The use of Open Innovation is to a large extent related to the rise of technology. Not only does technology smoothen Open Innovation, also the adaption of new technologies to the core business (model) can be accelerated by participating in Open Innovation networks. In fact, when talking to businesses the questions that they have do almost never directly include the use of Open Innovation as a goal. It’s assumed a logical and necessary step to take when dealing with actual problems. Simply said, many innovation questions that companies have follow the simple pattern: ‘How can we [change something] in order to improve our [business model construct] in line with [trending topic]?’. These are as such practical, design-oriented questions, not about why [something] happens, or what is the effect of [something] but about how to change to adapt to that [something].

Given that, without having done a lot of research, I created a small game that you could use in workshop, team meetings, or whatsoever, to lead the discussion in the right way. The [change something] is about Open Innovation, the [trending topic] is about smart technologies. Why? Because I think that covers most of the current questions available. Below, you’ll find the result.

Do you like it? I’m looking for great alternative ‘wheels’ to create. Drop a suggestion and I’ll add them to this post. Looking for a more sophisticated game on Innovation & Enterpreneurship, take a look at the simulations of Innovative Dutch.

In 1990 Kurzweil instantly incubated the way we think about Artificial Intelligence (AI) with his work The Age of Intelligent Machines. While there is now, almost 30 years later, still a long road ahead of us, the technology readiness level of AI is getting significantly closer and many applications are trying to implement AIs state-of-the-art features and starting to accelerate the creation of the Smart Business: AI-enabled organizations that thrive digitally, are hyperconnected (both digitally and physically), use machine learning and cognitive techniques to work smarter and that are increasingly becoming autonomous organizations. In his 2016 book the Fourth Industrial Revolution Klaus Schwab mentions 6 basic technologies that are based on AI and currently impacting business: 1) the Internet of Things (IoT), 2) Autonomous Vehicles, 3) Advanced Robotics, 4) 3D-printing, 5) new materials and 6) the biological revolution.

But while AI may speak to our imagination, it is in fact one of the slowest developing fundamental technologies and its current state is still highly debatable. Last week, on October 11 and 12, over 2000 professionals in AI gathered in Amsterdam at the World Summit AI 2017 and discussed the state of Artificial Intelligence and Machine Learning. Among the keynotes were Google, Intel, NASA, ING, Airbus, Facebook, Booking.com, Facebook, IBM Watson, Amazon, Alibaba and Uber. Or as Ebay CPO RJ Pittman said: “The brains in this room are worth trillions of dollars.”

These are some of the most important insights I deducted from the different keynotes, workshops and many people I met.

1. Anno 2017, Artificial Intelligence equals Machine Learning.

While AI has been around quite long – its main incentive is to simulate human thinking and human behaviour – the current applications of AI mostly just focus on Machine Learning. And Machine Learning is, as one of the keynotes pointed out just a form of very advanced programming, not AI per se. Or to say it more clearly: “what we are currently experiencing is not ‘general AI’, it’s just a lot of machine learning on big data”. The screenshot below is from a presentation of Google and show where Google is currently working on when it comes to AI:

2. From Machine Learning to Cognitive Learning

The problem with Machine Learning is that it focuses on learning machines how to think or behave like humans in stable environments, with repetitive information. So it works well for for instance face recognition or speech analytics (faces and language don’t change). But this is not how humans think. We also know what to do and what to think in unique situations. This asks for a complete different approach to AI, one that is called Cognitive Learning: a dynamic approach to data analysis and ‘intelligence’. With Machine Learning companies would be able to automate (routine) processes, but with Cognitive Learning techniques companies would be able to autonomously create new ideas, new inventions and new innovations. Images from the presentation of Gayle Sheppard, Intel.

3. We are wrong about the objective of AI

Currently, almost all AI is preprogrammed to attain a certain goal. But that’s a huge problem. Because when AI works well, it will thus try ‘to keep alive’ in order to attain its goal. So if humans put on off-button on the machine, in order to be able to still control it, the machine’s first action will actually be to break the off-button, because it thinks that it will likely get in its way when it tries to achieve its preprogammed goal.

