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 = 0.0568
正態分佈,威廉·西莉·格塞特(William Sealy Gosset)(學生) r = 0.0568
非正態分佈,Spearman r = 0.0023
分配非正常普通的普通的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
0.1387
Weak_positive
0.0259
Weak_negative
-0.0535
Weak_negative
-0.1027
Weak_positive
0.0603
Weak_positive
0.0328
Weak_negative
-0.0512
Answer 2-
Weak_negative
-0.0248
Weak_negative
-0.0642
Weak_positive
0.0045
Weak_positive
0.0068
Weak_negative
-0.0228
Weak_positive
0.0046
Weak_positive
0.0717
Answer 3-
Weak_positive
0.0319
Weak_positive
0.0025
Weak_positive
0.0127
Weak_negative
-0.0155
Weak_positive
0.0456
Weak_negative
-0.0623
Weak_negative
-0.0074
Answer 4-
Weak_positive
0.0403
Weak_positive
0.0652
Weak_positive
0.0029
Weak_negative
-0.0166
Weak_positive
0.0216
Weak_negative
-0.0525
Weak_negative
-0.0343
Answer 5-
Weak_negative
-0.0026
Weak_positive
0.0298
Weak_negative
-0.0503
Weak_positive
0.0321
Weak_positive
0.0216
Weak_negative
-0.0079
Weak_negative
-0.0256
Answer 6-
Weak_negative
-0.0170
Weak_positive
0.0172
Weak_positive
0.0526
Weak_negative
-0.0080
Weak_positive
0.0001
Weak_negative
-0.0188
Weak_negative
-0.0253
Answer 7-
Weak_positive
0.0471
Weak_positive
0.0249
Weak_negative
-0.0126
Weak_negative
-0.0553
Weak_positive
0.0219
Weak_negative
-0.0110
Weak_positive
0.0022
Answer 8-
Weak_negative
-0.0048
Weak_negative
-0.0093
Weak_positive
0.0003
Weak_negative
-0.0500
Weak_negative
-0.0249
Weak_positive
0.0620
Weak_positive
0.0306
Answer 9-
Weak_negative
-0.0460
Weak_positive
0.0550
Weak_positive
0.0356
Weak_positive
0.0564
Weak_negative
-0.0573
Weak_negative
-0.0205
Weak_negative
-0.0219


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好的


2023.12.09
Valerii Kosenko
產品主SaaS Pet ProjectSDTEST®

Valerii於1993年獲得社會教育學家的資格,此後已將其知識應用於項目管理。
Valerii在2013年獲得了碩士學位和項目和計劃經理資格。在他的碩士課程中,他熟悉Project Roadmap(GPM Deutsche GesellschaftFürProjektmanagemente。V.)和螺旋動力學。
Valerii進行了各種螺旋動力測試,並利用他的知識和經驗來適應當前版本的SDTest。
Valerii是探索V.U.C.A.的不確定性的作者。使用螺旋動力學和心理學中的數學統計數據,有20多個國際民意測驗的概念。
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