What part of the recommended schedule do you feel is the least useful? Is
there anything that’s missing from the schedule?

  * •

Do you feel that the recommended schedule gives you enough information to
decide whether you would want to follow it?

Finally, we’d like to ask about your overall opinion of the tool.

  * •

What did you like about this tool?

  * •

What did you dislike about this tool?

  * •

Did you feel that interacting with the tool took too much time, or that it was
too complicated or confusing for you? Why or why not?

  * •

What sort of information would increase the chance that you want to use this
tool and follow its recommendations? How big of a difference do you think that
having this information would make?

  * •

Do you believe that drivers would generally find a tool like this to be useful
for when they’re planning their driving? Why or why not?

###  B.2. Longitudinal Trials

The script below focuses on the guiding questions that we asked participants.
Some typical probing questions are also listed as sub-bullets.

####  B.2.1. Formative Questions

Let’s start with talking about your driving for rideshare/delivery services in
general.

  * •

Could you start by telling us why you are driving?

    * –

Is it primary or supplemental income?

    * –

What other commitments do you balance it with (jobs, family, hobbies)?

  * •

To what extent do you rely on making a target amount when you are driving?

  * •

Can you talk through your typical process for deciding when to drive?

####  B.2.2. Feedback on Constraint Design

Now, let’s think back to the times when you were interacting with the tool,
particularly when it asked you to enter your availability and goals.

  * •

How similar or different were the tool’s questions to the way you typically
make these decisions?

  * •

Did you feel like you were able to use the tool to adequately specify your
main considerations for when you’d like to drive?

    * –

Were you ever unsure of what information the tool was asking for?

    * –

Would you have preferred the tool to ask for information differently, or to
ask for different information?

  * •

Did you feel like you were able to influence the recommended schedule that the
tool generated for you?

    * –

Did you try to experiment with entering in different information?

  * •

Did you feel that interacting with the tool took too much time, or that it was
too complicated or confusing for you?

    * –

Could you see yourself spending more time interacting with the tool than you
did (e.g., to enter more details)? Why or why not?

####  B.2.3. Feedback on Schedules

Now, let’s talk about your how the tool’s recommended schedules may or may not
have influenced your driving activity over the last few days.

  * •

Did you find that the recommended schedules made sense?

    * –

Did you feel that you understood how the tool used your answers to generate
schedules? Why or why not?

  * •

To what extent did you rely on the email reminders of the schedules?

    * –

Did you ever miss the email reminders?

    * –

When did you typically check the schedule, if at all?

  * •

If you saw the recommended schedules, how did they impact your process for
deciding when to work?

    * –

To what extent did you follow the schedules?

    * –

Were there times at which you prioritised your own intuition over the
schedules?

    * –

If so, were there times at which you wished you followed the schedule more
closely? Why or why not?

  * •

How did your response to the recommended schedules change throughout the week,
if at all?

    * –

Did you look at the schedules more or less as time went on?

    * –

Were there any particular days on which you wanted to check the schedule more?
Why or why not?

  * •

Did you feel that the tool gave you more or different information than you
would otherwise get from the services that you drive for/from Gridwise? Why or
why not?

  * •

Are there some additional details which could have increased the chance that
you followed the recommended schedules?

    * –

For example, would you have preferred to see the schedule for the entire week
on every day?

  * •

Did the recommended schedules lead you to drive at different times and/or
locations than before?

    * –

Did this happen early on or later?

    * –

At what times of day?

  * •

When you followed the recommended schedules, did you feel that you ended up
making more money, less money, or about the same relative to before?

    * –

How closely do you track your earnings in general?

    * –

Did you track your earnings more closely when using the tool?

####  B.2.4. Feedback on Estimates

  * •

How much did you focus on the tool’s estimates for how much you could earn?

  * •

Did you feel like you could rely on the estimates to achieve your earning
goals?

    * –

Did you feel that these estimates were meant to be accurate projections of how
much you could earn, or that they were rough ballpark figures?

  * •

In general, did you feel that the estimated earnings had the right level of
detail, or would you have liked to see additional information?

    * –

If participants were in Conditions 1 or 2 Would you have preferred to see a
range for how much you could earn?

    * –

If participants were in Conditions 3 or 4 Would you have preferred to see a
single number for how much you could earn?

The interviewer selects a particular day on which the participant interacted
with the tool. If earnings data was available, this was a day on which the
participant earned more than the tool’s estimate; otherwise, this was the
sixth day of their interaction with the tool.

