Deciding on the basis of intuition is efficient, quick, and saves us from having to give justifications, but it has some drawbacks. Evolution has created conditions for our brain to specialize in making decisions without much conscious reflection. Our ancestors who didn’t react quickly to certain noises, glimpses, or smells probably didn’t reach reproductive age. However, the skills necessary for survival in nature can lead us to bad decisions in modern life. The 1974 paper by Daniel Kahneman and Amos Tversky initiated the study of cognitive biases and their impact on decision-making processes. For example, confirmation bias leads us to give greater relevance to information that confirms our beliefs, attribution bias makes us value a person’s contribution to an outcome at the expense of the conditions in which it occurred, and the Dunning-Kruger effect leads us to overestimate what we know about a given subject.

The way to avoid the interference of cognitive biases is to rationalize the decision-making process, that is, to analyze the different alternatives and try to estimate their probabilities of success. For this, we need to be able to characterize, in detail, the situation in which we find ourselves. Then we should look for past similar situations and analyze what happened next. For both cases, data is needed, some in real time about the current situation and historical data to try to predict the future.

In the 1990s, companies began using enterprise resource planning (ERP) systems for inventory and production management that quickly expanded into accounting, finance, and sales. This has resulted in the development of business intelligence (BI) technologies that not only analyze and present data in real-time but also make predictions for the future (predictive analytics) and suggest decision options (prescriptive analytics). These technologies had a huge boost with the development of Internet technologies, namely cloud computing, where it is easy to access data storage resources and computing power. There is, however, the difficulty that these decision support systems are critically dependent on the quality and quantity of the data on which they base their analyses. Much of this data is input by humans in an error-prone, time-consuming, and expensive process. Like our brain, a decision support system needs senses.

The name Internet of Things (IoT) was created in 1999 by a supply chain specialist in an advertising ploy to convince his superiors of the advantages of using technology to track transported objects. It was only 10 years later that the term took on the meaning of being a network of smart objects with processors, sensors, and actuators in order to exchange data with each other and with other devices connected to the Internet. This connection to cloud computing and the ability to obtain information and act on the environment is at the basis of the enormous growth of the IoT technology market. With it, we can provide decision support systems with large amounts of reliable data without human intervention.

In addition to being useful, IoT technology is surprisingly accessible thanks to the Arduino project. This project started in Italy and aimed to create a platform to facilitate the creation of low-cost electronic systems by non-engineers. It consists of an electronic circuit board with a microcontroller to which sensors and actuators can be connected together with an environment for developing programs on a personal computer. After the program is transferred to the microcontroller, it can run autonomously. As all project materials were made available in open source, there is now a huge community of users who develop and share all kinds of solutions. Whenever you hear about smart cities, agriculture, factories, or homes, it is very likely that Arduino systems are involved.

There are many introductory Arduino videos and inexpensive development kits that can be a nice gift for a daughter or son who likes to build things. My students have already used this technology to measure and predict the daily energy consumption of a home, locate mobile medical equipment in a hospital, count the number of people in a room, monitor manufacturing processes, and detect breaks in the daily routines of elderly people so that they can continue to live safely at home. All these projects use sensors that obtain information about the environment they are in without using image capture.

The ability to access large amounts of data and use them in decision-making processes is essential for the competitiveness of companies. W. Edwards Demming, the creator of the PDCA methodology for the continuous improvement of production systems, created a famous adaptation of the US motto in the following sentence: “In God we trust, all others must bring data”. Modern companies increasingly trust smart objects to collect the data on which to base their decisions.

Adapted from my article in i newspaper of July 12th, 2022

The first risk is that the result of the experiment is a false positive, that is, a misidentified effect that does not really exist. Could it be that what seemed to you as an approval of the quality of the pastries by your guests was just them being polite, knowing how proud you are of your culinary skills? It may also be the result of the so-called confirmation bias, which makes us give more importance to information that confirms our beliefs and less importance to the guest who was less enthusiastic about the quality of the pastries. If the repetition of the tasting experiment produces very different results, don’t be surprised, it happens to the best. Many results of  scientific studies were proven to be difficult or impossible to reproduce. A famous study tried to replicate experiments, published in relevant academic journals in the field of psychology, and only 36% of them gave the same results.

