TEDGlobal 2009
Dan Pink: The puzzle of motivation
Dan Pink - 出人意料的工作动机
Filmed:
Readability: 4
25,352,736 views
事业分析师 Dan Pink 揭开动机的秘密，什么是人文社会科学家知道而一般管理者却不知的？传统的报酬是否真的有我们想象的那样激励人心？听他讲述这个令人吃惊的发现 -- 它也许正是我们的未来。
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
开始前我必须先向你们告解
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
我做了一件让我后悔莫及的事
00:21
something that I'm not particularly proud of,
3
9000
4000
一件我丝毫不感到骄傲的事
00:25
something that, in many ways, I wish no one would ever know,
4
13000
3000
一件我希望没有任何人会知道的事
00:28
but here I feel kind of obliged to reveal.
5
16000
4000
但今日我认为我有必要揭发我自己
00:32
(Laughter)
6
20000
2000
(笑声)
00:34
In the late 1980s,
7
22000
2000
80年代晚期
00:36
in a moment of youthful indiscretion,
8
24000
3000
因为年少轻狂
00:39
I went to law school.
9
27000
2000
我进入法律学院就读
00:41
(Laughter)
10
29000
4000
(笑声)
00:45
Now, in America law is a professional degree:
11
33000
3000
在美国，法律学位是个专业学位
00:48
you get your university degree, then you go on to law school.
12
36000
2000
你得先拿到学士，才能进入法律学院
00:50
And when I got to law school,
13
38000
3000
当我进入法律学院时
00:53
I didn't do very well.
14
41000
2000
我的成绩不怎么好
00:55
To put it mildly, I didn't do very well.
15
43000
2000
客气地说，我的成绩不怎么好
00:57
I, in fact, graduated in the part of my law school class
16
45000
3000
我的毕业成绩成就了在我之上
01:00
that made the top 90 percent possible.
17
48000
4000
那其他九成的同学
01:04
(Laughter)
18
52000
4000
(笑声)
01:08
Thank you.
19
56000
3000
谢谢你们
01:11
I never practiced law a day in my life;
20
59000
3000
我这辈子从来没做过律师
01:14
I pretty much wasn't allowed to.
21
62000
2000
基本上那样做可能还会犯法
01:16
(Laughter)
22
64000
3000
(笑声)
01:19
But today, against my better judgment,
23
67000
3000
但今日，我违背我的理性
01:22
against the advice of my own wife,
24
70000
3000
违背我太太的忠告
01:25
I want to try to dust off some of those legal skills --
25
73000
4000
我想重拾那些过去所学的诉讼技巧
01:29
what's left of those legal skills.
26
77000
2000
所剩无几的诉讼技巧
01:31
I don't want to tell you a story.
27
79000
3000
我不要向你们说故事
01:34
I want to make a case.
28
82000
2000
而是提出一个陈述
01:36
I want to make a hard-headed, evidence-based,
29
84000
4000
提出一个有根有据，货真价实的
01:40
dare I say lawyerly case,
30
88000
3000
法庭陈述
01:43
for rethinking how we run our businesses.
31
91000
4000
来重新思考我们的管理方法
01:47
So, ladies and gentlemen of the jury, take a look at this.
32
95000
4000
陪审团的女士先生们，请看看这个
01:51
This is called the candle problem.
33
99000
2000
这便是有名的蜡烛问题
01:53
Some of you might have seen this before.
34
101000
2000
你们之中有些人可能已经看过了
01:55
It's created in 1945
35
103000
2000
它是在1945年
01:57
by a psychologist named Karl Duncker.
36
105000
2000
由心理学家 Karl Duncker 所创造的
01:59
Karl Duncker created this experiment
37
107000
2000
Karl Duncker 创造了这个实验
02:01
that is used in a whole variety of experiments in behavioral science.
38
109000
3000
在行为科学中被广泛运用
02:04
And here's how it works. Suppose I'm the experimenter.
39
112000
3000
情况是，假设我是实验者
02:07
I bring you into a room. I give you a candle,
40
115000
4000
我带你进入一个房间，给你一根蜡烛
02:11
some thumbtacks and some matches.
41
119000
2000
一些图钉和火柴
02:13
And I say to you, "Your job
42
121000
2000
告诉你说”现在
02:15
is to attach the candle to the wall
43
123000
2000
尝试把蜡烛固定在墙上
02:17
so the wax doesn't drip onto the table." Now what would you do?
44
125000
4000
让烛泪不要滴到桌上。“你会怎么做？
02:21
Now many people begin trying to thumbtack the candle to the wall.
45
129000
4000
许多人尝试用图钉把蜡烛钉在墙上
02:25
Doesn't work.
46
133000
2000
行不通
02:27
Somebody, some people -- and I saw somebody
47
135000
2000
有些人，台下也有些人
02:29
kind of make the motion over here --
48
137000
2000
做出这样的动作
02:31
some people have a great idea where they
49
139000
2000
有些人想到他们可以
02:33
light the match, melt the side of the candle, try to adhere it to the wall.
