Attention check

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

att_1 n
0 6
1 296

Demographics

Race

race N Perc
asian 17 5.74
black 42 14.19
hispanic 17 5.74
multiracial 9 3.04
white 208 70.27
NA 3 1.01

Gender

gender N Perc
man 119 40.20
woman 173 58.45
NA 4 1.35

Age

age_mean age_sd
38.98276 11.63768

Education

edu N Perc
GED 79 26.69
2yearColl 34 11.49
4yearColl 125 42.23
MA 42 14.19
PHD 16 5.41

Income

Employment

employment N Perc
Full-time 219 73.99
Full-time, Homemaker 1 0.34
Full-time, Student 1 0.34
Homemaker 1 0.34
Other 1 0.34
Part-time 53 17.91
Part-time, Homemaker 1 0.34
Part-time, Student 3 1.01
Retired 5 1.69
Student 2 0.68
Temporarily laid off 2 0.68
Unemployed 5 1.69
UnemployedHomemaker 1 0.34
UnemployedOther 1 0.34

Measures

Message chosen

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

dom: One of 15 crowd-sourced messages.
aff: One of 22 crowd-sourced messages.

message_choice N Perc
aff 272 91.89
dom 24 8.11

Predicted selection

Do you think your employee will choose to do the task and have a chance for a bonus of up to $2, 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
aff do task 126
aff skip task 146
dom do task 19
dom skip task 5

Prediction of affiliative message

message_choice choice_aff N
aff do task 268
aff skip task 4
dom do task 17
dom skip task 7

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 affiliative 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 affiliative 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 affiliative 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 $2.00.

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 = 0.78

Correlations

is_dom: dummy-coded message chosen (0 = aff; 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.

Preregistered Analysis

Model 1

Random effects linear regression: Competitive worldview as a predictor variable; expected relationship impact of dominant message as an outcome variable; messages shown as random effects.

term estimate conf.int statistic df p.value eta2
Intercept 1.81 [1.22, 2.39] 6.06 250.01 < .001 NA
CWV 0.29 [0.07, 0.50] 2.65 291.45 .008 0.024

Model 2

Random effects linear regression: Competitive worldview as a predictor variable; binary choice of dominant message as an outcome variable; messages shown as random effects.

term estimate conf.int statistic df p.value eta2
Intercept -0.05 [-0.16, 0.05] -0.98 251.30 .326 NA
CWV 0.05 [0.01, 0.09] 2.59 291.65 .010 0.022

Model 3

Random effects 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; messages shown as random effects.

term estimate conf.int statistic df p.value eta2
Intercept 0.08 [0.05, 0.11] 4.97 12.90 < .001 NA
Scaleattitude dom 0.06 [0.03, 0.09] 3.52 292.64 < .001 0.041
Scalecomp dom 0.01 [-0.03, 0.04] 0.39 292.85 .700 0.001

Random Effects Mediation Model

Predictor: Competitive worldview.
Mediator: Relationship expectancy.
Outcome: Selection of dominant message.
Random effects: dominant messages shown.

Effect Estimate X95..CI.Lower X95..CI.Upper p.value
ACME (indirect) 0.011 0.002 0.023 0.0064
ADE (direct) 0.040 0.001 0.080 0.0414
Total Effect 0.051 0.012 0.091 0.0096
Prop. Mediated 0.210 0.041 0.806 0.0156

Exploratory Analysis

Simultaneuous random effects mediation model

Predictor: Competitive worldview.
Mediators: Relationship expectancy, compliance expectancy.
Outcome: Selection of dominant message.
Random effect: dominant messages shown.

label est se ci.lower ci.upper pvalue
a1 0.2874319 0.1119319 0.0680494 0.5068145 0.0102312
a2 0.7604819 1.0204737 -1.2396097 2.7605736 0.4561361
b1 0.0372953 0.0099220 0.0178485 0.0567422 0.0001707
b2 0.0004684 0.0014003 -0.0022762 0.0032130 0.7380086
c_prime 0.0404075 0.0207452 -0.0002524 0.0810674 0.0514390
ind1 0.0107199 0.0048522 0.0012097 0.0202300 0.0271552
ind2 0.0003562 0.0009081 -0.0014236 0.0021360 0.6948640
ind_total 0.0110761 0.0047417 0.0017825 0.0203697 0.0194975
total 0.0514836 0.0209192 0.0104828 0.0924844 0.0138521

Expected recommendation in “good manager” survey as alternative to relationship expectancy

Linear model

Random effects linear regression: Competitive worldview as a predictor variable; Expected nomination for “good manager” survey as an outcome variable; messages shown as random effects.

term estimate conf.int statistic df p.value eta2
Intercept -0.16 [-0.29, -0.02] -2.32 225.06 .021 NA
CWV 0.11 [0.06, 0.15] 4.61 281.66 < .001 0.07

Mediation model

Predictor: Competitive worldview.
Mediator: Expected nomination for “good manager” survey.
Outcome: Selection of dominant message.
Random effects: dominant messages shown.

