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How to Follow Up with People After a Conference

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Attending a conference is a whir of activity — flying to a destination, engaging in several days of nonstop networking, and coming home to an inbox that has spiraled out of control in your absence. Back at work, most of us immediately go into catch-up mode; the last thing on your mind is following up with the people you just met. That’s especially true if you’re an introvert and feel overtaxed by the whole process.

But a small amount of focused effort can reap long-term benefits and ensure the arduous days you spent connecting face-to-face weren’t wasted. Here’s a framework for structuring your post-conference follow up to maximize the chances that your new connections turn into meaningful professional relationships.

First, it’s important to set aside “processing time.” The conference has probably left you with business cards scattered in your briefcase, pockets, and travel bag. Unless you transfer them quickly into whatever database system you use, they’re likely to get lost quickly. The system doesn’t much matter; it’s personal preference whether you use a business card app or add them to a spreadsheet manually.

What matters is capturing the data (including writing down where you met them, so you don’t forget over time), and also making a list of people you spoke with whose cards you didn’t obtain. That may be a substantial number if the conference discourages card exchange (some conferences fear that people trading business cards will make the gathering appear too “salesy”), or if your encounter has been brief, such as a chat in the lunch line. After I spoke at the recent Global Peter Drucker Forum in Vienna, I took close to 45 minutes to go through the program booklet listing conference attendees and circling the names of those I had conversations with. I hadn’t exchanged contact information with most of them, but they were still connections worth maintaining.

Second, for each person you’ve written down, take a moment to identify your goal for that relationship. You can’t invest in all connections equally, of course — so where should you prioritize your time? You may want to discard some connections upfront — for instance, someone who came up to you, handed you their card, and immediately started pitching you to buy their product or service. It’s not worth subjecting yourself to that in the future.

But most new relationships will fall into three categories. Specifically, those are “miscellaneous interesting people,” with whom there’s not an obvious point of connection; people with whom you have a specific reason to follow up; and people you’d like to build a deeper relationship with.

Miscellaneous interesting people. At the Drucker Forum, for instance, I had a nice conversation with a woman who is an executive with the Port of Vienna. My work doesn’t generally overlap with hers, but I’d be glad to keep in touch because it’s always nice to know a diverse set of people. For instance, in the future, I could imagine a hypothetical situation in which I was hired to speak to a shipping company. Having a contact knowledgeable about industry trends would be valuable as a way of understanding what was important to the client. Similarly, there may be unexpected ways I could assist her in the future.

For connections like that, I apply an “ambient awareness” strategy and send a friend request on LinkedIn, so that we can stay in touch through that channel and she may periodically be exposed to my posts in her news feed, and vice versa. Note that it’s important to be aware of national preferences related to social channels. Immediately after Vienna, I headed to Moscow to teach an executive education program and discovered that most Russians don’t have LinkedIn accounts because the service is officially blocked there. I connected with those colleagues on Facebook or Instagram, instead.

A specific reason to follow up. For other conference attendees, my mission is clearer: they mentioned specific business opportunities (an invitation to speak at a university, give a talk for a large company, etc.). I make a point of emailing them them in a timely fashion — within a week is ideal — to remind them of their suggestion and request a follow-up call.

Building a deeper relationship. Finally, you’ll meet some people with whom you’d like to build a long-term connection. Their work may be extremely salient to yours (they’re a VC and you’re an executive coach that works with startups), or you may just have great personal rapport. Either way, you want to develop a strategy to turn a one-time encounter into something more meaningful, as I describe in my e-book, Stand Out Networking. If they live in your city, the options are more plentiful; you can invite them to join you at a future professional event, such as a Chamber of Commerce gathering, a tech meetup, etc., or to a hybrid business/social event (after meeting a theater executive at a conference and hitting it off, he invited me to join him a couple of weeks later at a Broadway show for which he had an extra ticket).

If you live in different cities, you’ll need to develop a more deliberate strategy. Perhaps there are future conferences coming up they might be likely to attend; you could get in touch to inquire if they’ll be there, and if so, plan to meet up in person during the event. If you’re a frequent business traveler, you can also put them on the list of people you ping when you’re in town for visits. Even if it’s unlikely you’ll meet in person again anytime soon, you can be on the lookout for interesting articles to send them, or look for ways to be helpful (for instance, if they mentioned they’re looking for new contributors for the magazine they edit, you could suggest talented colleagues).

Of course, it’s essential to make sure the help you offer is actually helpful; there’s a big difference between connecting an editor actively seeking contributors with great candidates, and connecting an overwhelmed editor with would-be columnists they don’t have the time to deal with. It’s essential to listen to their stated needs, not make assumptions about what might be useful and risk turning yourself into a burden in the process.

