Digital social networks as complex adaptive systems

Abstract

PurposeThe purpose of this paper is to define optimal behavioral characteristics for members in digital social networks (DSNs). To this end, an assumption was tested that DSN members behave similarly to autonomous agents in a complex adaptive system (CAS) by maintaining a process of self‐branding.Design/methodology/approachOnline questionnaires were presented to 94 DSN users. Their answers were used to assess: different parameters of their behavior on the DSN; and their perception of the DSN's effectiveness. Statistical analyses were conducted to examine possible correlations between these parameters and the optimal behavioral characteristics of individual agents in the CAS model.FindingsSubjects who reported a higher degree of functional autonomy and self‐branding on the DSN also reported a higher degree of DSN effectiveness. A significant positive linear correlation was found between the degree of self‐branding and optimal behavioral characteristics described previously for individual agents in a CAS.Practical implicationsThe study provides first empirical evidence that the CAS model parameters can be used to explain DSN‐related phenomena in general, and the perception of DSN effectiveness in particular. This suggests that individual DSN members should generate and maintain a powerful self‐brand through autonomous activities to increase DSN effectiveness. Such activities can be manifested through behavioral processes characterizing individuals in CAS, and especially through maximizing situational sensitivity and integration of information.Originality/valueThis is the first study to empirically test the CAS theoretical model on DSNs. It specifies behavioral characteristics, which individual DSN members should incorporate to increase the perceived DSN effectiveness.