If you verify that the result of your experiment is not a false positive, you still need to confirm that the people involved adequately represent those you need to expand your idea. Will your pastries please in the same way people who live in another region or country? Will you be able to get help in the confection of the pastries to produce them in greater quantity? Remember that, in the case of teaching, many educational experiments fail to give good results when they are generalized to the entire education system because, generally, the teachers who volunteer for the experimental phase do not have the same characteristics as an average teacher.

The other validation you need to do is whether the circumstances in which you developed your experiment are generalizable. For example, your pastries were prepared just before your guests arrived. Will they have the same approval if they had been prepared earlier? What if I have to transport them from the cooking place to another one further away? This validation can lead a chef known for his elaborate dishes to decide not to open a chain of restaurants with his name, as he cannot guarantee the production of his dishes with the same quality. That’s why most restaurant chains ensure uniformity of quality by using simple recipes with standardized ingredients.

There is also a risk associated with the repercussions of expanding your experiment. These problems are difficult to test because they only occur after you scale up. For example, part of your product’s success may be due to the fact that it is handcrafted and challenging to acquire. Its greater accessibility may associate it with another category of products that are more common and less attractive to the audience of your original experiment. The expansion can lead to the emergence of competitors with similar products, increasing the offer and devaluing your pastries. 

There may be a risk that expanding your experiment will increase the cost of making your pastries. This could happen, for example, if its unique flavor depends on the use of unusual ingredients. This feature will make it difficult to copy the product, but expanding your business will increase demand for these ingredients and, consequently, their price.

Reducing the five scaling-up risks that I have described is essential for the second stage of startups when they are called scaleups. The knowledge of these five risks is useful for entrepreneurs and anyone who needs to decide whether or not an idea or activity is worth continuing or expanding. Many public policy proposals are based on the results of small experiments, without solid validation on whether their assumptions are generalized. Ensuring that ideas are capable of being scaled up is one of the goals of the book “The Voltage Effect”, by John A. List, a professor at the University of Chicago, where he states that there is no single recipe for the success of an idea, but that it will be difficult to achieve it lacking one of these five ingredients.

Returning to your idea, we can now assure you that, 185 years later, a similar idea had a huge impact. It remains to be seen whether the creators of pastel de Belém adequately assessed all the risks or if they simply did not think about it. In this case, the Anna Karenina principle may take the form: all ideas that have had an impact are equally good; every idea that failed, failed in its own way. 

Adapted from my article in i newspaper of April 26th, 2022

A good example of mutually beneficial collaborations are innovation projects: not only do the participating organizations win, but also the future customers of the resulting new products. It is, therefore, legitimate to ask the reason for the difficulties in establishing collaborations between the organizations best positioned to produce innovation: universities and companies. This was the question that the Rector of the Universitat Politècnica de Catalunya (Barcelona Tech, UPC) asked when he invited me to go to Barcelona last week to present to the university’s directors the structured model of collaboration with industry that we have at Técnico.

Like countries, organizations also have their own cultures that create barriers to collaboration among themselves. There are barriers related to the objectives of the organizations, for example in the need for companies to obtain more immediate results while universities seek a longer-term impact with their research activities. Even when the orientation-related barriers are overcome, transactional barriers arise in the contractualization of this collaboration. It is difficult to agree on the resources that the parties are willing to commit and on the sharing of eventual outcomes, namely intellectual property rights. Contracting often follows the supplier-client model, where the company contracts services to the university, in a model similar to that used with other companies. Transforming a collaboration opportunity into an innovation project is hard work on the part of the stakeholders, typically university and company employees linked to innovation. Once the project is finished, the process will have to be repeated again.

It is in the resolution of these difficulties that we can use the advice of Sun Tzu, in the form of what is currently called in the area of management as a strategic alliance. It is an agreement between organizations that decide to share resources to achieve their strategic objectives. Strategic alliances can involve companies in the same line of business (Star Alliance), companies with common customers (Disney and McDonald’s) or companies that partner to reduce costs, for example, in the development of a new product or in the approach of a new market. The book Cooperative Strategy (Child, 2019) systematizes the three perspectives in which the theories that support research on strategic alliances are grouped: economic, management and behavioural. These perspectives help in finding the areas where an alliance can produce mutual benefits: optimization and economy of scale, reduction of risks and uncertainty, access to resources and activities, greater credibility and reputation, access to customers, expansion of the product offering, internationalization, knowledge creation, etc.

