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##  Appendix A Questionnaire - Part One: Scenario Description

After the participant has chosen to consent to starting the questionnaire, a
detailed scenario description is provided to help them understand the subject
of the study (what we mean by HVAs):

_‘Imagine that you have an AI-based voice assistant at home which take care of
your health, and you can seek help without going out to see a GP. You can
issue commands directly to the voice assistant or have conservation with
third-party skills that deployed on the voice assistants. It can take care of
both your physical and mental health like a GP, such as: making a swift and
effective diagnosis based on the symptoms that appear; assessing the
likelihood of a certain illness; discussing and developing a treatment plan
with you; helping you monitor and manage your chronic disease. Explain to you
the test results such as blood test, x-ray diagnosis, etc. Combining with your
own experience with voice assistants, please answer the following questions,
we want your honest thoughts.‘_

##  Appendix B Questionnaire - Part Two: Item List

The second part of the survey is to let participants rate each of the items
listed below using a 5-point likert scale that goes from from ‘strongly
disagree’ (1) to ‘strongly agree’ (5).

Functional Factors
Anthropomorphism (A) - adapted from (Pitardi and Marriott, 2021):

A1. When I interacting with healthcare AI voice assistants, it would feel like
someone is present in the room.
A2. I feel that interactions with healthcare AI voice assistants would be
similar to those with a human.
A3. When communicating with healthcare AI voice assistants, I would feel like
I am dealing with a real person.
A4. I think I would communicate with healthcare AI voice assistants in a
similar way to how I would communicate with other people.

Effort Expectancy (EE) - adapted from (Chang et al., 2017; Chi et al., 2021):

EE1. I feel using healthcare AI voice assistants would take too much of my
time.
EE2. I feel working with healthcare AI voice assistants would be difficult and
I would not understand how to use them.
EE3. I feel it will take me too long to learn how to interact with healthcare
AI voice assistants.

Perceived Usefulness (PU) - adapted from (Cho et al., 2020):

PU1. I feel healthcare AI voice assistants would help me be more efficient
when dealing with health-related issues.
PU2. I feel healthcare AI voice assistants would be useful when dealing with
health-related issues.
PU3. I feel healthcare AI voice assistants could meets my needs.
PU4. I feel healthcare AI voice assistants would be able to do everything I
would expect in order to take care of my health.

Perceived Content Credibility (PCC) - adapted from (Cho et al., 2020):

Please indicate how well the following adjectives represent the healthcare AI
voice assistant, from 1 = describes very poorly to 5 = describes very well:

PCC1. Accurate
PCC2. Complete
PCC3. Expert
PCC4. Consistent
PCC5. Authentic

Perceived Relative Service Quality (PRSQ) - adapted from (Fan et al., 2020):

PRSQ1. I think the accuracy of information provided by a healthcare AI voice
assistant would be higher than that of the average doctors.
PRSQ2. I think question answering and diagnosis speed of healthcare AI voice
assistants would be faster than that of the average doctors.
PRSQ3. I think Healthcare AI voice assistants would provide a clearer and more
understandable service delivery process.

Personal Factors

Stance in Technology (ST) - adapted from (Chi et al., 2021):

ST1. I usually keep an eye on emerging products using AI technology,
especially those that will be beneficial to my health.
ST2. I always try out emerging products using AI technology earlier compared
to others especially if they will be beneficial to my health.
ST3. In general, I am willing to accept new emerging products using AI
technology, especially if they will be beneficial to my health.
ST4. If I heard about an emerging product using AI technology, especially that
is beneficial to my health, I would look for ways to use it.

Familiarity (F) - adapted from (Chi et al., 2021):

F1. I know a lot about healthcare AI voice assistants.
F2. I have much knowledge about healthcare AI voice assistants.
F3. I am more familiar than the average person regarding healthcare AI voice
assistants.

Technology Attachment (TA) - adapted from (Chi et al., 2021):

TA1. I feel that AI technology is a part of my identity .
TA2. I identify strongly with the use of AI technology.
TA3. Using AI digital technology says a lot about who I am.

