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People may be the heart of our organizations, but HR practices are often based on outdated ideas of human psychology and organizational design. When it comes to hiring decisions, employee motivation, and helping workers make better choices, behavioral insights and evidence-based practices can drive a new generation of HR strategies.
You spend more time working than doing anything else in life. It’s not right that the experience of work, even at some of the best employers, should be so demotivating and dehumanizing.—Laszlo Bock, Work Rules!1
The human resource function is at a crossroads. People are the heart of our organizations, yet many fundamental management and HR practices are based on outdated ideas of human psychology and organizational design. Often they are rooted in stories about successful business leaders operating in specific places and times (for example, Jack Welch at GE in the 1980s and ’90s). These anecdotes and examples evolve into management trends and, later, received wisdom that executives try to implement—at least while they’re on bestseller lists and magazine covers.
But it is rare for such practices to be rigorously evaluated. To paraphrase a well-known psychologist, they lack the character of scientific knowledge: “They tend neither to be refuted nor corroborated, but instead merely fade away as people lose interest.”2
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The time has come for a fresh look at evidence-based HR, founded on two key premises.
First: HR practices, policies, and programs should be designed to reflect our best understanding of human psychology. This simple idea carries huge implications, thanks to the revolution in our understanding of human psychology and behavior that Nobel laureate Daniel Kahneman and his collaborators and followers have ushered in over the past four decades. This work has fundamentally challenged and changed virtually every field involving human behavior, including behavioral economics and finance, marketing, behavioral health, and happiness research. HR should be next.
Second: HR practices, like all business programs, should be tested and validated. While the major findings of cognitive psychology and behavioral economics are well validated, the practical effectiveness of specific applications varies from context to context. A practice that flies at an online retailer, for instance, might flop at an ad agency. So whenever possible, researchers need to field-test new ideas using what the medical profession calls randomized controlled trials and what Internet companies call A/B testing: Try it on one randomly selected group and compare the results with those of a control group. Doing this enables HR departments to learn what works and what doesn’t and to quantify the economic value being created (or destroyed).
These two principles are the pillars of the “behavioral insights” movement that is reshaping the public policy world.3 Since the 2008 publication of Nudge by Richard Thaler and Cass Sunstein, policymakers have come to recognize that public sector interventions—ranging from government forms’ color and word choice to the design of job centers and after-school programs—should be designed to go with, rather than against, the grain of human psychology.4 And rather than simply follow tradition, accept authorities’ prescriptions, or adhere to industry benchmarks, policymakers are using A/B testing and data analysis to help design and evaluate programs.
The HR domain should embrace a behavioral insights movement of its own, founded on three premises that correspond to the major themes of behavioral economics (see the “Humans 101” sidebar):
The past 40 years have seen an explosion of research revealing dozens of surprises about how actual people (“Humans”) behave, often diverging from how rational economic actors (“Econs”) are presumed to behave. These findings fall into three major themes:5
Rationality: Traditional economic thinking holds that each of us makes decisions by gathering all available information and analyzing it with the kind of cost/benefit logic characteristic of Star Trek’s Mr. Spock. In reality, many of our intuitive decisions are predictably illogical—often in surprising ways. It turns out that even highly trained professionals—doctors, judges, underwriters, hiring managers—routinely lean on fallible rules of thumb and intuition (“thinking fast”) to make complex decisions that require careful data analysis (“thinking slow”).
Willpower: Econs enjoy perfect willpower and never let short-term distractions compromise long-term goals. Clearly, such beings exist only in Econ 101 textbooks. Understanding how and when we fall short of this ideal can help policymakers and HR executives design “choice environments” that steer us toward making smarter decisions.
Motivation: Econs act purely out of economic self-interest: If Dan puts in an extra hour on the job, it’s because he either relishes the expected reward and/or fears the punishment he’d face if he failed to do so. In reality, factors beyond economic self-interest—for example, fairness, professional pride, and societal responsibility—also strongly affect our decisions and choices.
Richard Thaler calls these characteristics of human behavior the “three bounds”: bounded rationality, willpower, and self-interest. Each of them points to major opportunities to rethink, redesign, and radically improve our management strategies.
Taken together, these principles offer a framework to drive a new generation of HR strategies that create happier, more motivated, and higher-performing teams. And thanks to the power of A/B testing, HR professionals can rigorously measure these strategies’ effectiveness.
