A part of my job is to debate with people about agile software development, sometimes, just analyzing discussions without taking part in it. I noticed one major factor that leads to arguments: misunderstanding. Two people may not agree on something only because they are simply not talking about the same thing, even though they are using the same words! In fact, words correspond to very different concepts or experiences in people. When the description is made using something that is unknown to us, we will first try, consciously or unconsciously, to link its properties to what we are already familiar with, and not necessarily take the required step back to interpret it globally by disregarding our existing knowledge. This understanding of things is partially or totally dependent on the subjective perception of the participants in a discussion. These perceptions are largely molded by the different cognitive biases that interact, self-reinforce. In psychology, we speak of heuristics in judgment. All of this can become very problematic in the context of so-called “psychological contracts,” where complexity emerges from these variable perceptions because of the positive (or negative) behavioral loops that become impossible to solve.

The debates can take long hours before we actually notice the problem, but in many cases, it remains unnoticed and everyone leaves with his or her beliefs and nothing changes. The fact that this article (and many others) exist is the consequence that there is widespread confusion caused by the creation of new terms to define similar (or existing) roles or behaviors. For example, the term “Carnism” or “Cisgender” create similar confusion and unnecessary division that prevents people from understanding each other.  Therefore, in some domains, knowing the good terms is so important that you don’t get your diploma if you don’t master them, such as in health or aeronautics. In some areas, using the right term is a matter of life and death.

What is the relationship with software development? The reason is that semantics are fundamental in the day-to-day work of knowledge workers and this is often the case with failed or poorly understood Scrum implementation. This goes from the very definition of a product owner to more fundamental things. But what motivated this post, besides my own observation of the problem (in many other contexts, as well), is the discovery that this phenomenon was much more widespread in the software industry than I thought and that it tended to annoy some (and that’s the least we can say).

In a recent blog post, Gregg Caines put it more gently, but very well:

When you want to get people to change the way they work, and you want them to understand the completely foreign concepts you’re bringing to them, it’s absolutely crucial that you name the thing in a way that also explains what it is not.

He continues with an example:

In Scrum, it’s also common to have a “sprint commitment” where the team “commits” to a body of work to accomplish in that time frame. The commitment is meant to be a rough estimate for the sake of planning purposes, and if a team doesn’t get that work done in that time, it tries to learn from the estimate and be more realistic in the next sprint. Developers are not supposed to be chastised for not meeting the sprint commitment — it’s just an extra piece of information to improve upon and to use for future planning. Obviously naming is hugely important here too, because in every other use of the word, a “commitment” is a pledge or a binding agreement, and this misnomer really influences the way people (mis)understand the concept of sprints. Let’s face it: if people see sprints as just more frequent deadlines (including those implementing them), the fault can’t be entirely theirs.

It is highly conceivable that Scrum defines new terms to impose a new way of thinking, as well (Agile Mindset). Ironically, this requirement is perhaps what contributes to the numerous failures of implementation of the framework, but also, and more importantly, to the proliferation of alternatives which infuriates its creator.

The lack of centralized and detailed information about Scrum can also contribute to the problem. The official scrum guide, with only a few dozen pages, is as highly subject to interpretation as your astrological sign of the day. The underlying psychological phenomenon for both is exactly the same:  it’s called the Barnum effect. It’s the tendency to interpret a general text to be specific to us. When it affects us, we filter the information and make it “match” our beliefs or knowledge, as I mentioned in the introduction. Sometimes it goes very far and I have already seen people develop theories about the functioning of the human brain based on sacred writings. This really makes me sad because the creator of Scrum probably agrees with me as they introduced transparency in the three pillars of Scrum along with inspection & adaptation:

Significant aspects of the process must be visible to those responsible for the outcome. Transparency requires those aspects be defined by a common standard, so observers share a common understanding of what is being seen.

OK, so far so good, but who benefits from the crime? Probably the agile consultants. It is possible for many of them to fill the electricity needs of an entire city simply talking in front of a wind farm. But how much does it cost? If I only rely on my experience to answer, I can attest that three generations of highly paid coaches are sometimes needed to make an organization understand agile fundamentals. Having been a “confluence archaeologist” several times, I can also attest that in some cases, it could never have been otherwise if the definition from my predecessors I read was the one submitted to the top management. They are not helped by the terms they need to promote.

That’s why I’m in favor of calling a spade a spade. Product Manager is the term everybody understands. It generally covers everything a “Product Owner” is supposed to do. I have the exact same opinion for the “Scrum Master” role (the “Servant Leader”), which creates further confusion, especially in different languages where the term servant is taken literally, and often leads to a “Scrum Janitor” or worse, a “Scrum Manager” in badly implemented Scrum. The guide mentions that it is simple to understand. I object. It also mentions that is it difficult to master. I agree. But one of the factors is definitely its inappropriate terms that create a breeding ground for misunderstanding. Therefore, a brave act would be to redefine the terms that would lead Scrum to the next level.

The Sprint Goal, as defined by the creators of Scrum, is a selection of product backlog items that form one coherent feature, which helps a development team to work together rather than pursue separate initiatives. Ian Mitchell, a writer on agile software development, summarizes the usefulness of the Sprint Goal by writing the following statement on a blog post:

What we are getting at here is that the whole purpose of a sprint is to meet a Sprint Goal, and Scrum is framed to support sprint-based delivery. Take away the Sprint Goal and the rationale for using Scrum disappears with it. You might as well operate on the basis of continuous flow. Without a Sprint Goal to meet, sprint backlogs can serve no useful purpose.

