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.
Researchers from CMU and MIT report a psychometric methodology for quantifying a factor termed “collective intelligence” (c), which reflects how well groups perform on a similarly diverse set of group problem-solving tasks.
Importance of Empathy
If online teams are good at building an environment with empathy, they could perform just as well as face-to-face teams.
Why Teams Don’t Work?
Harvard professor reveals some common fallacies that prevent teams from high efficiency.
Interactive Team Cognition
The interactive team cognition model shows that the effectiveness of a team depends on not only knowledge input, but also on the flow of information.
Whiznook ML Technologies
NLP on WhatsApp Chat Data
Chat data can reveal a lot about the communication among team members. Many insights are hidden between the lines - how frequent people talk, who they talk too, what is the sentiment of the chat, etc. We deploy cutting-edge NLP models that are related to this blogpost on WhatsApp chat data.
Networks reflect the dynamics of teams through illustrative graphs. Whiznook constructs network visualizations for teams on our platform and helps them to diagnose team health.