Many of us love competition and, more important, winning. Competition drives us toward our goals and motivates us to improve our performance, while the prestige and power that come from winning can provide a powerful morale booster. What’s more, winning increases testosterone and dopamine hormones, which, in turn, increases our confidence and willingness to take risks, and thus our chances of further success.
At the same time, the need to win can blind us to ethical considerations. It’s a potential problem in all kinds of areas: colleagues who have a strong rivalry at work, managers who need to make their numbers for the quarter, even political parties that spend campaign funds to attract votes. A common theme in these situations is that there are only a few winning slots — and maybe just one — with massive stakes in terms of money, advancement, and fame.
What’s often driving this fierce competition is the knowledge that our performance is being assessed not in absolute terms but in comparison with others’. In the workplace, such “rank-and-yank” methods — also known as the vitality curve, forced rankings, and stacking systems — are regularly used to judge performance, whereby, say, the top 20% of employees are categorized as high performers and the bottom 10% face redundancy. Similarly, the bell-curve grading in an MBA classroom ensures that students are categorized and graded relative to peers, without considering their overall performance.
In our research, recently published in the journal Human Resource Management, we found that performance evaluation schemes based on peer comparison can encourage unethical behavior. In one study, we asked 164 MBA students to read a hypothetical scenario (based on a true story) about an investment banker facing an ethical dilemma, and to estimate the likelihood that this banker would indulge in unethical behavior. The students were randomly assigned to three conditions for how the banker would be paid: a fixed salary with no bonus; a fixed salary with a bonus tied to the banker’s number of trades; and a fixed salary with a bonus tied to the banker’s performance relative to his peers. (For more details of this study and the ones below, see the sidebar “Our Studies.”) Our results showed that the students in the relative performance condition expected the banker to be more likely to behave in an unethical manner.
We asked 164 MBA students to (1) read a hypothetical scenario about an investment banker, Sam, who faced an ethical dilemma and (2) estimate the likelihood that he would indulge in unethical behavior. The scenario was motivated by the true story of an investment banker whose trading practices ultimately drove his bank to insolvency. According to the scenario, Sam was one of the key traders for his bank’s recently launched operations in Singapore. He had a successful trading career at the bank’s London operations: In the past two years his trades made millions, accounting for 8% of the bank’s annual profit. The bank had hired 10 other traders in its Singapore office, all of whom handled independent accounts without interfering or knowing much about the others’ work. Recently, the scenario continued, Sam had noticed that he had a big trading loss on one of the accounts, costing his bank $100,000. Sam was thinking about what he should do, as performance appraisals were coming soon. Now, Sam also managed the bank’s error account. Most banks have an account like this, which is used to account for genuine trading mistakes. Sam could use the error account to hide his losses without the knowledge of the bank. Of course, this is illegal and unethical.
Participants were randomly assigned to one of the three conditions that differed in the performance management system applied to Sam: control (a fixed salary of $300,000 with no additional bonus possibilities), absolute (a fixed salary of $300,000 with additional bonus related to the total profits from his trades), and relative (a fixed salary of $300,000 with additional bonus based on his performance as compared with the other traders’). We found that the average likelihood of using the error account in the relative performance condition was significantly higher than that in the absolute and the control conditions. Our results showed that the participants under relative performance evaluation expected the banker to be more likely to behave in an unethical manner.
We investigated people’s ethical behavior in self-reporting their performance. We invited 160 participants of U.S. origin on Amazon’s Mechanical Turk online platform to participate in a 10-question IQ quiz. They were asked to self-verify their answers and report their score to us. Again, participants were randomly assigned to one of the three groups that differed in their evaluation and compensation schemes: control, whereby all participants were given a fixed participation fee of 10 cents irrespective of their performance; absolute, with participants having a bonus possibility based on the number of correct answers they reported; and relative, where only the top scorers were to be rewarded with a bonus. Specifically, in the absolute condition, participants were informed that of the approximately 50 people who were participating, 10 of them would be randomly selected and we would pay an additional 10 cents for every point they scored. In the relative condition, participants were informed that of the approximately 50 people who were participating, at the end of the study we would award $1 to the 10 highest scorers based on their final scores. We deliberately kept the monetary incentives close to zero in order to study the effects of evaluation and comparisons instead of money and rewards.