What we actually should try to do is to learn AI that it has only one goal: to maximize the realization of human values. In order do that, it needs to be much smarter. It needs to ask us what‘s right and what’s wrong. It needs to ask us what we want. It needs to learn from our behaviour (see point 2) in order to behave adequately.

But then again, the question raises to what extent it should maximize our own believes. Too less or too much will result in absurd situations, such as these examples:

4. Next steps in AI

According to Ronny Fehling, Airbus, there are a few epochs that describe the road ahead for AI. We’re currently at Epoch 1 (using historical and operational data) and trying to get ahold of Analytical data. But we’re far from the next steps: predictive algorithms for business, descriptive algorithms for business (Epoch 2) and explorative algorithms for business (Epoch 3, more on that later).

5. We need Open Data. But we won’t get Open Data.

In order to get historical data (see point 4) and actually be able to learn from that (see point 2), we are in hard need of open data. There are many written and digitalized datasets available, that we can use the analyze human behaviour to understand our values better (see point 3). But the problem is that personal data will be much harder to get, due to tough (and rightfully so!) legislation on data. Or as Miles of Google said: ‘other companies will never get access to our data. Our data is our business. We will decide what you can retrieve from our data (through machine learning) and how you can achieve the data (through APIs).

6. AI is as good as the data it’s built upon

Because the current state of AI is that it is mostly based upon Machine Learning, and ML only functions when it can analyze repetitive and structured information, we could argue that AI is only as good as the datasets that it uses. And every company that tries to get into AI should therefore first extensively organize and structure its datasets. Keynotes that got into this fact were Airbus and SwedBank who argued that without thorough data they would never have made steps into AI.

7. The gateway to AI are APIs

As argued in point 5, the current gateway to AI for most companies are APIs: Application Programming Interface; small pieces of software that understand the underlying machine learning algorithms. This way, complex machine learning algorithm will become available for software programmers around the world: they can build dedicated apps. These apps can be digital (chatbots, software, business intelligence, intellegent systems), but can also be phyical (advanced robotics, biotech, internet of things, autonomous vehicles, and so on).

8. The misuse of AI is cybercrime on steroid

As one of the keynote speakers, Parry Malm, mentioned during the conference: “The misuse of AI is a cybercrime problem on steroids. And at the moment things are not going too well with cybersecurity.” It is a known fact that, although with products like Intel Saffron’s Anti-Money Laundring algorithms, criminals are in many ways always a step ahead of regular businesses and misusing the possibilities of Artificial Intelligence to their own benefit. While AI is hot, cybersecurity is much less so, but should be on the top of each companies agenda. Check this [Cyber Trends Index] of Owlin, another company that presented itself at the conference.

9. AI isn’t gender-neutral, and that’s a serious problem

Thursday early morning, there was an interesting panel discussion about AI and gender neutrality. The discussion started off with a few good examples of the fact that most AI-driven applications developed for both professional and personal use are created and developed for males rather than females. But the discussion then took another approach and discussed a much more serious problem: as a result of the fact that in the 80’s and 90’s earliest (game) consoles and PC’s were mostly branded as ‘toys for boys’, an unproportionate amount of males started studying computer sciences. A shift towards a ‘male industry’ started to happen and is still going on. Take the example of this conference, an approximate estimate would say that 95% of visitors are male. And that causes a serious, and ethical, problem – because Artificial Intelligence is not gender-neutral. And with artificial general intelligence in foresight, that would mean that a one-sided approach to intelligence could have an enormous impact on society – while a diverse approach would be much more needed. Interesting discussion: will the future artitificial intelligence be male or neutral?

10. AutoML and Autonomous AI

Many companies, amongst which Google, are taking steps on what they call AutoML, the machine learning on machine learning. In essence, that means that it won’t be necessary to create more machine learning algorithms, but that more intelligent algorithm learns about the effect of its own predecessors and automaticallys starts to create better algorithms. This technique, though still far off, could be a path the artificial general intelligence.