  * •

Let’s talk about [weekday], Day [day] of your interaction with the tool.

  * •

Do you recall the extent to which you looked at the recommended schedule?

    * –

If you did, do you remember how you decided whether you wanted to follow it?
Was this influenced by how much the tool estimated your earnings to be? Why or
why not? What did you think of the estimate that the tool gave you?

    * –

If you didn’t, do you happen to remember why? Was this influenced by how much
you earned on the previous day? Why or why not?

  * •

Do you recall whether you made more or less than the tool estimated on that
day?

    * –

If there were any differences, do you have any idea why?

  * •

Did that influence your decision to look at the recommended schedule for the
next day? Why or why not?

  * •

We checked your records briefly and found that you earned $xxx.xx, compared to
the tool’s estimate of $xxx.xx. Does that change how you feel at all?

####  B.2.5. Overall Thoughts

Now, we’d like to wrap up with a few general questions about the tool.

  * •

Did you feel that the time you spent interacting with the tool was worthwhile
or not worthwhile? Why or why not?

  * •

If you had the option of using a tool like this one, what are the chances that
you might actually use it to decide your driving schedule in the future? Why
or why not?

  * •

Beyond what you’ve mentioned already, is there anything else you believe might
increase the chance that you would use this tool in the future?

  * •

Do you have any other questions, comments, or concerns?

##  Appendix C Confounding Variable Analysis

In this section, we detail the models we used to analyse whether several
confounding variables could have impacted our measurements. We use the
variable notation introduced in Section 6.2.1; $C$ for a participant’s
selected constraint variant; $G$ for the participant’s city; and $M$ for the
participant’s level of motivation, on a scale of 1–5.

###  C.1. Constraint Variants

To ascertain that trust and reliance measures did not significantly vary
between different variants of constraints (maximum hours or minimum
earnings/daily or weekly), we encoded these variants of the constraints as a
categorical variable $C$, and added it as linear terms to the best-fitting
models for each measurement:

  * •

Survey 2 Trust (Table S1): OLS model $T_{2}\sim S+C$ from RQ3

  * •

Survey 3 Trust (Table S2): LMM $T_{3}^{i}\sim
D*\Delta_{G}^{i}+D*\mathds{1}_{E}^{i}+C$ from RQ1

  * •

Survey 3 Reliance (Table S3): LMM
$R^{i}\sim\mathds{1}_{E}^{i}+\mathds{1}_{X}^{i}+C$ modified from RQ2

  * •

Survey 4 Trust (Table S4): OLS model $T_{4}\sim\mathds{1}_{E}^{1}+C$ from RQ1

These revised models generally led to worse AIC values (with models including
interactions of $C$ being worse still), and most of the coefficients for the
constraint variants were not significant. As noted in Section 6.2.1, one
exception was the max/day group, who exhibited significantly higher reliance
for Survey 3. The AIC was slightly improved by adding the constraints to this
model. Fitted coefficients for each model are shown in the tables listed
above.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 3.1846 | 0.376 | 0.000***
$C=\textrm{min/week}$ | 0.4304 | 0.423 | 0.315
$C=\textrm{max/day}$ | 0.0459 | 0.338 | 0.893
$C=\textrm{max/week}$ | 0.0317 | 0.433 | 0.942
$S=2$ | 0.3341 | 0.383 | 0.387
$S=3$ | 0.9385 | 0.403 | 0.024*
$S=4$ | 0.1244 | 0.409 | 0.762

Table S1. OLS model for Survey 2 trust, verifying effect of adding constraint
$C$

Table of ordinary least squares model coefficients for pre-interaction Survey
2 trust, with constraint $C$ added. Significant coefficients are the intercept
(3.1846) and Condition 3 (0.9385), but not $C$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 2.992 | 0.271 | 0.000***
$C=\textrm{min/week}$ | 0.216 | 0.268 | 0.421
$C=\textrm{max/day}$ | 0.303 | 0.359 | 0.399
$C=\textrm{max/week}$ | 0.540 | 0.403 | 0.180
$D$ | 0.129 | 0.044 | 0.003**
$\Delta_{G}^{i}$ | 0.005 | 0.002 | 0.003**
$D:\Delta_{G}^{i}$ | -0.001 | 0.000 | 0.008**
$\mathds{1}_{E}^{i}$ | 0.517 | 0.232 | 0.026*
$D:\mathds{1}_{E}^{i}$ | -0.116 | 0.050 | 0.019*
Random effect | 0.499 | 0.246 |