The Sprint Goal is a description of the coherent increments that will be delivered at the end of the sprint and it provides guidance to the development team on why it is building an increment and how it should do so. If the development team discovers that they won’t be able to deliver that coherent feature during the sprint, they can negotiate the scope with the PO.

We can go even further by describing all the advantages of a well defined Sprint Goal and how it helps to get things done effectively. It helps …

• The team to understand the purpose of their work beyond the description of individual PBIs, which stimulates intrinsic motivation and compliance. This leads to better implementation as the team can keep the big picture in mind. A well known man built an empire on this concept alone.
• To keep the team focused on a small objective and avoid the negative impact of context switching. It can be seen as mini projects. These are clearly achievable multiple goals instead of a big one that is divided into chunks used to fill sprints (Waterfall disguised as Scrum). Focus is a very important concept to understand when you work with knowledge and creative workers (in the case of the vast majority of people in software development); it is probably the most important thing to work on in order to deliver value and quality to your customers.
• The team to collaborate on a single objective with required flexibility and empowerment (cohesion vs individualism). This leads to better shared responsibility as the success of the sprint will be evaluated based on whether the goal has been met, instead of counting all the individual tasks that can lead to individual developer comparison in the worst implementation of Scrum. This helps in getting cohesion which is the most important factor for a high performing team.
• The PO provide clear value beyond the selected product backlog items (PBI) and therefore improve prioritisation. This helps getting a realeasable product increment at the end of each sprint, which is the whole point of agile software development.
• The PO to communicate on progress based on working software, instead of a percentage of a list of individual tasks that means nothing to the customer. This also helps to create a valuable roadmap to communicate to users.
• The PO to get structured feedback from each meaningful increment instead of individual tasks. This provides the next sprints with new insights which automatically leverage the power of Scrum or any other iteration based framework.

The Sprint Goal, when properly used, is a proof that Scrum is understood and correctly implemented. Together with the (potential) release of a new product increment, this makes the Scrum Sprint really useful. Therefore, this is the first thing I check when I’m asked to audit an existing Scrum implementation. Sadly, this is the most overlooked artifact of Scrum, so if you begin a new implementation, start with the sprint goal and explain why it’s useful.

It may surprise some, but my interest in cognitive sciences was initiated by the bullshiters I frequently encountered in seminars, meetings and other events related to my business sector (entrepreneurship, creativity and computer science). I was bombarded with countless theories and concepts, and more importantly organisational ones, each one more ingenious than the other and which seemed able to solve anything. All these theories were of course backed up by so called “scientific” articles. Despite this evidence seen as irrefutable by their authors, I was wary of this “silver bullet” that was regularly marketed to me, sometimes advertised simply as such. In fact, some of these poorly argued theories sparked my interest more than others. On the one hand, some of the proposed arguments annoyed me by being inappropriate, and on the other, I felt that some theories were more credible than others, but poorly addressed or interpreted. Of course, at the time, I was not aware that my own reasoning based on my intuition was not appropriate, but I still felt that there was something to be done in this area, without really knowing what. A common trait in all these “stories” was that the “evangelists” (a regularly used term) of these methods seldom seemed to really master their subject, in my opinion. All that seemed important to them was to sell their ideas at any cost, including bulling, i.e. not actually lying, but using enumerations in keeping with the theory, to better convince their audience (Perry, 1963). Using reference philosophical texts on the epistemology of cognitive sciences, I will try to make the connection with the methods used in the Lean Startup, still very popular today, and try to explain the risks of such an approach.

Already in 1904, the geologist Thomas C. Chamberlin approached the topic of methodology in scientific research, in his article “The Methods of the Earth-sciences” (Chamberlin, 1904). According to him, the best method to verify a hypothesis is the one that is most precise, but also exhaustive and unbiased. To avoid this last risk, the researcher (or entrepreneur looking for a business model) must make his or her observations without any preconceptions. Without development, this approach is problematic as it prevents the researcher from detailing an observation to obtain relevant data. As Karl Popper (1969) demonstrated to his students, the simple instruction “observe” naturally leads to the following questioning: “What should be observed?” The choice of our subject of interest therefore arises from a starting hypothesis which arose from one or more observations. Moreover, a lot of our knowledge is built using the hypothetico-deductive model, as described by Bacon (1268). This procedure consists of first formulating a hypothesis, deducting its consequences and then testing them using experiments. Chamberlin (1904) proposed in opposition to “The Method of Colourless Observation” which has just been described, two other methods called “The Method of the Ruling Theory” and “The Method of the Working Hypothesis“. The first consists of carrying out as many experiments as needed to verify a first hypothesis. The second is to consider a hypothesis as tentative until observations contradict it. This is the method recommended in the business startup stimulation programs (including those in which I actively participate).

