Attention check

First, let’s see who passed the attention check.

att_1 n
0 5
1 198

Demographics

Race

race N Perc
asian 14 7.07
black 24 12.12
hispanic 11 5.56
multiracial 6 3.03
white 142 71.72
NA 1 0.51

Gender

gender N Perc
man 101 51.01
woman 92 46.46
NA 5 2.53

Age

age_mean age_sd
37.55897 10.82382

Education

edu N Perc
GED 40 20.20
2yearColl 29 14.65
4yearColl 88 44.44
MA 32 16.16
PHD 7 3.54
NA 2 1.01

Income

Employment

employment N Perc
Full-time 162 81.82
Full-time, Student 2 1.01
Other 2 1.01
Part-time 25 12.63
Part-time, Retired 1 0.51
Permanently disabled 1 0.51
Student 2 1.01
Temporarily laid off 1 0.51
Unemployed 2 1.01

Measures

Message chosen

Which of the following messages do you wish to send to your employee?

dom: Your job in this task is to select the shapes that match the description. Make sure you look at them carefully. If you don’t complete the task and do it well, I will not give you the full bonus.
nondom: Your job in this task is to select the shapes that match the description. Make sure you look at them carefully.

message_choice N Perc
dom 73 36.87
nondom 125 63.13

Predicted selection

Do you think your employee will choose to do the task and have a chance for a bonus of up to $1, depending on your choice? Or will they opt to skip the task and the chance for a bonus? Split here by message selection.

Prediction of dominant message

message_choice choice_dom N
dom do-task 68
dom skip-task 5
nondom do-task 70
nondom skip-task 55

Prediction of non-dominant message

message_choice choice_nondom N
dom do-task 52
dom skip-task 21
nondom do-task 121
nondom skip-task 4

Predicted Performance

If they choose to do the task after receiving your message, how well will they perform? Their performance can range from 0 points up to 50 points (for perfect performance). Split here by message selection.

Prediction of dominant message

Prediction of non-dominant message

Predicted Attitude

What will be the impact of this message on your employee’s attitude towards you? Split here by message selection.

Prediction of dominant message

Prediction of nondominant message

Predicted nomination

Do you think your employee will recommend you as a participant in this “good manager” paid follow-up survey? Split here by message selection.

Prediction of dominant message

Prediction of non-dominant message

Punish decision

For each level of their performance below, please indicate what bonus you would like them to receive. You can select between 0 and 100 cents for each. 100 cents is the full $1.00.

Competitive Worldview

1 = Strongly Disagree to 7 = Strongly Agree

1. It’s a dog-eat-dog world where you have to be ruthless at times
2. Life is not governed by the “survival of the fittest.” We should let compassion and moral laws be our guide [R]
3. There is really no such thing as “right” and “wrong.” It all boils down to what you can get away with
4. One of the most useful skills a person should develop is how to look someone straight in the eye and lie convincingly
5. It is better to be loved than to be feared [R]
6. My knowledge and experience tell me that the social world we live in is basically a competitive “jungle” in which the fittest survive and succeed, in which power, wealth, and winning are everything, and might is right
7. Do unto others as you would have them do unto you, and never do anything unfair to someone else [R]
8. Basically people are objects to be quietly and coolly manipulated for one’s own benefit
9. Honesty is the best policy in all cases [R]
10. One should give others the benefit of the doubt. Most people are trustworthy if you have faith in them [R]

Cronbach’s alpha = 0.82

Correlations

is_dom: dummy-coded message chosen (0 = nondom; 1 = dom).
attitude: predicted impact of the message on employee’s attitude towards the manager.
pred_nom: predicted nomination for “good maanger” survey
comp: score the employee will get in the task if sent this message.

Analysis

Model 1

Linear regression: Competitive worldview as a predictor variable; expected relationship impact of dominant message as an outcome variable.

Without controls

term estimate conf.int statistic df p.value
Intercept 1.63 [0.90, 2.36] 4.40 196 < .001
CWV 0.57 [0.32, 0.82] 4.57 196 < .001

With controls

term estimate conf.int statistic df p.value
Intercept 2.06 [0.61, 3.50] 2.81 179 .006
CWV 0.56 [0.29, 0.83] 4.12 179 < .001
Age 0.00 [-0.02, 0.02] 0.06 179 .954
Gender man -0.01 [-0.49, 0.46] -0.05 179 .957
Race white 0.08 [-0.45, 0.61] 0.29 179 .771
Income num -0.10 [-0.20, 0.00] -1.99 179 .048
Edu num 0.01 [-0.23, 0.25] 0.05 179 .960

Model 2

Linear regression: Competitive worldview as a predictor variable; binary choice of dominant message as an outcome variable.