50
141000
4000
点燃火柴，溶化蜡烛的底部，尝试把它黏在墙上
02:37
It's an awesome idea. Doesn't work.
51
145000
3000
好主意。但行不通
02:40
And eventually, after five or 10 minutes,
52
148000
3000
差不多过了五到十分钟
02:43
most people figure out the solution,
53
151000
2000
大部分的人便会想出解决办法
02:45
which you can see here.
54
153000
2000
就像图片上那样
02:47
The key is to overcome what's called functional fixedness.
55
155000
3000
重点是克服”功能固着“
02:50
You look at that box and you see it only as a receptacle for the tacks.
56
158000
4000
当你看到盒子，你不过把它当成装大头针的容器
02:54
But it can also have this other function,
57
162000
2000
但它还有其它功能
02:56
as a platform for the candle. The candle problem.
58
164000
4000
那就是作为放蜡烛的平台。
03:00
Now I want to tell you about an experiment
59
168000
2000
现在我想告诉你另一个实验
03:02
using the candle problem,
60
170000
2000
利用蜡烛问题
03:04
done by a scientist named Sam Glucksberg,
61
172000
2000
由一个现在在普林斯顿大学
03:06
who is now at Princeton University in the U.S.
62
174000
2000
叫做 Sam Glucksberg 的科学家所做的实验
03:08
This shows the power of incentives.
63
176000
4000
这实验让我们看见动机的力量
03:12
Here's what he did. He gathered his participants.
64
180000
2000
他是这么做的。他将参与者聚集在一个房间里
03:14
And he said, "I'm going to time you. How quickly you can solve this problem?"
65
182000
3000
告诉他们“我要开始计时。看看你们能多快解决这个问题？”
03:17
To one group he said,
66
185000
2000
他对其中一群人说，
03:19
"I'm going to time you to establish norms,
67
187000
3000
我只是想取个平均值
03:22
averages for how long it typically takes
68
190000
2000
看一般人需要花多久的时间
03:24
someone to solve this sort of problem."
69
192000
2000
才能解决这样的问题。
03:26
To the second group he offered rewards.
70
194000
3000
他提供奖励给另一群人
03:29
He said, "If you're in the top 25 percent of the fastest times,
71
197000
4000
他说“如果你是前25%最快解决问题的人
03:33
you get five dollars.
72
201000
3000
就能拿到五块钱。
03:36
If you're the fastest of everyone we're testing here today,
73
204000
3000
如果你是今日所有人里解答最快的
03:39
you get 20 dollars."
74
207000
2000
你就有20块钱。"
03:41
Now this is several years ago. Adjusted for inflation,
75
209000
3000
这个实验是几年前的事了，按照通货膨胀
03:44
it's a decent sum of money for a few minutes of work.
76
212000
2000
几分钟就能拿到20块是很不错的
03:46
It's a nice motivator.
77
214000
2000
是个不错的诱因
03:48
Question: How much faster
78
216000
3000
问题是：这群人比另一群人
03:51
did this group solve the problem?
79
219000
2000
的解题速度快了多少呢？
03:53
Answer: It took them, on average,
80
221000
3000
答案是：平均来说，他们比另一组人
03:56
three and a half minutes longer.
81
224000
4000
多花了三分半种。
04:00
Three and a half minutes longer. Now this makes no sense right?
82
228000
3000
整整三分半种。这不合理，不是吗？
04:03
I mean, I'm an American. I believe in free markets.
83
231000
3000
你想想，我是个美国人。我相信自由市场
04:06
That's not how it's supposed to work. Right?
84
234000
3000
这个实验不太对劲吧？对吗？
04:09
(Laughter)
85
237000
1000
(笑声)
04:10
If you want people to perform better,
86
238000
2000
如果你想要人们做得更好，
04:12
you reward them. Right?
87
240000
2000
你便给他们奖赏，对吗？
04:14
Bonuses, commissions, their own reality show.
88
242000
3000
红利、佣金、他们自己的真人秀
04:17
Incentivize them. That's how business works.
89
245000
4000
赋予他们动机。这就是商业法则
04:21
But that's not happening here.
90
249000
2000
但实验里却不是这样
04:23
You've got an incentive designed to
91
251000
2000
奖励是为了
04:25
sharpen thinking and accelerate creativity,
92
253000
4000
增强思考能力及创意
04:29
and it does just the opposite.
93
257000
2000
但事实却是相反。
04:31
It dulls thinking and blocks creativity.
94
259000
3000
它阻断了思考和创意能力
04:34
And what's interesting about this experiment is that it's not an aberration.
95
262000
3000
有趣的事情是，这个实验不是误差
04:37
This has been replicated over and over
96
265000
3000
它被一再重复
04:40
and over again, for nearly 40 years.
97
268000
3000
在过去的四十年间
04:43
These contingent motivators --
98
271000
3000
这些不同的诱因
04:46
if you do this, then you get that --
99
274000
2000
如果你这样做，你就得到那个
04:48
work in some circumstances.