Moneyball—the title of Michael Lewis’s famous book and the subsequent movie adaptation—has become shorthand for the type of data-driven decision making now widely used to improve decisions in realms far beyond professional sports: health care, financial services, entertainment, consumer business, public affairs, and more.6 So it is ironic that data-driven methods have made comparatively few inroads into the sorts of decisions at the heart of the Moneyball story: hiring. Hiring decisions are still routinely made largely on the basis of unstructured interviews—the corporate analog of baseball scouts’ use of gut feel to select players—which are notoriously unreliable predictors of future performance.7
To get a feel for how badly our mental shortcuts (“heuristics”) can lead us astray, take a moment to consider a young woman named Linda. Linda is 24 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of inequality and social justice and also participated in the Occupy movement.
Now ask yourself: Which of two alternatives is more likely?
a) Linda is a bank teller
b) Linda is a bank teller who is active in the Occupy movement
Most people answer (b), even though a moment’s thought reveals that this cannot possibly be the better response: The set of politically engaged bank tellers is a subset of the world’s bank tellers!8
This little test illustrates a fundamental feature of human cognition that psychologists have brought to light in the past four decades. Human thought comprises two sorts of mental operations that Daniel Kahneman calls “thinking fast” (System 1) and “thinking slow” (System 2). Thinking fast is automatic and effortless, valuing stories that possess narrative coherence. Thinking slow is controlled and effortful, valuing analyses with logical coherence. The bulk of our mental operations are System 1, and most of the time this serves us well—after all, we would be paralyzed if we had to logically analyze each of the thousands of decisions we make every day.
But there’s a rub: System 1 is terrible at statistics, and comes with dangerous unconscious biases. We humans are natural storytellers but poor natural statisticians. We are so far from being rational Econs that Kahneman calls the mind “a machine for jumping to conclusions.”9 Often without even realizing it, we tend to:
As if this were not bad enough, our susceptibility to cognitive biases is strongly tied to our physical energy level. For example, a study of eight judges making a total of more than 1,000 parole decisions over a 10-month span found that first thing in the morning, the judges granted parole to over 60 percent of the cases presented to them. This rate shrank steadily to zero just before the judges’ first meal break, after which it spiked back up to 60 percent and so on throughout the day. This phenomenon, called “decision fatigue,” is useful to keep in mind when scheduling full days interviewing job candidates or making year-end rating decisions.12
A psychological study, discussed by Google’s Laszlo Bock in Work Rules!, illustrates the havoc that cognitive biases routinely wreak in job interviews. (See the “Think about Linda” sidebar for further background.) Researchers showed study participants short clips of videos of actual job interviews:13
Slices were extracted from each interview, beginning with the interviewee knocking on the door and ending 10 seconds after the interviewee took a seat, and shown to naïve observers. Observers provided ratings of employability, competence, intelligence, nervousness, ambition, trustworthiness, confidence, nervousness, warmth, politeness, likeability, and expressiveness. For 9 of the 11 variables, thin-slice judgments correlated significantly with the final evaluation of the actual interviewers. Thus, immediate impressions based on a handshake and brief introduction predicted the outcome of a structured employment interview.14
In other words, the great bulk of the actual time spent in the interviews did little more than confirm the first impression that the interviewee made on the interviewer. The problem is that those first impressions are worthless at predicting success on the job.
Consider a second, and more dangerous, outcome of intuition-driven hiring: unconscious bias and discrimination. A study conducted by University of Chicago and Harvard economists studied the effects of randomly assigned “white sounding” and “African-American sounding” names atop otherwise identical résumés. The study found that the former résumés yielded call-backs for interviews 50 percent more often than the latter.15 This type of fast thinking—by time-crunched hiring managers quickly sorting candidates into yes and no piles—can lead to lawsuits, to say nothing of neglected job candidates and foregone workforce diversity.
For 9 of the 11 variables, thin-slice judgments correlated significantly with the final evaluation of the actual interviewers. Thus, immediate impressions based on a handshake and brief introduction predicted the outcome of a structured employment interview.