The results surprised us. Participants averaged 3.39 correct answers (out of 10 questions) with no significant differences between the three experimental conditions. However, most participants — 85.6% (137 out of 160) of our sample — overreported their performance. Moreover, both the incidence and magnitude of overreporting was higher in the relative performance condition than in the other two conditions. 100% (56 out of 56) of participants in the relative performance condition overreported their performance, which was significantly greater than the 86% (44 out of 51) in the absolute performance condition and the 70% (37 out of 53) in the control condition. The self-reported score in the relative performance condition was also significantly greater than in the absolute performance condition, as well as in the control condition. In short, the competitive pressure and comparison seemed to encourage rule breaking.
Again on Mechanical Turk, we invited 184 participants of U.S. origin to participate in a decision-making scenario. Participants assumed the role of a university professor who is close to tenure evaluation and is being considered for nomination to a prestigious national congress. The professor has a manuscript under review with a top journal, and its publication is key to both the tenure and nomination decisions. The data analysis for the manuscript had not provided desirable results and the professor is tempted to manipulate the data. Participants were asked to provide their likelihood of manipulating data on a scale of 0 (not at all) to 100 (certainly). They were randomly assigned to one of two conditions: control and consequential reflection. The only difference between the conditions was that participants in the consequential reflection condition were asked to list possible consequences (both positive and negative) of their decision before providing their likelihood judgment. We found that the average likelihood of data manipulation in the consequential reflection condition was significantly lower than in the control condition. We replicated our findings with another study, conducted with 142 MBA students who, instead of assuming the role of the professor, were asked to assess the likelihood that the academic would indulge in such data manipulation.
Across three additional experiments, we found that taking a moment to reflect helped to put short-term benefits and long-term potential losses into perspective. For example, in the consequential reflection study described above, after providing their likelihood judgment, all participants were asked to rate the magnitude of both the perceived risks and the perceived benefits involved in the situation they faced, using a scale of 0 (low) to 100 (high). For each participant, we combined these assessments to construct an assessment index. Our results showed that participants in the consequential reflection condition perceived significantly higher risks vis-à-vis benefits than those in the control condition.
In another study, we investigated people’s ethical behavior in self-reporting their performance. Using Amazon’s Mechanical Turk platform, we invited 160 participants of U.S. origin to participate in a 10-question IQ quiz. They were asked to self-verify their answers and report their scores to us. Again, participants were randomly assigned to one of three compensation groups: a fixed participation fee of 10 cents, irrespective of performance; a fixed fee with a bonus based on the number of correct answers they reported; and a fixed fee with a bonus for only the top scorers. The results surprised us. The groups didn’t differ much in performance, and most participants overreported their scores. But both the incidence and the magnitude of overreporting was highest in the third group, the one in which only top performers received a bonus. Notably, every single person in the group overreported their score. In short, the competitive pressure and the comparisons encouraged rule breaking.
Organizations continue to experiment with and debate the pros and cons of comparison-based performance management systems. In recent years, for example, Yahoo endorsed them, while Microsoft abandoned them. One thing is clear, though: Relative comparisons are widespread and here to stay. Given that, what can be done to limit possible temptations of ethical breaches that accompany such competitive comparative settings?
We propose a subtle and simple intervention we call consequential reflection: prompt individuals to reflect on the positive and negative consequences of their decisions. In another study of ours, participants who took a moment to think and write down such possible consequences were less willing to act unethically. Again on Mechanical Turk, we invited 184 participants of U.S. origin to participate in a decision-making scenario. Participants assumed the role of a university professor, close to tenure evaluation, who had a manuscript under review with a top journal. The data analysis for the manuscript had not provided desirable results, and as a result the professor was tempted to manipulate the data. Participants were asked how likely it was that they would manipulate the data, with some participants being prompted to consider the consequences. We found that those participants were significantly less likely to take unethical action.
Why would this kind of prompt be effective? Research on the human mind tells us we run on autopilot much of the time. The pressures of our jobs mean we often don’t take time to pause and reflect. Therefore, our intuitive, habitual behaviors take over. In matters of ethics, this can lead to a self-centered, “me-first” attitude, focused on the immediate benefits for ourselves and ignoring the long-term consequences of ethical lapses.