According to Chamberlin, this latter method can often degenerate into the first one if the experimenter is not vigilant. In both cases, a theory can be compared to the researcher’s child. It is easy to imagine how some people persist, like a magistrate who examines exculpatory and not always incriminatory evidence, a prisoner of his own beliefs and convictions, and only retains information that confirms his hypothesis, neglecting everything else. Because elaborating a theory based on a series of empirical observations, however numerous, confirming the underlying hypothesis, is not without a whole series of difficulties, including the problem of induction (Popper, 1969). Induction consists of a mental operation through which we go from given observations to a proposal that explains it. This method is therefore probabilistic since it is based on the principle that the greater the number of observations, the greater the likelihood that the general theory deduced from the latter be true.

However, according to Karl Popper (1969), no general theory can be justified using this method since most of the time it’s impossible to observe all instances of a type of observable facts since induction can only be justified by induction, which causes an infinite regress. Popper did not hesitate to characterize the generation of hypothesis by induction a myth in the development of objective scientific knowledge. His first argument concerns the selective nature of the observation. To observe, attention must be focused on a point of interest. This point of interest originates from a hypothesis that has been constructed from one or more observations. Which makes it a recursive process. This tendency to look for regularities in the world around us is a very powerful learning mechanism on which conditional reasoning is based. It seems natural, but can pose a whole new series of problems…

This method concerns multiple cognitive biases, of which the best known is the “confirmation bias“, the powerful effects of which have been demonstrated by Wason & Johnson-Laird (1972). Wason’s original experience (1960) suggested that participants discover a rule from the sequence of starting digits “2-4-6”. Participants could propose sequences of figures to test their hypotheses. They were then told whether this suite was compatible with the rule to be discovered.  After making several proposals, they were then asked to formulate a general rule, of which they were absolutely certain. A positive or negative feedback followed. This task uses our ability to generate hypotheses but also the ability to give up. According to the author, these two processes would interact. The experiment was created to highlight the method most commonly used by the participants to find the rule. It also highlights the fact that most of the participants are prepared to formulate a rule based solely on proposals that correspond to their initial hypothesis. Most subjects proposed the sequence of numbers “4 – 6 – 8” (valid), then “6 – 8 – 10” (also valid) to prematurely announce the rule “two is added to each next digit“. In fact, the rule is “figures in ascending order” and only barely 21% of the participants found it without having an erroneous solution. More surprisingly, more than 51% of the proposals, with an erroneous solution, remained consistent with the latter. Most people, even if very intelligent, seem incapable of considering that there may be a different or more basic rule than the one that seems to work (Miller, 1967). Where most participants used the strategy to verify their hypothesis, the others tended to invalidate it using proposals such as “2 – 4 – 10” (valid) or “10 – 6 – 4” (invalid) or “1 – 7 – 13” (valid). As Popper (1969) stated, there is no “valid” inductive method since it is impossible to guarantee that a generalization from observations confirming a hypothesis, be correct.

A second major problem appears with the “always correct” theories that nothing seems able to dispute. These theories are often referred to as pseudoscience, due to this characteristic and one of the most commonly used examples to illustrate it in scientific psychology is psychoanalysis. Thanks to concepts such as ambivalence, resistance, repression or denial, it is always possible to interpret a behaviour with hindsight. A set of behaviours can therefore go in one direction or in the opposite direction without ever questioning the theory. It is these peculiarities that make psychoanalytic theories difficult to experimentally test, giving them longevity (in the case of French speaking countries) and the high progressiveness in their decline contrary to scientific theories that disappear abruptly when they are proven to be false backed up by new data (Meehl, 1978). For Popper (1969), a theory is only scientific if it is falsifiable. This falsifiability would be the demarcation criterion between a scientific theory and one that would not be. The latter formalised, at the end of the 1920s, his conclusions as follows (slightly adapted by myself):

1. It is easy to obtain confirmations, or verifications for almost every theory, if confirmation is being looked for.
2. Confirmations should only be considered if they are the result of risky predictions; that is to say, we should have expected an event which was incompatible with the theory and that would have refuted it.
3. Every “good” scientific theory is a prohibition: it forbids certain things to happen. The more a theory forbids, the better it is.
4. A theory which is not refutable by any conceivable event is non-scientific. Irrefutability is not a virtue of a theory (as people often think), but a vice.
5. Every genuine test of a theory is an attempt to falsify or refute it. Testability is falsifiability; but there are degrees of testability; some theories are more testable, more prone to refutation, than others; they take as it were, greater risks.
6. Evidence confirming the theory should not count except when it is the result of a genuine test attempt; which means that it can be presented as a serious but unsuccessful attempt to falsify the theory. (I speak in such cases of “corroborating evidence“.)
7. Some genuine testable theories, when found to be false, are always upheld by their admirers, for example by introducing ad hoc an auxiliary hypothesis or by re-interpreting the theory in such a way that it escapes the refutation. Such a procedure is always possible, but it rescues the theory from being refuted at the cost of destroying, or at least lowering its scientific status. (I later describe such a rescue operation as a “conventionalist twist“).

Consequently, to increase the quality of a theory, it is necessary, not to increase the number of empirical observations that confirm it, but to actively seek events that make it impossible, such as the alibi of an alleged killer who prevents him from being physically at the scene of the crime at the time it occurred. Researchers (of a business model) must therefore formulate their theory by making clear how it can be refuted, and not the means by which it can be confirmed. Chamberlin (1904) proposed to use the method that he called “The Method of Multiple Working Hypothesis” which implies the elaboration, even before experimentation has begun, of multiple hypotheses, some of which contradict themselves.  So, the researcher must have an open mind and remain open to all possible interpretations of a studied phenomenon, including the possibility that none of the explanations are correct, and that even some new explanations may appear.

