economic conditions, public health factors, and behaviors, while resilience is shaped by healthcare system quality, institutional effectiveness, and individual capacities.

Quantitative methods, including exploratory data analysis and causal-comparative design, form the core of this study. Natural flood events are used to investigate cause-and-effect relationships, supported by a conceptual framework developed through desk studies and concept mapping. Systematic data collection and analysis explore the relationship between flood events, healthcare systems, and communicable diseases. Principal component analysis identifies key indicators linking health characteristics with increased communicable disease incidences post-flood.

Synthesizing insights from data analysis, the study proposes an empirically validated framework to guide effective disaster response strategies. Understanding flood impacts and enhancing community resilience are crucial for developing targeted mitigation measures. By identifying vulnerabilities and resilience factors in healthcare and emphasizing individual empowerment, this research contributes to holistic disaster preparedness and response approaches.

Data-driven health research, specifically the development of AI models, is hampered by poor data availability and associated administrative burdens, caused complex and fragmented data protection regulation. To reap the benefits of using high quality health data, while safeguarding data protection of patients, the synthetic data generation is seen as a promising privacy-enhancing technology to avoid the need for personal data sharing. Although synthetic data is widely discussed, research primarily focuses on a technical development and evaluation of data protection, leaving substantive matters on the use of synthetic data within its institutional context open. By combining legal and technical knowledge, this thesis aimed to bridge this gap, by analysing how synthetic data generation could enable health data sharing for research in a privacy-enhancing manner. Specifically, a design science research approach is followed to combine the requirements from the institutional environment, focusing on a use case with a Dutch healthcare provider and research institute, with scientific knowledge on synthetic data generation and data protection evaluations. The research objective was to design a framework that structures the data protection-related factors that influence the extent to which synthetic health data enables secondary use of health data for research. The identified barriers and drivers of synthetic data generation focus on the health data sharing process, the interplay between the legal definition of anonymisation and technical data protection evaluations, as well as data protection principles. Synthetic data can enable secondary use of health data for research, but, measures should be implemented in the various phases of synthetic data sharing to safeguard patients’ data protection. As policy opportunity, this thesis argues for narrower definition of personal data to support privacy-enhancing technologies such as synthetic data generation.

Hospitals are part of the vital and vulnerable infrastructure in the Netherlands. Failure of such infrastructure may have severe consequences on a national scale. The Dutch flood risk management approach aims to prepare hospitals for floods through spatial adaptation. However, the current policy does not prescribe concrete guidelines for consistent decision-making and implementation of flood strategies to increase flood preparedness of hospitals. The engagement of stakeholders in this process is also arbitrary.

The main research question is: “How can flood preparedness of hospitals in the Netherlands be assessed and improved?” The main aim of this research is to quantitatively assess the flood preparedness of hospitals to enable comparison between flood strategies and to make recommendations on which stakeholders should be engaged for the implementation of flood strategies and how this can be achieved.

Six different flood preparedness indicators were selected that together describe flood preparedness of hospitals. Based on these indicators, the impact of several flood scenarios on the Reinier de Graaf Gasthuis (RdGG) and Erasmus Medical Centre (EMC) was quantitatively assessed. The costs per flood strategy are highest for “shelter in place with additional measures”, followed by both “shelter in place without additional measures” and “preventive evacuation”. “Accept” is least expensive. The number of fatalities per flood strategy for the RdGG (0,2 to 2,1 fatalities) is much lower than for the EMC (18,8 to 195,3 fatalities). Furthermore, it was determined which stakeholders should be engaged for the implementation of each flood strategy. Experts who have experience with hospital floods were interviewed to obtain their view on how these stakeholders could be engaged.

In conclusion, essential hospital functions can be categorised based on the flood preparedness indicators and flood strategies can be quantitatively compared. For the RdGG and EMC, "shelter in place with additional measures" yield the least amount of fatalities and is therefore the "best" flood strategy. The Executive Board of a hospital, hospital personnel, water boards, municipalities and safety regions should be engaged to realise the implementation of flood strategies. Experts with lived experience recommended to engage stakeholders through legislation or creating awareness.

It is recommended to calibrate the number of fatalities based on historical flood events and to analyse the vulnerability of the regional healthcare system to supplier failure during floods.