Table S2. LMM for Survey 3 trust, verifying effect of adding constraint $C$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 trust, with constraint $C$ added. Significant
coefficients are the intercept (2.992), $D$ (0.129), $\Delta_{G}^{i}$ (0.005),
the interaction of $D$ and $\Delta_{G}^{i}$ (-0.001), $\mathds{1}_{E}^{i}$
(0.517), and the interaction of $D$ and $\mathds{1}_{E}^{i}$ (-0.116), but not
$C$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 0.431 | 0.088 | 0.000***
$C=\textrm{min/week}$ | 0.133 | 0.101 | 0.186
$C=\textrm{max/day}$ | 0.322 | 0.134 | 0.016*
$C=\textrm{max/week}$ | -0.144 | 0.153 | 0.346
$\mathds{1}_{E}^{i}$ | 0.230 | 0.073 | 0.002**
$\mathds{1}_{X}^{i}$ | 0.058 | 0.066 | 0.383
Random effect | 0.059 | 0.073 |

Table S3. LMM for Survey 3 reliance, verifying effect of adding constraint $C$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 reliance, with constraint $C$ added. Significant
coefficients are the intercept (0.431), $\mathds{1}_{E}^{i}$ (0.23), and $C$ =
max/day (0.322).

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 3.539 | 0.345 | 0.000***
$C=\textrm{min/week}$ | -0.021 | 0.256 | 0.934
$C=\textrm{max/day}$ | 0.267 | 0.323 | 0.416
$C=\textrm{max/week}$ | -1.139 | 0.732 | 0.131
$\mathds{1}_{E}^{1}$ | 0.274 | 0.349 | 0.439

Table S4. OLS model for Survey 4 trust, verifying effect of adding constraint
$C$

Table of ordinary least squares model coefficients for post-interaction Survey
4 trust, with constraint $C$ added. The intercept (3.539) is significant, but
the coefficients for $\mathds{1}_{E}^{1}$ (0.274) and $C$ are not.

###  C.2. Cities

To ascertain that trust and reliance measures did not significantly vary based
on the city in which participants were driving (Los Angeles, New York,
Chicago, or Houston), we similarly added a categorical variable $G$ for the
city to the best-fitting models:

  * •

Survey 2 Trust (Table S5): OLS model $T_{2}\sim S+G$

  * •

Survey 3 Trust (Table S6): LMM $T_{3}^{i}\sim
D*\Delta_{G}^{i}+D*\mathds{1}_{E}^{i}+G$

  * •

Survey 3 Reliance (Table S7): LMM
$R^{i}\sim\mathds{1}_{E}^{i}+\mathds{1}_{X}^{i}+G$

  * •

Survey 4 Trust (Table S8): OLS model $T_{4}\sim\mathds{1}_{E}^{1}+G$

These revised models all led to worse AIC values, and none of the coefficients
for the cities were significant.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 3.0656 | 0.467 | 0.000***
$G=\textrm{Houston}$ | 0.4695 | 0.497 | 0.350
$G=\textrm{Los Angeles}$ | 0.2597 | 0.417 | 0.537
$G=\textrm{New York}$ | 0.3459 | 0.494 | 0.488
$S=2$ | 0.2646 | 0.394 | 0.505
$S=3$ | 0.8850 | 0.430 | 0.045*
$S=4$ | 0.0353 | 0.405 | 0.931

Table S5. OLS model for Survey 2 trust, verifying effect of adding city $G$

Table of ordinary least squares model coefficients for pre-interaction Survey
2 trust, with city $G$ added. Significant coefficients are the intercept
(3.0656) and Condition 3 (0.885), but not $G$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 3.121 | 0.371 | 0.000***
$G=\textrm{Houston}$ | 0.174 | 0.440 | 0.693
$G=\textrm{Los Angeles}$ | 0.015 | 0.351 | 0.966
$G=\textrm{New York}$ | 0.053 | 0.402 | 0.895
$D$ | 0.132 | 0.044 | 0.003**
$\Delta_{G}^{i}$ | 0.004 | 0.002 | 0.004**
$D:\Delta_{G}^{i}$ | -0.001 | 0.000 | 0.011*
$\mathds{1}_{E}^{i}$ | 0.533 | 0.233 | 0.022*
$D:\mathds{1}_{E}^{i}$ | -0.120 | 0.050 | 0.016*
Random effect | 0.525 | 0.257 |

