TEDGlobal 2009
Dan Pink: The puzzle of motivation
Dan Pink談叫人意想不到的激勵科學
Filmed:
Readability: 4
25,352,736 views
個人事業發展專家Dan Pink探討工作動機的謎團。他舉出一個社會學家都知道，而企業管理階層卻不懂的道理：傳統的奬勵並非如我們所想的湊效。他道出發人深省的故事，更指向一條新出路。
Dan Pink - Career analyst
Bidding adieu to his last "real job" as Al Gore's speechwriter, Dan Pink went freelance to spark a right-brain revolution in the career marketplace. Full bio
Bidding adieu to his last "real job" as Al Gore's speechwriter, Dan Pink went freelance to spark a right-brain revolution in the career marketplace. Full bio
Double-click the English transcript below to play the video.
00:12
I need to make a confession at the outset here.
0
0
4000
我首先想坦白供認一件事，
00:16
A little over 20 years ago
1
4000
3000
二十多年前，
00:19
I did something that I regret,
2
7000
2000
我做了一件使我後悔至今的事，
00:21
something that I'm not particularly proud of,
3
9000
4000
這件事不太光采，
00:25
something that, in many ways, I wish no one would ever know,
4
13000
3000
我也不想有人知道，
00:28
but here I feel kind of obliged to reveal.
5
16000
4000
但今天，我覺得我有必要把它公開，
00:32
(Laughter)
6
20000
2000
(笑聲)
00:34
In the late 1980s,
7
22000
2000
在一九八零年代後期，
00:36
in a moment of youthful indiscretion,
8
24000
3000
因為年輕人的一時輕率，
00:39
I went to law school.
9
27000
2000
我居然入了法學院，
00:41
(Laughter)
10
29000
4000
(笑聲)
00:45
Now, in America law is a professional degree:
11
33000
3000
現今在美國，法律學位是專業學位，
00:48
you get your university degree, then you go on to law school.
12
36000
2000
你先要拿一個大學學位，才能入讀法學院，
00:50
And when I got to law school,
13
38000
3000
當年我進了法學院後
00:53
I didn't do very well.
14
41000
2000
並沒有好好學習，
00:55
To put it mildly, I didn't do very well.
15
43000
2000
這個已是客氣的說法，
00:57
I, in fact, graduated in the part of my law school class
16
45000
3000
老實點說，我畢業時的成績，
01:00
that made the top 90 percent possible.
17
48000
4000
成就了在我之上的十分之九的學生；
01:04
(Laughter)
18
52000
4000
(笑聲)
01:08
Thank you.
19
56000
3000
謝謝！
01:11
I never practiced law a day in my life;
20
59000
3000
我一生中從沒當過律師，
01:14
I pretty much wasn't allowed to.
21
62000
2000
基本上，我是沒有資格的，
01:16
(Laughter)
22
64000
3000
(笑聲)
01:19
But today, against my better judgment,
23
67000
3000
但今天，我要漠視自己的良好判斷，
01:22
against the advice of my own wife,
24
70000
3000
漠視我太太的忠告，
01:25
I want to try to dust off some of those legal skills --
25
73000
4000
把我那些封塵的法律知識拿出來，
01:29
what's left of those legal skills.
26
77000
2000
雖然已經所餘無幾，
01:31
I don't want to tell you a story.
27
79000
3000
我並不是要講故事，
01:34
I want to make a case.
28
82000
2000
我想作一個陳述，
01:36
I want to make a hard-headed, evidence-based,
29
84000
4000
一個實實在在，有根有據的陳述，
01:40
dare I say lawyerly case,
30
88000
3000
一個使我們重新思考
01:43
for rethinking how we run our businesses.
31
91000
4000
我們的企業運作的法律陳述。
01:47
So, ladies and gentlemen of the jury, take a look at this.
32
95000
4000
好了，各位陪審員先生女士，請看看﹔
01:51
This is called the candle problem.
33
99000
2000
這個叫作「蠟燭難題」
01:53
Some of you might have seen this before.
34
101000
2000
你們或者曾經見過，
01:55
It's created in 1945
35
103000
2000
這是由一位名叫頓克的心理學家
01:57
by a psychologist named Karl Duncker.
36
105000
2000
在一九四五年想出來的，
01:59
Karl Duncker created this experiment
37
107000
2000
頓克這個實驗，
02:01
that is used in a whole variety of experiments in behavioral science.
38
109000
3000
被廣泛的應用在各類的行為科學研究中，
02:04
And here's how it works. Suppose I'm the experimenter.
39
112000
3000
實驗是這樣的，設想我是實驗人員，
02:07
I bring you into a room. I give you a candle,
40
115000
4000
我帶你進入一個房間，給你一支蠟燭，
02:11
some thumbtacks and some matches.
41
119000
2000
一些按釘和幾根火柴，
02:13
And I say to you, "Your job
42
121000
2000
然後跟你說：「請你
02:15
is to attach the candle to the wall
43
123000
2000
把蠟燭黏到牆壁上去，
02:17
so the wax doesn't drip onto the table." Now what would you do?
44
125000
4000
但蠟不可滴到桌子上。」你會怎麼做？
02:21
Now many people begin trying to thumbtack the candle to the wall.
45
129000
4000
很多人會嘗試用按釘把蠟燭釘在牆上，
02:25
Doesn't work.
