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Diversity recruiting

Human Resources

Recruiting? Random selection can help you tap into diversity dividend

Published 29 September 2021 in Human Resources • 9 min read • Audio availableAudio available

When it comes to hiring, blind luck can trump biased reasoning and generate a less-is-more effect.

Recruiting a diverse workforce does not necessarily improve an organization’s performance. Diversity may generate a performance bonus when a task is so complex that no individual expert can be expected to master all the relevant knowledge, experience and perspectives needed to address the task. For less complex tasks, however, such as difficult mathematical problems, one expert can easily beat a crowd, regardless of how diverse it is.

The logic of generating a diversity bonus – the way organizations can improve performance by harnessing the power of differences in the way people think – seems straightforward, but can be difficult to put into practice. Organizations facing complex problems tend to put a misplaced belief in meritocracy – hiring the “best”. If a complex problem is like a jigsaw puzzle, the “best” may provide more pieces but unlikely all of them, because the “best” tend to be good in a homogeneous way. In addition, organizations tend to mistake identity diversity for cognitive diversity. Recruiting a demographically diverse team does not necessarily generate non-overlapping cognitive repertoires, which are essential to generating a diversity bonus. These biases imply that a performance bonus from recruiting a diverse workforce may exist as potential but can often be left unexploited. 

My research shows that random selection is an undervalued tool for overcoming several barriers to capturing the diversity bonus. Random selection cannot guarantee optimal hiring, when needing to locate additional team members who have exactly the missing pieces to the puzzle. But random selection can improve the baseline outcome of biased hiring decisions, such as those polluted by the meritocracy bias, nepotism, homophily, or stereotypes. Randomness may help organizations uncover more missing pieces by revealing how self-confirming processes may have blinded them from finding these pieces. Because random selection means deciding based on no reason, blind luck can trump biased reasoning and generate a less-is-more effect: one has less control over outcomes but achieves more by saving time and resources, as well as detecting and sanitizing biased decisions.

“If a complex problem is like a jigsaw puzzle, the “best” may provide more pieces but unlikely all of them”

In particular, random selection can capture the diversity bonus by addressing: (1) the paradox of merit, by avoiding fruitless deliberation; (2) biased reasoning, by deciding on the basis of no reason; (3) learning traps, by uncovering self-confirming false beliefs.

The paradox of merit, by avoiding unnecessary deliberation

Random selection cannot beat a decision based on good reasons. But is it always worthwhile to find a reason to justify why a hire is optimal. Suppose that several “good enough” candidates exist who have irreducible trade-offs, rather than one optimal hire. Spending more time and resources to judge these trade-offs may not significantly enhance decision quality but entail substantial opportunity costs: the hiring committee could have spent their time on other recruitment opportunities that would make a bigger difference. 

Random selections can help organizations avoid fruitless deliberation, particularly in the later stages of recruitment. For example, shortlisted candidates are, by definition, not bad options. How much deliberation by the committee is needed to select the best one from the shortlisted? Research on the paradox of merit suggests that possible improvement from deliberation at a later stage of selection is likely trivial. A paradox of merit occurs whenever the same selection criteria (for example, having a college degree or not; publishing a certain number of academic papers; reaching a sales target) are applied to all candidates. The implication is that the “survivors” – those who passed multiple rounds of selections, such as the candidates in the final round – are all very competent but the differences among them are very small and increasingly indistinguishable from those selected against. 

Random selection helps to attenuate the challenges generated by the paradox of merit; that is, the decreasing effectiveness of deliberate selection over time. Random selection can save organizations from wasting time in endless meetings in the later stage of recruitment, trying to choose the best from an already good-enough pool of candidates. Moreover, random selection may save firms from overspending on candidates who are not likely to provide more value to the firm than other candidates, and allows resource allocation to potentially more productive activities, such as R&D, while also potentially attenuating inequality. This may be particularly relevant when the board determining executive pay is dominated by conservatives, who tend to over-attribute superior performance to the individual and often over-pay the chief executives they have carefully chosen. Such excessive pay cannot be justified if those chosen are at best marginally better than the rest.

We live in a world characterized by uncertainty, limited foresight, and controllability. Insisting on deciding by reason can lead to an illusion of control when many factors are actually beyond our control

Biased reasoning, by deciding on the basis of no reason

Random selection can also help organizations to address biases in the recruitment and evaluation process that go beyond gender and racial discriminations, such as the “not invented here” syndrome, the discounting of the ideas of others that are thematically close to one’s own work, the overvaluing of one’s own ideas, the undervaluing of colleagues’ ideas owing to turf wars or competition for resources, and to be fooled by exceptional luck. These patterns are predictably irrational and random selections can help sanitize these biases.

Among the biases, the meritocracy bias is often overlooked but in fact fuels the problems generated by the paradox of merit. Consider an organization that is hiring an additional member for its top management team to address a complex task. Whom should the organization recruit? According to the logic of generating a diversity bonus, the organization should first evaluate the nature of the task — that is, what types of knowledge, tools, or experiences are essential to address this task. Next, the organization should recruit additional team members with cognitive resources that match the task requirements and do not overlap with those of existing members.

However, there is a “no test exists” rule when assembling a diverse team: “no test applied to individuals will be guaranteed to produce the most creative groups.” Complex tasks require a cognitively diverse team, but the team’s cognitive diversity cannot be recognized in isolation or ex ante (for example, through a test with objective criteria); rather, it has to be identified along with the team composition and expansion.