In the case of collaboration between universities and companies, it is easy to find a set of common goals: the need for more and better talent, the advantage of contact between students and future employers, the speed in converting knowledge into new products, sharing information that helps the organizations strategic planning or the increase of the competitive advantage of their region. Strategic alliances are designed on common goals, which must have two characteristics: they must be long-term and cover several areas of collaboration. In the present case, the long term makes it possible to maintain the collaboration in the intervals between projects and the multidisciplinarity extends the collaboration beyond the area of innovation. For each domain in which there are mutual benefits, it is necessary to design the different activities that will be carried out during the term of the partnership agreement: sponsoring of teaching laboratories, mentoring of students by professionals, theses in a business environment, among others. It is easy to understand that such an extensive collaboration is only possible with the express will of the top managers of the company and the university, which makes both parties responsible for the success of the partnership.

Often, some of the collaboration activities may already exist as a result of tactical alliances, created as a result of specific needs of the university and the company. What I tried to show in Barcelona is the value of its coordination within the framework of a strategic alliance where the long-term goals of the university and the company are made clear. As Sun Tzu said, strategy without tactics is the longest road to victory, but tactics without strategy are the noise before defeat. Unfortunately, it seems that the times we live in are not favourable to the advice of Sun Tzu for whom the supreme excellence was victory without a fight.

This change reminds me of Jacinto, the cosmopolitan main character of the novel “A Cidade e as Serras” by Eça de Queirós, who utters the phrase I use as a title, in a conversation with his friend Zé Fernandes. His mansion, on number 202 of the Champs Elysees, brings together all the innovations of that period: from a telegraph in his office to an elevator for food. Eça de Queirós takes us on a journey from Paris, the most cosmopolitan city of its time, to the beautiful countryside of Tormes which Jacinto falls in love with and where he went to live.

 Jacinto’s sentence was correct. Luis Bettencourt, a Portuguese researcher currently at the University of Chicago, compared the average number of patents granted with the number of inhabitants of the city of origin. He and his team found that a city of two million inhabitants has 2.4 times more patents than a city of one million. This factor holds for any city size: whenever we double the number of inhabitants, the number of patents per inhabitant increases by 20%. This increase in scale is observed in almost all socio-economic quantities: the gross metropolitan product, salaries, etc. In the case of infrastructure costs, the scale factor is lower than population growth. In a city with twice the population, the surface area of roads or the length of electric cables per inhabitant is 12% lower.

The advantages in the socio-economic outcomes of larger cities seem to be the result of the greater number of social connections provided by the greater number of inhabitants. To confirm, Luis Bettencourt’s team analyzed telephone communications in Portugal and the United Kingdom. In Portugal, they used a 15-month period, considering only calls between mobile phones that initiated calls with each other. The results show that, for every doubling of the city’s population, there are 12% more telephone contacts per person: in 15 months, an inhabitant of Lisbon made, on average, twice as many telephone contacts as the average of the 4,300 inhabitants of the city of Lixa.

This advantage of social interconnectedness in the production of socio-economic results also results from studies on the operation of innovation ecosystems. For example, AnnaLee Saxenian compared the evolution of the Silicon Valley and Boston’s Highway 128 ecosystems between the 60s and 90s of the last century. Her work shows that the initial advantage of the Boston ecosystem was lost in the early 1980s due to the difficulty in adapting to new technological developments, namely in the area of microelectronics. The greater geographic dispersion of companies and the employment contracts with non-compete clauses of Boston companies prevented the formation of horizontal links between workers, unlike what happened in the San Francisco Bay Area. These horizontal links made the ecosystem more flexible to change.

The realization that innovation grows exponentially with the population of cities and that the cause is the greater number of social interactions led to the development of the idea that the promotion of social interactions can increase innovation. European cities have created conditions to increase the links between the agents of their innovation ecosystem with surprising results. Europe is the region in the world with the most “unicorn cities”, with 65 cities with at least one unicorn, a company that investors value at more than a billion dollars. Portugal has 3 of these “unicorn cities”. Europe currently attracts 18% of the world’s venture capital funding. The entities that streamline the ecosystem connection vary from city to city and can be the local University, municipal authorities or even successful entrepreneurs like Xavier Niel in Paris.