Almost every professional attends at least a few conferences per year. By following these strategies, you can make sure the time, effort, and money you spend on them actually turns into true relationships, not just one-time conversations that are quickly forgotten.

AI in 2019: The Good, The Bad — And the Unknown - SPONSOR CONTENT FROM PWC

Twenty percent of executives at U.S. companies with artificial intelligence initiatives report that they will roll out AI across their business this year, according to PwC’s 2019 AI Predictions report. These companies expect their AI investment, which is often part of intelligent automation initiatives, to go beyond improving productivity and cutting costs. They see AI as a path to growing profits and revenue in 2019.

However, the executives in the survey said that there are challenges, such as training employees to use AI systems, and security threats remain a concern. Success in leveraging AI will be built on strategies for the organization and the workforce, for creating responsible AI and AI-ready data, for reinventing the business, and for integrating AI with other technologies.

For more practical steps to deliver on your 2019 AI priorities, click here.

 

When a Leader Is Causing Conflict, Start by Asking Why

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Not long ago, I received a call from an HR manager at a large corporation seeking an executive coach for one of their senior leaders. He was described as arrogant, tactlessly blunt, and lacking empathy. Despite his challenges, all of which hadn’t improved much despite several previous coaching interventions, the company hadn’t fired him because he was considered one of the industry’s most brilliant engineers, responsible for several of the firm’s most profitable patents. The company simply couldn’t afford to let him go.

How do you coach a leader whom others think is a hopeless case? Sometimes you can’t. The person may well turn out to be a jerk who won’t change their toxic ways. In that case, the company needs to fire the individual. Tolerating destructive behavior will send the signal that it’s ok to mistreat others as long as you get results. But, often, as was the case with my client, the leader who everyone thinks is hopeless is simply being misunderstood and their behavior misdiagnosed.

Whether you are a coach, an HR leader, or an executive trying to help a challenging subordinate, your credibility, and that of the leader you’re trying to help, depends on an accurate understanding of what’s actually going on. Here are three ways you can be sure you’re addressing the right problem with a challenging leader in the right way.

Manage your assumptions and judgements. Without realizing it, those of us in advisory roles often bring our own issues to our work helping others. We make assumptions and judgements based on our own experiences that often have little to do with the leader we’re trying to support. Before I even met this leader, I found myself feeling anxious, dismissive, and judgmental toward him based on what others had said. I imagined how I would respond to his insulting behavior and what I would say if he made an arrogant comment. But my defenses were unwarranted and my assumption that he was a jerk proved wrong. He was engaging, open to learning, and willing to accept his need to improve. When I asked him why he thought he was so harsh toward others, he seemed stumped and genuinely troubled by how others had characterized him.

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I’d heard from the company’s HR manager that this executive was especially cruel toward one colleague. Why had he singled out one person to treat in a uniquely nasty way? As we explored this, it became clear that something about the younger engineer triggered the executive’s anger and it eventually clicked: The young engineer reminded him of his older brother, with whom he had a contentious relationship. My client was raised in an excessively achievement-oriented family, that prized blunt candor over tact, and he was regularly sent the message that he was inferior. His brother had been the family’s golden child while he was never good enough. This direct report was a daily reminder of that pain. This back story in no way excused his behavior, but it did explain it. More importantly, it revealed a path forward toward changing it. But I had to set aside my biases and prejudgments to build the trust necessary to access these important insights.

Look past symptoms to contradictions. Determining what lies beneath seemingly destructive behavior requires looking beyond symptoms. My client’s colleagues had described him as mean and insensitive. His previous coaches had focused on various interpersonal techniques, like how to give constructive feedback, work with different personality styles, and delegate effectively. But they’d neglected to probe into the dynamic with that one engineer. To thoroughly diagnose a leader’s behavior, look for breaks in patterns. Are there people this person works especially well or poorly with? Specific circumstances in which they shine or falter? No one is the same all the time, so understanding where people deviate from predictable habits can isolate important clues. In my client’s case, his unique contempt toward one colleague was an important data point. Further, I learned later that his widely regarded technical expertise coupled with his family background made him feel anxiously responsible for the company’s technical reputation. His team members experienced this as micromanagement and dismissive of their expertise. If we’d focused on those symptoms, we wouldn’t have gotten very far. We needed to understand the root cause. It’s not uncommon to inaccurately diagnosis bad leadership behavior. One Arizona State University study found that toxic leadership pathologies are often confused with behaviors that might fall into a normal range of pathology. To avoid confusing common leadership shortfalls with serious pathologies, it’s critical to dig deeper behind symptoms.