Rather than appreciating the “no test exists” rule and hiring team members sequentially, organizations often believe that they can solve complex problems by recruiting the “best individuals” based on objective criteria. This belief not only wastes time and resources in finding a candidate who is, at best, marginally better than the rest, but also a worse candidate under the logic of generating a diversity bonus in teams.

To capture the diversity bonus, organizations should avoid the best and instead engage the luck of the draw among the rest. That is, for later rounds of selection, the best candidates should be dismissed because they are decreasingly likely to provide additional diversity to an existing team. The rest of the candidates may appear worse, but their inferiority to the “best” signals useful differences that may contribute to the team’s cognitive repertoire.

One caveat is that useful diversity may already be weeded out if the selection is highly efficient, suggesting that all later-stage survivors fail to provide viable cognitive diversity to the existing team. Random selection at an earlier stage is perhaps desirable: once apparently destructive candidates are removed (soft screening or shortlisting), organizations should hire a randomly picked candidate from the pool. Note that the selected may be unqualified according to objective criteria. But this is exactly the occasion where the diversity bonus may be created: the candidate is more likely to have perspectives or experience lacked by the “elites” — excellent human resources selected based on objective criteria.   

Learning trap, by revealing flaws in practices

Some biases in organizations persist because they are protected by learning traps: actions based on these biased beliefs strengthen their illusory validity instead of revealing their flaws. Competitive advantage can be created by discovering how rivals are fooled by these learning traps and exploiting their biased beliefs. 

A generic approach is “contrarian experimentation”: using randomized experiments to test against conventional wisdom or common beliefs in an industry (e.g., adopting practice X will lead to high performance). Most of these experiments would fail, in the sense that common sense and best practices probably reflect the wisdom of the crowd, channeled by effective emulation and diffusion. Yet when the results show that the conventional wisdom is flawed (for instance, not following practice X in fact leads to higher performance), this creates an attractive opportunity. Firms that discover and strategize with such self-confirming learning traps are likely to enjoy competition-free growth, as their rivals will likely misattribute such unorthodox successes to luck instead of trying to eliminate the threat.

Consider how Capital One discovered a flaw in the credit-card-financing industry and turned it into an advantage. In this industry, it was often best practice to interview customers, with low interview scorers being rejected. The founder of Capital One, Richard Fairbank, instead decided to randomly accept customers with “good enough” credit scores, regardless of their interview scores. They found interview scores did not predict loan repayment, so were able to tap into a customer base ignored by rivals and benefit from the diversity bonus. Randomness revealed how traditional “best practices” may have blinded rival organizations to see the flaws in their process.

Importantly, incumbent credit-card-financing firms cannot identify that they were wrongly rejecting some viable applicants because these errors are invisible to them. Instead, the importance of interviews is self-confirming: the incumbents’ profitable customers are increasingly like to be stereotypical ones, unless they decouple interviews from evaluations, like Capital One did.

Structuring incentives to encourage contrarian experimentation is crucial to prohibit learning traps from emerging in a contrarian strategist’s own backyard. For example, Capital One rewarded ideas rather than seniority by giving bonuses and promotions to employees whose proposed ideas were proven to work in randomized experiments. This policy enabled Capital One to sustain a contrarian culture, expressed in the hundreds of experiments the firm ran every year, while its competitors remained trapped in the illusory validity of outdated best practices. It took rivals at least two years to overcome these limits and emulate Capital One’s business model, a delay that allowed Capital One to gain a strong position in the credit-card-financing industry with almost no competition.

Capital one
Capital One Headquarters office building

When to incorporate random selection in organizational design

The main advantage of random selection, as outlined above, is to save time and outperform decisions based on biased reasons. However, even when random selection is likely to trump biased decisions, this does not necessarily imply that an organizational designer should apply it when career concern is considered.  Organizational designers should evaluate whether important stakeholders understand the reasons for deciding on the basis of no reason. If important internal and external stakeholders do not appreciate the logic of random selection, a diversity bonus may not be realized (due to failure of inclusion) or be discounted even when realized (due to lack of legitimacy). Moreover, the designer will likely be held accountable for any low performance resulting from the luck-of-the-draw approach, even when the failure may simply be a matter of bad luck. 

Thus, applying random selection is only feasible when one’s stakeholders appreciate your strategy or when you are insensitive to the evaluations of others. Otherwise, the organizational designer should abandon this random approach and prepare for the worst-case scenarios that may result from biased decisions. 

This consideration perhaps suggests why random selection is rarely observed in modern management.  Since the Enlightenment, many have been convinced that human reasoning can overcome all challenges. Decision by lottery has been degraded to an irrational belief in luck or to giving up control to divinity, like our ancestors did. This belief can create a self-confirming false belief: true believers that insist on reason-driven decisions can never discover that reason-absent decisions can sometimes be more effective. 

But we live in a world characterized by uncertainty, limited foresight, and controllability. Insisting on deciding by reason can lead to an illusion of control when many factors are actually beyond our control. Random selection can have a less-is-more effect – deciding by luck of the draw is to exercise less control over outcomes but to achieve more by detecting and sanitizing biased reasons.


Chengwei Liu

Associate Professor of Strategy and Behavioral Science and faculty lead of the Global Online MBA Program at ESMT Berli

Chengwei Liu is Associate Professor of Strategy and Behavioral Science and faculty lead of the Global Online MBA Program at ESMT Berlin. His book Luck: A Key Idea for Business and Society summarizes his research on how to quantify and strategize with luck in sports, investment, and business, as well as its implications on judging merit and social inequality. His current research focuses on how organizations should manage diversity and redesign themselves in the age of algorithms and AI.


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