100
276000
2000
在某些情况里是可行的
04:50
But for a lot of tasks, they actually either don't work
101
278000
3000
但在许多任务中，它们不是没作用
04:53
or, often, they do harm.
102
281000
3000
更有可能产生反效果
04:56
This is one of the most robust findings
103
284000
4000
这是在社会科学中一项
05:00
in social science,
104
288000
3000
最有力的发现。
05:03
and also one of the most ignored.
105
291000
2000
同时也是最为人忽略的
05:05
I spent the last couple of years looking at the science of
106
293000
2000
过去两年，我研究
05:07
human motivation,
107
295000
2000
人类的动机
05:09
particularly the dynamics of extrinsic motivators
108
297000
2000
尤其是那些外部的激励因素
05:11
and intrinsic motivators.
109
299000
2000
和内在的激励因素
05:13
And I'm telling you, it's not even close.
110
301000
2000
我可以告诉你，两者相差悬殊
05:15
If you look at the science, there is a mismatch
111
303000
2000
如果你使用科学方法查证，你会发现
05:17
between what science knows and what business does.
112
305000
4000
科学知识和商业行为之间有条鸿沟
05:21
And what's alarming here is that our business operating system --
113
309000
3000
我们必须要注意的是，我们的商业机制
05:24
think of the set of assumptions and protocols beneath our businesses,
114
312000
3000
想想这些商业的协议和假设
05:27
how we motivate people, how we apply our human resources --
115
315000
5000
我们如何激励人心，如何运用人资
05:32
it's built entirely around these extrinsic motivators,
116
320000
3000
全是以这些外部激励因素作为基础
05:35
around carrots and sticks.
117
323000
2000
打手心给块糖
05:37
That's actually fine for many kinds of 20th century tasks.
118
325000
4000
对许多20世纪的工作来说是可行的
05:41
But for 21st century tasks,
119
329000
2000
但面对21世纪的工作
05:43
that mechanistic, reward-and-punishment approach
120
331000
4000
这些机械化的，奖惩分明的作法
05:47
doesn't work, often doesn't work, and often does harm.
121
335000
4000
已经不管用了，有时更招致反效果
05:51
Let me show you what I mean.
122
339000
2000
让我呈现我想表达的
05:53
So Glucksberg did another experiment similar to this
123
341000
3000
Glucksberg 做了一个类似的实验
05:56
where he presented the problem in a slightly different way,
124
344000
2000
这次他给了他们一个比较不同的问题
05:58
like this up here. Okay?
125
346000
3000
像这个图里面的
06:01
Attach the candle to the wall so the wax doesn't drip onto the table.
126
349000
2000
实验对象必须要找出一个让蜡烛黏在墙上，又不会流下烛泪的方法
06:03
Same deal. You: we're timing for norms.
127
351000
3000
相同地，这边：我们要的是平均时间
06:06
You: we're incentivizing.
128
354000
3000
这边：一样的给他们不同诱因
06:09
What happened this time?
129
357000
2000
结果呢？
06:11
This time, the incentivized group
130
359000
2000
这次，有诱因的那组人
06:13
kicked the other group's butt.
131
361000
4000
远远地胜过了另一组人
06:17
Why? Because when the tacks are out of the box,
132
365000
4000
为什么？一旦我们把图钉从盒子里拿出来
06:21
it's pretty easy isn't it?
133
369000
4000
问题就变得相当简单不是吗？
06:25
(Laughter)
134
373000
2000
(笑声)
06:27
If-then rewards work really well
135
375000
3000
假设 - 在这个情况下
06:30
for those sorts of tasks,
136
378000
3000
奖励就变得非常有效
06:33
where there is a simple set of rules and a clear destination
137
381000
2000
在规则简单，目标明显
06:35
to go to.
138
383000
2000
的情况下
06:37
Rewards, by their very nature,
139
385000
2000
奖励，产生了作用
06:39
narrow our focus, concentrate the mind;
140
387000
2000
让我们集中精神，变得专注
06:41
that's why they work in so many cases.
141
389000
2000
这便是为何奖励在许多情况下有效的缘故
06:43
And so, for tasks like this,
142
391000
2000
当我们面对的工作是
06:45
a narrow focus, where you just see the goal right there,
143
393000
3000
范围狭窄，你能清楚见到目标
06:48
zoom straight ahead to it,
144
396000
2000
向前直冲时
06:50
they work really well.
145
398000
2000
奖励便非常有效
06:52
But for the real candle problem,
146
400000
2000
但在真正的“蜡烛问题”中
06:54
you don't want to be looking like this.
147
402000
2000
你不能只是这样看
06:56
The solution is not over here. The solution is on the periphery.
148
404000
2000
解答不在那里，解答是在周围
06:58
You want to be looking around.
149
406000
2000
你需要四处找寻
07:00
That reward actually narrows our focus
150
408000
2000
奖励却令我们眼光狭隘
07:02
and restricts our possibility.
