AI as a social skills equalizer
July 2024: Analysis
Materials
Description
Participants were asked to read the following:
Imagine you are a manager at a mid-size firm responsible for
overseeing a high-stakes project involving the development of a new
product, crucial for the company’s future growth. Alex, one of your key
employees, is leading this project. Alex has been with the company for
two years.
Recently, however, Alex’s team has not been performing up to the
usual standards. This drop in performance coincides with a critical
phase of the project, which demands an increased workload from Alex and
their team. Important deadlines have been missed, leading to significant
revisions in their work. The implications of these missed deadlines are
severe, including substantial financial losses and strained
relationships with important clients who are expressing dissatisfaction
and considering taking their business elsewhere.
As a manager, you are under pressure from company leadership who are
deeply concerned about the project’s current trajectory and its
potential impact on the firm’s financial health and client retention.
You need to motivate Alex to address these issues promptly and
effectively.
To address this situation, you can send one of two messages to Alex.
We will introduce these messages to you, ask you to choose the one you
would send, and then ask a few questions about your choice.
Messages
In random order, participants read the messages and selected one to send to Alex. Then, they answered questions about each of them. These are the messages:
Dominant message
Alex,
As we advance through this critical phase of our project, it’s
crucial to acknowledge our recent setbacks. The expectations for your
role encompass leading the team to meet stringent deadlines, which is
essential not only for the project’s success but also for maintaining
our client relationships and financial stability.
To address this, I propose we immediately review our project
strategy and implement a performance enhancement plan. Your leadership
is going to be key to navigate these challenges. Let’s schedule a
meeting early next week to discuss actionable steps and resources that
could support the team better. Your insight into the team’s hurdles and
your proposals for moving forward will be important as we adjust our
approach.
Regards,
[Your Name]
Non-dominant message
Dear Alex,
I understand the recent phase has been particularly demanding, and
it’s clear that the increased workload has taken its toll on you and the
team. It’s important to recognize the hard work everyone is putting in
and the challenges that come with such a high-stakes
endeavor.
To help us move forward, I’d like to explore ways we can adjust the
workload and provide additional support where needed. Your experience
and understanding of the team’s dynamics are crucial in crafting a plan
that ensures both the well-being of our team and the success of our
project. Let’s discuss how we can achieve this balance in a meeting
early next week. I’m here to support you in this challenging time and am
confident in our collective ability to meet our objectives.
Warm regards,
[Your Name]
Demographics
Race
df_aiss %>%
group_by(race) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| race | N | Perc |
|---|---|---|
| Asian | 4 | 4 |
| Black or African American | 18 | 18 |
| Hispanic, Latino, or Spanish origin | 5 | 5 |
| Middle Eastern or North African | 1 | 1 |
| White | 65 | 65 |
| multiracial | 7 | 7 |
Gender
df_aiss %>%
mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>%
group_by(gender) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| gender | N | Perc |
|---|---|---|
| man | 54 | 54 |
| other | 3 | 3 |
| woman | 43 | 43 |
Age
df_aiss %>%
summarise(age_mean = round(mean(age,na.rm = T),2),
age_sd = round(sd(age,na.rm = T),2)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| age_mean | age_sd |
|---|---|
| 37.6 | 10.28 |
Education
df_aiss %>%
group_by(edu) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| edu | N | Perc |
|---|---|---|
| GED | 25 | 25 |
| 2yearColl | 9 | 9 |
| 4yearColl | 46 | 46 |
| MA | 15 | 15 |
| PHD | 3 | 3 |
| NA | 2 | 2 |
Income
df_aiss %>%
ggplot(aes(x = income)) +
geom_bar() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
coord_flip()
Employment
df_aiss %>%
group_by(employment) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| employment | N | Perc |
|---|---|---|
| Full-time | 85 | 85 |
| Full-time, Homemaker | 1 | 1 |
| Full-time, Other | 1 | 1 |
| Other | 2 | 2 |
| Part-time | 11 | 11 |
Work Experience
df_aiss %>%
group_by(workex) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| workex | N | Perc |
|---|---|---|
| Less than 5 years | 11 | 11 |
| 5 - 10 years | 26 | 26 |
| 10 - 20 years | 37 | 37 |
| More than 20 years | 26 | 26 |
Measures
Empathic Concern
- I often have tender, concerned feelings for people less fortunate
than me.
- Sometimes I don’t feel very sorry for other people when they are
having problems. [R]
- When I see someone being taken advantage of, I feel kind of
protective towards them.
- Other people’s misfortunes do not usually disturb me a great deal.
[R]
- When I see someone being treated unfairly, I sometimes don’t feel
very much pity for them. [R]
- I am often quite touched by things that I see happen.
- I would describe myself as a pretty soft-hearted person.
Cronbach’s alpha = 0.83
df_aiss %>%
ggplot(aes(x = EC)) +
geom_histogram(binwidth = 0.3,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$EC,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Preference
If you had to choose one these messages to send to Alex, which one would you choose?
df_aiss %>%
group_by(pref) %>%
summarise(N = n()) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| pref | N |
|---|---|
| dom | 37 |
| nondom | 63 |
Much better!
Dominant message
Likelihood
How likely would it be for you to send this message?
df_aiss %>%
ggplot(aes(x = dom_likely)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$dom_likely,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Empathic
How empathic is this message?
df_aiss %>%
ggplot(aes(x = dom_emp)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$dom_emp,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Threat
How threatening is this message?
df_aiss %>%
ggplot(aes(x = dom_threat)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$dom_threat,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Perceived impact
What will be the impact of this message on Alex’s work? (1 = Greatly Harm to 7 = Greatly Help)
df_aiss %>%
ggplot(aes(x = dom_eff)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$dom_eff,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Non-Dominant message
Likelihood
How likely would it be for you to send this message?
df_aiss %>%
ggplot(aes(x = nondom_likely)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$nondom_likely,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Empathic
How empathic is this message?
df_aiss %>%
ggplot(aes(x = nondom_emp)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$nondom_emp,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Threat
How threatening is this message?
df_aiss %>%
ggplot(aes(x = nondom_threat)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$nondom_threat,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Perceived impact
What will be the impact of this message on Alex’s work? (1 = Greatly Harm to 7 = Greatly Help)
df_aiss %>%
ggplot(aes(x = nondom_eff)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$nondom_eff,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Correlation Matrix
EC: Empathic concern
pref_isdom: selecting dominant message over
non-dominant message
dom_likely: Dominant message | likelihood
dom_emp: Dominant message | empathic
dom_threat: Dominant message | threat
dom_eff: Dominant message | perceived impact
nondom_likely: Non-Dominant message | likelihood
nondom_emp: Non-Dominant message | empathic
nondom_threat: Non-Dominant message | threat
nondom_eff: Non-Dominant message | perceived impact
df_aiss %>%
mutate(workex_num = as.numeric(workex),
edu_num = as.numeric(edu),
income_num = as.numeric(income),
pref_isdom = ifelse(pref == "dom",1,0)) %>%
dplyr::select(EC,pref_isdom,dom_likely:nondom_eff,age,workex_num:income_num) %>%
corPlot(upper = TRUE,stars = TRUE,xsrt = 270)
Hmm, the preference is still pretty weakly correlated. The likelihood of selecting the dominant message, though, is correlated with PT.
EC by message chosen
df_aiss %>%
group_by(pref) %>%
summarise(N = n(),
EC = mean(EC)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| pref | N | EC |
|---|---|---|
| dom | 37 | 3.826255 |
| nondom | 63 | 3.852608 |