Broad-level competencies inspired by McClelland and Spencer
Big 5 personality framework
Schwartz theory of universal human values
Decades of academic science research
combined with groundbreaking technology.
There’s a difference between good and great hiring decisions, and the question of how to consistently make great hiring decisions has puzzled scientists for decades.
Imagine this everyday scenario: an executive search consultant has compiled a shortlist of candidates for a leadership role, including both internal and external candidates. How much more do you know about someone you have worked with, side-by-side?
There’s a notable difference in the information quality. With a colleague, you have first-hand knowledge about past performance, attitudes, work habits, and people skills. This imbalance of information is what Teamscope aims to solve.
It has been suggested that, for an internal candidate, the predictability of subsequent performance is about 80 to 90% accurate. Whereas, for an external hire, the same predictability is only around 55 to 65% correct.
To achieve overall predictability and candidate job fit into the 80 to 90% range, you need to know your external candidates as well as your internal ones! At Teamscope, we know that combining data science with personality and value surveys can provide insights and data points to increase the predictably of the hiring process.
Our goal is to help organizations build stronger and more engaged teams.
We do this through data. More specifically, we combine big data analytics with scientific testing to better understand people, and then use that data to help team leaders make better hiring decisions and build a culture where people can perform at their best.
We know that a superior manager produces superior results, thus hiring the best candidates can have an enormous effect on the success of your organization. This is why these types of decisions can’t be made on intuition or gut feeling. The only sure way to hire people who are both competent and motivated is to use repeatable, evidence-based decision-making processes.
We rely on three basic frameworks to better understand people:
In addition to traditional surveys, we rely on IBM Watson AI-powered linguistic analysis methods for predicting certain characteristics of individuals based on unstructured data sources.
Fit Theory is an important cornerstone of team analytics and candidate assessment. Fit is defined in organizational psychology literature as the degree to which individual and organizational attributes are compatible. Fit can take two forms:
There are many types of environments where a fit can occur, we are focused on the person-team fit, which means the fit between individual attributes and those of the work group. Research has suggested that person-team fit relates to attitudes towards peers, job attitudes and organizational citizenship. Values fit, on the other hand, has been found to strengthen organizational culture, improve efficiency, and help with employee retention.
Competencies are the knowledge, skills, and abilities that are required to perform a job successfully. Usually, there are a few core competencies that differentiate top performers in a given industry and job function.
Defining the critical competencies of a job and evaluating the competencies of candidates in a methodical and structured manner is the most objective way to decide which candidates to shortlist. However, while competencies help to evaluate the ability of a candidate to do the job, they do not help to predict how a candidate would influence team dynamics.
Personality can be defined as the distinct patterns of behavior that characterize each individual. Personality is considered to arise from within the individual and remains fairly consistent throughout life.
Personality assessment is often used to help people learn more about themselves and their unique characteristics. Understanding your own personality traits as well as those of your teammates will help you leverage the individual strengths of each person of your team and support them in aspects they do not feel confident in. Making personality data visible to every team member will help you communicate better and create an environment where everyone can perform their best.
The Big 5 personality model became popular thanks to the research led by Paul Costa and Robert R. McCrae in the 1970s. Since then, the Big 5 personality model has become the leading framework used in virtually every scientific study of human personality. In addition to the five personality traits (extraversion, openness, agreeableness, conscientiousness, and emotional range), research has also identified six unique facets of each personality trait that are often more predictive of situation-specific behavior.
At Teamscope, we also rely on the research of professor Jüri Allik from the University of Tartu and his colleagues for the latest science in human personalities.
The Big 5 measures five personality dimensions:
If personality best represents how we typically behave in a given situation, then values describe the motivational basis of our attitudes and behavior.
Teams that share similar values tend to be more cohesive, more engaged at work, and find it easier to reach consensus on important matters. Not surprisingly, candidates that share similar values to the team are up to four times more likely to stay with the company long-term.
We rely on the Schwartz theory of basic human values to understand what really motivates each individual and how values and motivation influence teams. Developed by Shalom H. Schwartz (2012), the theory defines values as intrinsic motivational goals that correspond to the following features:
We use the refined theory of basic human values (originally published by Shalom H. Schwartz in 2012) to analyze how values and motivation influence teams.
Basic human values form a circular continuum that reflects the motivational conflict or compatibility among them. The more compatible any two values, the closer they are on the circle; the more in conflict, the more distant.
While important values guide what people do, low importance values may influence what they do not do. A single behavior may be motivated by multiple values, and some behaviors are shaped by the trade-off between values that propel and those that oppose the behavior.
Individuals strive to fit, and sharing similar a value importance order leads to optimal outcomes. Fit on high importance values is considered to have the most impact, because it satisfies people’s core needs and is considered intrinsically motivating. Some studies have also suggested that a fit on a low importance level also deserves attention, because some low score values might represent an avoidance motivation.
Watson is the A.I. driven platform developed by IBM that first became famous in 2010 when it beat Jeopardy champions on live TV. Since then, IBM Watson has developed into an A.I. platform that powers medical research, customer service, risk management, education, financial service applications, and many others.
At Teamscope, we rely on Watson to help us process natural languages – which means that Watson helps us examine the interactions between computer languages and natural (human) languages. Watson enables us to understand the sentiment and topic of each conversation, and even helps predict the personality characteristics and values of each individual. Incorporating Watson into our analysis allows us to understand people based on their blog posts, cover letters, resumes, and basically any other text they have written.
We combined the findings from roughly 200 academic studies to develop our data model. Team relationships are complex and there is no single comprehensive theory of team performance, but most studies have concluded that:
Sounds simple, but implementing these principles gets quite complex. Simply calculating the team average of these characteristics is not sufficient - to really understand the team dynamics, we evaluate each interaction (user pair) in a team and compare how each team member and candidate influences the team as a whole. For even a small team of seven people, it adds up to thousands of data points.
In addition to the initial assessment, we gather feedback data from each team and use that to constantly look for new data patterns that help to refine our data model and offer additional insights for hiring and team development.
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