This latter effect, more than desirable, is also compatible with the social sciences (in entrepreneurship), which often suggests that behaviour originates from multiple causes that can interact with each other and thus make the repetition and use of statistical tests complicated. This is the major problem in cognitive science. Meehl (1978) does not hesitate to declare that, according to him, the field of research in psychology (clinical, social, etc.) based on correlations and tests of variance is so problematic, that it is scientifically irrelevant. In his original article, Meehl detailed the 20 reasons that led him to declare that Popper’s method (1969) was incompatible with statistical tests such as variance testing and analysis. He declared no more, no less that “The almost universal dependence of the rejection of the null hypothesis is a terrible mistake, is fundamentally unhealthy, is a bad scientific strategy, and is one of the worst things that ever happened in the history of psychology“. To illustrate his point, Meehl states that the variability of psychological processes is so highly dependent on context, that it is impossible to recognise an interesting and relevant meaning outside the experimental environment designed by the researcher. To draw up a list of these parasitic variables is as impossible as quantifying the precise effects on a given dependent variable, not to mention that the samples used are often far too small. He goes even further by questioning meta-analysis aimed at linking studies that study the same psychological construct and to draw a general conclusion on its validity. For Meehl, researchers in psychology who wish to highlight the explanation of a theoretical construct, should prefer consistency testing to significant statistical tests, i.e. use different means and not redundant estimations of a quantitative value for this construct. He concludes by declaring that it is the very nature of certain fields of study in psychology, such as social psychology or differential psychology, which are not compatible with the suggested method and are probably doomed to never have any major theory, scientifically speaking.

Considering everything mentioned previously, it should be noted that in social sciences (and therefore including entrepreneurship), many questions can be satisfied with dichotomous answers such as “Is this one better than the other?“. So, the question is not “what is scientific or not?“, or “what is the best method?“, but “what are the most appropriate tools to achieve this or that objective?” The entrepreneur’s is to use the appropriate tools for their research object and above all to correctly master them to avoid any erroneous interpretation of the results of their experiments, which could, in this precise case, lead to their loss with serious financial consequences. If the significance test was heavily abused in some areas of psychological sciences, to the detriment, for example, of the size of the effect test, is probably due to the way of funding research that drives the most brilliant theorists to publish or perish. The entrepreneur, very often put their own money, or their time, with no certainty of remuneration. Not addressing these issues seriously comes down to the same as putting all your eggs in one basket.

In conclusion, the entrepreneur (or professional researcher) must show intellectual honesty and be as rigorous as their role entails, even if in order to do so, they have to oppose the standards established by the markets (or institutions) in which they evolve. Paul E. Meehl (1989) said, in his famous courses on psychological philosophy the following: “If you get to a point where you say to yourself ‘I will cling to this blasted theory, whatever happens, come hell or high water’ you are no longer respecting the rules of scientific research”. The entrepreneur then enters the vicious circle of the optimistic entrepreneur where hope is fed by the information filtered through confirmation bias. And that’s exactly what you need to avoid at all costs.

References

Bacon, R. (1268). On Experimental Science.

Chamberlin, T. C. (1904). The methods of the earth-sciences. Popular Science Monthly, pp. 66:66-75.

Eccles, J. G. (1992). Under the Spell of the Synapse. In F. Worden, J. Swazey, & G. Adelman, The Neurosciences: Paths of Discovery, I (pp. 159-179). Boston: Birkhäuser Boston.

Meehl, P. E. (1978). Theoretical ricks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progressof soft psychology. Journal of Consulting and Clinical Psychology, pp. 46:806-34.

Meehl, P. E. (1989). Paul E. Meehl Philosophical Psychology Videos. Retrieved from Departement of Psychology of the University of Minnesota: http://www.psych.umn.edu/meehlvideos.php

Miller, G. A. (1967). The Psychology of Communication. New York: Basic Books.

Perry, W. G. (1963). Examsmanship and the Liberal Arts. In Examining in Harvard College: a collection of essays by members of the Harvard faculty (p. Examsmanship and the Liberal Arts.). Cambridge, MA: Harvard University.

Popper, K. (1969). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.

Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129-140. doi:10.1080/17470216008416717

Wason, P., & Johnson-Laird, P. (1972). Psychology of Reasoning: Structure and Content. Cambridge: Harvard University Press.

When I created the first version in 2012, I never suspected such a success. After a few years of practice in agile coaching and the resulting deeper understanding of the Scrum framework, I decided to update the slides with the latest changes from the official guide. I also decided to focus on some of the things that are not well understood in the many Scrum implementations I’ve had to observe.

First, Scrum is not always adapted to the environment that wants to implement it. Prior work on mentalities must be done. I specified that the Scrum Master was responsible for leading the entire organization towards that. The role of the Scrum Master is also in my experience, the most misunderstood element in the framework. Its related slide has been updated to allow you to explain what exactly a Scrum Master is doing on a daily basis.

Then, I also noted that important steps in the Scrum process were deliberately forgotten, often for lack of understanding of their usefulness. I added the notion of visibility and transparency of the backlog on the slide of the Product Owner but also the fact that the Definiton of Done could be the result of a reflection at the level of the entire organization. The slide on the Sprint Planning was already very clear about the usefulness of the Sprint Goal, but the importance of the latter is put more in the slide of the Daily Scrum which is undoubtedly one of the most important Scrum event but usually badly conducted by the team.