Table S6. LMM for Survey 3 trust, verifying effect of adding city $G$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 trust, with city $G$ added. Significant
coefficients are the intercept (3.121), $D$ (0.132), $\Delta_{G}^{i}$ (0.004),
the interaction of $D$ and $\Delta_{G}^{i}$ (-0.001), $\mathds{1}_{E}^{i}$
(0.533), and the interaction of $D$ and $\mathds{1}_{E}^{i}$ (-0.12), but not
$G$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 0.539 | 0.137 | 0.000***
$G=\textrm{Houston}$ | 0.002 | 0.179 | 0.990
$G=\textrm{Los Angeles}$ | -0.033 | 0.143 | 0.818
$G=\textrm{New York}$ | -0.023 | 0.163 | 0.888
$\mathds{1}_{E}^{i}$ | 0.213 | 0.073 | 0.004**
$\mathds{1}_{X}^{i}$ | 0.067 | 0.066 | 0.308
Random effect | 0.077 | 0.090 |

Table S7. LMM for Survey 3 reliance, verifying effect of adding city $G$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 reliance, with city $G$ added. Significant
coefficients are the intercept (0.539) and $\mathds{1}_{E}^{i}$ (0.213), but
not $G$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 3.4142 | 0.389 | 0.000***
$G=\textrm{Houston}$ | 0.1228 | 0.410 | 0.767
$G=\textrm{Los Angeles}$ | 0.0096 | 0.321 | 0.976
$G=\textrm{New York}$ | -0.3000 | 0.388 | 0.445
$\mathds{1}_{E}^{1}$ | 0.4629 | 0.331 | 0.173

Table S8. OLS model for Survey 4 trust, verifying effect of adding city $G$

Table of ordinary least squares model coefficients for post-interaction Survey
4 trust, with city $G$ added. The intercept (3.4142) is significant, but the
coefficients for $\mathds{1}_{E}^{1}$ (0.4629) and $G$ are not.

###  C.3. Motivation

To ascertain that trust and reliance measures did not significantly vary with
participants’ level of motivation, we needed to first select a survey item
that would allow us to assess this subjective dimension independent of the
participants’ perception of the tool. For this reason, we selected Question 5
of the formative Survey 1 (Section A.1), “When I drive, it’s important to me
that I make some minimum amount of money.” We encoded the Likert responses to
this question as an interval variable $M$ and added it as linear terms to the
best-fitting models for each measurement:

  * •

Survey 2 Trust (Table S9): OLS model $T_{2}\sim S+M$ from RQ3

  * •

Survey 3 Trust (Table S10): LMM $T_{3}^{i}\sim
D*\Delta_{G}^{i}+D*\mathds{1}_{E}^{i}+M$ from RQ1

  * •

Survey 3 Reliance (Table S11): LMM
$R^{i}\sim\mathds{1}_{E}^{i}+\mathds{1}_{X}^{i}+M$ modified from RQ2

  * •

Survey 4 Trust (Table S12): OLS model $T_{4}\sim\mathds{1}_{E}^{1}+M$ from RQ1

These revised models again generally led to worse AIC values, and most of the
coefficients for the motivation variable were not significant. As noted in
Section 6.2.1, one exception was the pre-interaction trust score, which had a
significant positive correlation with the motivation variable. The AIC was
improved by adding the motivation variable to this model. However, no such
effects were evident for the daily and post-interaction measures. This
suggests that the initial effects of the participants’ motivation on trust did
not persist over repeated interactions. Fitted coefficients for each model are
shown in the tables listed above.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 2.1844 | 0.475 | 0.000***
$M$ | 0.3001 | 0.105 | 0.007**
$S=2$ | 0.0725 | 0.352 | 0.838
$S=3$ | 0.7487 | 0.359 | 0.043*
$S=4$ | -0.0430 | 0.358 | 0.905

Table S9. OLS model for Survey 2 trust, verifying effect of adding motivation
$M$

Table of ordinary least squares model coefficients for pre-interaction Survey
2 trust, with motivation $M$ added. Significant coefficients are the intercept
(2.1844), Condition 3 (0.7487), and $M$ (0.3001).