The third version of the Green Deal Sustainable Healthcare 3.0 (GD3) in the Dutch elderly care sector represents a dynamic and shared governance model that promotes widespread participation and implementation of sustainability practices. The GD3 provides a blueprint for elderly care organisations to pursue sustainability goals, but its success depends on collaborative efforts supported by various tools and frameworks. This research explores how joining the GD3 facilitates the achievement of sustainability goals in elderly care organisations. Successful implementation of the GD3 can have far-reaching effects, of which inspiring other sectors to adopt similar collaborative approaches. However, its effectiveness depends on the performance of the participating organisations and their use of empowering tools such as the Milieuthermometer Zorg (MTZ). Interaction and cooperation between the diverse group of actors is essential to achieve the desired results, as the GD3 operates through a shared form of governance involving both bottom-up and governmental actors. Active participation, implementation of sustainable actions and maintaining trust and cooperation are crucial for the effectiveness of the GD3. Repeated experience with climate agreements such as the GD3 helps to build trust, reciprocity and reputation among participating actors. All of this is being explored in the context of other challenges specific to care for the elderly, such as staff shortages and an ageing population. Ultimately, the GD3 represents a valuable initiative within the health sector that provides a script for sustainability efforts, while allowing organisations the flexibility to adapt and take artistic license in achieving the intended outcomes. The aim is to present a dazzling sustainability show within three years, signalling a transformed system. Given the urgency of sustainability and the challenges posed by an ageing population, embracing GD3's collaborative and adaptive approach is essential for the future of healthcare, particularly in the context of elderly care in the Netherlands.

In Dutch primary care, there is a dilemma that needs to be solved: caregivers using different digital platform in information system (DPIS) have difficulties in transferring necessary medical information. Although DPIS, as an emerging technology, assists in the decision making, coordination and control that occurs in the organizational environment. Its lack of openness leads to difficulties in exchanging information data. Therefore, this study focuses on the architectural requirements that can facilitate the openness of DPIS for primary care in the Netherlands. A design science approach was used to answer this question. The study first clarifies the current situation using document analysis and literature review, then defines the factors influencing openness and the requirements of stakeholders regarding openness and identifies architectural requirements. Finally, a conceptual artifact was developed and the requirements were validated in discussion with domain experts. The results of the study suggest that a clear distinction should be made between open to share or open to use when defining the openness of an architecture, and in addition, we should focus on the development of standardized data exchange regulations.

In recent years, the medical world has seen rapid digitalisation. Digitalisation of healthcare and the opportunities of remote monitoring can help to keep the healthcare system affordable and accessible. A promising application of remote monitoring is the use of consumer-grade wearables for clinical care. However, vulnerabilities leave consumer-grade wearables susceptible to cybersecurity and privacy risks. When the wearables gain clinical care functionality, increased risks are problematic for the success of the technology. This research examined this problem by analysing the role of users in the cybersecurity and privacy environment and the impact of human factors on the cybersecurity and privacy system of medical wearables. Vulnerabilities and associated risks were identified with the help of notions from human-centric cybersecurity. To establish a user-centred approach to tackle these risks, the user needs and limitations were examined by performing a quantitative survey research. Based on the results of the survey and the human-centric components of user, usage, and usability, guidelines for the user-centred approach were formulated. These guidelines were subsequently linked with the challenges and attacker-oriented and user-oriented risks to establish recommendations for medical wearable providers. Providers can use these to steer the design of the cybersecurity and privacy system and the structuring of the system environment. The research showed that by taking into account the needs and limitations of users, the cybersecurity and privacy system design can be more effective in tackling user-oriented risks. The research concludes that a user-centred approach to cybersecurity and privacy can contribute to the successful use of consumer-grade wearables for clinical care purposes.

