One of the world’s renowned industrial gases companies sought to hire better fit candidates based on culture fit for long term success. They cooperated with Seedlink to create predictive index benchmark to increase the quality of future successful hires.
Seedlink’s “Culture Fit Prediction” Model is based on 2 theories:
Seedlink AI algorithm can help to identify such pattern, correlate the language with behavior and accordingly predict culture fit and future performance of the candidates.
In order to build a company model, the algorithm need two different data sets:
The internal employee language data with behavior label and it requires at least 80 – 100 respondents.
The training data that requires at least 5000 respondents. “The training data” includes various language patterns of ordinary people and their behavior labels.
Picture 1a. Language Space
Picture 1b. Language Space in graphic
If we see human language as the universe, the training data as the galaxy, then the data from selected employees are solar system. It is almost impossible to accurately locate solar system on the map of the borderless universe. But if we know where the galaxy is, it will be much easier to find solar system.
Likewise, we must ensure that the employee language data are clustered into the close space as that of the training data (as shown in Picture 1) so that AI technology is able to identify the specific pattern and correlate them with high performing behaviors for certain organization(De Fortuny et al., 2014; Gao, et al. 2013; Verhoeven & Daelemans, 2014) .
In this scenario, we have to use the same questions to collect both labelled language and training data. Most importantly, the training data must include the largest number of behavior labels in line with those selected by the organization.
The company’s Culture Fit Model Behaviors Set:
Seedlink AI recommended to use TOP 3 questions in the table below (Table 1), the training data of which includes all behavior labels selected by the company.