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 2.990 | 0.570 | 0.000***
$M$ | 0.046 | 0.119 | 0.699
$D$ | 0.126 | 0.044 | 0.004**
$\Delta_{G}^{i}$ | 0.002 | 0.001 | 0.001***
$D:\Delta_{G}^{i}$ | -0.000 | 0.000 | 0.004**
$\mathds{1}_{E}^{i}$ | 0.530 | 0.230 | 0.021*
$D:\mathds{1}_{E}^{i}$ | -0.116 | 0.049 | 0.018*
Random effect | 0.530 | 0.261 |

Table S10. LMM for Survey 3 trust, verifying effect of adding motivation $M$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 trust, with motivation $M$ added. Significant
coefficients are the intercept (2.99), $D$ (0.126), $\Delta_{G}^{i}$ (0.002),
the interaction of $D$ and $\Delta_{G}^{i}$ (-0.000), $\mathds{1}_{E}^{i}$
(0.53), and the interaction of $D$ and $\mathds{1}_{E}^{i}$ (-0.116), but not
$M$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Fixed effects
Intercept | 0.383 | 0.234 | 0.102
$M$ | 0.030 | 0.050 | 0.550
$\mathds{1}_{E}^{i}$ | 0.211 | 0.071 | 0.003**
$\mathds{1}_{X}^{i}$ | 0.074 | 0.064 | 0.250
Random effect | 0.081 | 0.094 |

Table S11. LMM for Survey 3 reliance, verifying effect of adding motivation
$M$

Table of linear mixed model coefficients (intercept, fixed effects, and random
effects) for daily Survey 3 reliance, with motivation $M$ added. The fixed
effect for $\mathds{1}_{E}^{i}$ (0.211) is significant, but not $M$.

Coefficient | $\mu$ | $\sigma^{2}$ | $p$
---|---|---|---
Intercept | 3.9645 | 0.676 | 0.000***
$M$ | -0.1259 | 0.127 | 0.330
$\mathds{1}_{E}^{1}$ | 0.4258 | 0.317 | 0.189

Table S12. OLS model for Survey 4 trust, verifying effect of adding motivation
$M$

Table of ordinary least squares model coefficients for post-interaction Survey
4 trust, with motivation $M$ added. The intercept (3.9645) is significant, but
the coefficients for $\mathds{1}_{E}^{1}$ (0.4258) and $M$ are not.

##  Appendix D Final Tool Design

The following figures show screenshots of the web-based tool used for the
longitudinal trials (Section 6), showing the constraints and recommended
schedule of interview participant F1.

Figure S1. Screenshot of final constraint page, showing constraints entered by
interview participant F1.

Screenshot of a webpage titled “Tool Interaction”. Below a set of
instructions, the constraint page is shown in a grey iframe. Various inputs
for constraints are set to values entered by interview participant F1. In
response to “Would you like to specify a maximum number of hours that you
would like to drive, or would you prefer to specify a minimum amount of money
that you would like to earn?”, F1 selected the latter. In response to “Would
you like to set these constraints for each day individually, or for the entire
week?”, F1 selected the latter. In response to “What is the minimum amount of
money you would like to earn for the entire week?”, F1 entered $1 400. In
response to “When are you available during the week?”, F1 selected time slots
(as indicated by blue cells in a tabular schedule) corresponding to 3 am to 7
am, 8 am to 4 pm, and 10 pm to 12 am on every day of the week.

###  D.1. Schedule Page Conditions

Figure S2. Screenshot of final schedule page, showing the recommended schedule
for interview participant F1 as it would be displayed if they were placed in
Condition 1 (reference condition; see Section 6.1.1).

Screenshot of a webpage titled “Tool Interaction”. Below a set of
instructions, the schedule page in Condition 1 for interview participant F1 is
shown in a grey iframe. The top of the schedule page reads, “Based on
historical data, it is estimated that you will earn a weekly average of”,
followed by an estimate of $1 412 below. Further below, a tabular schedule is
shown for each day of the week. Each cell reads, “Average”, followed by the
estimated hourly earning in bold. Recommended time slots are indicated by blue
cells, non-recommended time slots during which F1 is still available are
indicated by dark text, and time slots during which F1 is not available are
indicated by light text.

Figure S3. Screenshot of final schedule page, showing the recommended schedule
for interview participant F1 as it would be displayed if they were placed in
Condition 2 (daily estimates; see Section 6.1.1).

Screenshot of a webpage titled “Tool Interaction”. Below a set of
instructions, the schedule page in Condition 2 for interview participant F1 is
shown in a grey iframe. The top of the schedule page reads, “Based on
historical data, it is estimated that you will earn”. Unlike Condition 1, this
is followed by a table that reads, “An average of”, alongside estimates for
each day: $210 for Monday, $194 for Tuesday, $175 for Wednesday, $173 for
Thursday, $194 for Friday, $226 for Saturday, and $237 for Sunday. Further
below, a tabular schedule is shown for each day of the week. Each cell reads,
“Average”, followed by the estimated hourly earning in bold. Recommended time
slots are indicated by blue cells, non-recommended time slots during which F1
is still available are indicated by dark text, and time slots during which F1
is not available are indicated by light text.