46
133000
2000
那當然不成，
02:27
Somebody, some people -- and I saw somebody
47
135000
2000
我見到一些在座的朋友，
02:29
kind of make the motion over here --
48
137000
2000
在用動作示意，
02:31
some people have a great idea where they
49
139000
2000
有些人想到了，
02:33
light the match, melt the side of the candle, try to adhere it to the wall.
50
141000
4000
用火柴把蠟燭的邊融了，試圖把它黏到牆上，
02:37
It's an awesome idea. Doesn't work.
51
145000
3000
這個主意很好，但也不成功，
02:40
And eventually, after five or 10 minutes,
52
148000
3000
慢慢地，五分鐘、十分鐘後，
02:43
most people figure out the solution,
53
151000
2000
很多人都找到答案了，
02:45
which you can see here.
54
153000
2000
就是這個，
02:47
The key is to overcome what's called functional fixedness.
55
155000
3000
關鍵在於我們要克服一個叫「功能固着」的心理障礙，
02:50
You look at that box and you see it only as a receptacle for the tacks.
56
158000
4000
你見到這個盒子，就把它當成載釘子的工具，
02:54
But it can also have this other function,
57
162000
2000
但它也可以有其他用途啊，
02:56
as a platform for the candle. The candle problem.
58
164000
4000
若把它作為蠟燭的平台，難題便得到解決，
03:00
Now I want to tell you about an experiment
59
168000
2000
現在我要講一個
03:02
using the candle problem,
60
170000
2000
利用蠟燭難題的實驗，
03:04
done by a scientist named Sam Glucksberg,
61
172000
2000
這是由一位叫格魯茲堡的科學家做的，
03:06
who is now at Princeton University in the U.S.
62
174000
2000
他現在美國普林斯頓大學工作，
03:08
This shows the power of incentives.
63
176000
4000
這個實驗表現出奬勵的力量，
03:12
Here's what he did. He gathered his participants.
64
180000
2000
實驗是這樣的：他找來一批參與者，
03:14
And he said, "I'm going to time you. How quickly you can solve this problem?"
65
182000
3000
對他們說：「我會為你們計時，看看誰最快能找到問題的答案。」
03:17
To one group he said,
66
185000
2000
對其中一組，他說﹔
03:19
"I'm going to time you to establish norms,
67
187000
3000
「你們的時間
03:22
averages for how long it typically takes
68
190000
2000
會用作建立平均數，
03:24
someone to solve this sort of problem."
69
192000
2000
來量度解決這個問題需時的標準。」
03:26
To the second group he offered rewards.
70
194000
3000
對另外一組，他卻提供獎勵，
03:29
He said, "If you're in the top 25 percent of the fastest times,
71
197000
4000
他說﹔「最快解難的四分一人
03:33
you get five dollars.
72
201000
3000
可以得到五塊錢，
03:36
If you're the fastest of everyone we're testing here today,
73
204000
3000
而今天最快的一位，
03:39
you get 20 dollars."
74
207000
2000
可以得到二十塊錢。」
03:41
Now this is several years ago. Adjusted for inflation,
75
209000
3000
這個實驗在多年前進行，把通漲算在內，
03:44
it's a decent sum of money for a few minutes of work.
76
212000
2000
以幾分鐘的實驗來說，那是不錯的報酬，
03:46
It's a nice motivator.
77
214000
2000
也是一個很好的推動原素，
03:48
Question: How much faster
78
216000
3000
問題是：
03:51
did this group solve the problem?
79
219000
2000
這一組比另一組快了多少呢?
03:53
Answer: It took them, on average,
80
221000
3000
答案：
03:56
three and a half minutes longer.
81
224000
4000
是慢了三分半鐘，
04:00
Three and a half minutes longer. Now this makes no sense right?
82
228000
3000
是慢了三分半鐘！你會覺得沒有道理吧？
04:03
I mean, I'm an American. I believe in free markets.
83
231000
3000
我們美國人都相信自由市場嘛，
04:06
That's not how it's supposed to work. Right?
84
234000
3000
事情不應該是這樣的，你說是不是？
04:09
(Laughter)
85
237000
1000
(笑聲)
04:10
If you want people to perform better,
86
238000
2000
如果你想某人改善表現，
04:12
you reward them. Right?
87
240000
2000
你便要給他獎勵，對嗎？
04:14
Bonuses, commissions, their own reality show.
88
242000
3000
獎金啦，分紅利啦，讓他個人表演機會啦，
04:17
Incentivize them. That's how business works.
89
245000
4000
都是獎勵，商業社會就是這樣運作的，
04:21
But that's not happening here.
90
249000
2000
但這個實驗卻發現了事情並非這樣，
04:23
You've got an incentive designed to
91
251000
2000
你一心以為你設的奬勵，
04:25
sharpen thinking and accelerate creativity,
92
253000
4000
能使他們思考更快，創造力更高，
04:29
and it does just the opposite.
93
257000
2000
誰知卻弄巧反拙，
04:31
It dulls thinking and blocks creativity.
94
259000
3000
思考失色了，創造力受到窒礙，
04:34
And what's interesting about this experiment is that it's not an aberration.
95
262000
3000
你要知道，這個實驗結果並不是偶然的偏差，
04:37
This has been replicated over and over
96
265000
3000
重複又重複都得到同一結果，
04:40
and over again, for nearly 40 years.
