  In the last decade, conversational search has attracted considerable
attention. However, most research has focused on systems that can support a
\emph{single} searcher. In this paper, we explore how systems can support
\emph{multiple} searchers collaborating over an instant messaging platform
(i.e., Slack). We present a ``Wizard of Oz'' study in which 27 participant
pairs collaborated on three information-seeking tasks over Slack. Participants
were unable to search on their own and had to gather information by interacting
with a \emph{searchbot} directly from the Slack channel. The role of the
searchbot was played by a reference librarian. Conversational search systems
must be capable of engaging in \emph{mixed-initiative} interaction by taking
and relinquishing control of the conversation to fulfill different objectives.
Discourse analysis research suggests that conversational agents can take
\emph{two} levels of initiative: dialog- and task-level initiative. Agents take
dialog-level initiative to establish mutual belief between agents and
task-level initiative to influence the goals of the other agents. During the
study, participants were exposed to three experimental conditions in which the
searchbot could take different levels of initiative: (1) no initiative, (2)
only dialog-level initiative, and (3) both dialog- and task-level initiative.
In this paper, we focus on understanding the Wizard's actions. Specifically, we
focus on understanding the Wizard's motivations for taking initiative and their
rationale for the timing of each intervention. To gain insights about the
Wizard's actions, we conducted a stimulated recall interview with the Wizard.
We present findings from a qualitative analysis of this interview data and
discuss implications for designing conversational search systems to support
collaborative search.