151
410000
2000
限制了我们的想象力
07:04
Let me tell you why this is so important.
152
412000
3000
让我告诉你这个问题的重要性
07:07
In western Europe,
153
415000
2000
在西欧
07:09
in many parts of Asia,
154
417000
2000
亚洲的许多地方
07:11
in North America, in Australia,
155
419000
3000
北美洲、澳洲
07:14
white-collar workers are doing less of
156
422000
2000
白领工作者比较少处理
07:16
this kind of work,
157
424000
2000
这种问题
07:18
and more of this kind of work.
158
426000
4000
更多的是这种问题
07:22
That routine, rule-based, left-brain work --
159
430000
3000
那些例行的、常规性的、左脑式的工作
07:25
certain kinds of accounting, certain kinds of financial analysis,
160
433000
2000
一些会计、一些财务分析
07:27
certain kinds of computer programming --
161
435000
2000
一些电脑编程
07:29
has become fairly easy to outsource,
162
437000
2000
变得极为容易外包
07:31
fairly easy to automate.
163
439000
2000
变得自动化
07:33
Software can do it faster.
164
441000
3000
软件能处理的更快
07:36
Low-cost providers around the world can do it cheaper.
165
444000
2000
世界其他地方的低价供应商能以更便宜的成本来完成
07:38
So what really matters are the more right-brained
166
446000
4000
所以更重要的是右脑的
07:42
creative, conceptual kinds of abilities.
167
450000
3000
创意，概念式的能力
07:45
Think about your own work.
168
453000
3000
想想你的工作
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
你所面对的问题，甚至是我们
07:53
we've been talking about here,
171
461000
2000
今天所谈论到的问题
07:55
are those kinds of problems -- do they have a clear set of rules,
172
463000
2000
这些问题 - 它们有清楚的规则
07:57
and a single solution? No.
173
465000
3000
和一个简单的解答吗？不
08:00
The rules are mystifying.
174
468000
2000
它们的规则模糊
08:02
The solution, if it exists at all,
175
470000
2000
解答，如果有解答的话
08:04
is surprising and not obvious.
176
472000
3000
通常是令人意外而不明显的
08:07
Everybody in this room
177
475000
2000
在这里的每个人
08:09
is dealing with their own version
178
477000
3000
都在尝试解决他自己的
08:12
of the candle problem.
179
480000
2000
“蜡烛问题”
08:14
And for candle problems of any kind,
180
482000
3000
对所有形式的“蜡烛问题”
08:17
in any field,
181
485000
2000
在所有领域
08:19
those if-then rewards,
182
487000
3000
这些「如果 - 那就」的奖励
08:22
the things around which we've built so many of our businesses,
183
490000
4000
这些在商业世界里无处不在的奖惩系统
08:26
don't work.
184
494000
2000
其实没用
08:28
Now, I mean it makes me crazy.
185
496000
2000
这简直让我发狂
08:30
And this is not -- here's the thing.
186
498000
2000
这不是 - 重点是
08:32
This is not a feeling.
187
500000
3000
这不是一种“感觉”
08:35
Okay? I'm a lawyer; I don't believe in feelings.
188
503000
3000
我是个律师，我才不信什么感觉
08:38
This is not a philosophy.
189
506000
4000
这也不是哲学
08:42
I'm an American; I don't believe in philosophy.
190
510000
2000
我是个美国人，我才不信什么哲学
08:44
(Laughter)
191
512000
3000
(笑声)
08:47
This is a fact --
192
515000
3000
这是真相
08:50
or, as we say in my hometown of Washington, D.C.,
193
518000
2000
或是我们在华盛顿特区的政治圈常说的
08:52
a true fact.
194
520000
2000
一个“事实真相”
08:54
(Laughter)
195
522000
2000
(笑声)
08:56
(Applause)
196
524000
4000
(掌声)
09:00
Let me give you an example of what I mean.
197
528000
2000
让我给你一个例子
09:02
Let me marshal the evidence here,
198
530000
2000
让我收集这些证据
09:04
because I'm not telling you a story, I'm making a case.
199
532000
2000
因为我不是在告诉你一个故事，而是陈述一个案子
09:06
Ladies and gentlemen of the jury, some evidence:
200
534000
2000
陪审团的女士们先生们，证据在此：
09:08
Dan Ariely, one of the great economists of our time,
201
536000
3000
Dan Ariely,一位当代伟大的经济学家
09:11
he and three colleagues, did a study of some MIT students.
202
539000
4000
他和三位同仁，对麻省理工学院的学生做了一些研究
09:15
They gave these MIT students a bunch of games,
203
543000
3000
他给这些学生一些游戏
09:18
games that involved creativity,
204
546000
2000
一些需要创造力的游戏
09:20
and motor skills, and concentration.
205
548000
2000
需要动力和专注
09:22
And the offered them, for performance,
206
550000
2000
依照他们的表现给他们
09:24
three levels of rewards:
207
552000
2000
三种不同程度的奖励
09:26
small reward, medium reward, large reward.