97
268000
3000
四十年來，沒有例外，
04:43
These contingent motivators --
98
271000
3000
這種外設的推動力，
04:46
if you do this, then you get that --
99
274000
2000
你做了這個，便得到那個，
04:48
work in some circumstances.
100
276000
2000
在某些情況下可行，
04:50
But for a lot of tasks, they actually either don't work
101
278000
3000
但在很多其他事情上卻不行，
04:53
or, often, they do harm.
102
281000
3000
甚至有時會適得其反，
04:56
This is one of the most robust findings
103
284000
4000
這是在社會科學界裡，
05:00
in social science,
104
288000
3000
最穩健的研究發現之一，
05:03
and also one of the most ignored.
105
291000
2000
卻也是最被人忽略的一個，
05:05
I spent the last couple of years looking at the science of
106
293000
2000
這兩年來，我鑽研了
05:07
human motivation,
107
295000
2000
激發人類動機的科學研究，
05:09
particularly the dynamics of extrinsic motivators
108
297000
2000
尤其是外在和內在動機
05:11
and intrinsic motivators.
109
299000
2000
的互動，
05:13
And I'm telling you, it's not even close.
110
301000
2000
我可以告訴你，兩者簡直南轅北轍，
05:15
If you look at the science, there is a mismatch
111
303000
2000
從科學的角度看，
05:17
between what science knows and what business does.
112
305000
4000
科學認知的和企業實行的並不相符，
05:21
And what's alarming here is that our business operating system --
113
309000
3000
這很令人擔心，想想我們的商業運作，
05:24
think of the set of assumptions and protocols beneath our businesses,
114
312000
3000
建基於這樣的一套假設和慣例，
05:27
how we motivate people, how we apply our human resources --
115
315000
5000
無論去推動員工，運用人力資源，
05:32
it's built entirely around these extrinsic motivators,
116
320000
3000
都是利用這些外在動機，
05:35
around carrots and sticks.
117
323000
2000
一是物質奬勵，一是懲罰，
05:37
That's actually fine for many kinds of 20th century tasks.
118
325000
4000
那在二十世紀的多類工作上都可行，
05:41
But for 21st century tasks,
119
329000
2000
但到了二十一世紀，
05:43
that mechanistic, reward-and-punishment approach
120
331000
4000
那種機械性、獎與罰的方法非但不可行，
05:47
doesn't work, often doesn't work, and often does harm.
121
335000
4000
甚至會造成傷害，
05:51
Let me show you what I mean.
122
339000
2000
讓我解釋一下，
05:53
So Glucksberg did another experiment similar to this
123
341000
3000
格魯茲堡再做另一個相似的實驗，
05:56
where he presented the problem in a slightly different way,
124
344000
2000
他把問題稍作改動，
05:58
like this up here. Okay?
125
346000
3000
像這樣，
06:01
Attach the candle to the wall so the wax doesn't drip onto the table.
126
349000
2000
把蠟燭黏到牆上而蠟不能滴到桌子上，
06:03
Same deal. You: we're timing for norms.
127
351000
3000
同樣地：你的時間會用來定標準，
06:06
You: we're incentivizing.
128
354000
3000
而你會得到奬勵，
06:09
What happened this time?
129
357000
2000
結果怎樣？
06:11
This time, the incentivized group
130
359000
2000
這次，有奬勵的一組
06:13
kicked the other group's butt.
131
361000
4000
贏得很漂亮！
06:17
Why? Because when the tacks are out of the box,
132
365000
4000
為甚麼？因為那些按釘不是放在盒子裡，
06:21
it's pretty easy isn't it?
133
369000
4000
答案很明顯吧！
06:25
(Laughter)
134
373000
2000
（笑聲）
06:27
If-then rewards work really well
135
375000
3000
「因果」式的獎勵
06:30
for those sorts of tasks,
136
378000
3000
在這些有簡單規則，
06:33
where there is a simple set of rules and a clear destination
137
381000
2000
並有清晰目標的工作上
06:35
to go to.
138
383000
2000
很有效；
06:37
Rewards, by their very nature,
139
385000
2000
獎勵的本身，
06:39
narrow our focus, concentrate the mind;
140
387000
2000
把我們的視野收窄，思想聚焦，
06:41
that's why they work in so many cases.
141
389000
2000
它就是這樣發揮作用，
06:43
And so, for tasks like this,
142
391000
2000
所以，像這一類工作，
06:45
a narrow focus, where you just see the goal right there,
143
393000
3000
視線瞄準，目標明確，
06:48
zoom straight ahead to it,
144
396000
2000
思路就直向答案進發，
06:50
they work really well.
145
398000
2000
奬勵就有效了；
06:52
But for the real candle problem,
146
400000
2000
但對於真正的蠟燭難題，
06:54
you don't want to be looking like this.
147
402000
2000
你不會想這樣看，
06:56
The solution is not over here. The solution is on the periphery.
148
404000
2000
答案並不在那裡，答案在周邊，
06:58
You want to be looking around.
149
406000
2000
你要到處看去尋找靈感，
07:00
That reward actually narrows our focus
150
408000
2000
獎勵只會收窄我們的視線，
07:02
and restricts our possibility.
151
410000
2000
限制了可能性，
07:04
Let me tell you why this is so important.
