Research within this pillar concerns itself with questions relating to the influence that social networks have on enterprise behavior. We aim to understand the impacts of networks on enterprise over time. Studies are focused on 3 areas: (1) the impact of customer networks on strategic enterprise management, (2) various structures that exist in networks, and (3) the methodological tools used to detect network structures.
Analyzing the impact of customer networks on strategic enterprise management, we collaborate with an online shopping platform and focus on the way how should the retailer deal with customer unethical behavior. This platform combines online shopping with online trading card game. Customers are encouraged to purchase, collect and trade digital coupons to get higher discounts. However, certain number of customers have disobeyed the term of services defined by the platform in order to get additional benefits. How should the retailer react to this kind of customer unethical behavior? Our study suggests a practical way of quantifying the impact of customer unethical behavior in a fully interacting dynamic system over time. We have further developed a more nuanced theory about retailers’ response to unethical customer behavior.
Another topic that we are interested in is to model various network structures. Network structure is a property of networks that defines or describes the arrangement of various types of relationships. We extend the existing benchmark graphs that account for the heterogeneity in the distributions of node degrees and of community sizes by adopting the rule of constructing hierarchical networks. Our study closes the research gap in evaluating the performance of hierarchical community detection algorithms. Besides, to our knowledge, our model is the first benchmark graph that exhibits more than one stable hierarchical community structure.
The last topic we focus on is the methodological tools used to detect network structures. We have conducted a comprehensive analysis to evaluate the accuracies and computing complexities of state-of-the-art community detection algorithms. Based on our results, we provide the guidelines that help researchers in different fields to choose the most adequate community detection algorithm for a given network.