Based on the previous definition of talent, a self-report questionnaire, called Unique Talent Code (UTC), has been developed that includes the three aspects of talent: Drivers, Passions and Skills. We will explain all three aspects below.

The role of “Drivers” in our Unique Talent Code model

The iceberg model of David McClelland (1973) looks at a person’s visible behaviour, knowledge and skills and the underlying unexpressed and unconscious deeper layers, that we call driving factors (or drivers). In general, a person’s knowledge, skills and behaviours can be found above the waterline of the iceberg. Below the waterline we find terms such as values and beliefs, self-esteem, characteristics, and driving factors. According to us, analizing these driving factors gives us insight in an important part of people’s talent, namely that part of talent Dewulf (2009) states will give a lot of energy while performing a task.

As we said earlier, we believe that a simple model for talent is important for the understanding and communication about talent. Therefor we created 4 categories of driving factors that we have named AIR, WATER, EARTH and FIRE. In the UTC we developed a self-report questionnaire that measure these four driving factors (drivers):

1) AIR

The people who score high on the driver “Air” usually have a lot of curiousity, creativity, are very analytical and have a good helicopter view. Those who score low on Air usually have less of a natural tendency to ask questions, explore different options, be inquisitive.

2) WATER

The people that score high on the driver “Water” are very sociable, sensitive, accommodating and looking for harmony. Those who score low on Water are usually have less of a natural tendency to give immediate attention to others, network with those around and need more time to show empathy.

3) EARTH

The people that score high on the driver  “Earth” usually are very structured, pragmatic, persevere and reliable. Those who score low on Earth usually have less of a natural tendency to organise processes and time where possible, might not have a strict to-do-list and do not pretend things and people to be consistently reliable.

4) FIRE

The people that score high on the driver  “Fire” usually take a lot of initiative, very much enjoy a fast pace and tend to be quite  persuasive and sometimes competitive. Those who score low on Fire have less of a natural tendency to take the lead, try to influence others and do not desire to stand in the spotlight most of the time.

The role of “Passions” in our Unique Talent Code model

Since our goal is to help people become entrepreneurs of their own talent and in this way improve their well-being, we use Passions as an important ingredient of talent and it is a criterion in the matching algorithm that associates people with opportunities for collaboration on our platform.

We consider a person’s passions a useful road-sign indicating the type or field of work that could make this person happy professionally. Like Confucius (Confucius, “Choose a job you love and you will never have to work one day in your life”), we assume that if a person does something he/she loves this is a source of energy. The enthusiasm that is generated when a person works with his/her Passions has some powerful effects (Curran, T., Hill, A. P., Appleton, P. R., & Vallerand, R. J., (2015). The psychology of passion, A meta-analytical review of a decade of research on intrapersonal outcomes. In Motivation and Emotion):

1. It gives the motivation to learn new skills (to develop the passion).
2. It gives the energy to do some tasks or participate in team work without requiring an immediate financial return.
3. It is contagious and so attracts and motivates other people who are/become passionate too.

We ask people to code their passions to help them identify opportunities that are closely related to their passions. We have created our Passions dataset in a way that we can apply Machine Learning to analyse possible correlations between certain Passions and the person’s Drivers. We enrich this correlation hypothesis also by asking people to indicate their “Reason Why” for each Passion. Since many have probably never actually thought in a structured way about Passions before, we facilitate this coding by using the following breakdown, see the example below taken from our platform:

To satisfy the above functionalities, we have defined “Passions” more broadly than usual as the “things I love” (Cambridge Dictionary defines Passions as: “an extreme interest or wish for doing something such as a hobby or an activity that someone does for pleasure when they are not working”) and divided these further into leisure activities (things I do) and lifestyle (Cambridge Dictionary defines Lifestyle as: “the particular way that a person or group lives and the values and ideas supported by that person or group”) choices (things I like more in general).

Since we have not found pre-existing ontologies useful to our coding purposes, we are developing our own dataset of Leisure: Sports & Hobbies and Lifestyle: Preferences and Favourites. Sports and Hobbies are quite straightforward subcategories, and although very numerous they are easy to classify and keep updated. Preferences have been more complex to code since they aim to represent the lifestyle choices that people make, and when researching we found many different definitions and classifications. This is why we are aiming at developing our own list, also with the suggestions users will make on the platform. The Favourites category aims to represent the powerful impact of the social platforms on a person’s public and private profile. Since following certain topics on the different channels today has taken the place of many Hobbies, we consider this an important subcategory in the ontology we are building.

The way we have built our Passions (and Skills) data model and dynamic dataset as the platform backend allows us to measure a “distance” between single passions and between single passions and skills. This distance can be considered an indicator of potential success in the development of a specific competence. With support of Machine Learning and AI we will be able to create correlation hypothesis which can be tested to make predictions around career choice satisfaction potential.