208
554000
5000
小奖励、中奖励、大奖励
09:31
Okay? If you do really well you get the large reward, on down.
209
559000
4000
如果你做得好，你就得到大奖励，依此类推
09:35
What happened? As long as the task involved only mechanical skill
210
563000
4000
结果呢？只要是机械形态的工作
09:39
bonuses worked as they would be expected:
211
567000
2000
红利就像我们所认知的
09:41
the higher the pay, the better the performance.
212
569000
4000
奖励越高，表现越好
09:45
Okay? But one the task called for
213
573000
2000
是的。但如果这个工作需要
09:47
even rudimentary cognitive skill,
214
575000
4000
任何基本的认知能力
09:51
a larger reward led to poorer performance.
215
579000
5000
越大的奖励却带来越差的表现
09:56
Then they said,
216
584000
2000
于是他们说
09:58
"Okay let's see if there's any cultural bias here.
217
586000
2000
“让我们试试是否有什么文化差距
10:00
Lets go to Madurai, India and test this."
218
588000
2000
让我们去印度的马杜赖试试。”
10:02
Standard of living is lower.
219
590000
2000
生活水平较低
10:04
In Madurai, a reward that is modest in North American standards,
220
592000
3000
在马杜赖，北美标准的的中等奖励
10:07
is more meaningful there.
221
595000
3000
在这里有意义多了
10:10
Same deal. A bunch of games, three levels of rewards.
222
598000
3000
一样地，一些不同游戏，三种奖励
10:13
What happens?
223
601000
2000
结果呢？
10:15
People offered the medium level of rewards
224
603000
3000
中等奖励的人
10:18
did no better than people offered the small rewards.
225
606000
3000
做得不比那些小奖励的人好
10:21
But this time, people offered the highest rewards,
226
609000
4000
但这次，那些能够得到大奖励的人
10:25
they did the worst of all.
227
613000
4000
表现最差
10:29
In eight of the nine tasks we examined across three experiments,
228
617000
3000
三种实验中，在我们提供的九个游戏中有八个
10:32
higher incentives led to worse performance.
229
620000
5000
奖励越高的表现越差
10:37
Is this some kind of touchy-feely
230
625000
3000
难道这是一种感情用事的
10:40
socialist conspiracy going on here?
231
628000
3000
社会主义的阴谋诡计吗？
10:43
No. These are economists from MIT,
232
631000
3000
不。这些经济学家来自麻省理工
10:46
from Carnegie Mellon, from the University of Chicago.
233
634000
3000
卡内基梅隆、和芝加哥大学
10:49
And do you know who sponsored this research?
234
637000
2000
你知道赞助这实验的是谁吗？
10:51
The Federal Reserve Bank of the United States.
235
639000
4000
是美国联邦储备银行
10:55
That's the American experience.
236
643000
2000
完全的美国经验
10:57
Let's go across the pond to the London School of Economics --
237
645000
3000
让我们跨海到伦敦政经学院看看
11:00
LSE, London School of Economics,
238
648000
3000
LSE,伦敦经济学院
11:03
alma mater of 11 Nobel Laureates in economics.
239
651000
3000
十一位诺贝尔经济奖得主的母校
11:06
Training ground for great economic thinkers
240
654000
3000
训练伟大经济学家的地方
11:09
like George Soros, and Friedrich Hayek,
241
657000
3000
有乔治索罗斯、弗里德里希•哈耶克
11:12
and Mick Jagger. (Laughter)
242
660000
2000
和滚石乐团的米克•贾格尔（笑声）
11:14
Last month, just last month,
243
662000
4000
上个月，才刚过去的那个月
11:18
economists at LSE looked at 51 studies
244
666000
3000
政经学院的经济学家汇整了51个关于
11:21
of pay-for-performance plans, inside of companies.
245
669000
3000
企业内部绩效薪酬的研究
11:24
Here's what the economists there said: "We find that financial incentives
246
672000
3000
这些经济学家说，“我们发现金钱的诱因
11:27
can result in a negative impact on overall performance."
247
675000
6000
能对整体绩效带来负面效果。”
11:33
There is a mismatch between what science knows
248
681000
3000
科学知识和商业行为之间
11:36
and what business does.
249
684000
2000
有条鸿沟
11:38
And what worries me, as we stand here in the rubble
250
686000
3000
我所忧心的是，在我们站在金融风暴
11:41
of the economic collapse,
251
689000
2000
废墟之间的此刻
11:43
is that too many organizations
252
691000
2000
仍然有太多团体
11:45
are making their decisions,
253
693000
2000
仍然以一些过时的、
11:47
their policies about talent and people,
254
695000
2000
未经验证的、非科学的
11:49
based on assumptions that are outdated, unexamined,
255
697000
6000
几乎是来自天方夜谭的假设
11:55
and rooted more in folklore than in science.