Finally, I made some minor corrections, for example in the elements related to the Development Team, the practice of Backlog Refinement which should not exceed 10% of the team’s time during a sprint or what is a Product Owner.

This should now allow you to address the most important elements of Scrum fairly quickly. If you have comments about the content or if you notice an error on one slide or the other, do not hesitate to let me know.

Download the latest version here.

I’ve been very busy lately. First, by finishing my studies of experimental psychology, and then, by all the projects that I had in progress. I still think about the direction this blog should take. Since I have undertaken a doctorate in the field of cognitive neuroscience in which there is an artificial intelligence component, I would like to share with you a reflection I have been making for a few years. I am talking here about research on artificial intelligence called “general” and not machine learning based on statistics. That is to say that commonly understood as being the attempt to copy human intelligence, by creating a unique intelligent agent capable of learning and performing all human tasks as in the excellent TV series “Westworld”.

1. Computer scientists underestimate the complexity of the human brain. 
2. Neuroscientists overestimate the capacity of computing.

Although there are some extraordinary people in the field of artificial intelligence research, the domain is currently dominated by the first category. This has the effect of encouraging the creation of failed projects, some financed to the tune of billions of dollars of public money.

My approach was to try to become both, to play a coordinating and mediating role in a project bringing together the two profiles. After 5 years of intensive study at the University of Liège where you can find some of the finest scientists in fields like short term memory, I am now aware that we know almost nothing about the functioning of the human brain. That we just scratch the surface via indirect measurement methods. That psychology is so fragmented (the different fields), that it is difficult for a specialist to grasp the nuances of each of its components. This creates sterile wars between different schools of thought for example.

On the other hand, all computer scientists are aware of the limitations of computing, often more related to basic physics. The uninitiated have a vision of the computer biased by the films and the sensationalism of journalists whose industrialists profit shamelessly.

I would have the opportunity to come back to these points soon by supporting my analysis a little more. I just wanted to keep a written record somewhere to refer to it from time to time. Meanwhile, stay tuned 🙂

I realize that it has been nearly a year since I have posted on this blog. I was short of time rather than short of ideas. To make amends, I have come back with something slightly more ambitious than previous publications, the first of many I hope. In this article I will explain to you the results of a small study that attempts to verify a stereotype that was often mentioned to me during my career in IT: that software developers are introverts. This vision of the programmer runs counter to another persistent stereotype you might know: that software developers are attracted to novelty. Indeed, it has been shown that novelty seeking is positively correlated with extraversion and emotional stability (De Fruyt, Van De Wiele, & Van Heeringen 2000). This was confirmed to me by Michel Hansenne, professor of differential psychology at the University of Liège (2014). If these two stereotypes are contradictory, which one is true?

The first clue I found was in the search for novelty: it may be noted that headhunters specializing in the recruitment of software developers have not waited for the results of scientific studies to use this argument to entice new recruits by telling them about new technologies, they address this before even the issue of finance. This aspect of the personality of the average developer seems quite plausible because I have noticed many times that some of them adopt what I call “CV Driven Development” This is a counterproductive practice (for the company) which is to focus almost always new technologies, not for objective technical reasons but to be able to add this new knowledge to their resume or only for the pleasure of experiencing something new.

I then asked if anyone else had asked the question. Although I found no academic scientific study on the issue, I can still quote the Evans Data Corporation report “Developer Marketing Survey 2014“, the results were included and discussed in the popular online specialist magazine InfoQ (Avram, 2013). The report from this company, specializing in the analysis of the target population for this study attempts to provide an answer to this hypothesis through a questionnaire sent annually to its panel of over 75,000 developers in 85 different countries. Their results confirm the absence of introversion and confirms novelty seeking as a very common characteristic in the target population. My study attempted to determine whether similar results can be obtained using a standardized personality survey rather than self-evaluations in simple questionnaires.

To test my hypotheses, I used two personality surveys which compared the results to the mean population using the Student t test. One is the Temperament and Character Inventory-Revised (TCI-R: wiki) Cloninger. The second is the Revised NEO Personality Inventory (NEO PI-R: wiki) of Costa & McCrae.

The sample consisted of a total of 50 subjects, all masculine, some of whom responded to both questionnaires (32) and some only one (TCI-R: 38; NEO PI-R: 44). Note that this is a convenient sample since all these subjects are final year students of computer science who participated in a specialized coaching program of my design for which access was conditional on performance in an entry test.

If the results shed light on our question, they also give some interesting insight on other matters. First, with the TCI-R, there is a strong difference in the temperament “Novelty Seeking” (p = 0.000), which confirms the hypothesis that the developers would be eager for the new, this is not very compatible with introversion. One can also note an interesting way that the character “Self-Transcendence” which is associated with spirituality, is also significantly different from the mean population (p = 0.037), despite the fact that the sample was culturally eclectic.

The results from NEO PI-R found one statistically significant difference in the factor “Neuroticism” (p = 0.015) which determines emotional stability. This could be explained by the fact that most of the sample consisted of placed (ie successful) students. One can also note the interesting way that extraversion is a slightly above average level, which also confirms the hypothesis.

