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.
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:
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.
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.)
Robert G. Moulder, Quantitative Psychology Ph.D. candidate,
University of Virginia
Benjamin Pekaric, IUJ MBA, IT recruiter
, Web Developer