152
412000
3000
讓我告訴你這個認知的重要性，
07:07
In western Europe,
153
415000
2000
在西歐，
07:09
in many parts of Asia,
154
417000
2000
在亞洲很多地區，
07:11
in North America, in Australia,
155
419000
3000
在北美、澳洲，
07:14
white-collar workers are doing less of
156
422000
2000
白領員工越來越少做
07:16
this kind of work,
157
424000
2000
這類工作，
07:18
and more of this kind of work.
158
426000
4000
卻多做這類工作，
07:22
That routine, rule-based, left-brain work --
159
430000
3000
那些流水式的、循規蹈矩的左腦工作，
07:25
certain kinds of accounting, certain kinds of financial analysis,
160
433000
2000
如某類會計、財務分析、
07:27
certain kinds of computer programming --
161
435000
2000
某類電腦程式寫作，
07:29
has become fairly easy to outsource,
162
437000
2000
已經很容易被外判，
07:31
fairly easy to automate.
163
439000
2000
很容易自動化，
07:33
Software can do it faster.
164
441000
3000
用軟件可以做得更快，
07:36
Low-cost providers around the world can do it cheaper.
165
444000
2000
世界各地都有較低價的服務提供者，
07:38
So what really matters are the more right-brained
166
446000
4000
所以關鍵的是那些右腦式的
07:42
creative, conceptual kinds of abilities.
167
450000
3000
創造性、概念性的工作，
07:45
Think about your own work.
168
453000
3000
想想你自己的工作，
07:48
Think about your own work.
169
456000
3000
想想你自己的工作，
07:51
Are the problems that you face, or even the problems
170
459000
2000
你面對的問題，
07:53
we've been talking about here,
171
461000
2000
甚至我們現在在談的問題，
07:55
are those kinds of problems -- do they have a clear set of rules,
172
463000
2000
是不是那類問題？
07:57
and a single solution? No.
173
465000
3000
它們有沒有清晰的規則和單一的答案？沒有！
08:00
The rules are mystifying.
174
468000
2000
只有令人困惑的規則，
08:02
The solution, if it exists at all,
175
470000
2000
答案，如果有的話，
08:04
is surprising and not obvious.
176
472000
3000
是意想不到的、不明顯的，
08:07
Everybody in this room
177
475000
2000
在座的每一位，
08:09
is dealing with their own version
178
477000
3000
都在應付各自的
08:12
of the candle problem.
179
480000
2000
蠟燭難題，
08:14
And for candle problems of any kind,
180
482000
3000
而對於各式各樣的蠟燭難題，
08:17
in any field,
181
485000
2000
各行各業的蠟燭難題，
08:19
those if-then rewards,
182
487000
3000
現在的商業社會賴於的
08:22
the things around which we've built so many of our businesses,
183
490000
4000
那「因果」式的獎勵制度，
08:26
don't work.
184
494000
2000
是行不通的，
08:28
Now, I mean it makes me crazy.
185
496000
2000
那真叫我摸不著頭腦，
08:30
And this is not -- here's the thing.
186
498000
2000
這個問題，並非
08:32
This is not a feeling.
187
500000
3000
只是一種感覺，
08:35
Okay? I'm a lawyer; I don't believe in feelings.
188
503000
3000
我是律師嘛，我不信感覺的，
08:38
This is not a philosophy.
189
506000
4000
這也非哲學，
08:42
I'm an American; I don't believe in philosophy.
190
510000
2000
我是美國人，我不相信哲學，
08:44
(Laughter)
191
512000
3000
（笑聲）
08:47
This is a fact --
192
515000
3000
這是事實，
08:50
or, as we say in my hometown of Washington, D.C.,
193
518000
2000
像我們華盛頓人講的
08:52
a true fact.
194
520000
2000
一個「真的事實」，
08:54
(Laughter)
195
522000
2000
（笑聲）
08:56
(Applause)
196
524000
4000
（掌聲）
09:00
Let me give you an example of what I mean.
197
528000
2000
讓我用一個例子說明一下，
09:02
Let me marshal the evidence here,
198
530000
2000
讓我整理一下我的論據，
09:04
because I'm not telling you a story, I'm making a case.
199
532000
2000
因為我不是在講故事，我在陳述一個論點，
09:06
Ladies and gentlemen of the jury, some evidence:
200
534000
2000
陪審員先生女士，論據如下：
09:08
Dan Ariely, one of the great economists of our time,
201
536000
3000
當代數一數二的經濟學家阿拉利，
09:11
he and three colleagues, did a study of some MIT students.
202
539000
4000
他和三位同事，利用麻省理工的學生，做了一個研究，
09:15
They gave these MIT students a bunch of games,
203
543000
3000
他們給學生們一大堆各類
09:18
games that involved creativity,
204
546000
2000
需要創意、
09:20
and motor skills, and concentration.
205
548000
2000
靈巧和專注的遊戲，
09:22
And the offered them, for performance,
206
550000
2000
並因應表現，
09:24
three levels of rewards:
207
552000
2000
提供三重奬勵，
09:26
small reward, medium reward, large reward.
208
554000
5000
小奬勵、中奬勵、大奬勵
09:31
Okay? If you do really well you get the large reward, on down.