 What Eça de Queirós does not mention in his book is that number 202 of the Champs Elysees is practically equidistant from the Louvre, the Paris Opera, Levallois-Perret where the Gustav Eiffel factory was located, and Montmartre where Degas, Renoir, Toulouse-Lautrec, van Gogh and many others, lived and worked. At the same distance is Neuilly-sur-Seine, to where Eça moved in 1891 and where he died. Unlike Jacinto, he preferred the big city to his House of Tormes, which he only visited once. We still need to know if the communication tools we have today will allow us to move to our Casa de Tormes and continue to benefit from the growing network of contacts that city life offers us.

Adapted from my column in Jornal i of December 21st, 2021

The pervasiveness of text messaging communication is a surprising phenomenon. Science fiction predicted mobile devices for communicating by audio and video but not by text. In 1984, when the GSM protocol was defined in Europe, it included the SMS service to send text messages to customers about billing or new voice mail. It was only in 1993 that the first mobile phone able to send such messages appeared. Its adoption was slow and led by the younger generations who saw in it a new form of ubiquitous communication, non-intrusive and, above all, without the pressure of an immediate response. Exchanging text messages has become a way to carry on a conversation, leading to the emergence of many instant messaging applications such as WhatsApp. 

Physical interaction includes many non-verbal elements such as facial expressions, gestures, intonation and type of voice, which allow you to confirm that the message was well-received. Text messages have a greater risk of being misunderstood. The book “Digital Body Language”, by Erica Dhawan, presents many examples of problems created in companies by digital communication. By reducing the communication channel, there was a need to creatively compensate for the lack of these elements. We use punctuation excessively (“!!!”), repeat vowels to lengthen a word (“Nooo”), add emotion symbols (“:)”), and resort to acronyms (“LOL”).

Contrary to the general idea that this form of communication did not use punctuation or that it did so at random, Baron and Ling (2011) conclude that new generations follow coherent strategies in the use of punctuation if we include the repetition of symbols and emojis in it. At school, we were taught to use the exclamation mark sparingly. We were told that it serves to identify exclamatory phrases (“Good morning!”), imperative (“Get out of here!”) or interjections (“My God!”). In digital communication, a single exclamation mark has become an indicator of friendship (“Thank you!”). Its use has become so common that its absence has become an indicator of too much formality. While an exclamation point is read as favourable (“Excellent!”), a sequence of exclamation points is more difficult to interpret (“Excellent!!!!!”) as the repetition can be understood as either enthusiasm or irony. In contrast, the full stop almost disappears in digital communication. When used, it shows a lack of interest in continuing the conversation or that the statement is final, such as saying “and that’s it” at the end of a sentence. The ellipses, in addition to leaving the continuation of the text to the reader’s interpretation (“The early bird…”), also come to represent pauses in the oral discourse (“I went there… it was empty”). Baron and Ling identified gender differences in the use of this form of communication. Girls write more and use more punctuation than boys, who prefer succinct answers, and more often use the exclamation mark as a smooth way to finish a message, along with emojis.

Punctuation was first used to help to read a text aloud, the principle of rhetoric. The popularization of silent reading changed its role to a guide for the decoding of complex syntactic structures, the grammatical principle. Some researchers associate the new use of punctuation as a return to the principle of rhetoric. However, Busch (2021) proposes a new interactional principle, where punctuation appears as a mechanism to organize the sequence of written interactions, especially when it occurs at the beginning and end of the message.