Social Influence (SI) - adapted from (AlHogail, 2018):

SI1. Using healthcare AI voice assistants would be a status symbol in my
social networks (friends, family, co-workers).
SI2. People in my social networks who would utilize healthcare AI voice
assistants have more prestige than those who wouldn’t.
SI3. People whose opinions I value would encourage me to utilize healthcare AI
voice assistants.
SI4. People in my social networks who would utilize artificial intelligence
such as healthcare AI voice assistants have a high profile.

Risk Factors

Security Risk (SR) - adapted from (Cho et al., 2020):

SR1. I think it would be risky to interact with a Healthcare AI voice
assistant.
SR2. I would be concerned if I had to deal with my health-related issues via a
healthcare AI voice assistant.
SR3. There would be much uncertainty associated with my interactions with a
healthcare AI voice assistant.

Privacy Risk (PR): - adapted from (Cho et al., 2020):

PR1. I am concerned that a healthcare AI voice assistant would collect too
much information about me.
PR2. I am concerned about who might access my personal information I had given
to a healthcare AI voice assistant.
PR3. I feel that healthcare AI voice assistants would misuse my personal
information.
PR4. There would be a large potential loss associated with providing personal
information to healthcare AI voice assistant.

Perceived Substitution Risk (PSR) - adapted from (Fan et al., 2020):

PSR1. I think that healthcare AI voice assistants are likely to replace
doctors in the future.
PSR2. I think using healthcare AI voice assistants for a long time would make
doctors dependent on them.
PSR3. I think the rise and development of healthcare AI voice assistants would
likely lead to the unemployment of some doctors.
PSR4. I think using healthcare AI voice assistants for a long time would
decrease doctors’ own ability.

Trust and Intention to Use

Trust in HVAs (T) - adapted from (Cho et al., 2020):

T1. I feel Healthcare AI voice assistants would be interested in my well-
being.
T2. I feel like healthcare AI voice assistants would be truthful.
T3. I would characterize healthcare AI voice assistants as honest.
T4. I feel like a healthcare AI voice assistant would be sincere.

Intention to Use (IU) - adapted from (Pitardi and Marriott, 2021):

IT1. It is likely that I will use my voice assistant for Healthcare in the
future.
IT2. I intend to use my voice assistant for healthcare frequently.
IT3. I expect to continue using my voice assistant for healthcare in the
future.

##  Appendix C Questionnaire - Part Three: Post-survey questions

The last part of the survey contains both closed questions and open-ended
questions that aims to understand more about how users perceive healthcare
voice assistants. The responses to these questions were converted into a text
format that could be analysed using thematic analysis methods.

Q1: Would you consider to use a healthcare voice assistant in the future?

\- if No, then why you may NOT wish to use a healthcare AI assistant in the
future?

Q2: I would trust the following organisations to make a healthcare voice
assistant? not at all(1) - very much(5)

\- The NHS (on another platform)

\- Amazon

\- Google

\- Apple

\- Please briefly describe any other organizations you trust to develop
healthcare AI assistants (if any)

Q3: What aspects should be enhanced in terms of developing a trustworthy voice
assistant in healthcare?

##  Appendix D Thematic analysis - themes and codes

Table 6 displays the themes and the corresponding codes investigated from
participants’ response to the post-survey questions.