Effect Estimate X95..CI.Lower X95..CI.Upper p.value
ACME (indirect) 0.037 0.020 0.056 0.0000
ADE (direct) 0.016 -0.022 0.054 0.4100
Total Effect 0.053 0.013 0.092 0.0094
Prop. Mediated 0.696 0.341 2.273 0.0094

Simultaneuous mediation model

Predictor: Competitive worldview.
Mediators: Expected nomination for “good manager” survey, compliance expectancy.
Outcome: Selection of dominant message.
Random effect: dominant messages shown.

label est se ci.lower ci.upper pvalue
a1 0.1078650 0.0331700 0.0428530 0.1728769 0.0011464
a2 0.7604819 1.0204737 -1.2396097 2.7605736 0.4561361
b1 0.3255828 0.0994994 0.1305676 0.5205979 0.0010671
b2 0.0008986 0.0012249 -0.0015021 0.0032993 0.4631684
c_prime 0.0156813 0.0216389 -0.0267302 0.0580927 0.4686485
ind1 0.0351190 0.0157728 0.0042048 0.0660332 0.0259776
ind2 0.0006834 0.0008008 -0.0008861 0.0022529 0.3934392
ind_total 0.0358023 0.0154752 0.0054715 0.0661332 0.0206936
total 0.0514836 0.0209192 0.0104828 0.0924844 0.0138521

Add demographic controls

Model 1

Random effects linear regression: Competitive worldview as a predictor variable; expected relationship impact of dominant message as an outcome variable; messages shown as random effects. Controls: age, gender, race, income, education.

term estimate conf.int statistic df p.value eta2
Intercept 1.52 [0.37, 2.68] 2.59 272.99 .010 NA
CWV 0.21 [-0.02, 0.44] 1.79 271.43 .074 0.012
Age 0.02 [0.00, 0.03] 2.13 268.80 .034 0.017
Gender man 0.27 [-0.08, 0.62] 1.52 268.11 .129 0.009
Race white -0.26 [-0.64, 0.12] -1.35 264.19 .177 0.007
Income num -0.01 [-0.08, 0.06] -0.25 268.94 .804 0.000
Edu num 0.00 [-0.16, 0.15] -0.06 272.25 .955 0.000

Model 2

Random effects linear regression: Competitive worldview as a predictor variable; binary choice of dominant message as an outcome variable; messages shown as random effects. Controls: age, gender, race, income, education.

term estimate conf.int statistic df p.value eta2
Intercept -0.20 [-0.41, 0.01] -1.83 272.99 .069 NA
CWV 0.05 [0.01, 0.09] 2.33 272.92 .021 0.019
Age 0.00 [0.00, 0.01] 2.38 270.41 .018 0.021
Gender man -0.02 [-0.09, 0.04] -0.75 269.58 .453 0.002
Race white -0.04 [-0.11, 0.03] -1.05 264.84 .293 0.004
Income num 0.00 [-0.01, 0.01] -0.36 270.50 .717 0.000
Edu num 0.02 [-0.01, 0.05] 1.11 273.60 .268 0.004

Model 3

Random effects 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; messages shown as random effects. Controls: age, gender, race, income, education.

term estimate conf.int statistic df p.value eta2
Intercept -0.01 [-0.16, 0.14] -0.15 270.08 .882 NA
Scaleattitude dom 0.04 [0.01, 0.08] 2.39 271.35 .017 0.021
Scalecomp dom 0.00 [-0.03, 0.04] 0.18 273.00 .854 0.000
Age 0.00 [0.00, 0.00] 1.52 272.23 .131 0.008
Gender man -0.02 [-0.08, 0.04] -0.63 270.53 .528 0.001
Race white -0.04 [-0.11, 0.03] -1.06 265.87 .289 0.004
Income num 0.00 [-0.01, 0.01] -0.23 271.10 .816 0.000
Edu num 0.01 [-0.02, 0.04] 0.89 272.95 .376 0.003