The Netherlands is experiencing a significant increase in its aging population. Multi morbidity and a rapidly growing shortage of healthcare personnel are putting pressure on the healthcare system, leading to capacity issues. The capacity problems of care have medical, safety, social, and economic consequences. Policy measures are needed to address these challenges and improve the transfer care system. Therefore, the research question is: On which factors should policy interventions by the Ministry of Health, Welfare, and Sport be aimed, to improve the performance of the Dutch transfer care system? This study uses qualitative System Dynamics (SD), a method that uses causal loop diagrams, to analyze the relationships between factors that influence the transfer care system performance, such as the demographic changes and workforce shortage. The goal is to find strategies and solutions for a sustainable healthcare system. A visual SD model is used to highlight the complexity of the transfer care system and to reveal inter dependencies, interactions, and feedback loops, aiming to understand system dynamics. A literature review, multiple interviews and case studies have revealed factors and interactions that are modeled in a causal loop diagram. This qualitative System Dynamics analysis revealed both unidirectional (+ or -) and different polarity (+/-) interactions/feedback loops between/among the indicators, which implicates that certain policy interventions (mentioned in Dutch policy notes and other publications) may not achieve the intended results. Based on the interactions found, the Ministry of Health, Welfare and Sport can start using a bundle of interventions aiming on reducing workload/number of admissions and simultaneously keeping availability of aftercare constant. Additionally, the Ministry can improve other indicators, when keeping workload constant. However, to determine how many additional interventions are needed to keep workload and availability aftercare constant and to determine the net total effect of interactions with different polarities, quantitative System Dynamics is needed. In addition, quantitative System Dynamics is needed to be able to prioritize policy interventions, because qualitative System Dynamics is not able to determine the net effect of feedback loops. However, this qualitative System Dynamics study provides a solid foundation with which researchers (and the Ministry of Health, Welfare and Sport) can proceed to construct a quantitative model.

In India the COVID-19 pandemic resulted in a nationwide lockdown from March 25, 2020 till the end of May 2020. During this time public and private transportation activities were limited, economic activities came to a standstill and healthcare resources were redistributed. India’s healthcare system faced problems prior to the COVID-19 pandemic such as insufficient availability, suboptimal healthcare services and high out-of-pocket expenditures. The exponential rise in patient care during the COVID-19 pandemic exacerbated existing problems in the Indian healthcare system while introducing new ones. Realizing optimal care during the health crisis became more challenging as the focus was on mitigating the spread of the virus. The absent of public transportation and the pressure on healthcare resources impacted the healthcare accessibility of different demographic groups. During this study we assessed how access to healthcare was influenced due to the policy interventions that were meant to mitigate the spread of COVID-19. The hypothesis is that healthcare access for different demographic groups was negatively impacted by the policies meant to curb the spread. In order to analyse the impact, we performed a case study in the state of Goa using the Network-based Health Accessibility Index Method (NHAIM). This gravitational model allows us to study the spatial distribution of healthcare resources in the state of Goa. Based on our findings we can conclude that healthcare access is unevenly distributed in the state of Goa. Furthermore, we concluded that there is no significant correlation between healthcare availability and the urban and rural mortality rate for the state of Goa. However, there is a significantly strong positive correlation between geographical healthcare access and the urban and rural mortality for the state of Goa.

After the start of the COVID19 pandemic, many countries decided to develop digital contact tracing applications to assist in contact tracing. This aimed to warn people who were potentially infected as early as possible and stop them from spreading the virus further and bringing down the total amount of new infections. Even though there was some proof of the potential effectiveness of applications such as these, they had never been deployed at scale before the pandemic and there was no standardised and shared vision on how to best implement these applications, meaning that countries that wanted to include digital contact tracing as part of their COVID19 strategies needed to design and develop an application themselves...

Technology-based startups entail creating and developing sustainable value by capitalizing and commercializing new technologies, which accelerates the growth of the startups and promotes economic sustainability. Unlike new technology developed in commercial firms, technology-based startups go through a process of transformation from a non-commercial to a commercial environment. In this process, they may lack knowledge, network, or resources and face different critical junctures. In recent years, emerging startups have been confronted with difficult changes in their business environments. The life spans of the business models are drastically shortened by technological advancements and shifting patterns of client demand. Companies must continuously examine their established routines and procedures if they want to survive and grow in such unsettling environments. Therefore, in order to succeed, the technology-based startup may require market testing and business model adaptation.
By combining insights from the literature, this paper presents a comprehensive framework that conceptualizes business model dynamics. The framework includes different origins of business model change and different types of business model change as well as the growth stages and junctures. Following the conceptual framework's construction, the effectiveness of the framework is evaluated using explorative case studies of six Dutch technology-based startups.
The results indicate that all startups changed their business model often in the credibility juncture. In the early stages of development, the changes are often forced and caused by external factors, and when the startup matures, it has more freedom to make strategic decisions, particularly in credibility and sustainability junctures. Startups with lower market, managerial and entrepreneurial knowledge changed their business model frequently in a response to challenges, like resource constraints, while startups that were able to acquire knowledge were able to grow faster, access resources, and seize opportunities effectively. Finally, first movers to a specific market confronted technological challenges and changed their business model more often than other startups.