In our course, we will use sentiment analysis to introduce students to some of the natural language processing methods. The process begins with the preparation of training material, the tweets classified as positive or negative, performing a morphosyntactic analysis, which adds a label to each word indicating that it is a noun, verb, pronoun, etc. Certain forms, such as “lead” can be ambiguous: noun (“heavy as lead”) or verb form (“I lead the team”). Using the neighbouring words it is possible to choose the correct label. In a simple system, you can eliminate all words that are not verbs or nouns and convert the verb forms into the infinitive (“to lead”) and the nouns into the masculine singular, in a process called lemmatization. After reducing each word to its lemma, the training dataset is used to estimate the probabilities of occurring in positive and negative messages. Applying the same processing to the words of a new message, we combine the probability estimates to classify it as positive or negative. The simple system we use is 99.5% accurate in classifying the 3,000 tweets that we randomly reserve for testing. However, it is unable to deal with the irony and sarcasm that even more sophisticated classifiers have difficulties in identifying. So the next time you make a complaint, use strong negative words and avoid irony and sarcasm: you’re more likely to have your problem solved.

Adapted from my column in Jornal i of September 21st, 2021

Cases like the ones I’ve described are called conflicts of interest. This can be defined as a situation in which there is a conflict between an individual’s private interests and his or her professional obligations such that an independent observer might reasonably question whether the individual’s professional actions or decisions are affected by his or her private interest (Chinn and Kulakowski , 2006). One of the interesting characteristics of this definition is that it is based on what will be the perception of an independent observer and not on criteria about the greater or lesser economic or social value of any personal benefits.

There is, however, the difficulty with what cognitive scientists call motivated reasoning that leads us to think about a topic with the goal, consciously or unconsciously, of reaching the conclusion we want. It is motivated reasoning that makes us point out the faults that are not signalled to the other team and become indignant with those that are signalled to ours.

Motivated reasoning leads us to value arguments and facts favourable to our goals and to despise those that contradict them. Examples of goals or needs that can motivate reasoning can be private financial interests, the preservation of a positive self-image, or the protection of relationships with others to whom we are connected or on whom we materially or emotionally depend. Motivated reasoning prevents us from having the vision of the independent observer.

The usual way of dealing with this conflict has been to remove from the process all individuals with the potential to have a personal interest in the matter. A growing number of people, including myself, feel that this solution is ineffective as a conflict of interest is inevitable and organizations and individuals must know how to deal with it. For this, the situation needs to be analyzed by an independent observer to reduce the effect of motivated reasoning. In this way, even individuals with a conflict of interest can make relevant contributions to the decision-making process.

There are two essential instruments for an organization to deal with conflict of interest: conflict of interest policies and regulations and individual conduct. The first includes the form and mandatory nature of the conflict of interest declaration and who is responsible for reviewing and evaluating these declarations, as well as the mechanisms for the independent review of contracts and other decision proposals. Individual conduct is, for me, more important because it applies even when there are no explicit institutional policies or regulations for the conflict of interest. Professionals must be aware of it and able to identify potential conflicts and must assume with naturalness the need for a declaration of conflict of interest. This information should be included in the recommendation or justification for a certain action or decision. In particular, professionals should feel free to request an independent review whenever there is doubt on the interference of private interests.

Conflict of interest was the subject of a panel I was invited to at this year’s edition of the Science and Technology Transfer Professionals (ASTP) conference. It was moderated by George Summerfield, a lawyer with over 20 years of experience in intellectual property, and was attended by Jörn Erselius, director of Max-Planck Innovation. My role was to show the challenges facing Universities knowledge transfer professionals in the conflict between the requirements of companies seeking to license technologies and the University’s mission and fairness obligations. These challenges are more intense when there is a connection between inventors and companies seeking licensing, as in the case of startups created by researchers or when there is a long-term collaboration between them and the company.

One of the cases given as an example was that of the University of California, which has a review process where the proposed licensing decision of the technology transfer professional, together with the information on conflict of interest collected, are subject to independent analysis. The analyst evaluates the pieces of documentation that led to the proposed decision, relates it to other prior licensing decisions, and writes a recommendation. The decision proposal and the independent recommendation are taken together to the final decision-maker.

The opinion poll carried out in the session and the questions that followed the presentations demonstrated the audience’s concern with the topic, justifying the holding of the panel. For my part, I think I have managed to convey the inevitability of conflict of interest and the importance of individual conduct. On second thought, this conclusion is perhaps just the result of motivated reasoning. I need an independent observer.