256
703000
3000
来制定规则和管理人事
11:58
And if we really want to get out of this economic mess,
257
706000
3000
如果我们真的想要摆脱这个经济危机
12:01
and if we really want high performance on those
258
709000
2000
如果我们真的想要在这些
12:03
definitional tasks of the 21st century,
259
711000
2000
属于21世纪的核心工作中获取绩效的话
12:05
the solution is not to do more of the wrong things,
260
713000
6000
这解答无异是错上加错：
12:11
to entice people with a sweeter carrot,
261
719000
3000
用红萝卜来吸引人
12:14
or threaten them with a sharper stick.
262
722000
2000
或是用棍子来威胁人
12:16
We need a whole new approach.
263
724000
2000
我们需要一种新做法
12:18
And the good news about all of this is that the scientists
264
726000
2000
好消息是这些研究人类动机的科学家
12:20
who've been studying motivation have given us this new approach.
265
728000
3000
已经给了我们一个新方向
12:23
It's an approach built much more around intrinsic motivation.
266
731000
3000
这个新方向讲求内在的诱因
12:26
Around the desire to do things because they matter,
267
734000
2000
我们想做是因为它能改变世界
12:28
because we like it, because they're interesting,
268
736000
2000
因为我们喜欢，因为它很有趣
12:30
because they are part of something important.
269
738000
2000
因为它能影响的范围很广
12:32
And to my mind, that new operating system for our businesses
270
740000
4000
在我心里，这种新的商业机制
12:36
revolves around three elements:
271
744000
2000
围绕在三个基础上
12:38
autonomy, mastery and purpose.
272
746000
3000
自主性、掌握力和使命感
12:41
Autonomy: the urge to direct our own lives.
273
749000
3000
自主性，想要主掌自己人生的需求
12:44
Mastery: the desire to get better and better at something that matters.
274
752000
4000
掌握力，想要在举足轻重的事情上做得更好的欲望
12:48
Purpose: the yearning to do what we do
275
756000
3000
使命感，希望我们所做的事情
12:51
in the service of something larger than ourselves.
276
759000
3000
是为了更高远的理想的渴望
12:54
These are the building blocks of an entirely new operating system
277
762000
3000
这些便是建立新商业机制的
12:57
for our businesses.
278
765000
2000
基石
12:59
I want to talk today only about autonomy.
279
767000
4000
今天我只想提到自主性
13:03
In the 20th century, we came up with this idea of management.
280
771000
3000
20世纪产生了管理学的想法
13:06
Management did not emanate from nature.
281
774000
2000
管理学不是自然发生的
13:08
Management is like -- it's not a tree,
282
776000
2000
管理学像是 － 它不是一棵树
13:10
it's a television set.
283
778000
2000
而是个电视机
13:12
Okay? Somebody invented it.
284
780000
2000
对吗？有人发明它
13:14
And it doesn't mean it's going to work forever.
285
782000
2000
不代表它永远都好用
13:16
Management is great.
286
784000
2000
管理学很好
13:18
Traditional notions of management are great
287
786000
2000
传统的管理学的概念是好的
13:20
if you want compliance.
288
788000
2000
如果你需要的是服从
13:22
But if you want engagement, self-direction works better.
289
790000
3000
但如果你想要员工全心投入，自动自发更好
13:25
Let me give you some examples of some kind of radical
290
793000
2000
有关自动自发，让我给你一些
13:27
notions of self-direction.
291
795000
2000
革命性的例子
13:29
What this means -- you don't see a lot of it,
292
797000
3000
代表着 --这样的例子不多
13:32
but you see the first stirrings of something really interesting going on,
293
800000
3000
但是你可以发现一些有趣的事情正开始发生
13:35
because what it means is paying people adequately
294
803000
2000
它代表着付给人们合理与
13:37
and fairly, absolutely --
295
805000
2000
足够的工资
13:39
getting the issue of money off the table,
296
807000
2000
让钱不再是问题
13:41
and then giving people lots of autonomy.
297
809000
2000
然后给人们很大的自治权
13:43
Let me give you some examples.
298
811000
2000
让我举一些例子
13:45
How many of you have heard of the company Atlassian?
299
813000
4000
在座谁听过一家叫 Atlassian 的公司？
13:49
It looks like less than half.
300
817000
2000
看起来一半都不到
13:51
(Laughter)
301
819000
2000
（笑声）
13:53
Atlassian is an Australian software company.
302
821000
4000
Atlassian 是一个澳大利亚的软件公司
13:57
And they do something incredibly cool.
303
825000
2000
他们做了一件很酷的事
13:59
A few times a year they tell their engineers,
304
827000
2000
一年有几次，他们跟公司里的软件工程师说
14:01
"Go for the next 24 hours and work on anything you want,
305
829000
4000
“接下来的24个小时，去做你自己想做的事，
14:05
as long as it's not part of your regular job.
306
833000
2000
只要它和你每天的工作无关
14:07
Work on anything you want."
307
835000
2000
随便你要做什么都行。”
14:09
So that engineers use this time to come up with
308
837000
2000
这些工程师便利用这些时间
14:11
a cool patch for code, come up with an elegant hack.