To put this idea into practice, we propose that leaders try the following:
Conduct pre-mortems. Ask employees and teams to regularly stop and reflect before making crucial ethically charged decisions. Instead of diagnosing decisions after the fact, take the time to think about their positive and negative consequences early on.
Organize ethics hackathons. On a regular basis, get team members together to share upcoming decisions. Let peers dissect them, play devil’s advocate, and raise possible issues with various stakeholders.
Train for reflection. Encourage employees to embrace a reflective, mindful approach to decision making. Training sessions on mindfulness can be beneficial for helping employees to slow down and think critically.
Make ethics part of culture. Include consequential reflection in values statements and culture guidelines in your organization. Reminders such as “Think first” and “Seek opinions” can be placed prominently in offices.
We believe the strengths of our intervention are that it’s effective, cheap and easy to implement, and unlikely to provoke strong objections from people. As our research shows, simple psychological interventions can be a valuable part of an organization’s tool kit for creating an ethical culture.
A new generation of philanthropists, whose wealth was created via entrepreneurship in technology-driven fields, has the unique opportunity to make a real difference in speeding the pace of progress in the fight against cancer. Not content with having hospital pavilions named for them or with giving large, open-ended gifts for academic research, they want to use their wealth to have a direct and visible impact on patients’ health. Research we have conducted has revealed a variety of new, highly impactful investment approaches that can help accelerate the pace of the development, approval, and commercialization of new cancer therapies. By embracing these new approaches this new generation of philanthropists has the opportunity to truly help cure cancer.
The results-oriented attitude of the new generation of philanthropists couldn’t have come at a better time. Rapid advances in precision medicine and immunotherapy are ushering in a new era in the treatment and cure of many cancers. And new approaches to philanthropy, often termed impact investing, have emerged as a path to meet their goals. As part of our work with the Harvard Business School-Kraft Precision Medicine Accelerator, funded by a $20 million gift from the Robert and Myra Kraft Family Foundation, we have been studying these approaches. It is our belief that they have the potential to dramatically speed the pace at which more and more cancers are either cured or become chronic, rather than deadly, conditions.
Three big ideas underlie these new approaches: precision medicine, disease-focused investing, and investing at scale. Precision medicine refers to delivering the right medicine to the right patient, at the right time, and in the right sequence. It can only be realized when the scientific understanding of a particular cancer includes knowledge of the genetic and molecular aberrations that that are causing the disease. Once the science reaches this point, the chances of creating a disease-modifying therapy go way up. To illustrate, 10 years ago personalized medicines accounted for less than 10% of the U.S. Food and Drug Administration’s drug approvals. By 2017, that number had increased to 34% and is heading to over 40% this year.
The improved odds of success in drug discovery are providing new opportunities for donors to back what has become known as venture philanthropy. In this approach, drug discovery is developed around a specific disease and is financed by the efforts of a disease-focused foundation. For example, it was the venture philanthropy of the Cystic Fibrosis Foundation that allowed Vertex Pharmaceuticals to refine and test the drugs that have resulted in three FDA-approved treatments that enable 90% of CF patients to live symptom free. Because CF is a relatively rare disease, affecting roughly 70,000 people worldwide, pharmaceutical companies were unwilling to invest in potential cures. But that didn’t stop the Cystic Fibrosis Foundation which raised over $200 million specifically earmarked as venture philanthropy to back drug-discovery and clinical-trial efforts. As Josh Boger, the founder of Vertex, has stated, “Without Cystic Fibrosis Foundation funding, Vertex would not be in CF.”
This same approach offers an enormous opportunity in the cancer space. What is needed are many investments aimed at the different underlying causes of each specific cancer type. While this creates concentration risks, which are typically avoided by venture funds, they are precisely what disease foundations should be doing and where the new generation of philanthropists can make an enormous difference by taking on one particular type of cancer.
