IEEE Technology and Society Magazine - December 2015 - 78
With the help of SAS, a full factorial design was
created with 54 stimuli (2x3x3x3). We recommended
presenting respondents with 8 to 15 choice tasks [12].
We therefore decided to use SAS to create a near optimal design with 24 stimuli divided over 12 choice
tasks (with two alternatives). In addition, we included
two dominant decision tasks with one option that was
cheaper and provided better transparency tools compared to the alternative. Based on these control tasks
we omitted respondents who apparently did not conduct the survey tasks seriously. This resulted in 14
choice tasks for respondents to complete. Table 2
provides an overview of the selected choice tasks. The
presentation of the tasks to respondents was in randomized order.
The decision task was presented to respondents
through an online survey programmed on Limesurvey and respondents were sent a message containing a link to the survey. The survey consisted of
three parts. The first part was an introduction to
the survey with an explanation of all attributes and
levels used in the choice tasks. Respondents were
asked to select the alternative they considered the
best and were informed that they could subsequently
indicate if they considered both options undesirable
(i.e., "no choice").
The second part included the 12 choice tasks and 2
control tasks. Figure 1 provides an example of how the
choice tasks were presented to the respondents, followed by the "no choice" question.
The third part included several questions addressing demographic characteristics of the respondent (i.e.,
age, gender, and education), and in the end respondents
were thanked for their participation.
utility scores and we will therefore refer to the parameters as utility. For the attribute transparency the level
high was used as baseline, for reliability the level IA,
and for audience the level publicly. Costs were entered
as a linear function into the model.
Based on the results, the null hypothesis, that the
utilities obtained for the levels within each attribute do
2
not differ, was rejected, X (6) = 867.02, p < .001. All
utilities within an attribute differed significantly from
each other. An overview of the model is provided in
Table 3. We included the no choice option as a variable
to control for the instances in which respondents would
not have made use of the preferred option in real life.
As was hypothesized, the medium level (b = -.61) of
transparency provided less utility than the high level
(b = 0), X 2 (1) = 42.04, p < .001, and the low level
(b = -1.56) provided even less utility, X 2 (1) = 114.70,
p < .001. For cost, a linear function was found with a
negative relationship between costs per year and utility
(b = -.05; i.e., utility decreases with .05 for each euro
2
the costs per year increases), X (1) = 427.28, p < .001,
in support of the hypothesis. The level CPA (b = -.48)
2
provided less utility than the level IA (b = 0), X (1) =
22.56, p < .001. However, opposite to our hypothesis,
the level CP (b = -.41) was found to provide slightly less
utility compared to the level CPA, but still significantly
2
more than the level IA, X (1) = 11.29, p < .001. Finally,
the level only you (b = .33) was found to provide more
2
utility then publicly (b = 0), X (1) = 15.73, p < .001, as
was hypothesized.
With the help of these utility scores, we are able to calculate the total utility (TU) that respondents would assign
to hypothetical cloud services with the following formula:
TU = b * Low + b * Medium + b * High + b * Costs +
b * CP + b * CPA + b * IA + b * OnlyYou + b * Publicly
Results
Utility Associated with Attribute Levels
A proportional hazard regression was performed in
SAS to obtain the utilities for each attributes' levels.
The parameters obtained from this analysis provide the
In this formula the appropriate utility estimates can
be entered instead of b. In addition, replace attribute
levels with a 0 or 1 based on whether they are present,
except for cost which in this case will have value of 0
to 60.
Take for example, Cloud A that provides high transparency, costs 30 euro per year, has an IA checking the
reliability, and the transparency information provided is
only available to you. In this case the formula would be:
TU = (-1.56 * 0 + -.61 * 0 + 0 * 1) + (-.05 * 30) +
(-.41 * 0 + -.48 *0 + 0 * 1) + (.33 * 1 + 0 * 0)
TU = 0 * 1 + -.05 * 30 + 0 * 1 + .33 * 1
TU = -1.17
Figure 1. Decision task as presented to respondents.
78
Individually, this total utility score provides little information. Only when we compare the total utility of two
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