Figure S4. Screenshot of final schedule page, showing the recommended schedule
for interview participant F1 as it would be displayed if they were placed in
Condition 3 (ranged estimates; see Section 6.1.1).

Screenshot of a webpage titled “Tool Interaction”. Below a set of
instructions, the schedule page in Condition 3 for interview participant F1 is
shown in a grey iframe. The top of the schedule page reads, “Based on
historical data, it is estimated that you will earn”. Unlike Conditions 1 and
2, this is followed by a table that reads, “On an average week: $1 412”, “On a
bad week: $391”, and “On a good week: $3 032”. Further below, a tabular
schedule is shown for each day of the week. Each cell reads, “Average”,
followed by the estimated hourly earning in bold. Unlike Conditions 1 and 2,
this is also followed by “Low”, with the 10th percentile of earnings, and
“High”, with the 90th percentile of earnings. Recommended time slots are
indicated by blue cells, non-recommended time slots during which F1 is still
available are indicated by dark text, and time slots during which F1 is not
available are indicated by light text.

Figure S5. Screenshot of final schedule page, showing the recommended schedule
for interview participant F1 as it would be displayed if they were placed in
Condition 4 (ranged and hedged estimates; see Section 6.1.1).

Screenshot of a webpage titled “Tool Interaction”. Below a set of
instructions, the schedule page in Condition 4 for interview participant F1 is
shown in a grey iframe. Unlike Conditions 1, 2, and 3, the top of the schedule
page reads, “In general, every driver may encounter different conditions while
they are driving, which leads to differences in earnings from individual to
individual and from one day to the next. On average, based on historical data,
a driver following this schedule will earn”. Also unlike Conditions 1 and 2,
this is followed by a table that reads, “On an average week: $1 412”, “On a
bad week: $391”, and “On a good week: $3 032”. Further below, a tabular
schedule is shown for each day of the week. Each cell reads, “Average”,
followed by the estimated hourly earning in bold. Unlike Conditions 1 and 2,
this is also followed by “Low”, with the 10th percentile of earnings, and
“High”, with the 90th percentile of earnings. Recommended time slots are
indicated by blue cells, non-recommended time slots during which F1 is still
available are indicated by dark text, and time slots during which F1 is not
available are indicated by light text.

##  Appendix E Figma Prototype Design

The following figures show screenshots of the Figma prototype used for the
pilot interviews (Section 5).

Figure S6. Screenshot of Figma prototype for the constraint page. On behalf of
participants, the interviewer clicked on radio buttons, input fields, and
table cells to set constraints. For technical reasons, input fields for the
numerical constraints were implemented as dropdown menus.

Screenshot of a webpage titled “Shift Recommendation Tool”. The constraint
page is shown, with various inputs being set to mock values. In response to
“Would you like to set a maximum number of hours that you would like to drive
to earn as much money as possible, or a minimum amount of money that you would
like to earn in as few hours as possible?”, the latter is selected. In
response to “Would you like to set these constraints for each day
individually, or for the entire week?”, the former is selected. In response to
“What is the minimum amount of money you would like to earn for each day?”,
$140–$150 is entered for each day of the week. In response to “When are you
available during the week?”, time slots corresponding to 7 am to 2 pm on every
day of the week are selected (as indicated by dark cells in a tabular
schedule).

Figure S7. Screenshot of Figma prototype for the schedule page. A summary of
mean, pessimistic, and optimistic weekly earnings is shown at the top of the
page, followed by an hourly schedule where recommended cells are highlighted
in darker colours. For technical reasons, this was shown as a static page not
depending on previously-entered constraints.

Screenshot of a webpage titled “Shift Recommendation Tool”. The schedule page
is shown with mock estimates. The top of the schedule page reads, “Based on
historical data, it is estimated that you will earn a weekly average of”. This
is followed by a table that reads, “On an average week: $593”, “On a bad week:
$152”, and “On a good week: $1 135”. Further below, a tabular schedule is
shown for each day of the week. Each cell reads, “Average”, followed by the
estimated hourly earning in bold. Recommended time slots are indicated by dark
cells, and non-recommended time slots are indicated by light cells.