Adapted from my column in Jornal i of June 8th, 2021

Knowing that the main reason for startups to fail is the lack of market for their product, you decide to use the customer development methodology created by Steve Blank to do the initial validation of your idea. You will then interview people representing the product’s different customer segments to assess interest in this new television content distribution service. Diligently, you scripted the interview with your potential clients. You begin by explaining, in general terms, your idea and you continue with a sequence of questions: Do you feel that you lack time to watch television series? Do you think it’s a good idea to be able to use small breaks of free time to watch series? Would you use your cell phone to watch a short episode of a series, while waiting for an answer or while commuting on public transportation? How much would you pay to have this service?

Empowered by this script, you look among the people you know who could represent potential customers and you schedule some time to interview them. The results you get are very promising, more than 90% of respondents said that they would like to have more time to watch tv shows and think your idea is good. More than 70% would like to see tv shows while waiting to be attended or while travelling. They were also willing to pay for it, more than half said that they would pay €4 or more per month. Although excited by these results, you may, however, suspect that they may be due to the small sample size. That’s why you prepare an online questionnaire with the same questions, which you publish on social networks and in conversation groups about tv shows. The more than one hundred responses confirm the data obtained from the interviews.

It is at this stage that you decide to interview a friend with some experience in validating business ideas and who, upon hearing her questions with some horror, suggests that you should read Rob Fitzpatrick’s book “The Mom Test”. Only later will you understand the reason for the title: the book teaches how to do interviews in a way that even our mother can give you honest information about the potential of our business idea. The desire to succeed and the desire to be pleasant lead your mother, and many other people, to praise and give positive opinions about hypothetical future situations, misleading the unwary interviewer. You quickly realize the mistakes you’ve made. You started by exposing the idea, leading your interviewee to the assumption that confirmation is a way of being pleasant to you. In the first question, the positive answers probably validate the lack of time more than the desire to watch tv shows. The remaining questions ask for opinions about hypothetical future situations and not concrete facts that could predict the future behaviour of the respondent.

Based on what you read, you decide to reformulate your script. You will no longer present your idea at the beginning of the interview and, instead, you will start with the following question: Are you a subscriber of streaming services for tv shows or movies? With this question, you will learn about the respondent’s interest in the topic. The following questions are focused on facts about the interviewee’s life. What did you do the last time you had to wait 10 to 15 minutes? How often do you have these waiting times? How often do you watch series or movies on your mobile phone? More than knowing the interviewee’s opinion about your business idea, these questions are necessary to provide you with more information about his or her life. If the interest is confirmed, you can then ask about the possibility of using a product like yours. Finally, you decide to end your interview with the question: Do you know other people I should talk to? The request for references is more than the possibility of having more people to interview, it is the confirmation that this interviewee has given enough value to the interview and the product, to the point of providing the contacts of his or her acquaintances.

This change of strategy produced the opposite results. Potential customers who subscribe to streaming services have other ways to occupy their short idle breaks. Those who have a larger number of small breaks occupy it watching free access content. These conclusions quickly led you to discard your project. And that was good for you, as the streaming platform Quibi, which pursued a similar idea, closed its operations in December 2020. It closed just 6 months after launch, in part due to the withdrawal of most customers at the end of the free trial period of 90 days. Of the $1.75 billion received from investors, it ended up with $100 million of programming rights that were purchased last January by Roku. 

You have now more time to start validating your next business idea. You can start by interviewing your mother.

Adapted from my column in Jornal i of April 6th, 2021

Later in my engineering education, I was often faced with the difficulty of combining the need for language precision with the need to make it easier for a non-specialist to understand a concept. To show the difference, I have the habit of asking students at the beginning of the semester to define the meaning of the word startup. The answers usually refer to the age of the company, the use of technology, and the need to involve some form of innovation. It is easy to find counterexamples of the first two characteristics, but that of an innovative nature requires one more entry in the “caderno de significados”. How is innovation defined?

A dictionary lookup doesn’t help much, particularly if we want to use the word in the context of business and economics. Even when looking in academic publications, there is a great diversity of definitions depending on the specific area of the study. Since this is an activity where there are enormous advantages in multidisciplinary collaboration, a team of British researchers set out to find a common definition of innovation. For this, they collected 60 articles from 7 different areas, from management to engineering, with definitions of innovation from which they drew descriptors for a set of common attributes. They started by representing the attributes and main descriptors in a diagram from which they produced a textual version that can be translated as “Innovation is the multi-stage process whereby organizations transform ideas into new/improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace” (Baregheh et al. 2009). 