Random Effects Mediation Model

Predictor: Competitive worldview.
Mediator: Relationship expectancy.
Outcome: Selection of dominant message.
Random effects: dominant messages shown.
Controls: age, gender, race, income, education

Effect Estimate X95..CI.Lower X95..CI.Upper p.value
ACME (indirect) 0.006 0.000 0.015 0.0796
ADE (direct) 0.044 0.002 0.086 0.0404
Total Effect 0.050 0.008 0.092 0.0212
Prop. Mediated 0.104 -0.025 0.576 0.0964

Add demographic controls: for expected nomination in “good manager” survey

Model 1

Random effects linear regression: Competitive worldview as a predictor variable; Expected nomination for “good manager” survey as an outcome variable; messages shown as random effects. Controls: age, gender, race, income, education.

term estimate conf.int statistic df p.value eta2
Intercept -0.19 [-0.45, 0.06] -1.50 272.42 .135 NA
CWV 0.11 [0.06, 0.16] 4.21 261.23 < .001 0.063
Age 0.00 [0.00, 0.00] 0.33 266.16 .744 0.000
Gender man -0.07 [-0.15, 0.01] -1.73 269.33 .085 0.011
Race white 0.03 [-0.06, 0.11] 0.61 260.45 .540 0.001
Income num 0.00 [-0.02, 0.01] -0.59 263.35 .557 0.001
Edu num 0.01 [-0.02, 0.05] 0.67 265.34 .501 0.002

Random Effects Mediation Model

Predictor: Competitive worldview.
Mediator: Expected nomination for “good manager” survey.
Outcome: Selection of dominant message.
Random effects: dominant messages shown.
Controls: age, gender, race, income, education

Effect Estimate X95..CI.Lower X95..CI.Upper p.value
ACME (indirect) 0.031 0.015 0.049 0.0000
ADE (direct) 0.020 -0.021 0.060 0.3390
Total Effect 0.050 0.009 0.092 0.0178
Prop. Mediated 0.599 0.249 2.358 0.0178

Simultaneuous mediation model

Predictor: Competitive worldview.
Mediators: Expected nomination for “good manager” survey, compliance expectancy.
Outcome: Selection of dominant message.
Random effect: dominant messages shown.

label est se ci.lower ci.upper pvalue
a1 0.1069755 0.0314337 0.0453665 0.1685845 0.0006660
a2 1.7588232 1.3749701 -0.9360686 4.4537150 0.2008365
b1 0.2745952 0.0916470 0.0949703 0.4542201 0.0027334
b2 0.0003112 0.0011437 -0.0019305 0.0025528 0.7855709
c_prime 0.0195032 0.0212104 -0.0220685 0.0610748 0.3578292
ind1 0.0293750 0.0132021 0.0034993 0.0552506 0.0260799
ind2 0.0005473 0.0018627 -0.0031035 0.0041981 0.7688958
ind_total 0.0299223 0.0127331 0.0049658 0.0548787 0.0187759
total 0.0494254 0.0173244 0.0154702 0.0833807 0.0043318

Simultaneuous mediation model

Because there’s a difference between the full random effects (both dom and aff random effects) and the reduced random effects (one dom random effects)… I have to use the full model (random effect for dom message + random effect for aff message). Let’s see if it replicated below

Predictor: Competitive worldview.
Mediators: Expected nomination for “good manager” survey, compliance expectancy.
Outcome: Selection of dominant message.
Random effect: dominant messages shown.

label est se ci.lower ci.upper pvalue
a1 0.1069755 0.0318087 0.0446317 0.1693193 0.0007707
a2 1.7588232 1.1710289 -0.5363514 4.0539978 0.1331108
b1 0.2745952 0.0833224 0.1112864 0.4379041 0.0009822
b2 0.0003112 0.0012202 -0.0020804 0.0027027 0.7987124
c_prime 0.0195032 0.0198399 -0.0193824 0.0583887 0.3255947
ind1 0.0293750 0.0119094 0.0060329 0.0527170 0.0136429
ind2 0.0005473 0.0021500 -0.0036666 0.0047612 0.7990646
ind_total 0.0299223 0.0121923 0.0060258 0.0538187 0.0141201
total 0.0494254 0.0218608 0.0065790 0.0922718 0.0237645

Great. It holds.