Thinking fast can also lead to gender bias. For example, renowned classical music conductors have been known to dismiss female musicians as having “smaller techniques,” being more temperamental, and so on. When orchestras began holding blind auditions, female musicians began to be hired in greater numbers. Physical blinds overcame the mental blinders resulting from thinking fast.16
The problem is clear and pervasive, but what can be done? As Moneyball illustrates—and psychological research dating back to the 1950s clearly demonstrates—the answer is using evidence, experimentation, and predictive models as thinking-slow correctives to biased, unaided judgment.17 While few HR departments have as much “big data” as statistics-obsessed baseball teams, we are clearly moving in this direction. Companies have mountains of data about their people, and we are only now starting to learn how to use that information to improve HR practices.
More fundamentally, a lot can be accomplished with traditional data sources. Laszlo Bock notes that the results of work samples, tests of general cognitive ability, the results of structured interviews, and tests of such non-cognitive abilities as conscientiousness are all (to varying degrees) predictive of future job performance. As one might expect, combining these assessment techniques into a single predictive model outperforms any one of them individually.18
We have spoken with insurance companies, consumer goods companies, and retailers who are using analytics techniques to identify the characteristics of high performers and then apply them on the job.
With different workforces, practices, and needs, companies should experiment and learn from experience to refine the hiring process over time. Google, which shares much of its experimentation publicly, has analyzed its own data to dramatically alter its hiring practices. For example, the company:
The common theme here is to be scientific: Gather, standardize, and analyze the data resulting from interview processes rather than skipping a System 2 process in favor of raw intuition. As Linda, our philosophy major, can attest, this results in better decisions.20
Furthermore, as organizations become ever more adept at data science, it is increasingly practical to build predictive models to facilitate the hiring process. HR departments’ hard drives and file cabinets are filled with data that can fuel such applications: employees’ and candidates’ job history, educational experience, prior employers, roles and job titles, performance ratings, and even tests and assessments. One major software company, for example, found that candidates with successful tenures at a particular competitor almost always proved to be effective employees in sales positions. Another found that the most predictive factor among candidates who did not succeed in their first year was “typos and spelling errors on their résumé.”
HR departments can also use innovative behavioral and lifestyle data sources to supplement traditional data. A fundamental truth about making nearly any kind of predictions about people is that past behavior is the best predictor of future behavior.21 Furthermore, the “digital breadcrumbs” we continually leave behind in our digitally mediated lives are behavioral data sources that can be used in novel ways. For example, we used the sort of lifestyle data traditionally applied to target marketing promotions and catalogs to help a major financial services company better predict which sales agents were likely to make it through the interview process and succeed on the job.
An emerging type of behavioral data relevant to HR is sociometric data. Sociometric badges are recording devices capable of measuring patterns of nonverbal communication and team interaction. For example, sociometric data have been found to be predictive of which call-center conversations are likely to end well, which doctors are relatively likely to be sued for malpractice (hint: patients sue likeable doctors less frequently), and which banking employees are likely to sell the most products and services.22
Sociometric data and email metadata can also provide useful insights by measuring patterns of collaboration and piecing together an organization’s social-network graph. For example, several studies have shown that in roles such as engineering, research, and consulting, individuals with larger “internal collaboration networks” outperform those who operate and work independently. Tribal wisdom and fast thinking might advise that we just hunker down and do our work, but data-rich slow thinking tells us to take some extra time, meet our colleagues, and build a network of partners in the organization.
A core theme of classical economics is that once an Econ has logically calculated the optimal choice, he or she acts on it without hesitation. But decades of behavioral economics research reveal that a large number of what Thaler calls “supposedly irrelevant factors” affect our choices—often strongly.23
For example, we are more likely to tip a taxi driver 20 percent (rather than less) if the payment touchscreen offers a 25 percent option; we tend to cut our electricity usage when informed that it’s much higher than that of neighbors living in similar homes (this is more effective than either economic arguments or environmental pleas); and we are more likely to purchase a jar of jam if we are presented with five flavors than if we see a choice-overloading array of 25 flavors.24
The choice-architecture lessons of Thaler and Sunstein’s Nudge are, we believe, entirely applicable to HR: Given that “everything matters” in the way environments affect our behaviors, we can intentionally design those environments in ways that prompt—nudge—people to take the short-term actions that are consistent with their long-term goals.
Consider the challenge of getting people to use the stairs at work. If the office stairs are stylish and centrally located (think of the set in the later seasons of Mad Men25), while the elevator is nondescript and requires a key card to use, people are likely to use the stairs more often than were the arrangement reversed. This encourages exercise, gives people a chance to move around, and even creates a more open work environment. (Similar research has shown that people with more light in their workspace were happier and more collaborative than those in dimmer offices.26Apparently, many Humans—unlike Econs—are heliotropes.)
