Based on the key insights we have identified some managerial implications. First, managers should not choose between vertical and shared leadership, but combine them. They need to maintain a clear strategic direction while encouraging collaboration and allowing team members to take the lead based on their expertise, in order to balance coordination and flexibility. Second, leadership should be adapted to the stage of the startup. In early stages, a more vertical approach is needed. However, as the organization grows, shared leadership becomes essential to manage challenges. Managers should therefore progressively delegate responsibilities and involve more team members in decision-making. Finally, managers and policy makers should focus on building collaborative teams rather than relying on a single leader. This means recruiting individuals who can work effectively in teams but also investing in team development to strengthen shared leadership and overall performance.

After analysing the key insights and their implications, we identified certain limitations. First, shared leadership is not optimal in crisis situations. In such situations, organizations must respond as quickly as possible to mitigate risks, but the collaboration and consensus-building processes inherent in shared leadership slow down the speed at which these situations must be addressed. Second, team members in shared leadership tend to be either too similar or too different. Teams that are too homogeneous may lack critical thinking and fall into tunnel vision, while teams made up of members from very different backgrounds, and who therefore have different working styles, will take longer to find an ideal way to collaborate and understand each other. Finally, shared leadership can lead to situations of collaborative overload, where team members spend more time discussing what tasks to do and how to do them rather than actually carrying them out. Furthermore, some people are naturally more inclined to take the lead, while others prefer to follow, which can lead to situations of overwork and even burnout for some individuals. 

Recent research (Zaghmout and Harrison, 2025; Hensellek et al., 2023) confirm that shared leadership is a good predictor for startups. It additionally shows that it enhances cohesion, innovation, and performance, but its effectiveness depends on context, sector, and organizational characteristics.

– Zaghmout, B., & Harrison, C. (2025). Distributed leadership: a systematic literature review. Strategy and Leadership, 53(3), 299–320. https://doi.org/10.1108/sl-10-2024-0119
– Hensellek, S., Kleine-Stegemann, L., & Kollmann, T. (2023). Entrepreneurial leadership, strategic flexibility, and venture performance: Does founders’ span of control matter? Journal of Business Research, 157, 113544. https://doi.org/10.1016/j.jbusres.2022.113544

Main article: Gartner, W. B. (1985). A conceptual framework for describing the phenomenon of new venture creation. Academy of Management Review, 10(4), 696–706. https://www.jstor.org/stable/258039
Additional articles: Carland, J. W., Hoy, F., Boulton, W. R., & Carland, J. A. C. (1984). Differentiating entrepreneurs from small business owners: A conceptualization. Academy of Management Review, 9(2), 354–359. https://doi.org/10.2307/258448
Ardichvili, A., Cardozo, R., & Ray, S. (2003). A theory of entrepreneurial opportunity identification and development. Journal of Business Venturing, 18(1), 105–123. https://doi.org/10.1016/S0883-9026(01)00068-4

Article : Brockhaus Sr, R. H. (1980). Risk taking propensity of entrepreneurs. Academy of Management Journal, 23(3), 509-520. https://doi.org/10.2307/255515