11. When artificial general intelligence starts supervising AI: singularity

As always, there was a lot of reference to technological singularity: the moment when artificial general intelligence becomes more sophisticated than human intelligence. General consensus is that we’re still far away from singularity, but that we’re getting closer. Some advanced robots already have the function of controling or supervising other robots. Though only in its own specialization, this is a form of singularity within that specialization.
But we’re much closer to singularity than you might think: in a keynote of NASA, one of their most recent projects is creating an interstellar space station, that will take dozens of years to reach its destination. Because communication will be extremely delayed, and humans won’t survive for such a long time, the ship will contain AI-driven robots that gather research data. But the fun fact is that these robots will have to sustain on itself for a long time, they will have to find new ways of doing research based on what they find, they will have to deal with unforeseen circumstances, and so – all without human intervention. Although interstellar, this is definitely a form of singularity. If they run into extraterrestrial life, what will they do? What decisions will they make? What will we think of those decisions when we finally hear about them years after?

All in all a very inspiring and interesting conference!

Introduction

Entrepreneurial thinking is described as one of the most relevant skills for the 21st-century workforce (Bacigalupo, Kampylis, Punie, & Brande, 2016). And for that reason it has become an integral criteria in many prescriptive regulations for (higher) education and in increasing numbers also explicitly and implicitly part of curricula (Saavedra & Opfer, 2012). As opposed to entrepreneurship, entrepreneurial thinking is not necessarily bound to entrepreneurs (to be); it is an essential skill for ‘strengthening human capital, employability and competitiveness’ (Bacigalupo et al., 2016).

This white paper has been originally published to the SSRN: Spruijt, Jan, Paradoxes of Entrepreneurial Thinking: Why Entrepreneurship Can Hardly Be Taught (May 17, 2017).

Entrepreneurship

However, the definition of ‘entrepreneurial thinking’ – and the different skills and competences that are related with it – is not obvious. Entrepreneurship has seen a theoretical divide that has existed since the Schumpeter vs. Kirzner debate started. Whereas Schumpeter describes an entrepreneur as disequilibrative – destroying the pre-existing stage of the equilibrium ((Kirzner, 1999) – Kirzner chooses to describe the role of the entrepreneur as more equilibrative – entrepreneurs systematically displace disruptive conditions in order to create stabilized market conditions (Kirzner, 1999). These two extremes – and everything in between – have been topic of discussion ever since. In essence it is nowadays recognized as the difference between creativity (Schumpetarian) and alertness (Kirzner) – creation versus discovering.

From that perspective alertness is the discovery of hitherto overlooked current facts and the entrepreneur’s perception of the way in which those discoveries could shape the future market conditions (Kirzner, 1999) and creativity could best be described as the way entrepreneurs deal with radical uncertainty by building upon their qualities of boldness, innovativeness and creativity (Kirzner, 1999). In the Schumpetarian view, opportunities arise from the internal willingness to change the industry. The entrepreneur is an innovator and disturbs the economy (De Jong & Marsili, 2010; Schumpeter, 1934). In the Kirznerian view, opportunities are already existing and be discovered by opportunity-alert entrepreneurs. Research has shown that innovation is mostly linked to the Schumpetarian view: innovative companies are more likely to be started by Schumpetarian-type founders (Samuelsson & Davidsson, 2009), are more likely to be started by engineering students (Ilozor et al., 2006) and are more likely to be created by making new and unique combinations (S. A. Shane, 2003). In contrary, the Kirznerian view is more linked to an economic perspective: are entrepreneurs able to see where a good can be sold at a better price higher than that for which it can be bought (Busenitz, 1996).