To simplify this definition of innovation it is useful to introduce the concept of “business model”. This concept aims to answer Peter Drucker’s questions about who the customer is and what he values, ??and how the company makes money. Alex Osterwalder and other authors define the business model as the set of hypotheses or assumptions that constitute “the rationale for how an organization creates, delivers and captures value” (Osterwalder and Pigneur, 2010). In this perspective, the value does not necessarily have to be income for the owner of the organization and the bases of the model are hypotheses whose validity can be tested. For example, a hotel’s business model is based on offering the traveller a simple way to book and enjoy accommodation at their property. Airbnb removed the need to own the property by resorting to available hosts that are willing to welcome strangers into their homes, offering a more authentic experience to the traveller. Ease of booking and sharing the opinions of travellers and hosts made the difference. Differentiation is also part of the business model.

The business model thus includes, among other things, the products, services or processes that the organization uses to create value. On the other hand, ideas need basic knowledge, and their generation process is part of the innovation process. It is thus possible to simplify the previous definition to form: “Innovation is the multi-stage process whereby organizations transform knowledge into a successful business model”.

Now that we have the definitions of innovation and business model in our “caderno de significados”, we can better understand the way Steve Blank defines a startup, which seems to me to be the most rigorous: “A startup is a temporary organization designed to search for a repeatable and scalable business model”. That’s why we tell students that in the entrepreneurship classes they will experience the challenges of creating a startup. We actually use this analogy to teach them how to design and develop a knowledge-based business model, that is, how to do innovation. And that, like the “caderno de significados”, will be useful to them for the rest of their lives.

Adapted from my column in Jornal i of February 2nd, 2021

Governments and entities like the World Bank spend billions of dollars on programs for these microentrepreneurs, to teach them good management practices such as accounting, stock management, marketing, sales, etc. There is, however, little evidence that this training has a significant contribution to increasing the profits of these businesses. On the other hand, studies on entrepreneurs show us little correlation between their success and their level of knowledge in management practices or in economic variables. In previous articles, have already mentioned the importance of factors such as the mindset and the process of thinking of successful entrepreneurs (“Do you have an innovation mindset”, 7-Jan-2019, and “How to think like an innovator”, 20-Mar-2018). Entrepreneurship education is focused on the development of this mindset and process of thinking.

A group of researchers from the World Bank and a German university decided to do an experiment to try to determine the differences between the impact of traditional management training and entrepreneurship education. To do this, they took candidates for a financing program of the World Bank in Togo to select 1500 microentrepreneurs for their study. The chosen businesses had an average monthly profit of $199, an average of 3 employees and 53% of the owners were women. They were stratified by gender, sector of activity and profits and, in each stratum, were randomly divided by 3 groups of 500 companies. Entrepreneurs in the first group received traditional training in management, those in the second group received training in entrepreneurship and the third group was used as a control group with no training.

The on-site training period lasted 4 weeks, followed by 4 monthly visits by trainers to each company. From then on, companies were followed up with 4 semi-annual surveys and the results after two years were surprising. The businesses of entrepreneurs who received training in entrepreneurship had an average profit growth of 30%, while those of entrepreneurs who received traditional training had an average non-significant increase in profits of 11%. In addition, entrepreneurs with training in entrepreneurship have created more new products with greater innovation and increased their access to credit. Interestingly, it was noted that this group also increased the use of good management practices. You can learn more about these results in the Science journal article “Teaching personal initiative beats traditional training in boosting small business in West Africa”, (F. Campos et al. 2017).

I recently used this work in a class that I give to Técnico’s first-year students to motivate them to seek value for society in the projects they will carry out throughout their engineering training, with the aim of increasing their entrepreneurial mindset. Since it was an online class, I was able to see in the exchanges of messages where some students discussed whether the propensity for entrepreneurship was born or made. In fact, just as the intelligence quotient increases between 2.7 and 4.5 points per year of schooling, entrepreneurial capacity can also be developed. There will certainly be some innate propensity for some of the aspects that contribute to an entrepreneurial mindset, but success in entrepreneurship requires the development of a variety of behaviours. Many of these microentrepreneurs in Togo will have been so under the circumstances, but they have been more successful with training focused on developing their personal initiative.

