Influence of Argument Types
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The Influence of Argument Types of Expert Systems on Users
Justin Scott Giboney
There are an increasing number of computer systems that try to influence user decisions.
These systems are found in a variety of tasks including school enrollment (Maltz, Murphy, &
Hand, 2007), quality control (Chakraborty & Tah, 2006), credibility assessment (Jensen, Lowry,
Burgoon, & Nunamaker, 2010), online shopping (Wang & Benbasat, 2007), forecasting (Arnold,
Clark, Collier, Leech, & Sutton, 2006), and medical support (Portet, et al., 2009).
Just because these systems are implemented, and have logical or supported answers from
developers, does not mean that the answers influence users (Ye & Johnson, 1995). There are
many factors that can cause a user to not accept the answer of a system including responsibility
(Ye & Johnson, 1995) and level of expertise (Jensen, Lowry, Burgoon, & Nunamaker, 2010).
This system influence can be as low as 10% acceptance, in a system that is 80% accurate (see
Jensen et al 2010). This results in organizations spending valuable resources on systems that will
not be used and/or spending valuable resources on inefficient employees because they could
benefit from a system to do their jobs more efficiently.
Systems need to this lack of influence. Researchers have examined what happens when
systems explain why and how their answers are created (Mao & Benbasat, 2000; Gönül, Önkal,
& Lawrence, 2006). Prior research shows that explanations can improve system influence (i.e.
the user’s response more closely resembles the systems response). To explain this influence,
researchers have more rigorously investigated explanations by looking at the content of the
explanation (Gönül, Önkal, & Lawrence, 2006), type of feedback (Arnold, Clark, Collier, Leech,
& Sutton, 2006; Kayande, De Bruyn, Lilien, Rangaswamy, & van Bruggen, 2009), type of
explanation (Ye & Johnson, 1995; Mao & Benbasat, 2000), whether experience has an impact
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(Jensen, Lowry, Burgoon, & Nunamaker, 2010), and self-confidence (Jiang, Klein, & Vedder,
2000).
One area researchers have not studied yet, and a call for research has been made, is the
impact of the argument type in the explanation on the user (Ye & Johnson, 1995). There are
multiple types of arguments that can be used to support a claim (see Table 1). These argument
types are suggested to have different amounts of influence when coming from a system. I plan to
address this by exploring the following research question:
RQ: Are systems more influential when using certain types of arguments?
Table 1 – Argument Types Based on (Brockriede & Ehninger, 1960)
Type/Subtype
Authoritative
Motivational
Substantive
Cause
Sign
Generalization
Parallel Case
Analogy
Classification
Summary
Report or statement from a reliable source
Associated with a desire, emotion, drive, or aspiration
Assumptions about the way things are related
Specifies the result of accepted facts
Interprets meanings from data
Assumes what is true in a group applies to the individual
One instance is like another
Similar relationship exists between pairs
Truths of a class apply to the individual
There are specific elements that are common to most arguments. Figure 1 shows an
example of a Sign type argument. This structure was created by Toulmin and has been used by
rhetoricians (Brockriede & Ehninger, 1960). The main difference in argument types lies in what
the warrant states. Since computers are often seen as more logical (Gönül, Önkal, & Lawrence,
2006) it would make sense for arguments that are most logical in nature to be strongest when
coming from a computer. Cause, generalization, and classification type arguments seem to be the
most logical. This is what leads to my hypothesis (see Figure 2):
H1: Logical arguments (cause, generalization, and classification) will influence a user
more than non-logical arguments (authoritative, motivational, sign, parallel case, and analogy).
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Figure 1 – Argument Structure (Substantive/Sign)
Following standard arrangement (Ye & Johnson, 1995)
Explanations
System Influence
Logical
Arguments
Figure 2 – Hypothesis
Methodology
To test my research questions I will run an experiment (after running a pilot test) where
participants make a credibility assessment of a set of videos. The participants will be randomly
assigned to a control group or a treatment group. The control group will watch some videos of
either deceitful or truthful individuals. After each video, the participant will make a judgment as
to whether or not the person should be investigated further, the participant will record his/her
credibility assessment on a Likert scale, and the participant will record the reasons why he/she
answered that way.
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The participants in the treatment group will do the same exact thing that the control group
does. The only difference is at the end of the video, the system will give an answer for what it
predicts on a Likert scale what it thinks the persons credibility is. The system will also give an
explanation using a random argument type to explain why or how it came up with the result.
A pretest will also be given to measure participant attributes. Some of these attributes
will include culture and big 5 personality types. This will be an exploratory part of the study.
To make sure that some of our explanations aren’t more convincing than others, I will
have the explanations rated by a panel of experts. These experts will judge the explanations on
argument strength and code them by type/subtype. When all of the explanations are equal in
strength and coded correctly, I will run the experiment.
Discussion
To test my hypothesis, I will examine how close the participant’s result is to the system’s
result. I will compare the results across the argument types. My results will hopefully show that
some argument types are stronger than others when given by a system.
I will also be able to see that some explanations are stronger when presented by a system.
Computer systems intended to influence the decision of an individual should focus on the
explanation types/subtypes with higher influence as shown by my results.
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References
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