One timely example illustrates the point. Senator John McCain recently died from glioblastoma, a relatively rare but very deadly form of brain cancer. Ted Kennedy and Beau Biden, former Vice President Joe Biden’s son, died from the same disease. Treatments to cure or modify glioblastoma could come from a large, say $150 million, venture philanthropy fund whose only mission is to identify and fund start-up companies with a variety of approaches to conquering this disease. Developing such funds — be it in glioblastoma, ovarian cancer, or any of the other less-common cancers for which no effective treatments exist — is a unique opportunity for young and older philanthropists who want to see their dollars create cures.
While venture philanthropy funds represent a way to invest at scale in a particular cancer, larger funds are beginning to emerge that invest at much greater scale in a broader range of cancers. Andrew Lo, a finance professor at MIT, has been a trailblazer in this area. Armed with numerous simulations, Lo has argued that a large megafund of investments in cancer companies could not only help find cures but also produce more predictable returns for investors.
An illustration of this concept comes from the UBS Oncology Impact Fund which raised $471 million in 2016 to invest solely in ventures that would “accelerate the development of new cures” from investors who had to commit a minimum of $500,000, an amount within reach of UBS’s private wealth clients, many of whom are looking for investments that have social impact. UBS’s role was to market the fund to its private wealth clientele. The selection of investments and nurturing of new ventures is handled exclusively by the highly respected and experienced venture capital firm MPM, which has a track record of achieving high returns in the cancer space. We believe the success of the fund represents a model that others could emulate or build upon to attract large amounts of new capital to the cancer space in either general funds as with UBS-MPM or large focused funds focused on say immunotherapies or data analytic start-ups.
Curing cancer will require brilliant science and lots of investments dollars. It is our hope that the new generation of philanthropists, with their entrepreneurial and results-oriented approach, will lead the way in having their philanthropy and investment make a real difference in halting the onslaught of this devastating disease.
Today, Explained Podcast, Nov. 30 Episode (Humans 2.0)
Babylon Berlin (Netflix)
“The Prison Inside Me” (Reuters)
Robert Stavins (follow on Twitter)
FRED (Federal Reserve Economic Data)
RBG (Documentary on Amazon Video)
The Man in the High Castle (Amazon Video)
The Ringer website
Janesville (Amy Goldstein)
Small Fry (Lisa Brennan-Jobs)
You can email your comments and ideas for future episodes to: firstname.lastname@example.org. You can follow Youngme and Mihir on Twitter at: @YoungmeMoon and @DesaiMihirA.
HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.
As my company grew, I’d periodically be accused of making decisions slowly. I couldn’t figure out why, until one day it dawned on me: While employees thought they were asking me to weigh in on a choice that had to be made, I thought they were making small talk at the coffee machine. It wasn’t always clear that I’d been asked to decide anything. I simply figured I’d been listening to colorful commentary about issues that team members were working to resolve.
When a business is growing fast, decisions can get lost in the fray — especially if it’s unclear that a decision even needs to be made. The result, for our company, was that initiatives would stall without my input.
That got me thinking about the most effective ways for people to present their ideas and get the responses they need to either take action or go back to the drawing board. Clarity about how to communicate and what strategies to employ gives people a better chance of getting approval for their recommendations.
Research, Then Tailor Your Message
There is a process for how actionable recommendations evolve. You explore data and find a problem or opportunity. You craft a well-structured recommendation. Then, once it’s approved, you lead by influencing others to act on it. (See “Progression of a Recommendation.”)
Progression of a Recommendation
When you’re making a recommendation, keep in mind your final goal: to make change and get people to act.
There are four audiences to whom people in the workplace bring recommendations: those who approve a recommendation (a manager or top executive) and those who execute a recommendation (peers or a broader audience). It’s important to understand the different ways to speak to each group. (See “Making a Recommendation? Understand Your Audience.”)
Sell the Deciders
After identifying a problem or opportunity, your first audience is the person or team who has the authority to approve your recommendation. Your job is to shape your message.
To sell your manager, provide lots of detail. To get a recommendation approved, you’ll inevitably be communicating up. Your manager may be the one to make the decision, or she or he might sponsor the idea to your executive suite. In either case, you need to show that you’ve done your homework. Many times you’ll want to prepare a document that outlines the problem or opportunity, builds your case for action, and includes a comprehensive appendix with research and other material that supports your decision. We use (and give away for free) a presentation tool we developed called Slidedocs, which is designed for creating something to be read and referenced instead of projected. It’s a resource that makes it easy to blend text and visuals and gives you the space to shape your idea. Whatever presentation software you use, your manager must feel confident that you’re well-informed and that the idea is defensible ― because both of your reputations will be on the line.