Have a broad repertoire of solutions. For many in advisory roles, their diagnostic lens is narrowed to problems they are best equipped to solve. Every hammer looks like a nail, as the saying goes. For example, I’ve seen some consultants whose specialty was team building, so it was no surprise that their findings and recommendations were all around improving team trust. Leadership coaches use their favorite personality instruments to solve everything from poor financial performance to low morale. It’s important to be open-minded to solutions that fall outside your expertise. Ineffective leadership behavior can originate from deep-seated pathologies to problems with organizational culture. Having a repertoire of tools and approaches helps avoid the dangers of applying a one-size-fits-all solution to all situations. And don’t be afraid to refer people to others who have different expertise that may be able to better help your clients with particular issues. In the case of my client, I recommended he also see a therapist to work on his anxiety and unresolved family issues. He and I worked on more effective ways to engage, teach, and empower his team, and how to recognize when his triggers were getting in the way of doing so.

Consistent scholarly research suggests when it comes to empirically measuring the effectiveness of those advising leaders, we fall far short. Mislabeling behavior or a person as beyond help is one way we fail leaders. If you don’t look for contradictions, get to the root cause, and have a range of solutions, you could unwittingly limit someone’s growth or, even worse, derail their career. But if you do those things, with an open mind, you may be able to help save the job of a valuable leader who might otherwise have been let go, and in turn, provide great value to those you serve.

Using AI to Improve Electronic Health Records

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Electronic health record systems for large, integrated healthcare delivery networks today are often viewed as monolithic, inflexible, difficult to use and costly to configure. They are almost always obtained from commercial vendors and require considerable time, money, and consulting assistance to implement, support and optimize.

The most popular systems are often built around older underlying technologies, and it often shows in their ease of use. Many healthcare providers (including the surgeon and author Atul Gawande) find these systems complex and difficult to navigate, and it is rare that the EHR system is a good fit with their preferred care delivery processes.

As delivery networks grow and deploy broad enterprise EHR platforms, the challenge of making them help rather than hinder clinicians is increasing. Clinicians’ knowledge extends far beyond their clinical domain — care procedure knowledge, patient context knowledge, administrative process knowledge — and it’s rare that EHRs can capture all of it efficiently or make it easily available. What’s more, in the U.S., regulatory, billing and revenue cycle requirements add additional complexity to the electronic healthcare workflow and further reduce the time clinicians have to engage with patients.

The options for improving this misalignment between systems and processes are limited. One is to design EHR systems to be more integrated and streamlined from the beginning. One Medical, for example, a concierge medical practice across 40 cities in the U.S., developed its own EHR system that is closely aligned with the care and patient relationship practices it employs. Flatiron Health, a data and analytics-driven cancer care service recently acquired by Roche, bought a company with a web-based EHR and tailored it to fit its OncoCloud EHR for community-based oncology. Although these bespoke systems do seem to fit clinician workflows better, they are themselves difficult and time-consuming to develop (One Medical required ten years to build its system) and they are relatively narrow in scope. Building a system from scratch or extensively customizing a commercial one would probably not work for large delivery networks.

Using an open source EHR is a second option. However, most current ones are designed for small medical practices and aren’t easily scalable or need substantial configuration. And even though the software is free, considerable programming and IT infrastructure is required to implement it and tailor it to the individual practice. Further, open source EHRs are less carefully maintained and less frequently updated than commercial ones and so can quickly become obsolete. Finally, regulatory requirements and reimbursement rules change rapidly. Relying on either open source or internally developed systems in keeping up with those requirements creates both compliance risks and financial challenges.

A third and more promising option is to use AI to make existing EHR systems more flexible and intelligent. Some delivery networks, sometimes in collaboration with their EHR platform vendor, are making strides in this direction. AI capabilities for EHRs are currently relatively narrow but we can expect them to rapidly improve. They include:

Data extraction from free text Providers can already extract data from faxes at OneMedical, or by using Athena Health’s EHR. Flatiron Health’s human “abstractors” review provider notes and pull out structured data, using AI to help them recognize key terms and uncover insights, increasing their productivity. Amazon Web Services recently announced a cloud-based service that uses AI to extract and index data from clinical notes.

Diagnostic and/or predictive algorithms Google is collaborating with delivery networks to build prediction models from big data to warn clinicians of high risk conditions such as sepsis and heart failure. Google, Enlitic, and a variety of other startups are developing AI-derived image interpretation algorithms. Jvion offers a “clinical success machine” that identifies patients most at risk as well as those most likely to respond to treatment protocols. Each of these could be integrated into EHRs to provide decision support.

Clinical documentation and data entry Capturing clinical notes with natural language processing allows clinicians to focus on their patients rather than keyboards and screens. Nuance offers AI-supported tools that integrate with commercial EHRs to support data collection and clinical note composition.

