Knowledge seekers’ and contributors’ reactions to recommendation mechanisms in knowledge management systems

We examined the behavior of knowledge seekers and contributors to an internal Knowledge Management System (KMS) in a multinational organization. The system has two selection mechanisms, based on semantic algorithms and user ratings. The first utilizes an algorithm to ‘measure’ the quality of knowledge contributions and ranks them accordingly, while the second averages the ratings that knowledge items receive from KMS users. Building on appraisal theory, we found that knowledge seekers and contributors reacted differently to the two mechanisms. The rating-based rankings positively influenced knowledge seekers’ tendency to access, comment on, and spread the knowledge shared in the KMS, while the algorithm-based ranking positively influenced knowledge contributors’ to continue sharing knowledge via the system. Moreover, shorter (or longer) time delay between the time that the knowledge was shared and the time when knowledge contributors received their first comments seemed to positively (or negatively) influence the contributors’ tendency to continue sharing knowledge via the KMS. Our study adds to the existing KMS literature by investigating knowledge seekers’ and contributors’ reactions to the two different knowledge recommendation mechanisms, and recommends that managers understand the importance of implementing algorithm-based rankings in their KMS as well as the simpler and more commonly adopted rating-based ranking.