My results show that, for my sample, the dominant personality type is far from introversion. In fact we can demonstrate a clear difference from the general population in the dimension of novelty seeking, the opposite of introverted behaviour.

It is possible that the origin of this stereotype comes from a misunderstanding about what introversion is. The concept could be confused with another facet of the personality: ie sociability. But even here, the results of my survey do not support the idea of introversion (Agreeableness in the NEO Pi-R or reward dependence of the TCI-R). I think the genesis of this stereotype is in the very nature of computing that by its apparent complexity, may have created a divide between IT and others, in specific contexts, and perhaps even at different times. To test this idea would require the development of a larger study.

With the limitations of this study, one might assume that the difference is only statistically significant due to a sampling problem. Indeed, the subjects were all involved in an internship supervised by a center for innovation and creative projects. One could easily imagine that these subjects have applied to do their internship with us because they were originally already very interested in innovation and new technologies.

In conclusion, I would say that this study and the various initiatives in this area show that studying this particular population could prove to be a very interesting opportunity. The financing of an international study could also be possible: most customers mentioned on the website Evans Data Corporation consist primarily of multinational technology companies like Microsoft, IBM, HP, Google or Adobe for example. It seems to me particularly appropriate to find collaborators with one foot in the industry of these companies and the other in an academic institution doing research in psychology….

Want to participate in one of my future studies? Please fill in this form and I will contact you: http://bit.ly/psydevform

References

• Avram, A. (2013, Février 20). Are Developers Introverted or Extroverted? Are They Intuitive or Logical? Retrieved from InfoQ: http://www.infoq.com/news/2013/02/Introverted-Intuitive-Logical
• De Fruyt, F., Van De Wiele, L., & Van Heeringen, C. (2000). Cloninger’s Psychobiological Model of Temperament and Character and the Five-Factor Model of Personality. Personality and Individual Differences, 441-452.
• Hansenne, M. (2014). Entrevue & echange d’emails. (P. Mengal, Interviewer)

Entrepreneurs are often trapped in a vicious circle of hope.  Hope clouds judgement and can be what prevents the entrepreneur seeing things clearly and taking the appropriate decisions.  Hope is so seducing that it’s what is used in most personal development or “get rich in x lessons” books.  Hope is also so powerful and rewarding that it is fed by proofs generated by the confirmation bias, that is itself fueled by hope. Entrepreneurs must learn its underlying concepts and learn to get objective opinions from others.

Confirmation bias refers to a type of selective thinking whereby one tends to notice and to look for what confirms one’s beliefs, and to ignore, not look for, or undervalue the relevance of what contradicts one’s beliefs.  [Skeptics Dictionnary]

Before talking about confirmation bias, let’s understand what is hope from a psychological point of view.  Hope is one of the many mental defense mechanism we have that is triggered in order to disconnect us from hurtful emotions (anxiety, sadness, despair, ….).  It uses thoughts to construct a positive scenario of the future.  People often grab these thoughts as a life buoy to avoid the reality of the present moment. We usually hope for a better future when we are  uncomfortable with the present.  Hope is what drives many wantrepreneurs.  Hope will make entrepreneurship’s book writers rich, not you.  Happy people hope for the best, once, then stop thinking about it.

In order to fuel hope, you need proofs that what you see in the future is possible and is likely to happen. This is when the confirmation bias enters into action. Every single proof you see that makes your predictions credible is highlighted, while every single piece of evidence that it won’t is denied. Those proofs stimulate hope that itself forces you to suffer from the confirmation bias. It’s a vicious circle.  One great example of the relationship between hope and the confirmation bias is seen with believers of the 2012 end of world event.  They can point to you dozens of  scientific  studies that prove it will happen, ignoring the hundred other proofs that it won’t.  One seducing thought in the 2012 case is that the event can potentially make them better than they are now.  Hope is triggered by a desire for change and fueled by confirmation bias.  Desire for change is not the only way to trigger hope.  Any unwanted emotion, such as fear, can also be a very good motivator: some religions claim that if you are not a good practitioner, you won’t go to paradise, but burn in hell forever. With entrepreneurs the desire for change is the key to the process.

Conscious thinking is not the only factor: things get worse when we take into consideration theories of biological psychology.  Desire to change is not the only motivator for hope. There is a physiological reward for the behavior. Dr. Robert Sapolsky, professor of biology and neurology at Stanford University conducted experiments that showed that there is a direct link between hope and dopamine (pleasure hormone) releases in the brain.  Studies even show that we get higher dopamine releases when there are more uncertainties. Uncertainties? That is certainly something that entrepreneurs can relate to.

The ability to cope with temporary difficulties is one of the entrepreneur’s required abilities. Too many people can’t go through what Seth Godin called The Dip.  Defeatism, the opposite of hope, is one of the obvious failure factors in entrepreneurship.  Often they decide to stop.  They think their efforts are not worth it anymore.  Defeatism works just like hope. Your judgment is biased and you see things from the negative side.  It is also fueled by the confirmation bias in the same way.

Optimism is often suggested as a strategy to fight defeatism.  It’s true that if you tend to be defeatist, hope can counter balance the feeling by replacing negative thoughts by positive ones.  It’s positive psychology. And it’s even suggested by the Emotional Intelligence guru Daniel Goleman.

