无人管理会怎样？

Barely managing

User-rating systems are cut-rate substitutes for a skilful manager

无人管理
用户评级系统只是优秀经理人的次级替代品

无人管理会怎样？

IT OFTEN arrives as you stroll from the kerb to your front door. An e-mail with a question: how many stars do you want to give your Uber driver? Rating systems like the ride-hailing firm’s are essential infrastructure in the world of digital commerce.

经常是这样，当你刚从马路边沿滑到前门，一封附有问题的电子邮件就到了一：你打算给你的优步司机打几颗星？像＂乐乘公司＂一样的评级系统，是电子商务世界中基础设施。

Just about anything you might seek to buy online comes with a crowdsourced rating, from a subscription to this newspaper to a broken iPhone on eBay to, increasingly, people providing services. But people are not objects. As ratings are applied to workers it is worth considering the consequences—for rater and rated.

就像任何一件你想要在网上购买的商品，从订阅报纸，到ebay上的旧手机，总是跟随着一个＂众包＂评级，越来越多地要求人们提供这种评论。但是，人不是物品。由于评级被用于工作人员，其对评级考核和被评级者的影响，都值得我们认真思考。

User-rating systems were developed in the 1990s. The web held promise as a grand bazaar, where anyone could buy from or sell to anyone else. But e-commerce platforms had to create trust. Buyers and sellers needed to believe that payment would be forthcoming, and that the product would be as described.

用户评级系统是从二十世纪90年代发展起来的。只要人们从他人处买入或者卖出物品，网络就像一个巨大的集市汇集了人们的承诺。但是，电子商务平台必须建立信任。买者和卖者都需要确认付款可以顺利到达，产品与描述相符。

E-tailers like Amazon and eBay adopted reputation systems, in which sellers and buyers gave feedback about transactions. Reputation scores appended to products, vendors and buyers gave users confidence that they were not about to be scammed.

电子零售商，比如亚马逊和易贝采用了声誉系统，买者和卖者都可以对交易作出反馈。给产品、零售商和买者的信誉评分，给用户以信心，使他们不会被欺骗。

Such systems then spread to labour markets. Workers for gig-economy firms like Uber and Upwork come with user-provided ratings. Conventional employers are jumping on the bandwagon.

这样的评级系统随后转到到劳动力市场。像优步和＂＂随意工＂这样的零工经济公司，开始对工作人员实施用户评级机制。传统的雇主也开始随之采用。

A phone call to your bank, or the delivery of a meal ordered online, is now likely to be followed by a notification prompting you to rate the person who has just served you.

现在给银行一个电话，或者网上叫一个外卖，也可能跟随着一个通知，提醒你给提供服务的人员作出评价。

Superficially, such ratings also seem intended to build trust. For users of Uber, say, who will be picked up by drivers they do not know, ratings look like a way to reassure them that their ride will not end in abduction.
Yet if that was once necessary, it is no longer.

表面上看来，这样的评级是想建立信任。对于优步用户来说，驾驶员选择谁作为乘客，他们并不知道，评级看起来好像是确认，使他们搭乘行为不会变成一次不愉快之旅。然而，如何说以前是必须的，那么现在己经不必要了。

Uber is a global firm worth tens of billions of dollars and with millions of repeat customers. Its customers know by now that the app records drivers’ identities and tracks their route. It is Uber’s brand that creates trust; for most riders, waiting for a driver with a rating of 4.8 rather than 4.5 is not worth the trouble.

优步是一家全球性公司，价值上百亿美元，拥有几百万名老客户。它的客户知道，他们的App记录着驾驶员的身份和他们的行车路线，是优步的品牌创建了信任。对很多乘客来说，等待一个评级4.8分而不是4.5分的驾驶员并不必要。

Rather, ratings increasingly function to make management cheaper by shifting the burden of monitoring workers to users. Though Uber regards its drivers as independent contractors, in many ways they resemble employees. The firm seeks to provide users with a reasonably uniform experience from ride to ride.

而且，由于评级的广泛使使用，把监督工作人员的重任转移了给用户，使得管理工作的成本下降。虽然，优步将驾驶员是独立的合同工，但在很多方面，公司力图通过为用户的每一趟搭乘都提供一个合理的、统一的体验，驾驶员更类似于雇员，

And because drivers are randomly assigned to customers, it is the platform that cares whether rides lead to repeat business and which therefore bears the cost of poor behaviour by drivers.

由于客户是随机分配给驾游员的，因此，平台对搭乘行为是否会带来重复性业务十分重视，否则就要承担因为驾驶员的不良行为所带来导致的经营成本。

Ordinarily a firm in such a position would need to invest heavily in monitoring its workers—hiring staff to carry out quality assurance by taking Uber rides incognito, for instance.

一般情况下，处于这种地位的公司需要在监控工作人员方面投入大量成本－雇佣人员去执行质量控制，比如以化名搭乘优步的车子。

A rating system, however, reduces the need for monitoring by aligning the firm’s interests with those of workers. (Drivers with low ratings risk having their profile deactivated.)