209
559000
4000
做得越好，奬勵越大，
09:35
What happened? As long as the task involved only mechanical skill
210
563000
4000
結果怎樣呢？只要遊戲只要求機械性的技巧
09:39
bonuses worked as they would be expected:
211
567000
2000
奬品就如所料的發揮作用，
09:41
the higher the pay, the better the performance.
212
569000
4000
奬勵越大，表現越好，
09:45
Okay? But one the task called for
213
573000
2000
但只要那個遊戲，
09:47
even rudimentary cognitive skill,
214
575000
4000
要求即使是最低層次的思考方法，
09:51
a larger reward led to poorer performance.
215
579000
5000
奬勵越大，表現越差，
09:56
Then they said,
216
584000
2000
他們想：
09:58
"Okay let's see if there's any cultural bias here.
217
586000
2000
「文化差異是不是一個因素呢？
10:00
Lets go to Madurai, India and test this."
218
588000
2000
讓我們到印度馬度拉去測試一下。」
10:02
Standard of living is lower.
219
590000
2000
在馬度拉，生活指數較低，
10:04
In Madurai, a reward that is modest in North American standards,
220
592000
3000
一個在美國一般的奬勵，
10:07
is more meaningful there.
221
595000
3000
在那裡已經很有吸引力，
10:10
Same deal. A bunch of games, three levels of rewards.
222
598000
3000
同一樣的規則，一堆遊戲，三重奬勵，
10:13
What happens?
223
601000
2000
結果呢？
10:15
People offered the medium level of rewards
224
603000
3000
得到中級奬勵的人，
10:18
did no better than people offered the small rewards.
225
606000
3000
並不比拿小奬勵的做得好，
10:21
But this time, people offered the highest rewards,
226
609000
4000
但這次，得到大奬的人，
10:25
they did the worst of all.
227
613000
4000
表現是最差的，
10:29
In eight of the nine tasks we examined across three experiments,
228
617000
3000
在九個遊戲當中，有八個
10:32
higher incentives led to worse performance.
229
620000
5000
是奬勵越高，表現越差的，
10:37
Is this some kind of touchy-feely
230
625000
3000
這是不是甚麼社會主義者
10:40
socialist conspiracy going on here?
231
628000
3000
裝神弄鬼的陰謀？
10:43
No. These are economists from MIT,
232
631000
3000
可不是呢，他們是麻省理工、卡内基·梅隆、
10:46
from Carnegie Mellon, from the University of Chicago.
233
634000
3000
芝加哥大學的經濟學家啊
10:49
And do you know who sponsored this research?
234
637000
2000
還有，你猜是誰贊助這個研究？
10:51
The Federal Reserve Bank of the United States.
235
639000
4000
是美國的聯邦儲備銀行，
10:55
That's the American experience.
236
643000
2000
好一個美國式體驗！
10:57
Let's go across the pond to the London School of Economics --
237
645000
3000
又讓我們越洋到倫敦經濟學院，
11:00
LSE, London School of Economics,
238
648000
3000
是LSE，倫敦的經濟學院！
11:03
alma mater of 11 Nobel Laureates in economics.
239
651000
3000
十一位諾貝爾經濟學得主的母校，
11:06
Training ground for great economic thinkers
240
654000
3000
偉大經濟思想家的搖籃，
11:09
like George Soros, and Friedrich Hayek,
241
657000
3000
例如索羅斯、海耶克、
11:12
and Mick Jagger. (Laughter)
242
660000
2000
和米積加，（笑聲）
11:14
Last month, just last month,
243
662000
4000
就在上個月，
11:18
economists at LSE looked at 51 studies
244
666000
3000
LSE的經濟學家分析五十一個研究，
11:21
of pay-for-performance plans, inside of companies.
245
669000
3000
都是圍繞企業内據工作表現決定薪酬的計劃，
11:24
Here's what the economists there said: "We find that financial incentives
246
672000
3000
經濟學家們的結論是這樣的：
11:27
can result in a negative impact on overall performance."
247
675000
6000
「我們發現金錢的奬勵可以造成整體表現的負面影響。」
11:33
There is a mismatch between what science knows
248
681000
3000
科學的結論和企業的實踐
11:36
and what business does.
249
684000
2000
背道而馳，
11:38
And what worries me, as we stand here in the rubble
250
686000
3000
使我憂慮的是，我們今天
11:41
of the economic collapse,
251
689000
2000
站在經濟陷落後的廢墟中，
11:43
is that too many organizations
252
691000
2000
卻仍有太多的機構，
11:45
are making their decisions,
253
693000
2000
還是根據一些過時、沒受考驗的，
11:47
their policies about talent and people,
254
695000
2000
只憑傳統而非科學驗證的想法，
11:49
based on assumptions that are outdated, unexamined,
255
697000
6000
去作出決定，
11:55
and rooted more in folklore than in science.
256
703000
3000
去製定人力資源的政策，
11:58
And if we really want to get out of this economic mess,
257
706000
3000
而我們極需走出這個經濟爛攤子，
12:01
and if we really want high performance on those
258
709000
2000
如果我們真正想在二十一世紀的工作上，
12:03
definitional tasks of the 21st century,
259
711000
2000
有高水準的表現，
12:05
the solution is not to do more of the wrong things,
260
713000
6000
就要改變那行不通的路，
12:11
to entice people with a sweeter carrot,
261
719000
3000
利用甜頭去利誘人，
12:14
or threaten them with a sharper stick.