While some view choice architecture as a form of Big Brother central planning—and yes, it can be abused along such lines—its inspiration is rooted in user-centric design.27 For example, iPads and iPhones are designed so that even children can pick them up and start using them with little instruction. Why can’t we design forms, policies, programs, and indeed all physical and choice environments in a similarly transparent, human-centered spirit?28
As it happens, one of the greatest choice-architecture success stories resides in the HR domain: prompting employees to save more for retirement. Knowing that idiosyncratic Humans tend to shrug and select the “default choice” led Thaler to suggest that companies automatically enroll employees in retirement savings accounts (requiring them to unenroll if they don’t want the default choice). Classical economics predicts that the default setting—a “seemingly irrelevant factor”—would have little effect: What self-respecting Econ would let filling out a silly form stand in the way of a more prosperous retirement? Yet the effects can be huge: An early study reported participation jumping from 49 percent to 86 percent.29 This is a cost-free bit of choice architecture that yields an outsized effect that benefits employees (and society).
When one thinks along these lines, any number of workplace choice-architecture applications—both actual and eminently plausible—leap to mind:
We believe that HR departments should assign someone to “own” such choice-architecture applications. Just as online retailers employ webmasters who use A/B testing to optimize the design of their websites, perhaps HR departments should have chief nudge officers to better understand and improve the small things that have outsized effects on workers’ lives.
“We’re adding a little something to this month’s sales contest. As you all know, first prize is a Cadillac Eldorado. . . . Second prize is a set of steak knives. Third prize is you’re fired.”—David Mamet, Glengarry Glen Ross36
Perhaps HR organizations’ biggest opportunity is to design policies and programs that really drive superior performance. We talk a lot about employee engagement and what we need to do to make our organizations “irresistible.”37 But do we really understand what motivates people in the first place?
Many traditional HR and management practices—command-and-control management, pay-for-performance, rank-and-yank, year-end ratings, and so on—are based on the premise that employees’ primary motivation is economic self-interest. As Adam Smith himself put it: “It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest.”38
The third principle of behavioral economics is that Humans, unlike textbook Econs, are motivated by factors other than economic self-interest. We also value peer recognition, respect, freedom to contribute, and the sense of self-esteem—not just a bigger paycheck. (Our research shows that “saying thanks” is one of the best things you can do to drive performance.39) And punishment, while it may work with children, is problematic.
Yet many core management and HR practices (bonuses, pay for performance, merit raises, performance ratings, potential ratings, high-potential lists, economic consequences for not reaching goals) are based on the idea that people work harder and produce more output in exchange for money and positional power.
Psychologists call such carrot-and-stick tactics extrinsic motivation. It turns out that such external rewards are really not what drives superior performance—it’s intrinsic motivation that matters: the desire to do a job for its inherent rewards.40
The behavioral economist Dan Ariely illustrates the idea: “Imagine you go to a dinner party and offer to pay your host a $100 reward for cooking such a wonderful meal. He or she is likely to be insulted, and may never invite you back again.”41 The host is motivated not by the prospect of remuneration but by the intrinsic rewards of cooking for friends and taking pleasure in their appreciation.
Wharton economist Jonathan Kolstad recently studied the effect of physician report cards on a group of heart surgeons. Contrary to expectations, the report cards had little effect on consumer demand—though they did produce improved physician performance, resulting in significant declines in mortality rates. Kolstad found that intrinsic motivations—professional pride and the desire to do well—trumped economic incentives by a wide margin.
Ariely’s example also hints at a subtler effect: Economic incentives can actually crowd out the inherent motivators that drive superior performance and ethical behavior. A well-known experiment suggests why. An A/B test involving a group of day care centers in Israel found that imposing fines for picking up children late resulted in increased tardiness, since the fines transmuted what parents had previously viewed as a social obligation into an economic transaction. Furthermore, the damage was lasting: The higher level of tardiness remained even after the fines were lifted.42
Think about the potential impact of a pay-for-performance program. A number of studies have shown that when we crowd out intrinsic motivation with pay, we end up with poor service quality (for example, focusing on time per call instead of customer satisfaction), poor product quality (for example, rushing products through the production line), and even poor health outcomes.43
Evidence is not hard to find:
What can HR teams do to promote intrinsic motivations? We suggest two fundamental actions. First, apply “design thinking” to all your HR programs. Rather than rely on the classical economics worldview, traditional “best practices,” or business-guru books, think about people from the inside out; begin with psychological insights about what motivates superior performance and ethical behavior. Second: Test everything.