When taking into account a wider definition of entrepreneurship, a more organizational perspective on the matter has described the same divide as causation versus effectuation. Whereas causation is more oriented at a managerial, Kirznerian, perspective on entrepreneurship, effectuation is oriented at a more experimenting, Schumpeterian, perspective on entrepreneurship (De Jong & Marsili, 2010). Many scholars have researched the impact of causation and effectuation on entrepreneurial outcomes. Generally, it can be concluded that entrepreneurship – in the meaning of creating new ventures – a right balance has to be found between the both extremes. Enterpreneurship is about finding the right mix between both causation and effectuation (Reymen et al., 2015). And a successful entrepreneur knows when to act causational and when to act effectuative. When to be creative, when to be managerial. They have researched the number of explicit decisions in new venture creation regarding both perspectives and created the following figure:

Figure 1: Percentage of effectuation and causation dimensions (Reymen et al., 2015).

Another study has indeed proven that specifically within SMEs, entrepreneurs use both causation and effectuation at the same time (Berends, Jelinek, Reymen, & Stultiëns, 2014). Quantitative studies show that entrepreneurs use effectuation logic in their early stages, which increasingly turned towards causation over time. Qualitative analysis shows that entrepreneurs actually use both logic at the same time, in contrast to the way larger organizations deal with innovation (in a more structured way). Therefore, entrepreneurs should not learn from large corporations’ best cases on (innovation) management, but learn entrepreneurial thinking in a more Schumpeterian way.

Entrepreneurial thinking

That brings us to entrepreneurial thinking. Entrepreneurial thinking is described as having an entrepreneurial expert mindset (Krueger, 2007). The difference between entrepreneurship and entrepreneurial thinking lies in the fact that entrepreneurship is about actions and intentions and entrepreneurial thinking is about attitude and beliefs. This is best described by Krueger: “Behind entrepreneurial action are entrepreneurial intentions. Behind entrepreneurial intentions are known entrepreneurial attitudes. Behind entrepreneurial attitudes are deep cognitive structures. Behind deep cognitive structures are deep beliefs.” (Krueger, 2007).

Simple logic leads to the fact that entrepreneurial thinking is more common than entrepreneurship. Entrepreneurial thinking focuses on the deep beliefs that lead to behavior that is positively related to entrepreneurial outcome. Based on the same logic as mentioned above, entrepreneurial thinking is prone to both causational thinking and effectuation-based thinking (Krueger, 2007). From a more psychological perspective, the same difference is described as a growth mindset (effectuation) versus a fixed mindset (causation) (Dweck, 2012). Dweck argues that – and that is specifically relevant for early stage entrepreneurs – a gowth mindset makes them more likely to embrace challenges, learn from criticism and dealing with setbacks.

Corporate entrepreneurship

Corporate entrepreneurship, and intrapreneurship, are direct effects of entrepreneurial thinking applied to organizations, and then more specifically organizational culture. Questions that come to mind when talking about corporate entrepreneurship are: are there resources available to explore new ideas? Are managers prepared to allow experimentation? Does the organization encourage risk-taking? Do they tolerate mistakes? Is it easy to create autonomous team and projects? Critical elements for an entrepreneurial climate are both causational (goals, rewards) and effectuation-based (feedback, reinforcement, trust) and are built upon stimulating entrepreneurial thinking in the organization (Kuratko, Hornsby, Naffziger, & Montagno, 1993).

Corporate entrepreneurship has been widely studied because it is believed that it directly leads to innovation and an organization is not able to sustain competiveness over time without renewal or regeneration (Covin, Green, & Slevin, 2006; Damanpour & Gopalakrishnan, 2001; S. Shane, Venkataraman, & MacMillan, 1995; Venkataraman, 2014). Current research focuses mainly on the creation of internal processes, innovation adoption, governance, and the knowledge, skills and attitudes of individuals (A. Corbett, Covin, O’Connor, & Tucci, 2013).

