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Trust and Human-Machine Teaming: A Qualitative Study 111

Fig. 6.3 Percentage of teaming dimensions by teaming category (teammate versus not).

6.4 DISCUSSION

Advances in technology presence and capability are forcing research to
examine social attitudes toward complex machines. Two variables of note
include evaluations of trust—one’s willingness to be vulnerable (and the
associated antecedents of which)—and the construct of human-machine team-
ing, an elusive yet omnipresent term among contemporary researchers.
The current study examined the antecedents of trust and the dimensions
of human-machine teaming using a qualitative sample and a broad cross-
section of US workers. Participants were simply asked to list an intelligent
technology that they use on a regular basis and then describe their use. The
majority of these technologies were home-based technologies, such as the
Amazon Echo, or mobile technologies, such as an iPhone equipped with
Siri. First, the participants were asked to describe why they either trust or
distrust the technology. Next, they were asked whether they viewed the
technology as a tool or a teammate (and why). An emerging model of
teammate-likeness was used to create a coding scheme for examining the
qualitative data. The data largely confirm the extant trust antecedents and
the utility of the teammate-likeness construct overall.
When considering trust antecedents, participants emphasized the con-
structs of reliability, predictability, and support (both task-oriented and pro-
active support). These results are consistent with prior literature on trust in
automation. Several meta-analyses have confirmed the importance of reli-
ability (i.e., high performance) on trust (Hancock et al., 2011; Schaefer,
Chen, Szalma, & Hancock, 2016). Further, predictability is fundamental