This article questions the long-standing myth that entrepreneurs possess a higher propensity for risk. It demonstrates that there is no significant difference, from a statistical point of view, between entrepreneurs and managers. The author analysed new ventures in order to avoid the survivorship bias and demonstrated that entrepreneurs’ risk profiles are similar to those of the general population. It also highlighted that most of the population shows a moderate appetite for risk. At the end of the day, the author concludes that risk-taking behavior is a universal human trait rather than a specific characteristic of entrepreneurship, refuting earlier literature that relied on more subjective methods and lacked comparative control groups.
The main lesson to take away is that risk-taking is not the defining characteristic of entrepreneurship, and relying on this belief leads to important misjudgments at different levels. In corporate recruitment, companies looking for intrapreneurs should not focus primarily on candidates perceived as risk-takers, but rather on qualities such as creativity, opportunity recognition, and perseverance in uncertain contexts. In terms of public policy, programmes designed to support entrepreneurship are often flawed because they replicate the profile of successful entrepreneurs rather than identifying potential ones; selecting beneficiaries based on traits such as high risk tolerance may exclude promising individuals while not guaranteeing success for those selected. Finally, entrepreneurial mythology plays a significant role in discouraging capable people, as the image of the entrepreneur as someone who “takes all the risks” creates a misleading standard. As a result, individuals who do not see themselves as bold risk-takers may mistakenly believe they are not suited for entrepreneurship, ultimately leading to the loss of untapped potential in society.
Brockhaus’ conclusions present several limitations. First, while he rejects risk-taking as a distinguishing trait, he does not provide alternative criteria, offering little practical guidance for recruitment or identifying entrepreneurial profiles. Second, the study is based on a small sample in a specific 1975 U.S. context, limiting its applicability to modern, more uncertain environments such as high-tech industries. Finally, by measuring risk-taking only at the entry stage, the study ignores how entrepreneurs may evolve over time, meaning that higher risk tolerance could still characterize experienced entrepreneurs.
To complement Brockhaus (1980), modern papers refined the link between risk and entrepreneurship. Arteaga-Fonseca et al. (2024) show that risk is a heterogeneous concept (financial, behavioral, perceptual), explaining why general measures fail to distinguish entrepreneurs. Meanwhile, Kraft et al. (2022) highlight overconfidence as a key trait: it encourages venture creation but can harm performance. Together, these studies suggest that entrepreneurs are not defined by risk tolerance, but by how they manage uncertainty and evolve across stages.
Arteaga-Fonseca, J., Rutherford, M. W., Phillips, D., & Hill, A. D. (2024). What is risk, exactly? Reviewing construct heterogeneity across business fields and implications for entrepreneurship research. Journal of Management, 51(1). https://doi.org/10.1177/01492063241293129
Kraft, P. S., Günther, C., Kammerlander, N. H., & Lampe, J. (2022). Overconfidence and entrepreneurship: A meta-analysis of different types of overconfidence in the entrepreneurial process. Journal of Business Venturing, 37(4), 106207. https://doi.org/10.1016/j.jbusvent.2022.106207

From this, three clear managerial implications emerge.

1. Act early when you have strong complementary assets.
If the firm already possesses assets that naturally support the new business model (efficient supply chains, strong brands, loyal customers) speed matters. Early action allows the firm to leverage these assets before competitors catch up. Early entry also signals commitment to partners and internal stakeholders. The message is simple: when your existing strengths give you an edge, moving early amplifies their value.

2. Isolate the new business model when existing assets create friction.
Not all assets help. Some parts of the legacy organization (store formats, incentive systems, political structures) can hold back the new model. The paper shows that integrating the new model within the old one often leads to conflict and underperformance. The recommended solution is an Autonomous Business Unit (ABU). This provides the new model with its own leadership, culture, KPIs and freedom from old routines. When assets conflict, separation is not just useful, but necessary.

3. Diagnose your assets before choosing timing or structure.
Managers should not assume that “more resources” always help. The real challenge is to distinguish between complementary and conflicting assets. This diagnostic determines whether the firm should move fast or slow, integrate or separate. A good diagnosis prevents misalignment, such as entering too early with the wrong assets or isolating a model that actually needed support. Managers need to diagnose first, decide second.

At the same time, these implications have limits.

Acting early is not always beneficial, especially when the market is uncertain, when assets do not actually transfer to the new model or when the organization lacks the bandwidth for rapid expansion. Similarly, creating an ABU can introduce fragmentation or become symbolic rather than real if internal politics persist. Even the diagnostic step can be biased, outdated or overly simplistic, since many assets have both supportive and conflicting effects depending on the situation.

Follow-up readings reinforce these ideas. Sosna et al. (2010) highlight that incumbents often innovate through trial-and-error rather than precise planning, adapting their models gradually. McGrath (2010) presents business model innovation as a discovery-driven process where assumptions are tested and revised. Foss & Saebi (2017) provide a broader theoretical map, linking resources, capabilities, and performance to different types of business model change. Together, these works show that business model innovation is not a single strategic choice but an ongoing process of alignment between assets, timing, structure and learning.