309
839000
3000
写出一套有趣的编程，优雅地包装这些想法
14:14
Then they present all of the stuff that they've developed
310
842000
3000
在那天的最后
14:17
to their teammates, to the rest of the company,
311
845000
3000
在这个全员到齐，万众一心的会议中
14:20
in this wild and wooly all-hands meeting
312
848000
2000
对他们的组员和整个公司
14:22
at the end of the day.
313
850000
2000
介绍他的发明
14:24
And then, being Australians, everybody has a beer.
314
852000
2000
当然，身为澳大利亚人，大家都得来罐啤酒
14:26
They call them FedEx Days.
315
854000
3000
他们叫这是 FedEx 联邦快递日
14:29
Why? Because you have to deliver something overnight.
316
857000
6000
因为你必须在隔夜交出你的作品
14:35
It's pretty. It's not bad. It's a huge trademark violation,
317
863000
2000
很不赖的想法。虽然违反商标法
14:37
but it's pretty clever.
318
865000
2000
但这个想法很聪明。
14:39
(Laughter)
319
867000
1000
（笑声）
14:40
That one day of intense autonomy
320
868000
2000
在高度自主的一日中
14:42
has produced a whole array of software fixes
321
870000
2000
他们做出了许多软件编程的革新
14:44
that might never have existed.
322
872000
2000
之前根本没人想到的
14:46
And it's worked so well that Atlassian has taken it to the next level
323
874000
2000
这个计划的成功，让 Altlassian 更进一步的发明了
14:48
with 20 Percent Time --
324
876000
2000
五分之一时间
14:50
done, famously, at Google --
325
878000
2000
谷歌把这个想法发扬光大
14:52
where engineers can work, spend 20 percent of their time
326
880000
2000
工程师可以用五分之一的时间
14:54
working on anything they want.
327
882000
2000
做所有他们想做的事情
14:56
They have autonomy over their time,
328
884000
2000
他们可以自由的分配他们的时间
14:58
their task, their team, their technique.
329
886000
2000
工作，组员，和作法
15:00
Okay? Radical amounts of autonomy.
330
888000
2000
就是这样。完全的自主权
15:02
And at Google, as many of you know,
331
890000
4000
诚如大家所知，在谷歌
15:06
about half of the new products in a typical year
332
894000
2000
一年中有一半的新商品
15:08
are birthed during that 20 Percent Time:
333
896000
3000
都来自这五分之一时间
15:11
things like Gmail, Orkut, Google News.
334
899000
3000
像谷歌信箱、Orkut、谷歌新闻
15:14
Let me give you an even more radical example of it:
335
902000
3000
让我给你一个更具革命性的例子
15:17
something called the Results Only Work Environment,
336
905000
2000
一个叫做“只论结果的工作环境”
15:19
the ROWE,
337
907000
2000
简写是ROWE
15:21
created by two American consultants, in place
338
909000
2000
由两个美国分析师所创造
15:23
in place at about a dozen companies around North America.
339
911000
2000
用在十多家北美公司上
15:25
In a ROWE people don't have schedules.
340
913000
4000
在 ROWE 之中，人们没有日程表
15:29
They show up when they want.
341
917000
2000
他们想来就来
15:31
They don't have to be in the office at a certain time,
342
919000
2000
他们不需要在特定时间到公司
15:33
or any time.
343
921000
2000
任何时间
15:35
They just have to get their work done.
344
923000
2000
他们只需要把工作完成
15:37
How they do it, when they do it,
345
925000
2000
怎么做、何时做
15:39
where they do it, is totally up to them.
346
927000
3000
在哪里做、都取决于他们自己
15:42
Meetings in these kinds of environments are optional.
347
930000
4000
甚至连开会都是选择性的
15:46
What happens?
348
934000
2000
结果呢？
15:48
Almost across the board, productivity goes up,
349
936000
3000
几乎所有公司的生产力都提升了
15:51
worker engagement goes up,
350
939000
3000
工作投入度提升
15:54
worker satisfaction goes up, turnover goes down.
351
942000
3000
工作满意度提升，人力流失降低
15:57
Autonomy, mastery and purpose,
352
945000
2000
自主性、掌握力和使命感
15:59
These are the building blocks of a new way of doing things.
353
947000
2000
这便是新工作方式的新基础
16:01
Now some of you might look at this and say,
354
949000
3000
在座的某些人可能会看着然后说
16:04
"Hmm, that sounds nice, but it's Utopian."
355
952000
3000
”嗯，听起来不错，就是太理想化了。“
16:07
And I say, "Nope. I have proof."
356
955000
5000
我说”错了。我有证据。“
16:12
The mid-1990s, Microsoft started
357
960000
2000
在90年代中，微软开始了一个
16:14
an encyclopedia called Encarta.
358
962000
2000
叫做 Encarta 的百科全书计划
16:16
They had deployed all the right incentives,
359
964000
2000
他们使用了所有正确的诱因
16:18
all the right incentives. They paid professionals to
360
966000
3000
所有的诱因。他们付钱给专业人士
16:21
write and edit thousands of articles.
