The Role of Peer Influence in Churn in Wireless Networks

Subscriber churn remains a top challenge for wireless carriers. These carriers need to understand the determinants of churn to confidently apply effective retention strategies to ensure their profitability and growth. In this paper, we look at the effect of peer influence on churn and we try to disentangle it from other effects that drive simultaneous churn across friends but that do not relate to peer influence. We analyze a random sample of roughly 10 thousand subscribers from large dataset from a major wireless carrier over a period of 10 months. We apply survival models and generalized propensity score to identify the role of peer influence. We show that the propensity to churn increases when friends do and that it increases more when many strong friends churn. Therefore, our results suggest that churn managers should consider strategies aimed at preventing group churn. We also show that survival models fail to disentangle homophily from peer influence over-estimating the effect of peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing (Socialcom 2014), Best Paper Award Winne

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Global interactions between firms and universities: Global Innovation Networks as first steps towards a Global Innovation System

This paper aims to broaden the horizon as well as to shed further light on the studies of interaction between firms and universities in a global context. Its starting point is thus a review of two different strands of the literature on innovation. First, the literature on interaction by Klevorick et al (1995) and Nelson (1993), and second, the more recent literature on Global Innovation Networks (GINs) by Ernst ( 2006) and The Economist Intelligence Unit ( 2007). These strands share a common problem: each has a blind spot in relation to the core focus of the other strand. The literature on interaction does not consider the international dimension in any depth, and the GINs literature does not integrate the university dimension adequately. This paper addresses the common weakness through a combination of the two approaches, searching for interactions between firms and universities globally. In doing so, the paper also puts forward a tentative framework on global interaction between firms and universities.interactions between firms and universities, National Innovation Systems, Global Innovation Networks.