Let’s start with the psychological insights. There is an emerging consensus that intrinsic motivation is characterized by the desire to achieve mastery of a craft, the desire for autonomy in going about one’s work, and the need to infuse one’s work with a sense of purpose. Psychologist Barry Schwartz is eloquent on this point:
We want work that is challenging and engaging, that enables us to exercise some discretion and control over what we do, and that provides us opportunities to learn and grow. We want to work with colleagues we respect and with supervisors who respect us. Most of all, we want work that is meaningful—that makes a difference to other people and thus ennobles us in at least some small way.49
Such principles are often honored more in the breach than in the observance. But successful organizations increasingly exemplify them in their practices:
These examples can help motivate good HR “design thinking.” But of course, organizations differ, and you will need to experiment to nail the execution. Harvard Business School professor Teresa Amabile points out that finding the right mix of intrinsic and extrinsic rewards can be tricky.58 For example, Google experimented with—but ultimately dropped—a start-up-like “Founders’ Award” program rewarding exceptional performance with commensurate (up to seven-figure) financial rewards. The company found that the program inadvertently celebrated money above other values and pleased almost no one. People in vital but back-office roles knew they had little chance of winning, near-winners experienced excruciating cases of loss aversion, and even many of the winners were disappointed by smaller rewards than they expected. Learning from this experience, Google has shifted from providing monetary rewards to experiential rewards such as gifts and dinners out.59
Culture, engagement, and retention of people are the foremost issues on the minds of business and HR leaders. Behavioral economics provides a new and powerful set of principles to help us improve the slippery and complex process of hiring and promoting the right people, motivating our teams, and driving superior performance.
Performance management should be a big area of focus: Research shows that simply rating people reduces their self-esteem and creates animosity and competition among peers.60 Amabile found that manager evaluation of subordinates (characteristic of traditional performance evaluation and rank-and-yank) can crowd out creativity, since people hesitate to suggest unusual ideas for fear of a harsh evaluation. Yet absence of feedback also causes problems—it makes people feel uncertain and nervous. The right balance is work-relevant feedback that is frequent, informative, and constructive. Some organizations are beginning to experiment with new approaches such as a frequent “check-in” process.61
So for example, a large health care company is simultaneously A/B testing four different forms of performance evaluation: One group is using the traditional rating and ranking process, the second is using a simplified rating process, the third is using no ratings at all, and the fourth group is letting people rate each other. After six months, managers plan to look at how each group performed, to see which one delivered the highest output measured by employee performance, engagement, retention, and learning.
Consider one of the biggest issues businesses face today: corporate scandals and the increasing need for regulation in almost every industry. Research by Deloitte Australia suggests that threatening more draconian punishment actually results in lower compliance than does building a “culture of compliance.”62 Other research shows that “toxic employees” (that is, those who steal or cheat) are infectious and create patterns of bad behavior among those who sit nearby.63 Rather than punish or threaten people to comply, we have to build a culture of “doing the right thing”—driving the intrinsic motivation to “be good” and model the behavior of others.
HR professionals—the architects and designers of the people and management processes by which we work—play a crucial role in their organizations. By applying the proven principles of behavioral economics to rethink programs, HR can make them simpler, more effective, more scientific, more economically efficient—and more “human.”
Marketing science, another domain concerned with human behavior, has taken this lesson to heart: Online retailers use A/B testing to evaluate advertisements and optimize the placement of web pages, and even test offers, prices, colors, and fonts. Similarly, HR departments should be testing new ideas to see what policies, programs, and messages best motivate people. HR owns the choice architecture of the workplace and should design it carefully, iterating and using data wherever possible.
Both the US and British governments now have “nudge units” dedicated to the people-centric design of programs and policies, and the data-driven evaluation of what works.64 HR departments can do the same, perhaps even including their own behavioral insights teams led by a chief nudge officer.
Culture, engagement, and retention of people are the foremost issues on the minds of business and HR leaders.65 Behavioral economics provides a new and powerful set of principles to help us improve the slippery and complex process of hiring and promoting the right people, motivating our teams, and driving superior performance.
It might take more than a nudge to get there, but the goals are worth striving for.