Feathered Correlations: Color Predicting Culture in Diverse Flocks After Disaster

There were once colorful flocks of birds that lived together in a big forest. These flocks of birds had different ratios of beige birds, purple birds, red birds, blue birds, orange birds, green birds, yellow birds, and turquoise birds. These different proportions of colors influenced each flock's values, culture, and atmosphere. Their leaders made decisions based on the most common bird colors.


One day, after a bad storm, the flock leaders made tough choices to help their birds survive.


In Swift's flock, purple and green birds predominated. True to their adventurous nature, Swift sent the purple and green birds to find new food sources (unpaid vacation). She asked the orange birds to scout for materials to rebuild damaged nests because they loved to compete.


Feather's flock mainly had blue, who valued stability, and orange birds, who loved competition. The no-nonsense orange birds got right to work on repairs (no reduced staff). And the peaceful blue birds kept singing to lift spirits, so Feather changed nothing about their routines (nothing changed).  


Hootie's flock had many sociable yellow birds who could always find a profitable solution for everyone and possessed a Win-Win-Win behavior. When the yellow birds' food storage was damaged, instead of reducing their rations (reduced salaries), Hootie asked the red birds to share the extra food they had gathered. So, yellow birds' food storage wasn't changed (no reduced salaries).


Over in Willa's flock, fun-loving turquoise birds were the majority. After the storm, Willa kept all her turquoise birds in their usual nests, singing songs since their cheerfulness helped the whole flock recover (nothing changed). But she had to ask the few red and orange birds to rebuild damaged nests belonging to elder beige birds temporarily.


Each diverse flock recovered based on its colorful culture and connections. But all birds worked cooperatively despite difficulties to continue thriving in the forest.


The SDTEST® gives clues to someone's motivational values. However, additional polls can provide more pieces of the puzzle.


Imagine also giving an "Actions of companies in relation to personnel in the last month (yes / no)" poll. It asks people about actions of companies in relation to personnel in the last month. 


Now imagine 1'000 people who took both tests. You could match up each person's SDTEST® colors with their answers about actions of companies in relation to personnel in the last month.


Comparing tests gives an expanded picture of values in action. More puzzle pieces make the whole image more apparent!


Multiple tests can work together, like colors blending on a palette. Other polls reveal what engages your values, like what is the perception of the actions of companies in relation to personnel in the last month. Combined, they paint a richer picture of what motivates our thoughts and deeds.


Below you can read an abridged version of the results of our VUCA poll "Actions of companies in relation to personnel in the last month (yes / no)". The full results of the poll are available for free in the FAQ section after login or registration.


上個月與人員有關的公司的行動(是 /否)

Country
Lang
-
Mail
重新計算
Critical_value_of_the_correlation_coefficient
正態分佈,威廉·西莉·格塞特(William Sealy Gosset)(學生) r = 0.0521
正態分佈,威廉·西莉·格塞特(William Sealy Gosset)(學生) r = 0.0521
非正態分佈,Spearman r = 0.0021
分配非正常普通的非正常普通的普通的普通的普通的普通的
所有問題
所有問題
上個月對員工採取了什麼行動
上個月對員工採取了什麼行動
Answer 1-
Weak_positive
0.1213
Weak_positive
0.0327
Weak_negative
-0.0412
Weak_negative
-0.0965
Weak_positive
0.0377
Weak_positive
0.0201
Weak_negative
-0.0309
Answer 2-
Weak_negative
-0.0030
Weak_negative
-0.0350
Weak_negative
-0.0032
Weak_negative
-0.0111
Weak_negative
-0.0091
Weak_negative
-0.0085
Weak_positive
0.0606
Answer 3-
Weak_positive
0.0334
Weak_negative
-0.0146
Weak_positive
0.0181
Weak_negative
-0.0163
Weak_positive
0.0350
Weak_negative
-0.0423
Weak_negative
-0.0072
Answer 4-
Weak_positive
0.0349
Weak_positive
0.0613
Weak_negative
-0.0090
Weak_negative
-0.0315
Weak_positive
0.0194
Weak_negative
-0.0392
Weak_negative
-0.0148
Answer 5-
Weak_negative
-0.0090
Weak_positive
0.0321
Weak_negative
-0.0391
Weak_positive
0.0154
Weak_positive
0.0250
Weak_negative
-0.0101
Weak_negative
-0.0175
Answer 6-
Weak_negative
-0.0243
Weak_negative
-0.0016
Weak_positive
0.0424
Weak_negative
-0.0073
Weak_negative
-0.0022
Weak_positive
0.0064
Weak_negative
-0.0187
Answer 7-
Weak_positive
0.0271
Weak_positive
0.0351
Weak_negative
-0.0204
Weak_negative
-0.0346
Weak_positive
0.0090
Weak_negative
-0.0018
Weak_positive
0.0009
Answer 8-
Weak_negative
-0.0126
Weak_negative
-0.0018
Weak_negative
-0.0118
Weak_negative
-0.0379
Weak_negative
-0.0342
Weak_positive
0.0655
Weak_positive
0.0338
Answer 9-
Weak_negative
-0.0569
Weak_positive
0.0362
Weak_positive
0.0356
Weak_positive
0.0668
Weak_negative
-0.0571
Weak_negative
-0.0105
Weak_negative
-0.0216


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好的


2023.12.09
Valerii Kosenko
產品負責人 SaaS SDTEST®

Valerii 於 1993 年獲得社會教育心理學家資格,此後將他的知識應用於專案管理。
Valerii 於 2013 年獲得碩士學位以及專案和專案經理資格。
Valerii 是探討 V.U.C.A. 不確定性的作者。使用螺旋動力學和心理學數理統計的概念,以及 38 個國際民意調查。
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