en-gager makes commitment visible

What is En-gager

“It is the history of humankind; those who learned to collaborate and improvise most effectively have prevailed.” Charles Darwin


En-gager is a people intelligence instrument, which tracks, traces, and predicts the commitment of community members over a longer period.

Do you always know how members of your project are doing? Even when they are dispersed across the globe? Do you know how other teams are doing when a project consists of several independent teams? And do you really know how people will react to a strategic change in your organization? Do you think you know? In all these instances, we need to know if and when to intervene.

With En-gager you can monitor and analyse in real time how members of your community are holding up in any collaborative situation. Such people intelligence is essential today’s complex world of work.

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As En-gager tracks, traces, and predicts commitment over time, it delivers analytics, which can be directly translated into strategic action and timely intervention in all organizations. It is a great people intelligence tool for situations where collaboration and work attitudes are essential.

Engager uses an intuitive data entry mechanism. Participants are regularly requested to indicate how engaged they are in the context of project collaboration or the company as a whole. The results are registered and depicted graphically on a time line. Everyone can see their own history and compare it with peers or related groups. Participants obtain insight in their own functioning and that of the team as a whole.

En-gager also has a built-in forecasting dashboard, which gives insight where the different teams, and community as a whole is going. This is crucial information for any organization that whish to prevent problems before they occur and intervene where necessary.

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Gamification is the use of game thinking and game mechanics in a non-game context in order to engage users and solve problems. Gamification is used by large companies for marketing purposes and customer engagement, but it is also a great tool to increase employee engagement.

In En-gager gamification elements ensure the commitment required for regular and sustained input. With the use of an incentive scheme all participants collect credits. Credits are given for every time data is entered in the system. Bonus credits can be generated by entering data on a regular bases or entering data within a certain part of the day.

With these credits al participants can work towards one or more extrinsic goals. The company can determine what goal or goals participants can play for. Could be one large philanthropic goal, or a series of smaller goals as simple as a better lunch buffet… Over time new goals can be added to the system to keep all participants engaged.

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the ScIENCE behind en-gager

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En-gager is based on thorough academic research. The researchers built a new measurement instrument as a means to track people over a longer period of time while measuring often (a practice known as ‘experience sampling’ in the social sciences). Using this new instrument, they found that the patterns of our commitments to projects and organizations over time can be compared to a hike through a rugged landscape: many hills (high points) and valleys (low points), sharp turning points, and occasional stability. To make sense of all this data they used a scenario-based approach. These scenarios help us recognize where we are and make predictions for the future. For example, in an article recently published in Organization Science (article) they found that there are 5 scenarios:

  • Honeymoon-Hangover (initial high commitment, then decline)
  • Learning to Love (slow start, but then consistent growth of commitment),

  • And three relatively stable patterns:

  • Perfect match
  • Moderate Match
  • Mismatch.
These scenarios help us recognize and predict team sentiment, allowing us to take necessary action. For example, both Learning to Love and Mismatch trajectory types start low, but one grows quite rapidly in the first months and ends up high (Learning to Love), while the other remains low (Mismatch). Prediction algorithms allow for early recognition allowing early intervention with a Mismatch scenario or prevent unnecessary or possibly hurtful intervention in case of a Learning to love scenario.

En-gager app is a much extended and more professionally developed version of the original instrument. In 2013 the En-gager initiative was funded by the Netherlands Organisation for Scientific Research (NWO), in the form of a VENI–grant (awarded to dr. Omar Solinger). The purpose of a VENI grant is to stimulate innovative research by talented creative researchers who belong to the top 10% of their peers in the Netherlands.