Modern business models are rapidly adapting digital platforms and moving past traditional methods of knowledge circulation. In an age where successful innovation, collaboration, and knowledge management platforms rely heavily on the flow of knowledge, a constant challenge that is faced is inconsistent participation and difficulty to engage employees in the knowledge circulation process.

At present, businesses are using basic gamification strategies at the workplace, which reward employees based on the activity they perform. While this may boost the quantity of work done and bring minimal improvements in employee engagements, it only lasts for short bursts of time. It does not assure the expected transformation in behaviour. These methods rely on a general sense of improvement. They are unable to target specific individuals who need the most or least change and different kinds of motivational triggers to engage.

A solution that has not been extensively explored just yet is the idea of taking a deep dive into an individual’s socio-cognitive psychology and analyzing the root of the problem and what is causing it, which can then be followed up by proactive intervention. It is possible that through the usage of intelligent agents, participation may be strategically stimulated according to a participatory profile created from the observation of employee’s online behaviour. It, in turn, can be used to determine an individual’s intervention that may have the most impact in a scenario. Such interventions can be used proactively to keep employees engaged.

A well-developed, artificial intelligence-powered assistant can do a lot to solve the problem of employee engagement. It can set up theautomatic construction of a behavioral profile of each member which is formed by analyzing the various activities performed by him on a day-to-day basis. Most impactful nudges (low-cost technology interventions) can be created based on the characteristics observed. Using this approach, one can reach a large-scale audience that can be stimulated, hence, improving employee engagement in any digital initiative. Let us analyze this process step-by-step.

As mentioned previously, the first step for the success of these intelligent agents would be to construct an automatic behavioral profile. It would rely on a collection of rules and constraints put in place by the stakeholders in various digital programs. The different behavioral patterns, according to which a particular user can be categorized. Such as including the level of involvement (Is he often present?) and the nature of his contributions (Is he only a lurker? Is he a contributor of knowledge assets? Does he participate in the discussions? Does he initiate conversations?). Additionally, heuristic rules may be based on the amount of activity. For example, a user that has not logged on for a certain period may be deemed inactive while a user that only logs once a week could be deemed committed to that digital platform. The construction of this behavioral profile primarily relies on analyzing an individual’s personality traits, cognitive style/traits, and their attention state.

The next phase of the process deals with using the data collected previously to understand what the best approach may be to nudge a specific type of profile which engages more in the community.However, this cannot be done over a short period. Theory of innovation diffusion states that people do not adopt straightaway, a new attitude but go through a series of phases of adoption (awareness, interest, trial, and adoption). To avoid interventions that are not relevant to the user, the agent needs to know about the current participatory level of the user (e.g. is the member already familiar with some of the digital practices or is he unaware?) when selecting the most effective intervention. For instance, a user that is entirely out of touch from that digital initiative may be informed about the participation of other members and how he may be missing out. Alternatively, an active user that has recently not been participating much could be nudged with an exciting possibility that he may have missed out on.

Another distinction made by the theory of innovation diffusion is regarding one’s attitude towards innovation. For instance, the innovators are principally driven in their action by their curiosity. In contrast, the late adopters are very sensitive to social pressure and change their practices once they realize that they are isolated in their behaviors. An intervention that is likely to have the most effect on an innovator (identified as such by observing his behaviour) will be one that emphasizes novelty. In contrast, the intervention that will have the most impact on a late adopter will be one that emphasizes social conformance (“everybody does it that way”).[i]

Key Takeaways 
Set up the automatic construction of a behavioral profile of each member which is formed by analyzing the various activities performed by him on a day-to-day basis. Most impactful nudges specific to those profiles can reach a large-scale audience that can be stimulated, hence, improving employee engagement in any digital initiative.

 


[i] https://www.academia.edu/590226/Using_artificial_agents_to_stimulate_participation_in_virtual_communities