Further References:
– Sosna, M., Trevinyo-Rodríguez, R. N. & Velamuri, S. R. (2010). Business Model Innovation through Trial-and-Error Learning: The Naturhouse Case. Long Range Planning, 43(2–3), 383–407 https://doi.org/10.1016/j.lrp.2010.02.003
– McGrath, R. G. (2010). Business Models: A Discovery-Driven Approach. Long Range Planning, 43(2–3), 247–261 https://doi.org/10.1016/j.lrp.2009.07.005
– Foss, N. J. & Saebi, T. (2017). Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go? Journal of Management, 43(1), 200–227 https://doi.org/10.1177/0149206316675927

The main implication of the study is that managers should focus on stabilizing project goals early, as goal changes are the strongest factor harming project efficiency and they trigger additional plan changes whose negative impact outweighs the benefits of good planning. Clear early goal definition, strong user involvement, and freezing requirements as soon as possible help prevent this instability. At the same time, managers must control external pressures such as staffing shortages, personnel turnover, parallel projects, and technological risks (e.g. building a product around a hardware component still in development), which often drive both goal and plan changes and disrupt performance. Ensuring stable staffing, limiting parallel workloads, and anticipating technological uncertainty reduces the likelihood of
such disruptions. As a final implication, the study notes that successful projects keep goals concrete and stable while allowing plans to stay flexible. Stable goals reduce confusion and rework, because everyone knows exactly what the project is aiming to achieve. Meanwhile, plans must remain adaptable so the team can adjust to challenges and changes in the project environment.

The paper has some limitations. Firstly, there are projects with strong legal constraints or norms of security where the objectives and requirements hardly change. Indeed, sometimes there are legal constraints that are difficult to overcome in order to make the change. Moreover it can be complicated to implement change, or even risky and therefore should be avoided. Then, the size of the company has an impact on its capability to choose whether or not it can implement change. SMEs or start-ups may be constrained by their investors and therefore are quite limited in terms of change. They can’t decide for themselves to make changes. Unlike large companies, which have the power to decide to implement changes. Finally, it is true that in general the fact of being able to adapt matters more than the initial plan. However, there are sectors, for example the automobile industry, where this adaptability has a limit because beyond a certain threshold changes cost more than they bring. A strategy, mentioned by the article, that can be put in place by these sectors is the « freeze gates » which means that after a certain date no more modifications can be made.

Recent research expands Dvir and Lechler’s (2008) framework by showing that the effects of plan changes are not uniform but depend strongly on contextual factors such as industry and project complexity (Carvalho et al., 2015). Meanwhile, the second article (Ika and Pinto, 2022) broadens the notion of project success by emphasizing stakeholder satisfaction, benefits realization, issues of timing and sustainability. Their findings suggest that plan changes are not necessarily failures but can be opportunities to generate value through flexible management.

References:

(Article) Carvalho, M. M. de, Patah, L. A., & de Souza Bido, D. (2015). Project management and its effects on project success: Cross‑country and cross‑industry comparisons. International Journal of Project Management, 33(7), 1509–1522.
https://www.sciencedirect.com/science/article/pii/S0263786315000733 

(Article) Dvir, D., & Lechler, T. (2004). Plans are nothing, changing plans is everything: the impact of changes on project success. Research Policy, 33(1), 1-15.

(Article) Ika, L. A., & Pinto, J. K. (2022). The “re‑meaning” of project success: Updating and recalibrating for a modern project management. International Journal of Project Management, 40(7), 835–848.
https://www.sciencedirect.com/science/article/pii/S0263786322000990

These modes are each connected to different types of knowledge : know-what, know-why, knowhow, and know-who. STI mainly uses and produces know-what and know-why, which can be acquired through reading or formal education. DUI, on the other hand, develops know-how and know-who, which are more established in practical experience and social relations. The central message is that the most innovative companies are those who manage to combine the two
modes of innovation. 

From this paper, there are several managerial implications that managers should focus on.

First, managers should adopt a dualistic strategy (STI + DUI) tailored to the sector and type of work. Tailoring the dual strategy to the type of work ensures that innovation processes are both systematic and grounded in practical experience, maximizing the organization’s capacity to adapt in a complex and uncertain environment. However, this implies that they must address the tensions between STI and DUI before their combination. Therefore, they have to make sure that coordination mechanisms and knowledge-management practices align with both approaches, as resolving these conflicts maximizes organizational adaptability and innovation performance.

Moreover, they should create an environment that promotes knowledge exchange, as the four types of knowledge are acquired through different channels. Indeed, as tacit knowledge requires prior skills and social interaction, practices like job rotation, cross-functional teams, and collaboration with external stakeholders help employees integrate it and support innovation grounded in expertise and practical experience. 