The role of “Skills” in our Unique Talent Code model

As mentioned before skills are considered the most visible part of a person’s talent portfolio. Skills are typically defined as “the ability and capacity acquired through deliberate, systematic, and sustained effort to smoothly and adaptively carryout complex activities or job functions involving ideas (cognitive skills), things (technical skills), and/or people (interpersonal skills)” (Business Dictionary). As such, a certain set of skills (or competencies) are the result of the choices a person has made in terms of education, practice and work experience. Skills can be learned with study and practice and the market for skills has changed continuously over the last 300 years (Marina Gorbis: The History and Future of Work)

The skillsets that are most in demand today are rapidly changing, as the WEF (World Economic Forum 2015: “The future of jobs”) and other institutions are pointing out. The transformation of the workplace due to digital technology requires millions of people to reconsider which skills to invest in in terms of time and money to assure a satisfying profession. On the one hand, there is a major focus on the development of vocational (Cedefop 2008: “Mind the gap: Europe’s potential skills deficit”, Worldfinance 2017: “US must tackle growing skills gap”) and STEM (United Nations goals: Close the global Science Technology Engineering Mathematics skill gap) skills for which demand exceeds supply; on the other hand, WEF and other institutions advocate for the investment in the development of skills that robots will not easily be able to perform, such as Communication, Collaboration and Critical Thinking. The current discussion on skills is more intense than talent on social media (Google gives 2.2 billion mentions of skills on the search engine, versus 157 million on talent) and various institutions are working on skills classifications (Cedefop, ILO, Disco EU, ESCO, SOC (USA), Hays, Mercer, The House of Skills etc). However, since none of these satisfy our criteria for matching people with the right collaboration opportunities, we have developed our own classification to fit the weighting logics in our matching algorithm.

This segmentation leads to 3 usable skill categories:

Professional Capabilities: These typically receive most attention on an average CV or LinkedIn profile. Today, they determine a person’s options in the traditional job market and both human and digital recruitment criteria put these as a minimum condition for hiring/sourcing. A person’s portfolio of professional capabilities can vary significantly in a lifetime, depending on education and career tracks.

Enabling Competencies: These skills are useful in many professions, since they enable and support a person’s professional capabilities in action. An obvious example is “business English” but we have also considered others that allow a person to interact and work more effectively: the more digital languages and general computer skills and the artistic languages like music, painting etc. These enabling competences are measurable with external norms and standards. These latter skills are much undervalued in today’s workplace, but they “nourish” many other skills (For example Tish Seabrook: Creatubbles.com, Michela Tramonti: “Enhancing STEM skills through Art” (2017), Khan Zada: “Learning through Art and Creativity”) and are worth investing in, at any age.

Human-Centric Competencies: These skills are gaining more attention today in the new world of work where automation and robots are transforming many jobs (WEF: The Future of Jobs (2015), Frey &Osborne: The future of Employment (2013), McKinsey: Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages (2017)). Often referred to as “soft skills”, they receive an increasing share from large corporate training budgets since they are proven to make a difference in a persons’ job performance. They are powerful enablers, and as of today there are many different ways to measure, assess and develop these skills. There are no global references yet, for example as to how much “leadership” or “negotiation” skill a certain person is able to apply at work.

Therefore, these skills have less weight in our matching algorithm, but we invest more time and effort to coach our users on the self-development of these skills since they can really make the difference in terms of professional effectiveness and competitive positioning on the job market. Whereas our dataset has thousands of professional capabilities and hundreds enabling competencies we have selected only 50 human centric competencies to support this goal.

Human-centric competencies are more closely linked to personality (See Daniel Goleman’s work on Emotional Intelligence) and because of this tend to be more “embedded” in our behaviour. Improving these skills requires some education (in general less than Professional Capabilities) but a significant amount of practice. Typically, one receives less useable feedback on their human-centric competencies which makes it more difficult for a person to measure his/her improvement. To facilitate the self-development process, we have further segmented this cluster in 5 categories, with 10 competencies each.

All the skills and passions are linked to the user’s profile through self-assessment with a step-by-step online guidance and will show in the user’s Unique Talent Code on the site. Users that look for potential collaborators on the site for participating in an Opportunity, fill out a “Talent Need Definition” drawing from the same dataset items.

For matching purposes, our algorithm calculates the distances between the Talent Need Definition and the users, showing a ranking with those who have the highest fit.

References:

Buckingham, M., & Clifton, D. O. (2001). Now, discover your strengths. New York: Free Press.

Curran, T., Hill, A. P., Appleton, P. R., & Vallerand, R. J., (2015).  The psychology of passion, A meta-analytical review of a decade of research on intrapersonal outcome. Article in Motivation and Emotion, 39(5), 631-655.

Dewulf, L., (2012). Ik kies voor mijn talent. Uitgeverij Scriptum.

Luthans, F. (2002). The need for and meaning of positive organizational behavior. Journal of Organizational Behavior, 23, 695-706.

McClelland, D., (1973). Testing for competence rather than intelligence. American Psychologist, 28, 1-14.

Seligman, M. (2002). Authentic happiness: Using the new positive psychology to realize your potential for lasting fulfillment. New York: Free Press.

United Nations (2011). Resolution 65/309 Happiness: towards a holistic approach to development.