About Us
MayGraph was
started with one Goal: to help people find the best teams, and to help
companies find star performers while decreasing turnover.
To achieve this goal, I delved deeply into organizational
and psychological research on group dynamics, individual performance, and team
science. During this research, I learned some very interesting things about how
teams behave and how individuals contribute to a teamfs overall success.
For instance, research shows that a single productive
employee can increase the productivity of a group at both the group and
individual levels.
Research out of MITfs Human Dynamics Laboratory found
that teams who gfit wellh together communicated differently than lower
performing teams and individuals within these teams felt more as equals than as
competitors. In one instance, similarly built teams performed extremely
differently. In this instance, researchers found something many business
leaders may find counter-intuitive. By improving gslack-timeh
communication and collaboration among the teams with lower performance, the
company saw $15 million in productivity gains.
"We found that you could pretty
accurately predict how well the group or individual would do without knowing
any of the group or the content of their work.h
The data suggested that the success of
teams had much less to do with experience, education, gender balance, or even
personality types; it was closely correlated with a single factor: "Does
everybody talk to each other?g
Alex
Pentland, professor of media arts and sciences at MIT
Meta-analyses of person/organization (P-O) fit indicate
that P-O fit is positively related to employee job performance, job
satisfaction, organizational commitment, and organizational citizenship
behaviors, and is negatively related to intent to leave and turnover
(Kristof-Brown, Zimmerman, & Johnson, 2005; Verquer,
Beehr, & Wagner, 2003).
Good cultural fit is associated with many more positive
outcomes too. A 2005 meta-analysis by Kristof-Brown reported that employees
who fit well with their organization, coworkers, and supervisor:
·
Had greater job satisfaction
·
Identified more with their company
·
Were more likely to remain with their
organization
·
Were more committed
·
Showed superior job performance
Studies of cultural fit across many countries have also
found a relationship between cultural fit and mental and physical health. If a
personfs job fits his/her personality well, he/she is less likely to exhibit
signs of depression, anxiety, and may even live longer. Research also
shows that nearly half (43%) of the variation between different teams on
individualsf job satisfaction is explained by good cultural fit.
Why
fit is rewarding?
·
Increased ease of communication, improved
predictability, increased interpersonal interaction and increased trust between
individuals (Edwards and Cable, 2009).
·
Employees who understand their companyfs
culture and are aligned with it outperform competition three-fold.
What are the consequences of poor fit?
In gGood to Greath, Jim Collins proposed that companies
where employees are culturally aligned with the company are 6 times more
profitable than competition.
Current
Directions
With our science backed tests and questionnaires, we help
people find great teams and help great teams find their missing link to greater
success. These tests are multifaceted and are used to build a quantitative
profile of qualitative aspects of individuals and companies.
Our goal is to use the results from these AI algorithms
to fit people to team, and fit teams to people, so as to maximize every aspect
of the interactions between people and the teams to which these people belong.
We combine data from these tests with complex AI decision
algorithms to ensure our results are accurate. As more and more people take our
tests our AI system gets more and more accurate.
MayGraph Matching Algorithm:
Our matching algorithm works on two steps:
SIMILARITY:
Our
similarity rating index is based on the personality and life styles of
individuals who take our tests. In a broadest sense our tests measure: common
goals, values, extroversion etc.
DIVERSITY
Some
people claim that diversity is always good and some claim that it's always bad.
Based on our extensive study of industrial/organizational psychology, personality
and social psychology, and management science, we developed a set of algorithms
that capture the best qualities of diversity.
We try to make teams as diverse as possible in terms of
demographics, gender, age, education, international exposure, experiences etc.
We want good diversity, versus bad
diversity. That is, we want diversity that is beneficial for team and
individual well-being, as well as company goals and profit.
Attitude
AND Aptitude
There is also a debate on attitude vs. aptitude. Should
an employer hire people with a good attitude and train for aptitude, OR hire
people with the right skills and tolerate a bad attitude?
At MayGraph, we believe it is
possible to find people who are both star performers and good colleagues. We believe that the person with the
right fit is there, but that companies and candidates often miss each other.
Our algorithm sifts through thousands of companies and teams and finds teams
where you are good match.
Why
algorithmic hiring is better?
Traditional recruitment relies heavily on manual resume
screening, interviewing candidates multiple times and select the one which an
interviewer has a ggut feelingh that this person is best.
Research shows that this is imperfect: typical job post gets
total of 250 resumes and it is either infeasible cost-wise or time-wise to
interview every possible candidate (Source article: Why
You Canft Get A Job c Recruiting Explained By the
Numbers).
Relying solely on interviews is not good. According to Don Moore, an associate
professor at the Haas School of Business at the University of California,
Berkeley, gInterviews favor candidates
who are attractive, sociable, articulate, and tall. They also favor
manipulative candidates, or ones who know how to make a positive impression
even in a brief interview. But those arenft always the best job performers.h
Along with streamlining the hiring process, we want also
to help companies to make better decisions. According to Kuncel,
Ones and Kliegerfs article gIn Hiring, Algorithms
Beat Instincth: Algorithms greatly
out preformed individuals at fitting people to jobs.
Working in global environment, we meet people who come from
great schools, which are not well known. Have you heard about: IIT? Grand Ecole? Sharif Institute of Technology? These are MITs in
their countries. Bruce A. Wooley, a former chair of the Electrical Engineering
Department at Stanford University, said that: gSharif Institute of Technology now has one of the best undergraduate
electrical-engineering programs in the world.h Our algorithm helps you to
become noticeable as well.
We
help companies to find those candidates that they would not find otherwise. Or,
they find them, but did not recognize they are the right ones.
In one research example from Yale School of Management,
76 students were asked to interview other students. Using information gleaned
from the interview along with previous academic results and an upcoming course
schedule, the interviewer was then asked to predict the future success of the
interviewee. They were then asked to predict the future success of a second
student based on paper alone — that is, without the interview. (Source: https://www.indy100.com/article/interviews-jobs-yale-school-of-management-new-york-times-study-research-7684301)
The result? The predictions made without the interview
turned out to be by far the more accurate.
For years, similar studies have shown unstructured job interviews are poor predictors of future work
performance. This is because
interviews have own biases (How the person looks like? How he compares to
previous candidate? Is he overweight?). Extroverts in general do better in
interviews than introverts, but for many, if not most jobs, extroversion is not
what a company is looking for. Biases
cost your business.
It is also hard for you to evaluate each team member in 1
or 2 hours.
I am currently working with my partners to find out what
makes great team, why people leave companies, and how to help people find their
tribe.
I am constantly reading research done by scientists to
make algorithm.
In
the end, our goal is to help you find:
- A job you like
- A team where you are good fit. (We also recommend you
to companies as a good fit. With our algorithm, companies will not miss a great
employee.)
- A company that is most likely to hire you. (So you do
not waste time going to various interviews.)
Benjamin Pekaric
Contributors:
Robert
G. Moulder, Quantitative Psychology Ph.D.
candidate, University of Virginia |
Benjamin
Pekaric, IUJ MBA, IT recruiter |
, Web Developer |