但是，评价系统使得公司整合公司利益和驾驶员利益的需求降低。（低评级风险不利于驾驶员改进行为）。

Outsourcing management like this appeals to cost-conscious firms of all sorts; hence the proliferation of technological nudges to rate one service worker or another. To work as intended, however, ratings must provide an accurate indication of how well workers conform to the behaviour that firms desire.

类似的外包管理，对成本敏感型的各类公司都有吸引力；因此技术的广泛使用推动评级从一种服务人员扩展到另一种类。但是，要达成期望的结果，评级必须提供其工作人员应遵守的行为标准的准确指示。

Frequently, they do not. Raters may have no incentive to do their job well. They may ignore the prompt to rate a worker, or automatically assign the highest score. They may adhere to social norms that discourage leaving a poor rating, just as diners often leave the standard tip, however unexceptional the service.

然而，很多公司并未提供此类标准。评级者可能没有激励他们做好这项工作的动力。人们可能忽视提醒他们立即评价的提示，或者自动给出最高的分数。他们可能遵循社会常规，不愿意留下一个糟糕的评级，就像吃饭的人总是留下标准小费，即使他们接受的服务乏善可陈。

Uber’s customers often award drivers five stars rather than feel bad about themselves for damaging a stranger’s work prospects. And even when users are accurate, their ratings may reflect factors beyond a service provider’s control, such as unexpected traffic. Systems that allow users to leave more detailed feedback (as Uber’s has begun to) could address this, but at the cost of soaking up more time, which could mean fewer reviews.

优步的用户经常给予驾驾驶员五颗星，而不愿因为影响到一个陌生人的工作前途而感到内疚，即使用户的感觉是准确的，他们的评级也可能反映的是服务提供者难以控制的因素，比如突如其来的交通事故。系统可以让用户留下详细内容，反馈可以解决这个问题（优步已经开始这么做了），但是成本是花费更多的时间，意味看评价的人更少。

When the quality of a match between a worker and a task is particularly important, the problem of sorting the signal from the noise in rating systems grows. Skilled managers can tell when a worker struggling in one role might thrive in another; rating systems can capture only expressions of customer dissatisfaction.

当工作人员和任务之间的质量显得特别重要时，难以从评级系统的杂音中梳理出有用信号的问题就出现了。有经验的经理可以辨别出工作人员在一个岗位上痛苦不堪，而在另一个岗位上积极投入的情况；而评级系统只能采集到客户满意或者不满意的信息。

Such difficulties also affect gig-economy platforms. Poor ratings on a job-placement site could reflect an inappropriate pairing between a worker with one set of skills and a firm that needs another, rather than the worker’s failure of effort or ability.

这些问题也影响到零工经济平台。糟糕的评级可能会影响到工作人员和公司之间不适当的匹配，比如拥有某种的工作人员，而公司需要的却是另一种技能，而这并不是员工不够努力或者缺少某种能力。

Platforms can reduce the potential for such errors by including more information about tasks and the workers who might tackle them. Yet they may discover to their chagrin that more information also provides users with more opportunities to discriminate.

平台可以通过提供更多关于任务，以及可能处理此事的工作人员的信息来减少此类错配。然而他们却失望地发现，更多的信息也给予用户歧视性对待的机会。

An analysis of Upwork, for example, found that employers of Indian descent disproportionately sought Indian nationals for their tasks. True, this particular sort of information could be concealed—and conventional management permits plenty of discrimination. But firms typically have a legal obligation not to discriminate, and to train managers accordingly.

例如，一份对Upwork的分析表明，源自印度的雇主总是倾向于寻找印度籍人士完成工作任务。的确，这类特别的信息可能会被掩盖－传统的管理中有大量的歧视性对待。因此，公司一般都有法定责任训练经理人，不应采取歧视性对待。

Overrated

过度依赖

Management is underappreciated as a contributor to success. Recent work by Nicholas Bloom, John Van Reenen and Erik Brynjolfsson suggests that good management matters more than the adoption of technology for a company’s performance.

管理层作为促进成功的作用被低估了。根据尼古拉斯.布鲁姆最新的工作成果，John Van Reenen和Erik Brynjolfsson认为，良好的管理比采用技术来提高公司业绩更为重要。

Even so, the use of ratings seems sure to grow. They are, as “Left Outside”, a pseudonymous blogger, puts it, a genuine disruptive technology: cheap enough to be adopted widely even if inferior to established practice.

即便如此，评级系统的使用的确在增长。正如＂Left Outside”，一个匿名博主所说的，他们是真正的颠覆性技术：成本极其低廉，适宜广泛使用，即使他们不如己有的操作惯例。

Further advances could improve such systems, as is common with disruptive technology. Artificial-intelligence programs may one day know how much people enjoyed a taxi ride better than they do themselves. In the meantime, management risks being left to the wrong sort of stars.

一般来说，随着颠覆性技术的进一步发展，这样的系统会逐步完善。终有有一天，人工智能可能会比人类自己更清楚他们是否喜欢所乘的出租车。与此同时，管理层则冒着看错明星的风险。