  Human-machine networks affect many aspects of our lives: from sharing
experiences with family and friends, knowledge creation and distance learning,
and managing utility bills or providing feedback on retail items, to more
specialised networks providing decision support to human operators and the
delivery of health care via a network of clinicians, family, friends, and both
physical and virtual social robots. Such networks rely on increasingly
sophisticated machine algorithms, e.g., to recommend friends or purchases, to
track our online activities in order to optimise the services available, and
assessing risk to help maintain or even enhance people's health. Users are
being offered ever increasing power and reach through these networks by
machines which have to support and allow users to be able to achieve goals such
as maintaining contact, making better decisions, and monitoring their health.
As such, this comes down to a synergy between human and machine agency in which
one is dependent in complex ways on the other. With that agency questions arise
about trust, risk and regulation, as well as social influence and potential for
computer-mediated self-efficacy. In this paper, we explore these constructs and
their relationships and present a model based on review of the literature which
seeks to identify the various dependencies between them.