262
722000
2000
或用手段去恐嚇人，
12:16
We need a whole new approach.
263
724000
2000
我們需要一個全新的取向，
12:18
And the good news about all of this is that the scientists
264
726000
2000
好消息是，
12:20
who've been studying motivation have given us this new approach.
265
728000
3000
那些研究行為動機的科學家，已經給了我們答案，
12:23
It's an approach built much more around intrinsic motivation.
266
731000
3000
新的理念是多利用內在動機，
12:26
Around the desire to do things because they matter,
267
734000
2000
我們對重要的事、喜歡做的事、
12:28
because we like it, because they're interesting,
268
736000
2000
有意思的事、可以有更大貢獻的事，
12:30
because they are part of something important.
269
738000
2000
都有想把它做好的渴望，
12:32
And to my mind, that new operating system for our businesses
270
740000
4000
我認為，新的企業運作模式，
12:36
revolves around three elements:
271
744000
2000
圍繞三個元素，
12:38
autonomy, mastery and purpose.
272
746000
3000
自主性、掌握度、使命感，
12:41
Autonomy: the urge to direct our own lives.
273
749000
3000
自主性是主宰自己生命的動力，
12:44
Mastery: the desire to get better and better at something that matters.
274
752000
4000
掌握度，是對自己重視的工作有做好的願望，
12:48
Purpose: the yearning to do what we do
275
756000
3000
使命感，是對自己工作
12:51
in the service of something larger than ourselves.
276
759000
3000
能對個人以外有所貢獻的渴望，
12:54
These are the building blocks of an entirely new operating system
277
762000
3000
這都是全新企業運作系統
12:57
for our businesses.
278
765000
2000
的基石，
12:59
I want to talk today only about autonomy.
279
767000
4000
我今天只想談一談自主性，
13:03
In the 20th century, we came up with this idea of management.
280
771000
3000
二十世紀，冒出了「管理」這個概念，
13:06
Management did not emanate from nature.
281
774000
2000
管理並非由自然產生出來，
13:08
Management is like -- it's not a tree,
282
776000
2000
它不像一棵樹，
13:10
it's a television set.
283
778000
2000
它像一台電視機，
13:12
Okay? Somebody invented it.
284
780000
2000
它是由人發明出來的，
13:14
And it doesn't mean it's going to work forever.
285
782000
2000
那並不表示，它能永遠運作良好，
13:16
Management is great.
286
784000
2000
管理學很管用，
13:18
Traditional notions of management are great
287
786000
2000
若你要追求遵從規矩的制度，
13:20
if you want compliance.
288
788000
2000
傳統的管理概念很有效，
13:22
But if you want engagement, self-direction works better.
289
790000
3000
但如果你要對工作投入，自我主導會更見效，
13:25
Let me give you some examples of some kind of radical
290
793000
2000
讓我用一些例子，解釋一下
13:27
notions of self-direction.
291
795000
2000
自我主導的概念，
13:29
What this means -- you don't see a lot of it,
292
797000
3000
你不會常常見到自我主導的出現，
13:32
but you see the first stirrings of something really interesting going on,
293
800000
3000
但你已經可以見到一些很有意思的事情在發展，
13:35
because what it means is paying people adequately
294
803000
2000
它的意思是：給予人足夠的、
13:37
and fairly, absolutely --
295
805000
2000
合理的薪金，
13:39
getting the issue of money off the table,
296
807000
2000
錢不再是著眼點，
13:41
and then giving people lots of autonomy.
297
809000
2000
然後給他們很多的自決空間，
13:43
Let me give you some examples.
298
811000
2000
讓我給你一些例子，
13:45
How many of you have heard of the company Atlassian?
299
813000
4000
你們有沒有聽過一間叫阿拉斯安的公司？
13:49
It looks like less than half.
300
817000
2000
好像不夠一半人聽過，
13:51
(Laughter)
301
819000
2000
（笑聲）
13:53
Atlassian is an Australian software company.
302
821000
4000
阿拉斯安是一間澳洲的軟件公司，
13:57
And they do something incredibly cool.
303
825000
2000
他們做的東西真夠酷，
13:59
A few times a year they tell their engineers,
304
827000
2000
每年有幾次他們會對工程師們說：
14:01
"Go for the next 24 hours and work on anything you want,
305
829000
4000
「由現在起二十四小時内，你們做甚麼也可以，
14:05
as long as it's not part of your regular job.
306
833000
2000
只要不是你平常的工作，
14:07
Work on anything you want."
307
835000
2000
做甚麼也可以。」
14:09
So that engineers use this time to come up with
308
837000
2000
他們的工程師就用了這些時間，
14:11
a cool patch for code, come up with an elegant hack.
309
839000
3000
編些很有趣的碼、絕妙的駭客程式等，
14:14
Then they present all of the stuff that they've developed
310
842000
3000
在當天完結前，
14:17
to their teammates, to the rest of the company,
311
845000
3000
他們舉行一個意念橫飛的會議，
14:20
in this wild and wooly all-hands meeting
312
848000
2000
在組員和公司的其他同事面前，
14:22
at the end of the day.
313
850000
2000
發表他們想出來的東西，
14:24
And then, being Australians, everybody has a beer.
314
852000
2000
然後，身為澳洲人，當然都喝啤酒盡興，
14:26
They call them FedEx Days.