361
969000
2000
让他们写和编辑这些文章
16:23
Well-compensated managers oversaw the whole thing
362
971000
2000
收入颇丰的主管们监督着整个计划
16:25
to make sure it came in on budget and on time.
363
973000
5000
确定它不会超过预算和时间
16:30
A few years later another encyclopedia got started.
364
978000
2000
几年后另一个百科全书计划开始了
16:32
Different model, right?
365
980000
3000
完全不同的模式
16:35
Do it for fun. No one gets paid a cent, or a Euro or a Yen.
366
983000
4000
为了兴趣而作。没有人能拿到任何一毛钱
16:39
Do it because you like to do it.
367
987000
3000
因为自己喜欢做而做
16:42
Now if you had, just 10 years ago,
368
990000
3000
如果你在十年前
16:45
if you had gone to an economist, anywhere,
369
993000
2000
到一个经济学家那里去
16:47
and said, "Hey, I've got these two different models for creating an encyclopedia.
370
995000
4000
对他说”我有两种撰写百科全书的模式
16:51
If they went head to head, who would win?"
371
999000
3000
拿来相比，谁会赢？“
16:54
10 years ago you could not have found a single sober economist anywhere
372
1002000
4000
十年前你绝对不会找到任何一个清醒的经济学家
16:58
on planet Earth
373
1006000
2000
在这个地球的任何角落
17:00
who would have predicted the Wikipedia model.
374
1008000
2000
能够预知维基百科的模式
17:02
This is the titanic battle between these two approaches.
375
1010000
3000
这是一个两种模式之间的世纪战役
17:05
This is the Ali-Frazier of motivation. Right?
376
1013000
3000
动机的阿里与弗雷泽之战
17:08
This is the Thrilla' in Manila.
377
1016000
2000
就像那场在马尼拉的拳王之战
17:10
Alright? Intrinsic motivators versus extrinsic motivators.
378
1018000
3000
是吗？内在动机和外在动机
17:13
Autonomy, mastery and purpose,
379
1021000
2000
自主性、掌握力和使命感
17:15
versus carrot and sticks. And who wins?
380
1023000
2000
和胡萝卜和棍子。谁赢了？
17:17
Intrinsic motivation, autonomy, mastery and purpose,
381
1025000
3000
内在动机、自主性、掌握力和使命感
17:20
in a knockout. Let me wrap up.
382
1028000
4000
获得压倒性胜利。结论是
17:24
There is a mismatch between what science knows and what business does.
383
1032000
3000
科学知识和商业行为之间
17:27
And here is what science knows.
384
1035000
2000
有条鸿沟
17:29
One: Those 20th century rewards,
385
1037000
2000
一：这些20世纪的奖励
17:31
those motivators we think are a natural part of business,
386
1039000
3000
这些我们当作商业中自然一部分的诱因
17:34
do work, but only in a surprisingly narrow band of circumstances.
387
1042000
4000
是有用的。但意外地只在一个非常狭窄的情况下
17:38
Two: Those if-then rewards often destroy creativity.
388
1046000
4000
二：这些奖励往往会破坏创造力
17:42
Three: The secret to high performance
389
1050000
2000
三：高绩效的秘密
17:44
isn't rewards and punishments,
390
1052000
2000
不是奖励和惩罚
17:46
but that unseen intrinsic drive --
391
1054000
2000
而是看不见的内在动力
17:48
the drive to do things for their own sake.
392
1056000
3000
让人为了自己而做的动力
17:51
The drive to do things cause they matter.
393
1059000
2000
让人有使命感的动力
17:53
And here's the best part. Here's the best part.
394
1061000
2000
最好的是
17:55
We already know this. The science confirms what we know in our hearts.
395
1063000
3000
我们了然于心。科学不过确认了我们心里的声音
17:58
So, if we repair this mismatch
396
1066000
3000
如果我们改变
18:01
between what science knows and what business does,
397
1069000
2000
科学知识和商业行为之间有的那条鸿沟
18:03
if we bring our motivation, notions of motivation
398
1071000
3000
如果我们把我们的动机，对诱因的想法
18:06
into the 21st century,
399
1074000
2000
带进21世纪
18:08
if we get past this lazy, dangerous, ideology
400
1076000
4000
如果我们越过懒惰的、危险的、理想化的
18:12
of carrots and sticks,
401
1080000
2000
胡萝卜和棍子的想法
18:14
we can strengthen our businesses,
402
1082000
3000
我们可以强化我们的公司
18:17
we can solve a lot of those candle problems,
403
1085000
3000
解决许多的“蜡烛问题”
18:20
and maybe, maybe, maybe
404
1088000
4000
那么或许，或许，或许
18:24
we can change the world.
405
1092000
2000
我们便能改变世界。
18:26
I rest my case.
406
1094000
2000
陈述完毕。
18:28
(Applause)
407
1096000
3000
（掌声）
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