Science – Whiznook

science behind whiznook

Being at the intersection of behavioral science, industrial-organizational psychology, AI and gamification, Whiznook strives to rely on solid scientific methods while being innovative.

Whiznook’s theoretical framework is based on research endeavors from various perspectives.

A study by CMU, MIT and Union College researchers found that there exists such a thing as team intelligence, which is uncorrelated with individual intelligence and significantly correlated with equal communication and emotional understanding within a team. Team intelligence directly contributes to the performance.

An earlier study by MIT’s Center for Collective Intelligence revealed that remote team communication, surprisingly, yields higher effectiveness for teams that handle communication well. On the other hand, european researchers discovered that the key to unlock high team performance lies within the stages of information processing (Woerkom, Croon; Dreu). Whiznook aims to maximize team intelligence by integrating these findings. As we promote team intelligence on a gamified platform, we also aspire to transform information processing into data-driven metrics for our teams to receive quantified evaluations.

Challenges and General Solutions

By deploying cutting-edge machine learning techniques, we generate the most relevant analytics results to make teams better. Team performance largely depends on the efficiency of processing large amounts of information, but the flow of information in many companies can be very opaque. By breaking down the information processing steps, Whiznook understands and improves team intelligence at each stage.

We can all agree on the vital importance of communication in team performance, but a failed communication can be led by many different reasons – lack of interpretation when acquiring information, ineffective distribution of information, or poor integration of information that leads to bad actions

Whiznook’s solution can better pinpoint the problems of team communication by establishing metrics at several “checkpoints” in the information processing chain. We then use behavioral analytics and machine learning algorithms at each checkpoint to deliver data-driven, interpretable results so teams understand how well they are functioning and how to become better.