Without controls

term estimate conf.int statistic df p.value
Intercept -0.05 [-0.26, 0.16] -0.44 196 .662
CWV 0.15 [0.08, 0.22] 4.07 196 < .001

With controls

term estimate conf.int statistic df p.value
Intercept 0.53 [0.12, 0.93] 2.58 179 .011
CWV 0.13 [0.05, 0.21] 3.41 179 < .001
Age -0.01 [-0.02, 0.00] -2.71 179 .007
Gender man -0.08 [-0.21, 0.06] -1.16 179 .249
Race white 0.07 [-0.08, 0.21] 0.86 179 .388
Income num -0.04 [-0.07, -0.01] -2.77 179 .006
Edu num 0.00 [-0.07, 0.07] 0.04 179 .965

Model 3

Linear regression: Expected relationship impact of dominant message as a predictor variable; binary choice of dominant message as an outcome variable; expected compliance impact of dominant message as a control variable.

term estimate conf.int statistic df p.value
Intercept 0.37 [0.31, 0.43] 11.81 195 < .001
Scaleattitude dom 0.19 [0.12, 0.26] 5.60 195 < .001
Scalecomp dom 0.04 [-0.03, 0.11] 1.15 195 .251

With controls

term estimate conf.int statistic df p.value
Intercept 0.86 [0.54, 1.18] 5.31 178 < .001
Scaleattitude dom 0.16 [0.09, 0.23] 4.54 178 < .001
Scalecomp dom 0.05 [-0.02, 0.12] 1.47 178 .144
Age -0.01 [-0.02, 0.00] -3.17 178 .002
Gender man -0.06 [-0.19, 0.07] -0.93 178 .353
Race white 0.05 [-0.09, 0.19] 0.73 178 .465
Income num -0.04 [-0.06, -0.01] -2.63 178 .009
Edu num 0.02 [-0.05, 0.08] 0.50 178 .621

Model 4

Mediation model: Competitive worldview as a predictor variable; expected relationship impact of dominant message as a mediator; binary choice of dominant message as an outcome variable.

Without controls

a = 0.57 (p = 0)
b = 0.11 (p = 0)
direct = 0.15 (p = 0)
indirect = 0.09 (p = 0.015)

With controls

Exploratory Analysis

“Good manager” survey

All models from the analysis plan, but, instead of the continuous expected relationship impact measure, we will insert the binary expected recommendation to add the manager as a participant in the “good manager” follow-up.

Model 1

Linear regression: Competitive worldview as a predictor variable; expected nomination in follow-up for dominant message as outcome.

Without controls

term estimate conf.int statistic df p.value
Intercept -0.07 [-0.27, 0.14] -0.64 196 .523
CWV 0.13 [0.06, 0.20] 3.73 196 < .001

With controls

term estimate conf.int statistic df p.value
Intercept 0.43 [0.04, 0.83] 2.18 179 .030
CWV 0.12 [0.05, 0.19] 3.19 179 .002
Age -0.01 [-0.01, 0.00] -2.06 179 .041
Gender man -0.06 [-0.19, 0.07] -0.92 179 .361
Race white 0.00 [-0.15, 0.14] -0.02 179 .987
Income num -0.03 [-0.06, 0.00] -2.18 179 .030
Edu num -0.01 [-0.07, 0.06] -0.30 179 .768

Model 2

Linear regression: expected nomination in follow-up for dominant message as a predictor variable; binary choice of dominant message as an outcome variable; expected compliance impact of dominant message as a control variable.

term estimate conf.int statistic df p.value
Intercept 0.18 [0.12, 0.24] 5.62 195 < .001
Nompred dom 0.64 [0.52, 0.75] 10.78 195 < .001
Scalecomp dom 0.06 [0.01, 0.12] 2.35 195 .020

With controls

term estimate conf.int statistic df p.value
Intercept 0.46 [0.16, 0.76] 3.05 178 .003
Nompred dom 0.56 [0.44, 0.69] 8.84 178 < .001
Scalecomp dom 0.07 [0.01, 0.13] 2.49 178 .014
Age -0.01 [-0.01, 0.00] -2.09 178 .038
Gender man -0.03 [-0.14, 0.09] -0.45 178 .650
Race white 0.06 [-0.07, 0.18] 0.88 178 .379
Income num -0.03 [-0.05, 0.00] -2.31 178 .022
Edu num 0.02 [-0.03, 0.08] 0.75 178 .454

Model 4

Mediation model: Competitive worldview as a predictor variable; expected nomination in follow-up for dominant message as a mediator; binary choice of dominant message as an outcome variable.

Without controls

a = 0.13 (p = 0)
b = 0.62 (p = 0)
direct = 0.15 (p = 0)
indirect = 0.07 (p = 0.028)

With controls