AI as a social skills equalizer
May 2024c: Analysis
Materials
Description
Participants were asked to read the following:
Imagine that you are a manager at a mid-size firm. You recently met
with leadership, and they expressed discontent with your team’s
performance.
One of your employees, Alex, is leading a high-stakes project
involving the development of a new product, crucial for the company’s
future growth. Alex has been with the company for two years.
Alex’s recent outputs have not met usual standards. This coincided
with a critical phase of the project, demanding an increased workload
from Alex and their team. Important deadlines have been missed, and
there have been a few instances where Alex’s work required significant
revisions.
You want to motivate Alex to improve their work.
To motivate Alex, you can send one of two messages. We will now
introduce those messages to you, ask you which one you would send, and
ask a few questions about each one.
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,
I want to address the critical issues we’ve observed in your recent
performance. Given the high stakes of our current project, the
expectations for your role are significantly elevated. Over the past few
months, there has been a noticeable decline in your output and the
quality of your work, which has led to missed deadlines and necessary
revisions.
It is crucial that we see immediate and sustained improvement.
Failure to meet these standards can have serious repercussions,
potentially impacting your future at our firm. I would like us to meet
as soon as possible to formulate a clear and actionable plan for turning
this situation around. Please come prepared for a detailed discussion of
your current projects and performance metrics.
Regards,
[Your Name]
Non-dominant message
Dear Alex,
I’ve been considering the challenges you’ve faced recently,
especially with the increased demands of our high-stakes project. I
understand that ramping up efforts during this critical phase has not
been easy, and I appreciate all the effort you’re putting in.
If there is anything that you are struggling with at work or outside
of work, I hope I can help overcome those challenges. Let’s schedule a
time to discuss how we can better support your needs, adjust your
workload, and set realistic goals that consider the demands you’re
currently facing. I’m here to help you overcome these challenges and
ensure you continue to succeed and grow with us.
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 | 5 | 4.95 |
| Black or African American | 14 | 13.86 |
| Hispanic, Latino, or Spanish origin | 5 | 4.95 |
| White | 69 | 68.32 |
| multiracial | 8 | 7.92 |
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 | 53 | 52.48 |
| other | 2 | 1.98 |
| woman | 46 | 45.54 |
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 |
|---|---|
| 39.77 | 12.63 |
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 | 17 | 16.83 |
| 2yearColl | 18 | 17.82 |
| 4yearColl | 51 | 50.50 |
| MA | 10 | 9.90 |
| PHD | 4 | 3.96 |
| NA | 1 | 0.99 |
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 | 75 | 74.26 |
| Part-time | 22 | 21.78 |
| Permanently disabled | 2 | 1.98 |
| Retired | 1 | 0.99 |
| Unemployed | 1 | 0.99 |
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 | 10.89 |
| 5 - 10 years | 23 | 22.77 |
| 10 - 20 years | 26 | 25.74 |
| More than 20 years | 41 | 40.59 |
Measures
Perspective-Taking
- I sometimes find it difficult to see things from the “other
person’s” point of view [R]
- I try to look at everybody’s side of a disagreement before I make a
decision
- I sometimes try to understand my friends better by imagining how
things look from their perspective
- If I’m sure I’m right about something, I don’t waste much time
listening to other people’s arguments [R]
- I believe that there are two sides to every question and try to look
at them both
- When I’m upset at someone, I usually try to “put myself in their
shoes” for a while
- Before criticizing somebody, I try to imagine how I would feel if I were in their place
Cronbach’s alpha = 0.89
df_aiss %>%
ggplot(aes(x = PT)) +
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$PT,na.rm = T),
color = "black",
size = 1.5,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank())
Preference
If you had to choose one these messages to send to Alex, which one would you choose?
df_aiss %>%
group_by(pref) %>%
summarise(N = n()) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| pref | N |
|---|---|
| dom | 17 |
| nondom | 84 |
Oh boy. Still very low. A bummer. We need to get that up.
Dominant message
Likelihood
How likely would it be for you to send this message?
df_aiss %>%
ggplot(aes(x = dom_likely)) +
geom_histogram(binwidth = 1,
fill = "lightblue",
color = "black") +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8,1)) +
ylab("frequency") +
geom_vline(xintercept = mean(df_aiss$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
PT: Perspective-taking
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(PT,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.
PT by message chosen
df_aiss %>%
group_by(pref) %>%
summarise(N = n(),
PT = mean(PT)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| pref | N | PT |
|---|---|---|
| dom | 17 | 3.722689 |
| nondom | 84 | 3.799320 |