315
854000
3000
他們叫這天為聯邦快遞日，
14:29
Why? Because you have to deliver something overnight.
316
857000
6000
為甚麼？因為他們要在一天之內「交貨」，
14:35
It's pretty. It's not bad. It's a huge trademark violation,
317
863000
2000
這個做法不錯，雖然侵用了別人的商標，
14:37
but it's pretty clever.
318
865000
2000
但這個主意很聰明，
14:39
(Laughter)
319
867000
1000
（笑聲）
14:40
That one day of intense autonomy
320
868000
2000
這一天的高度自主，
14:42
has produced a whole array of software fixes
321
870000
2000
促進了各類
14:44
that might never have existed.
322
872000
2000
原本可能永不會出現的軟件提升，
14:46
And it's worked so well that Atlassian has taken it to the next level
323
874000
2000
發現這個方法的好處，阿拉斯安把它提升到更高的層次，
14:48
with 20 Percent Time --
324
876000
2000
叫作「五分一時間」，
14:50
done, famously, at Google --
325
878000
2000
這個方法在谷歌得到發揚光大，
14:52
where engineers can work, spend 20 percent of their time
326
880000
2000
谷歌的員工，可以花五分之一的時間，
14:54
working on anything they want.
327
882000
2000
做任何他們想做的事，
14:56
They have autonomy over their time,
328
884000
2000
他們在自己的工作時間、
14:58
their task, their team, their technique.
329
886000
2000
工作內容 、所屬團隊 、所需技巧等都有自主權，
15:00
Okay? Radical amounts of autonomy.
330
888000
2000
那是很全面性的自主權，
15:02
And at Google, as many of you know,
331
890000
4000
有很多人都知道，在谷歌，
15:06
about half of the new products in a typical year
332
894000
2000
差不多一半的新產品，
15:08
are birthed during that 20 Percent Time:
333
896000
3000
都是在這五分一時間内誕生的，
15:11
things like Gmail, Orkut, Google News.
334
899000
3000
例如Gmail、Orkut、谷歌新聞等，
15:14
Let me give you an even more radical example of it:
335
902000
3000
讓我再舉一個更革命性的例子，
15:17
something called the Results Only Work Environment,
336
905000
2000
有一種叫「只看結果的工作環境」
15:19
the ROWE,
337
907000
2000
英文簡稱為ROWE，
15:21
created by two American consultants, in place
338
909000
2000
由兩位美國的顧問專家創造，
15:23
in place at about a dozen companies around North America.
339
911000
2000
在十多所北美的公司實行，
15:25
In a ROWE people don't have schedules.
340
913000
4000
在ROWE的制度下，員工沒有工作時間表，
15:29
They show up when they want.
341
917000
2000
他們喜歡就上班，
15:31
They don't have to be in the office at a certain time,
342
919000
2000
沒有設定待在辦公室的時段，
15:33
or any time.
343
921000
2000
甚至不用到辦公室，
15:35
They just have to get their work done.
344
923000
2000
他們只需把工作完成，
15:37
How they do it, when they do it,
345
925000
2000
至於他們怎麼完成，甚麼時候完成，
15:39
where they do it, is totally up to them.
346
927000
3000
在甚麼地方完成，完全由他，
15:42
Meetings in these kinds of environments are optional.
347
930000
4000
在這種工作環境下，會議並不是必須的，
15:46
What happens?
348
934000
2000
效果怎樣呢？
15:48
Almost across the board, productivity goes up,
349
936000
3000
差不多所有此類公司的生產力都上升，
15:51
worker engagement goes up,
350
939000
3000
員工的投入感上升，
15:54
worker satisfaction goes up, turnover goes down.
351
942000
3000
員工的滿足感上升，人力流失降低，
15:57
Autonomy, mastery and purpose,
352
945000
2000
自主性、掌握度、使命感，
15:59
These are the building blocks of a new way of doing things.
353
947000
2000
這都是新工作方式的基本單元，
16:01
Now some of you might look at this and say,
354
949000
3000
你們或許會說：
16:04
"Hmm, that sounds nice, but it's Utopian."
355
952000
3000
「嗯，雖然很好聽，但那是烏托邦，並不存在的。」
16:07
And I say, "Nope. I have proof."
356
955000
5000
我回答：「錯，我有證據。」
16:12
The mid-1990s, Microsoft started
357
960000
2000
在一九九零年代中期，
16:14
an encyclopedia called Encarta.
358
962000
2000
微軟著手建立一套叫Encarta的百科全書，
16:16
They had deployed all the right incentives,
359
964000
2000
他們調動了最理想的獎勵，
16:18
all the right incentives. They paid professionals to
360
966000
3000
請來一群專家，
16:21
write and edit thousands of articles.
361
969000
2000
去撰寫幾千篇文章，
16:23
Well-compensated managers oversaw the whole thing
362
971000
2000
還高薪聘請好些管理人員去統籌，
16:25
to make sure it came in on budget and on time.
363
973000
5000
以期能在預定的時間和開支範圍內完成，
16:30
A few years later another encyclopedia got started.
364
978000
2000
幾年後，另外一個百科全書誕生了，
16:32
Different model, right?
365
980000
3000
用不同的模式運作，
16:35
Do it for fun. No one gets paid a cent, or a Euro or a Yen.