Entrepreneurship Competence

Because of the above-sketched complexity that arouses out of the ambidextrous nature of entrepreneurship, teaching entrepreneurship has always been prone to different views and methods. Most literature however, indeed suggests that teaching entrepreneurship is about dealing with paradoxical situations, such as uncertainty. From an educational point of view it is therefore more obsolete to teach entrepreneurship through the determinants of entrepreneurship – in education often called the entrpreneurship competence. That term is in itself a very complex one and continuously under debate. Lans et al. (2008) have written an excellent paper on the discussion of what entrepreneurship competence actually. Generally, an entrepreneurship competence includes the knowledge, skills and attitude (Fiet, 2001). In education, two approaches are currently used: a more ‘bolt on’ approach, where the entrepreneurship competence is seen as a fixed way of thinking about entrepreneurial competencies, and more ‘interpretive/integrative’ approach, where the entrepreneurship competence rather is seen as a context-dependent set of skills and attitude (Lans et al., 2008). Especially the latter follows the direction of this paper. From that perspective personality traits are seen as conditions for entrepreneurship, but not as learnable. The ‘learned entrepreneurship competence’ is a competence not acquired at birth, but through education, training or experience (Bird, 1995; Lans et al., 2008). According to Bird (1995) there is no use in developing a model for entrepreneurship competencies without considering that these competencies should be learnable.

Can entrepreneurship be taught at all? There is a difference between teaching entrepreneurship and teaching about entrepreneurship. The entrepreneurship competence is about teaching entrepreneurship and if that’s possible at all has been part of many studies. Hindle (2007) argues that, depending on what we see as a result, it can be taught if we define the result as ‘the entrepreneurship exists’ after she ‘underwent a process of education that contributed to the nature of her current existential state.’

A recent publication of the European Commission has used this view on entrepreneurship competence to define a framework for education. Although one of many different views, it takes an interesting in approach in the fact that it tries to ‘bridge the world of education and work’. The framework defines three main competence areas: ‘ideas and opportunities’, ‘resources’ and ‘into action’ – and another fifteen competences (Bacigalupo et al., 2016). The framework however, has not been validated yet. A framework that has been validated is the FINCODA-framework. Although a bit more focused on innovation skills, it validated five key competences for innovation and entrepreneurship: creativity, critical thinking, initiative, teamwork and networking (Marin-Garcia et al., 2016).

Figure 2: EntreComp Framework (Bacigalupo et al., 2016 figure 2, p. 11)

Teaching Entrepreneurship

Even when knowing which competences should be taught in order to increase entrepreneurial success in a complex and paradoxical-setting, one could wonder how to teach these competences, from a more didactical point of view. Neck et al. (2014) argue that the ‘effective doing of entrepreneurship requires a set of practices and these practices are firmly grounded in theory’. They call this actionable theory learning, as depicted in the following matrix (A. C. Corbett & Katz, 2012; Neck & Greene, 2011):

The authors argue that are five different practices of educating entrepreneurship:

• Practice of play: ‘the skill of play frees the imagination, opens up our minds to a wealth of opportunities and possibilities, and helps us to be more innovative as entrepreneurs’ (Neck, Neck, & Murray, 2017). Play includes the used of simulation games that challenge to think like an entrepreneur.
• Practice of Experimentation: ‘the skill of play is best described as acting in order to learn – trying to something, learning from the attempt and building that learning into the next iteration’ (Neck et al., 2017).
• Practice of Empathy: can be taught using creative research methods.
• Practice of Creativity: can be taught using creative techniques and methods such as design thinking (Neck et al., 2017)
• Practice of Reflection: can taught by using different ways of reflection in class, such as narrative reflection, emotional reflection, analytics reflection and critical reflection. Although its benefits have been supported widely, reflection is not brought into practice at all (Neck et al., 2017).

Figure 3: The theory-practise matrix (Neck et al., 2014).
Figure 4. The practices of entrepreneurship education (Neck et al., 2014).