The results of this study in Togo should lead to reflection not only on how best to train microentrepreneurs in Africa but on how we prepare the next generations for the uncertainties of a global economy. Especially because last week’s winning Web Summit startup was founded and is headquartered in Ethiopia.

Adapted from my column in Jornal i of December 8th, 2020

As usual, the problems that students wanted to address fell into the classification of “wicked problems”, that is, problems that are hard to solve due to incomplete, contradictory or variable requirements and difficult to recognize and specify. We use various methodologies to find creative solutions to these types of problems, benefiting from the collective intelligence of students and using visual tools such as screens and sticky notes posted on the wall or inside digital collaboration tools, such as Miro.

In these ideation processes, we use sticky notes (physical or virtual) for two reasons: summarization and interconnection. The participants need to summarize their ideas in a text that fits in the available space in a way to be readable by the other team members. Another advantage is that they can be moved without losing their content, allowing similar ideas to be grouped or prioritized.

We try to show students that ideas can stand on their own, but often have more value when associated with others. The power of collective intelligence used in these sessions is able to change silly or out-of-scope ideas into new ideas that would not arise otherwise.

The linking of ideas has long been used in academic articles where authors recognize the contribution of previous works. The study of frequency and citation patterns between documents is currently used, not only to identify important articles but also to rank the journals that publish them, their authors and their universities or research centres.

The value of connections is at the base of the “concept map”, a tool used to organize and structure knowledge. Created in the 70s of the last century, by Joseph D. Novak of Cornell University, the concept map is a graph in which ideas are in boxes (nodes) connected by arrows with tags (arcs). They are used to stimulate the generation of ideas or for the analysis of complex ideas.

At this point, you should be asking yourself the relationship between what you have been reading about creation and knowledge management and the title of this chronicle. The answer is that Zettelkasten is a method of knowledge management that was popularized by sociologist Niklas Luhmann (1927-1998). The method, whose name can be translated as “card box”, is inspired by the old bibliographic card files that summarize the documents of a library’s estate. The forms have the physical location of the document and its title, author, subject and date of publication. Zettelkasten is a collection of cards containing a small text with an idea or some information that was recorded when it happened or when it was taken from a source, for example, when reading a book. Each card also contains meta-information with references to other cards and labels that describe important aspects of the card’s text. This method of summarizing and interconnecting knowledge was used extensively by Niklas Luhmann, creating an archive of 90,000 records that he used to write more than 70 books and 400 academic articles.

Zettelkasten was rediscovered in 2017 in the book “How to Take Smart Notes”, by Sonke Ahrens, and has been adapted to various digital tools for taking and connecting notes such as Evernote, Notion, OneNote, etc. The basic concepts of summarization and interconnection can be used in these tools, but with some effort for the user. These limitations and the recognition of the method’s advantages led to the creation of a new generation of tools designed from scratch for this purpose. Its main feature is the cross-linking of notes: whenever a note is referred to by another, a link to the note that quoted it is added to the original note. Thisconceptbacklinkfacilitates navigation between notes. Another feature is the ease of making references, just use your title in the note text and use a key to transform it into a hyperlink. It is also common to be able to change the title of a note and thereby change the text of all references made to that note. For example, the note with the title “Networks” may initially be limited to the concept of computer networks. When wanting to introduce a new note on the concept of “Social Networks” it may make sense to rename the note “Networks” to “Computer Networks” making more precise not only the title of the note but that of all references to it. Finally, a common feature in tools with this method is the possibility of graphically visualizing the interconnection of notes, easily distinguishing those that are central from those that are peripheral.

We are still at a very early stage in the development of these new tools, the most well-known of which, Roam Research, was launched less than a year ago. The main competitors are RemNote and Obsidian, both free for personal use. In addition to price, an important choice factor is the confidentiality and portability of the information. In this domain, Obsidian stands out for storing information exclusively on the user’s computer using the Markdown format that can be read by various text editing programs.

Even without sophisticated tools, anyone can benefit from using the principles of the Zettelkasten method: summarization and interconnection. For this, a notebook, a pen and sticky notes are perfect.

Adapted from my column in Jornal i of September 29th, 2020