The STI/DUI framework has key limitations, especially for digital innovation. It assumes stable knowledge bases, while digital products like AI systems and apps evolve rapidly, update continuously, and often develop outside traditional R&D. Because digital innovation depends on fast iteration and real-time data, the model cannot fully capture how these processes work. Managers should therefore treat STI/DUI as a baseline and complement it with agile, data-driven
capabilities.

Furthermore, industries such as construction, trade, and basic services often lack internal R&D making it unrealistic to adopt STI and DUI. These firms fall into the paper’s “Low Learning” category, meaning they first need to build fundamental learning or basic R&D partnerships. Managers in these sectors should approach the combination of STI and DUI as a long-term goal rather than an immediate strategy.

Finally, some industries operate under strict regulatory and documentation requirements. Pharmaceuticals, aerospace, medical devices, and fintech require highly codified, traceable, and stable processes, which reduces the usefulness of informal, experience-based DUI learning. In such environments, managers should prioritize STI routines, documentation, and standardisation, since these are essential for compliance and legal certainty.

Additional research helps contextualize these findings. Parrilli et al. show that the effectiveness of STI, DUI, or combined modes varies across regions depending on local capabilities, while HervasOliver et al. highlight that SMEs in catching-up countries tend to rely on DUI due to limited R&D resources, making the combined mode harder to implement. Together, these studies underline that innovation strategies must be adapted to the firm’s environment, capabilities, and institutional
context.

References : 

(Article) Hervas-Oliver, J.-L., Parrilli, M. D., & Sempere-Ripoll, F. (2021). SME modes of innovation in European catching-up countries: The impact of STI and DUI drivers on technological innovation. Technological Forecasting and Social Change, 173, 121105. https://www.sciencedirect.com/science/article/pii/S0040162521006004 

(Article) Jensen, M.B., Johnson, B., Lorenz, E., & Lundvall, B.A. (2007). Forms of knowledge and modes of innovation. Research Policy, 36, 680-693. https://www.researchgate.net/publication/222526843_Forms_of_Knowledge_and_Modes_of_Innovation 

(Article) Parrilli, M. D., Balavac, M., & Radicic, D. (2020). Business innovation modes and their impact on innovation outputs: Regional variations and the nature of innovation across EU regions. Research Policy, 49(8), 104047. https://www.sciencedirect.com/science/article/pii/S004873332030125

These insights lead to clear managerial takeaways.

1. Reduce causal ambiguity – Managers should make practices easier to understand by clarifying the “why” behind them, breaking them into essential steps, and providing examples.

2. They also need to treat knowledge transfer as an ongoing learning process rather than a one-off handover. Problems will appear along the way and follow-up support really matters.

3. Strengthen collaboration and trust between source and recipient – Building a strong relationship between the source and the recipient helps too, because trust and good communication make learning much easier.

4. Finally, managers should not over-focus on motivation; most failures happen because the recipient is confused or overwhelmed, not because they don’t want to adopt the practice.

At the same time, these recommendations have limits.
Some practices are simply hard to codify and not everything can be explained perfectly. Building strong relationships takes time, which is not always available. And if managers put too much effort into adapting practices during transfer, they risk losing consistency across the organization.

Follow-up research reinforces these ideas. Studies show that tacit or complex knowledge naturally increases ambiguity, that different stages of the transfer process require different forms of support, and that trust and diverse networks help knowledge flow more smoothly. Overall, the paper highlights that transferring best practices is mainly a learning journey: success depends on clarity, support, trust, and patience.

Further References (updated after the presentation on December 1st 2025):
– Ugur Uygur (2013) – Determinants of causal ambiguity and difficulty of knowledge transfer within the firm, Journal of Management & Organization, 19(6), 742-755.
→ Links transfer difficulty to knowledge complexity and tacitness.
– Szulanski, Ringov & Jensen (2016) – Overcoming stickiness: How the timing of knowledge transfer methods affects transfer difficulty, Organization Science, 27(2), 304-322.
→ Shows that adapting transfer timing reduces internal stickiness.
3. Reagans, R. & McEvily, B. (2003). “Network Structure and Knowledge Transfer: The Effects of Cohesion and Range.” Administrative Science Quarterly, 48(2), 240–267.
-> The article shows that knowledge is transferred inside a firm more easily when people have strong, trusting ties with their close colleagues and also have many diverse contacts across the organisation.