Missing Literature

Many educational programs follow the golden rule of first defining competences and its body of knowledge, skills and attitudes which then lead to curriculum, teaching and learning objectives that can be assessed, examined and form the foundation for learning material (Biggs, 1996; Brown, 1995). These objectives are an essential part of education. They are more specific than competencies, but less specific than (grading) criteria. Although the latter are widely covered in literature (Bacigalupo et al., 2016), specific objectives that take into account the ambidextrous nature of entrepreneurship are not. While it could be relatively easy to formulate learning objectives for the five competences of the FINCODA model – something like ‘Upon successful completion of this course the student is able to understand and paradoxical nature of dealing with taking initiative in the context of entrepreneurship in order to prepare him/her for effective decision-making as an entrepreneur.’ – these learning objectives are to broad and don’t take into account the actual body of knowledge and skills needed as an entrepreneur. A true set of learning objectives for dealing with ambidextrous entrepreneurship is not yet out there and it is the purpose of this paper to create one.
The fact that the most relevant competence for entrepreneurship – which is, as was argued before, ambiguity, or to say more specifically learning how to deal with or to tolerate ambiguity – has not been integrated in the present-day competency frameworks is not so strange. Teaching ambiguity is widely recognized as one of the most difficult competences for a teacher himself (Chang, Yang, Martin, Chi, & Tsai-Lin, 2016). It is however, as the subtitle of this article suggested, never as satisfying as you want it to be. Dealing with ambiguity, causation and effectuation, is like trying to solve an unsolvable equation. Or as Ludwig van Mises wrote:

“entrepreneurs defy any rules and systematization. [Entrepreneurship] can be neither taught nor learned.” (Klein & Bullock, 2006; Lewin, 2011; Von Mises, 1949)

More recent literature however suggest that it can be learnt, but it still cannot be taught:

“Some business professors dream of finding a grand algorithm that will allow them to guide entrepreneurial decisions and to judge in advance which decisions are good and which bad. [This has been revealed to be] a form of magical thinking. We need entrepreneurs to make their decisions for themselves precisely because it is impossible for us to make those decisions for them.” (Koppl, 2008; Lewin, 2011)

The paradoxes depicted below are merely a single-minded and largely invalidated solution to the unsolvable equation.

Paradoxes of Entrepreneurial Thinking

Based on conclusions from the literature study above, I will now propose a set of teaching objectives that are constructed in such a way that they deal with the dilemma that arose in the Kirznerian and Schumpetarian literature on entrepreneurship and following the line of practices as proposed by Neck at al (Neck et al., 2014).

#1: The Uncertainty Paradox

This paradox was framed by Peter Lewin (Lewin, 2011) in a refreshing article named Entrepreneurial Paradoxes as followed: “entrepreneurial opportunities are complicated by uncertainty but would not exist without uncertainty.”

#2: The Strategic Paradox

This specific paradox is closely related to the before-mentioned literature on organizational ambidexterity, which deals with the difficulty of exploration and exploitation: for a long-term sustainable business model an entrepreneur would need to focus on exploration but that his company would not be able to sustain itself without a short-term exploitative strategy. This is also referred to as the choice between pivoting or persevering (Ries, 2011).

#3: The Opportunity Paradox

This is a complicated paradox, but basically it described the fundamental way entrepreneurs see and recognize opportunities. On the one hand, opportunities may exist and be discovered – as was depicted earlier in the article – but on the other hand it could be claimed that opportunities are created and exploited. This paradox is questionable however, because one could wonder if an opportunity created by a specific entrepreneur was not actually an existing opportunity missed by anyone else (Lewin, 2011).

#4: The Experience Paradox

This one is rather simple to understand: an entrepreneur never could have enough experience to always make wise decisions in hindsight. The paradox causes, for instance, also the not-invented-here syndrome. An entrepreneur would rather base its decisions on prior experience which does not completely reflect the current situation.

#5: The Momentum Paradox

When confronted with a more complex decision, often arises the dilemma if I do it now, will it be too soon for the market, or if I wait will it be too late for my business? Choosing the right time for the right decision is often paradoxical, because an entrepreneur will be too early, if no one else was too early, will be right on time when somebody was too early and will be too late if everyone else was earlier.