366
983000
4000
大家純為興趣而做，
16:39
Do it because you like to do it.
367
987000
3000
沒有人收到分毫，
16:42
Now if you had, just 10 years ago,
368
990000
3000
請你設想，十年前，
16:45
if you had gone to an economist, anywhere,
369
993000
2000
如果你問任何一個經濟學家，
16:47
and said, "Hey, I've got these two different models for creating an encyclopedia.
370
995000
4000
「我手上有這兩個編輯百科全書的方法，
16:51
If they went head to head, who would win?"
371
999000
3000
若兩個競賽，那個會勝？」
16:54
10 years ago you could not have found a single sober economist anywhere
372
1002000
4000
十年前，踏遍全球，你都不會找到
16:58
on planet Earth
373
1006000
2000
一個頭腦清醒的經濟學家，
17:00
who would have predicted the Wikipedia model.
374
1008000
2000
能預見維基百科的成功，
17:02
This is the titanic battle between these two approaches.
375
1010000
3000
這是兩個動機方式的世紀決戰，
17:05
This is the Ali-Frazier of motivation. Right?
376
1013000
3000
就像當年拳王阿里和費舍的大戰，對嗎?
17:08
This is the Thrilla' in Manila.
377
1016000
2000
就是那場《決戰馬尼拉》，
17:10
Alright? Intrinsic motivators versus extrinsic motivators.
378
1018000
3000
內在動機對撼外在動機，
17:13
Autonomy, mastery and purpose,
379
1021000
2000
自主性、掌握度、使命感，
17:15
versus carrot and sticks. And who wins?
380
1023000
2000
對抗獎與罰，誰勝?
17:17
Intrinsic motivation, autonomy, mastery and purpose,
381
1025000
3000
內在動機：自主性、掌握度、使命感，
17:20
in a knockout. Let me wrap up.
382
1028000
4000
獲得技術性擊倒。
17:24
There is a mismatch between what science knows and what business does.
383
1032000
3000
現在讓我總結，科學所知與企業所行並不相符，
17:27
And here is what science knows.
384
1035000
2000
科學所知的如下﹔
17:29
One: Those 20th century rewards,
385
1037000
2000
第一：那些二十世紀的獎勵方式，
17:31
those motivators we think are a natural part of business,
386
1039000
3000
那些我們以為是企業運作的自然元素，
17:34
do work, but only in a surprisingly narrow band of circumstances.
387
1042000
4000
只能在一個比我們想像更狹窄的環境下發揮作用，
17:38
Two: Those if-then rewards often destroy creativity.
388
1046000
4000
第二：那種「因果」式的獎勵往往摧毀創意，
17:42
Three: The secret to high performance
389
1050000
2000
第三：提高工作表現的祕訣，
17:44
isn't rewards and punishments,
390
1052000
2000
不在獎與罰，
17:46
but that unseen intrinsic drive --
391
1054000
2000
而是在見不到的內在動力，
17:48
the drive to do things for their own sake.
392
1056000
3000
那種為做好工作的動力，
17:51
The drive to do things cause they matter.
393
1059000
2000
那種因為工作有意義而做的動力，
17:53
And here's the best part. Here's the best part.
394
1061000
2000
最精采的是，
17:55
We already know this. The science confirms what we know in our hearts.
395
1063000
3000
科學已經把我們心知的道理證實，
17:58
So, if we repair this mismatch
396
1066000
3000
所以，如果我們修補企業所行
18:01
between what science knows and what business does,
397
1069000
2000
與科學所知的落差，
18:03
if we bring our motivation, notions of motivation
398
1071000
3000
如果要把我們的工作動機，和對工作動機的理解，
18:06
into the 21st century,
399
1074000
2000
帶到二十一世紀，
18:08
if we get past this lazy, dangerous, ideology
400
1076000
4000
如果我們克服這種因循、危險、理想化
18:12
of carrots and sticks,
401
1080000
2000
的獎罰制度，
18:14
we can strengthen our businesses,
402
1082000
3000
我們可以強化企業，
18:17
we can solve a lot of those candle problems,
403
1085000
3000
我們可以解決很多「蠟燭難題」
18:20
and maybe, maybe, maybe
404
1088000
4000
而可能，可能，可能，
18:24
we can change the world.
405
1092000
2000
我們能夠改變世界，
18:26
I rest my case.
406
1094000
2000
我的陳述完畢。
18:28
(Applause)
407
1096000
3000
（掌聲）
ABOUT THE SPEAKER
Dan Pink - Career analystBidding adieu to his last "real job" as Al Gore's speechwriter, Dan Pink went freelance to spark a right-brain revolution in the career marketplace.
Why you should listen
With a trio of influential bestsellers, Dan Pink has changed the way companies view the modern workplace. In the pivotal A Whole New Mind, Pink identifies a sea change in the global workforce -- the shift of an information-based corporate culture to a conceptual base, where creativity and big-picture design dominates the landscape.
His latest book, The Adventures of Johnny Bunko, is an evolutionary transformation of the familiar career guide. Replacing linear text with a manga-inspired comic, Pink outlines six career laws vastly differing from the ones you've been taught. Members of the Johnny Bunko online forum participated in an online contest to create the seventh law -- "stay hungry."
A contributing editor for Wired, Pink is working on a new book on the science and economics of motivation for release in late 2009.
Dan Pink | Speaker | TED.com