Table 6. Themes and codes of thematic analysis. Relation to the model | Themes | Codes
---|---|---
Retained | Security and Privacy | Reduce general privacy concern
|  | Data and usage transparency
|  | Ensure data storage security
|  | Obey regulations such as HIPAA
|  | Prevent data selling
Retained | Anthropomorphism | Provide natural conversation
|  | Empathy
|  | Compassionate
|  | Customize voice and care
|  | More human-like features
Retained | Other functional-related | Higher accuracy
|  | Higher usability
|  | Knowledgeable
|  | Timely
|  | Ease of use
|  | Help support avaliable
|  | Reliable information & advice
Excluded | Substitution Risk | Assist instead of replace
|  | Supervised by doctors
|  | Help contact doctors
Newly found | Financial Causes | Affordable to more people
|  | Special care to underprivileged
|  | Not designed for profit
Newly found | Discrimination Issues | Eliminate discrimination
|  | Treat patients equally
Newly found | Develop Organisation |  NHS¿Google$\approx$Apple¿Amazon
|  | Private tech companies
|  | Hospital & academic institutions

##  Appendix E Item Loadings

The final version of item loadings are shown in the Table 7. As can be seen
from the table, the loadings are much larger than the preferred level of 0.7,
which according to Hair et al. indicates that the construct explains more than
50 per cent of the item’s variance, thus providing acceptable item
reliability, except ST1 (0.479), SR2 (0.256), PR1 (0.410), A3 (0.569), PCC4
(0.357), PCC5 (0.365) and PSR2 (0.612), which were therefore removed and are
not included in the table.

Table 7. Item Loading for Each Factor Factor | Item | Loading |  Factor | Item | Loading
---|---|---|---|---|---
Perceived Substitution Risk (PSR) | PSR1 | 0.906 |  Anthropomorphism (A) | A1 | 0.861
| PSR3 | 0.830 |  | A2 | 0.872
| PSR4 | 0.817 |  | A4 | 0.810
Perceived Usefulness (PU) | PU1 | 0.760 |  Social Influence (SI) | SI1 | 0.864
| PU2 | 0.760 |  | SI2 | 0.713
| PU3 | 0.843 |  | SI3 | 0.857
| PU4 | 0.760 |  | SI4 | 0.885
Trust in HVA (T) | T1 | 0.787 |  Effort Expectancy (EE) | EE1 | 0.879
| T2 | 0.803 |  | EE2 | 0.899
| T3 | 0.869 |  | EE3 | 0.894
| T4 | 0.818 |  Perceived Relative Service Quality (PRSQ) | PRSQ1 | 0.871
Intention to Use (IU) | IU1 | 0.833 |  | PRSQ2 | 0.845
| IU2 | 0.824 |  | PRSQ3 | 0.822
| IU3 | 0.833 |  Security Risk (SR) | SR1 | 0.926
Technology Attachment (TA) | TA1 | 0.913 |  | SR2 | 0.863
| TA2 | 0.909 |  | SR3 | 0.914
| TA3 | 0.777 |  Privacy Risk (PR) | PR2 | 0.923
Stance in Technology (ST) | ST2 | 0.766 |  | PR3 | 0.923
| ST3 | 0.889 |  | PR4 | 0.907
| ST4 | 0.869 |  Perceived Content Credibility (PCC) | PCC1 | 0.838
Familiarity (F) | F1 | 0.828 |  | PCC2 | 0.853
| F2 | 0.893 |  | PCC3 | 0.772
| F3 | 0.869 |  |  |

##  Appendix F Post-hoc Analysis of the Model

As mentioned in Section 5.2.3, out of interest in whether the use of HVA and
user-reported demographics also influenced trust in HVAs, we ran a second
model which added the control variables to capture users’ general demographics
like _age_ and _gender_ , as well as whether they have _experience_ in using
voice assistant for healthcare purpose, and the voice assistant (_device
type_) they normally use (e.g. Amazon Alexa, Google Assistant, etc.). Note
that, from the demographics of the survey participants (see Table 2), 3 of the
300 participants reported that they were not willing to state their gender
status, and in order to include the gender variable as a binary variable in
the model, we evaluated the models after removing the data associated with
these three participants. To summarise, the first model is the model _without_
these three participants and _without_ the control variables, and the second
is the model _without_ these three participants but _with_ the control
variables. The numbers in brackets in the table 8 are the results of the
second model. Finally, the resulting model showed no significant differences
in terms of the factors previously analyzed; those significantly and
substantially influencing trust in HVA remained so, and those that were not
significant remained so as well. In terms of the control variables, only
_gender_ was statistically significant, suggesting women to be more inclined
to trust in HVAs (Sample Mean = 0.107, $p\textless$0.001).