Having hope means that one will not  give in to overwhelming anxiety, a defeatist attitude, or  depression in the face of difficult challenges or setbacks. [Daniel Goleman]

Just as defeatism must be avoided, hope can’t be a strategy on the long term. It can even  hurt as much as defeatism.  Do you remember how you felt after you really hoped that something would happen and it did not? You would be really surprised if you noted each prediction you make and compare them with what actually happened.  Hope will play against you.  It will hurt, it will hide a more concrete problem, and more importantly, it will bias your judgment.

Self awareness, again, is the key. To manage the process, consider hope (or defeatism) as a signal you must decode by being aware of the physical and psychological mechanisms. If we suspect we are biased, we must not believe entirely what we think and assess every hypothesis we make with realistic information.  Market studies and/or customer development are ways to assess our assumptions, as well getting mentorships from more experienced people who have learnt the hard way. With some practice, you will be able to acquire the self awareness required to avoid being trapped in those loops. Be a mindful hacker.

There are two common types of behavior I have noticed in people regarding their competition. You have the competition driven people, and the competition denial people .

The first constantly monitor their competitor’s activity by visiting their websites & forums or googling them constantly, while the latter meticulously try to avoid any piece of text mentioning their names.  Sometimes, the latter behaves like the former by accident, triggered by some source of information they have read by mistake… Both emotionally driven behaviors are very dangerous for their business.

A third widespread behavior, linked to the same concept, is to not enter a market because of the presence of one or more competitors or even worse, entering a market without studying the competition in detail. All these types of behavior will make your business decisions weaker. The solution is to take your competition for what it is, no more, no less and try to intelligently decode your emotions and thoughts (both are linked).

When someone is competition driven, he will often take any of the competition’s initiatives as something he doesn’t have (and therefore he must have), instead of taking it for what it is: an initiative that can be equally good or bad.  The resulting action is to try copying the competitor’s ideas (but perhaps doing them better…). The problem with that behavior is that the competition will always be significantly in advance and the market will inevitably perceive him as the follower, not the leader, constantly competing on basic stuff such as features & pricing.  It’s not only his innovation that will be affected, but his overall entrepreneur’s ability to take good decisions.  Being so abnormally obsessed by a competitor can be the start of very serious trouble possibly leading to burnout or worse, to the abandonment of the venture.  His fear of competition is irrational.  Many of his important decisions will be biased by his distorted perception of his challengers.  It’s not how he should develop a wealthy company.

Competition denial on the other hand, is the action of ignoring the competition completely. This is a very commonly suggested strategy to solve the results of competition driven behavior. That is, by ignoring anything the competition do, his judgment, innovation & decisions are not affected by what they are doing. After all, he may say, listening to the market and customers is the only valuable thing to do.  Instead of being competition driven, he becomes customer driven.  It’s a seducing way to drive your business, but ignoring the opponent can hurt you as much as being obsessed by them.

If you know the enemy and know yourself, you need not fear the result of a hundred battles.  If you know yourself but not the enemy, for every victory gained you will also suffer a defeat.  If you know neither the enemy nor yourself, you will succumb in every battle.

Sun Tzu, The Art Of War

This is something I learnt from practicing martial arts. Before an important fight, you must watch videos of your opponents previous battles (or previous fights in a competition).  Watch how he moves, what he is good at, what he sucks at.  It will helps you develop effective ways to attack him and defend yourself.  Strategy is not the only important thing in a battle.  Attitude can make all the difference.  If you have a very determined opponent in front of you, this will affect your morale during the battle, and therefore your performance.  Invest in strategy by analyzing the market, including competitors, and in self confidence.  The former will help your differentiation in the market.  The later will surely contribute to making your competitor fear you, leading them to one or both of the unwanted behaviors described in this post, and making you the leader. Maybe.

In this post, I’ll talk about a problem that affected me personally really badly and that I see in too many other fellow entrepreneurs & developers.

Twelve years ago I thought that increasing my productivity would solve my problems.  It did the exact opposite.  My problems did not disappear.  They became bigger as I became more highly productive.  Until I learnt that I was missing an obvious piece of perfect time management: commitment or what I prefer to call: controlled input.  For those who don’t see the obvious coming (like me a few years ago), this is for you.

In 2000, I started freelancing as I was hit by the entrepreneurial fever. Very quickly I became overwhelmed by work and projects. Sometimes, I had to completely stop moving and think “what is it you are doing?”  I was doing 3 things at the same time, in addition to reacting to every external disturbance such as phone calls. That’s when I decided to invest in something I hadn’t been taught at school or by my parents: organizing myself.  At the time, delegating was out of my reach.

I purchased top rated books on the subject and went to training courses. I started to learn and to put everything into practice.  Productivity increased dramatically.  I became an unstoppable working machine.  In the less than 2 years that followed this, I was able to create 5 companies (with the satisfaction that all still exist today) in addition to freelancing and working on the numerous side projects I had.   This was made possible with increased productivity and the fact that almost 95% of my conscious time was spent working. I started to earn a lot of money, more than I could handle.  But all of this had a price: I became like a zombie and eventually, I burnt out.

I had missed something very important that I hadn’t learnt how to manage yet: my commitments.  I was tempted to say yes to everyone, and more importantly, to myself.  As an example, any new idea I had would be turned into a new company, immediately.  I finally learnt how to solve that problem the hard way.

