Analyzing the Flow of Knowledge with Sociometric Badges
Procedia - Social and Behavioral Sciences
This paper presents a collection of "best practices" for the use of "Sociometric Badges" that support automatic collection of faceto-face interaction between workers within an organization. The practices presented aim to improve data quality over legacy methods allowing insights into the processes and structures of an enterprise's de-facto communication networks. Our approach uses dynamic Social Network Analysis (dSNA) to make it easier for executives to analyze and manage communications
... mmunications networks. The practical applicability of the approach was evaluated by case studies conducted in three different organizations: (1) the marketing department of a medium sized bank in Germany, (2) the post-anesthesia care unit at a large US hospital, (3) teams of software developers in a Nordic European country. For the analysis, we tracked, amongst others, all personal interactions between the knowledge workers in a department or team using sociometric badges worn by each employee for the duration of the case studies. We analyzed this sociometric data as well as emails and instant messages exchanged between the employees and compared it with performance data of individuals and teams. The paper highlights 16 key lessons learnt during these studies. The first nine lessons focus on overcoming the employee's privacy concerns to set up the necessary technology infrastructure, and the final seven provide general findings for efficient management of knowledge workers based upon the results of the case studies. Reciprocity in communication means that, for example, if employee A sends an email to employee B, employee B will also send an email to employee A in return. To measure reciprocity, the authors used a contribution index that reflects the ratio of messages sent by employee A to other team members vs. messages received by employee A other team members . Afterwards the authors aggregated these individual statistics to a network statistic at team level. Our findings suggest that teams composed of individuals that have a more balanced communication have a better (objective) performance than teams composed of members that have an unbalanced communication. However, when teams have to evaluate their own performance, teams with a more balanced communication do not rate their performance higher than teams with an unbalanced communication. Note that instead of messages the contribution index can also measure face-to-face interaction through sociometric badges, where in an unbalanced case one person looks at the other, while the other person never looks the first one into the face.