#6: The Generalization Paradox

Crazy enough, there is more literature suggesting that an entrepreneur is characterized by the fact that his personality traits are unique to anyone else, thus making a general set of competences, skills or behaviourial actions impossible to depict. Or as Lewin says it: “The elements of the category “entrepreneur” are all unique individuals whose characteristics (almost) defy generalization.” (Lewin, 2011). An entrepreneur should therefore always wonder if he should learn from the best practices of other entrepreneurs, or that he should learn from other entrepreneurs in a way that purposely does not want to copy their best practices.

#7: The Decision-making Paradox

The simple restriction of limited time, limited budget forms the backbone of almost each decision made in business. With limited budget in mind, would it be wise to spread your money over a longer time (a lower burn rate) or over more different strategic directions (exploration) – but then does limited time not ask for quicker spendings?

#8: The Impact Paradox

Sharp (2010) has found that there is strong paradox in (entrepreneurial) leadership when it comes to innovation at the personal level of the entrepreneur. He depicts that only 99% of all leaders are unable to demonstrate both humility and will at the same time, thus creating a paradox (Collins, 2001; Sharp, 2010). In a way this could be related to the much more discussed paradox: social impact versus economic impact. Most entrepreneurs have to struggle continuously between trying to create social impact with their business or trying to have economic impact with their business.

#9: The Risk-taking Paradox

Risk is one of the most-researched elements of entrepreneurship. Many scholars come to the conclusion that entrepreneurs actually don’t take a lot of risk; risk a merely the smart use of statistics and therefore the term calculated risk is often used. But statistics often counteract with each other and in practice each entrepreneur will ask himself over and over: is this worth taking the risk or not?

#10: The Knowledge Paradox

An entrepreneur does not have time, nor does he have the intention, of knowing all information that he may use for taking effective decisions for his enterprise. This so-called ‘knowledge gap’ is prevalent in day-to-day actions and entrepreneur finds himself choosing between learning and doing.

#11: The Trust Paradox

As an entrepreneur won’t be able to know everything himself, the knowledge paradox, he finds himself relying on others information. This raises the question: can he trust the information he gets? The trust paradox is mostly visible when dealing with outsiders, collaborators, clients, suppliers, et cetera and is of great difficulty for entrepreneurs.

Conclusions and Discussion

While effort has been put in discussing the phenomenon of teaching entrepreneurship from different perspectives, I do not even try to claim that this research is close to complete. There is a wide range of research available which both – and arguably with evidence – claim that entrepreneurship could be taught or could not be taught. This discussion adds to the on-going debate around nature versus nurture. This white paper is nothing more than my two cents on teaching entrepreneurship and bringing up the topic of paradoxes in entrepreneurial. To my opinion entrepreneurship as much as entrepreneurial thinking cannot be taught, but we can teach tolerance to ambiguity and therewith a self-reflectivity needed to autodidact entrepreneurial thinking. In order teach tolerance for ambiguity, lecturers need to cope with the above-mentioned paradoxes themselves rather than trying to translate them into teaching material. This makes learning entrepreneurial more tacit than explicit. But we already knew that, didn’t we?

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As a member of ISPIM, we’re proud to be part of the ISPIM 2016 Conference in Porto again.

Organised by ISPIM, and supported by ANI – Agência Nacional de Inovação (the National Innovation Office of Portugal), this event is for innovation researchers, industry executives, thought leaders and policy makers.

• Understand the latest innovation management thinking in 50+ workshops, keynotes, tours and discussions
• Broadcast your insights to 500 innovation experts from 50 countries
• Get feedback, get published and share understanding
• Deep dive into the Portuguese innovation scene

Please sign up for the conference at their website if you want to be present.

ISPIM Grand Prize for Innovation Management Excellence

Just like last year, the ISPIM Grand Prize will be awarded to a company or organization with the best idea or performance on innovation management. Do you have an idea? Don’t hesitate and submit it before April 8th.