When I talk about it to friends, employees or students, I use the illustration of the tap and the funnel. The tasks coming in flow from the tap, your input, while the bottleneck of the funnel is you, your maximum output, your productivity. What’s in the funnel is your commitment.

Below is an illustration of three possible scenarios.

• Overwhelmed: you have too much work and you can’t face it. The fact you are overwhelmed affects your productivity negatively because of stress and other technical factors, eg having to multi task. Not to count the waste of unfinished tasks (or low quality).
• Increased Capacity: you decide to learn GTD to increase your productivity. It works, you have a larger bottleneck, but you are still overwhelmed. You do more with the same time.  By your new behavior, you teach others (and yourself) that you can do even more. Instead of solving your problem, this actually worsens it.
• Controlled Input: you control both external solicitations and personal commitments. Input is controlled and matches your capacity. Everything is under control. This is a part of self-awareness.

Properly or improperly managing your commitments has many other effects, for example – trust. The more commitment you fail to meet, the more you teach others (by conditioning) that you are not reliable. They will progressively lose trust. Everything you say will be seen as something said by the unreliable guy. It works both sides: if you succeed in meeting almost every commitment you make, you will teach others that you are very reliable. You will build trust and increase your circle of influence. This includes trust in yourself.  

Here are few ideas on how to manage input:

• Deadlines set by others: in the developer’s world, we often face situations in which other people set deadlines for us. When I face such situation, I re-estimate the task myself and compare it to my actual commitments. If there is a difference, I confront the person who set the deadline. In short, I learn to say no, but with a proper argument. Saying no without any explanation is not only rude, but unprofessional.
• External disturbances: I’m always amazed when I see someone looking at his ringing phone saying: “oh no, not him, he disturbs me all the time“. Why not simply ignore the call? You are NOT committed to answer the phone, you can call him back at a better time. This statement is valid for everything including emails. They can wait another 3 hours to get an answer, right? In addition, these interruptions are real productivity killers (Nass, Ophir, Wagner 2009).
• Ongoing projects: Limit your ongoing projects. Don’t involve yourself in two big projects at the same time. I limit myself to one large project and one or two much smaller ones. In order to do that, I put every idea or thing I would like to do on a list. I update the list often with new stuff, but nothing goes out of it until I have the free room (time) for it.

Increasing your productivity is very easy. The techniques work and are easy to learn. The hard part is learning to say no. To others, but also yourself.  If you are like me, it will take some time to be completely healed from this bad behavior.  But being aware is certainly one big step.  Be productive, control your input, be happy (Oswald, Proto, Sgroi 2009).

There is a difference of judgment in any activity depending if we are an actor or an observer (Malle, Knobe, Nelson 2007). I realized this fact when I was practicing martial arts, long before my studies in psychology. The audience, usually parents, downplayed the difficulty of the discipline and very often judged the activity incorrectly. One of the most common expressions was, “but it is only dancing.”

This comment annoyed many of the new practitioners, but not the more experienced who knew that this was a gross misjudgement. You could see the difference when these same people decided to try the discipline once. As you can imagine, most did not usually go beyond the first attempt as it was physically demanding and much more difficult than they had imagined. Their judgement of the activity observed radically changed when they became an actor.

In our everyday life, we alternate between the positions of observer and actor. In both situations, we make judgments and many of the various decisions we take are based on them. As an observer, we can judge incorrectly the behavior of others. As an actor, we take into account the judgment of observers. It is important to recognize these situations and act accordingly.

Let’s take a concrete example: starting a software business. This is a subject dear to my heart as I far too often watch the growing disillusions of entrepreneurs when they take in the reality of things. As the disillusionment grows, only those predisposed (very rare) or those working for pleasure keep going.

The reality is that entrepreneurs work very hard and take on tasks that we would probably never have agreed to do as an employee.  You must also be aware that very few projects succeed at first; most stop after the first failure.

When we see talented entrepreneurs in the newspapers, we think it’s easy and as simple as registering a domain name, writing 10 lines of code and then selling your company for millions. The reality is that most successful people have worked hard, often in physical and psychological pain, for many years and have faced all kinds of problems. They made the difference by persevering. All my successes have been preceded by hard work and pain.

It is difficult to realize this when you have not been there yourself. But how do you know before you make a judgement or take an important decision?

The first step is certainly to become aware of any bias and take it into account. It is very difficult as these behaviors are unconscious and judgment is rooted in how we operate.

When you realize that you are in an observer’s position and about to judge and eventually take a decision, you should not be satisfied with the information available. As you may have noticed, observing is insufficient to get an opinion, even if one is aware of the bias. Failing testing it yourself (and you can’t start a business as a test in the same way you can attend a single martial art class), the only solution is to ask questions of the actors. Those who have experience in the field or are in the situation. Do not just question one of them. The more people you question, the more relevant your information.

The only concern with this approach is that it can block you from moving forward. Indeed, if you ask too many questions you can start to stagnate. Everyone now knows that one of the primary qualities of an entrepreneur is his ability to move forward quickly. Many are also characterized by a certain impulsive trait which will be discussed in a future article.

Awareness of the actor / observer asymmetry is directly related to critical thinking: identifying biases, separating fact from opinion and analyzing data. Awareness of our mental functioning is, again, the key.