To get a decision from an executive, provide a clear recommendation. For a C-suite audience, you need to get to the point quickly by crafting a recommendation with a sound structure and skimmable content. Many of my CEO friends spend one full day per month listening to and approving or declining recommendations as a key part of their executive function. Some executives give a half-hour time slot to each idea, but if you’re scheduled for 30 minutes, formally prepare only a solid 10 minutes — because you will get interrupted. Lots of senior executives can swiftly “see” most of the idea, and they will cut in either to gain clarity on the bits they cannot see or ask what you think they need to do. If you want to show visuals, keep your executive’s pace in mind and have an interactive table of contents so you can easily jump around your deck and appendix to show supporting evidence.
Here’s an example of how this can work effectively: At my company, we created an extended leadership team made up of 12 executives who are next in line to manage and drive change. At an off-site meeting, we asked the team to make recommendations about where the company should head and how we should get there. The 10-minute presentation they came up with was stunning.
The first part proposed that we operate more like a hospital, with a diagnosis center and specialists with distinct skills to solve client problems. This would address a pain point of senior team members feeling fatigued from having to be all things to all clients ― the metaphor they used was that they felt like they ran all over the floors of this hospital. The second part spelled out six structured recommendations with clear actions that would get us to a different kind of organization.
The team nailed it.
Engage the Implementors
Once your recommendation is approved, you start the harder part. Every recommendation requires a group of people to act, whether it’s peers in your department or a broader audience if the recommendation is company-wide or client-based. You need different persuasion techniques for each group.
To get your peers on board, speak your geek. When explaining a new project to people in your department, you already have a common language. The people closest to you organizationally may already understand why you’re making the recommendation and how it may play out; there’s a high probability they helped you craft the recommendation. With them, you can refer to the shared visual and verbal shorthand you use on a day-to-day basis. Acronyms, departmental verbiage, and complex charts are all OK, as long as your visual and verbal density is familiar to all involved.
To spread enthusiasm to a wider group, add emotional arguments. If your recommendation has corporate-wide appeal and becomes a key organizational initiative, or if it’s an idea that you’re taking to a client or an external partner, you’ll need to rally a larger group of people. This requires you to modify your day-to-day way of communicating and use a more empathetic approach, especially since those affected by the decision may resist change. Adding a layer of emotional appeal will help your audience understand why something needs to get done and find meaning in doing it. Consider how close your audience is to adopting your idea and create content with the appropriate amount of emotional appeal to reach them where they are.
We worked with a client, a vice president of human resources at a Fortune 50 retail company, who had this challenge. She was new to the company and had to give a hard speech to 3,000 people at the organization, telling them that they needed to change how they operated with the business in flux. We urged her to find common ground by talking to her audience about their shared experiences of their first days at the company. They all had felt the pride and excitement of working at a company their customers love. This was the feeling they would continue to aspire to, even as the company underwent difficult transformations.
Making a Recommendation? Understand Your Audience
Always consider what your audience needs to hear. When your audience changes, so should the language you use to make yourself understood.
Leaders: Clarify the Process
Communicating clearly what is being recommended, and why, will get to decisions faster. Those who are seeking an answer should not hesitate to be unambiguous when they need a decision — and acknowledge what the impact will be if they don’t get one.
With hindsight, I can see that part of the reason I made decisions slowly is because my company didn’t have a formal recommendation process. Employees didn’t know how decisions were made or when a decision was approved. Leaders can change this from the top. They can make it clear how employees should propose recommendations and be transparent about when a major decision is made and when it’s rejected. Having a well-defined approval cycle for recommendations will add metaphorical grease to the decision-making gears, rendering the process smoother and more efficient.
Hopes are high for Africa’s long-term growth, driven by technology and basic services rather than resources. Survey respondents in Africa report more optimism—and better business practices—than peers elsewhere.