Table 8. PLS-SEM Analysis Results: Hypotheses testing results (O: Original
Sample Mean, M:Sample Mean, STDEV: Sandard Deviation). The numbers in brackets
are results for models with control variables added.

| Hypotheses | O | M | STDEV | T statistics |  $p$ Value | Supported?
---|---|---|---|---|---|---|---
H1 | Anthropomorphism ->Trust in HVA | 0.076(0.073) | 0.076(0.073) | 0.038(0.036) | 2.006(2.015) | 0.045(0.044) |  $\surd$($\surd$)
H2 | Effort Expectancy ->Trust in HVA | -0.059(-0.065) | -0.059(-0.065) | 0.030(0.030) | 1.977(2.147) | 0.048(0.032) |  $\surd$($\surd$)
H3 | Perceived Usefulness ->Trust in HVA | 0.278(0.281) | 0.278(0.282) | 0.046(0.047) | 6.011(5.956) | 0.000(0.000) |  $\surd$($\surd$)
H4 | Perceived Content Credibility ->Trust in HVA | 0.159(0.156) | 0.159(0.156) | 0.034(0.035) | 4.669(4.504) | 0.000(0.000) |  $\surd$($\surd$)
H5 | Perceived Relative Service Quality ->Trust in HVA | 0.106(0.104) | 0.105(0.103) | 0.034(0.033) | 3.125(3.108) | 0.002(0.002) |  $\surd$($\surd$)
H6 | Familiarity ->Trust in HVA | 0.029(0.030) | 0.029(0.031) | 0.024(0.024) | 1.2(1.215) | 0.230(0.225) | x(x)
H7 | Technology Attachment ->Trust in HVA | 0.039(0.024) | 0.041(0.025) | 0.029(0.028) | 1.376(0.864) | 0.169(0.388) | x(x)
H8 | Social Influence ->Trust in HVA | 0.024(0.016) | 0.025(0.015) | 0.032(0.032) | 0.761(0.505) | 0.447(0.614) | x(x)
H9 | Stance in Technology ->Trust in HVA | 0.112(0.100) | 0.11(0.099) | 0.032(0.032) | 3.549(3.082) | 0.000(0.002) |  $\surd$($\surd$)
H10 | Security Risk ->Trust in HVA | -0.138(-0.107) | -0.138(-0.108) | 0.044(0.045) | 3.104(2.399) | 0.002(0.016) |  $\surd$($\surd$)
H11 | Privacy Risk ->Trust in HVA | -0.175(-0.158) | -0.173(-0.156) | 0.033(0.033) | 5.224(4.840) | 0.000(0.000) |  $\surd$($\surd$)
H12 | Perceived Substitution Risk ->Trust in HVA | 0.032(0.015) | 0.03(0.015) | 0.029(0.028) | 1.087(0.516) | 0.277(0.606) | x(x)
H13 | Trust in HVA ->Intention to Use | 0.738(0.738) | 0.738(0.739) | 0.031(0.030) | 24.155(24.293) | 0.000(0.000) |  $\surd$($\surd$)
| Gender ->Trust in HVA | (0.108) | (0.107) | (0.027) | (4.005) | (0.000) | ($\surd$)
| Age ->Trust in HVA | (0.009) | (0.009) | (0.023) | (0.375) | (0.708) | (x)
| Experience ->Trust in HVA | (0.002) | (0.003) | (0.026) | (0.093) | (0.926) | (x)
| Alexa ->Trust in HVA | (0.002) | (0.003) | (0.026) | (0.093) | (0.926) | (x)
| Google ->Trust in HVA | (0.026) | (0.052) | (7.595) | (0.003) | (0.997) | (x)
| Others ->Trust in HVA | (0.023) | (0.106) | (7.596) | (0.003) | (0.998) | (x)