  The collective behaviour of people adopting an innovation, product or online
service is commonly interpreted as a spreading phenomenon throughout the fabric
of society. This process is arguably driven by social influence, social
learning and by external effects like media. Observations of such processes
date back to the seminal studies by Rogers and Bass, and their mathematical
modelling has taken two directions: One paradigm, called simple contagion,
identifies adoption spreading with an epidemic process. The other one, named
complex contagion, is concerned with behavioural thresholds and successfully
explains the emergence of large cascades of adoption resulting in a rapid
spreading often seen in empirical data. The observation of real world adoption
processes has become easier lately due to the availability of large digital
social network and behavioural datasets. This has allowed simultaneous study of
network structures and dynamics of online service adoption, shedding light on
the mechanisms and external effects that influence the temporal evolution of
behavioural or innovation adoption. These advancements have induced the
development of more realistic models of social spreading phenomena, which in
turn have provided remarkably good predictions of various empirical adoption
processes. In this chapter we review recent data-driven studies addressing
real-world service adoption processes. Our studies provide the first detailed
empirical evidence of a heterogeneous threshold distribution in adoption. We
also describe the modelling of such phenomena with formal methods and
data-driven simulations. Our objective is to understand the effects of
identified social mechanisms on service adoption spreading, and to provide
potential new directions and open questions for future research.