Clinical decision support  Decision support, which recommends treatment strategies, was generic and rule-based in the past. Machine-learning solutions are emerging today from vendors including IBM Watson, Change Healthcare, AllScripts that learn based on new data and enable more personalized care.

While AI is being applied in EHR systems principally to improve data discovery and extraction and personalize treatment recommendations, it has great potential to make EHRs more user friendly. This is a critical goal, as EHRs are complicated and hard to use and are often cited as contributing to clinician burnout. Today, customizing EHRs to make them easier for clinicians is largely a manual process, and the systems’ rigidity is a real obstacle to improvement. AI, and machine learning specifically, could help EHRs continuously adapt to users’ preferences, improving both clinical outcomes and clinicians’ quality of life.

However, all of these capabilities need to be tightly integrated with EHRs to be effective. Most current AI options are “encapsulated” as standalone offerings and don’t provide as much value as integrated ones, and require time-pressed physicians to learn how to use new interfaces. But mainstream EHR vendors are beginning to add AI capabilities to make their systems easier to use. Firms like Epic, Cerner, Allscripts, and Athena are adding capabilities like natural language processing, machine learning for clinical decision support, integration with telehealth technologies and automated imaging analysis. This will provide integrated interfaces, access to data held within the systems, and multiple other benefits — though it will probably happen slowly.

Future EHRs should also be developed with the integration of telehealth technologies in mind (as is the EHR at One Medical). As healthcare costs rise and new healthcare delivery methods are tested, home devices such as glucometers or blood pressure cuffs that automatically measure and send results from the patient’s home to the EHR are gaining momentum. Some companies even have more advanced devices such as the smart t-shirts of Hexoskin, which can measure several cardiovascular metrics and are being used in clinical studies and at-home disease monitoring. Electronic patient reported outcomes and personal health records are also being leveraged more and more as providers emphasize the importance of patient centered care and self disease management; all of these data sources are most useful when they can be integrated into the existing EHR.

Most delivery networks will probably want to use a hybrid strategy — waiting for vendors to produce AI capabilities in some areas and relying on third party or in-house development for AI offerings that improve patient care and the work lives of providers. Starting from scratch, however, is probably not an option for them. However necessary and desirable, it seems likely that the transition to dramatically better and smarter EHRs will require many years to be fully realized.

The Student Debt Crisis, and the FIRE Movement

Youngme Moon, Felix Oberholzer-Gee, and Mihir Desai discuss staggering student debt levels, the FIRE (Financial Independence, Retire Early) Movement, and share their After Hours picks for the week.

Download this podcast

For interested listeners:

The Basics of FIRE
Mr. Money Moustache (FIRE blog)

Some recent picks:

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)
Airtable (software)
Small Fry (Lisa Brennan-Jobs)

You can email your comments and ideas for future episodes to: harvardafterhours@gmail.com. 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.

Impact Investing Could Accelerate the Fight Against Cancer

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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.

When Competition Between Coworkers Leads to Unethical Behavior

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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.

Our Studies

Study 1

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.

Study 2

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.

Study 3

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.

Further Studies

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.

How Timeboxing Works and Why It Will Make You More Productive

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Five years ago I read Daniel Markovitz’s argument for migrating to-do lists into calendars. Since then, my productivity has at least doubled.

That momentous (at least for me) article describes five problems with the to-do list. First, they overwhelm us with too many choices. Second, we are naturally drawn to simpler tasks which are more easily accomplished. Third, we are rarely drawn to important-but-not-urgent tasks, like setting aside time for learning. Fourth, to-do lists on their own lack the essential context of what time you have available. Fifth, they lack a commitment device, to keep us honest.

This was enough for me. I converted from my religiously observed to-do list (daily work plan) to this calendar system, also known as timeboxing (a term borrowed from agile project management). All five of Markovitz’s criticisms of to-do lists have manifested for me. In a study we conducted of 100 productivity hacks, timeboxing was ranked as the most useful. And over the last few years, I have also discovered several additional benefits of timeboxing, which I would like to share.

First, timeboxing into a calendar enables the relative positioning of work. If you know that a promotional video has to go live on a Tuesday and that the production team needs 72 hours to work on your copy edits, then you know when to place the timebox. In fact, you know where to place the timebox: it’s visual, intuitive, obvious. Working hard and trying your best is sometimes not actually what’s required; the alternative — getting the right thing done at the right time — is a better outcome for all.

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Second, the practice enables you to communicate and collaborate more effectively. If all of your critical work (and maybe just all of your work, period) is in your calendar, colleagues can see it. So not only are you more likely to plan your work to accommodate others’ schedules (the paragraph above), others are able to check that your work schedule works for them. Shared calendars (with attendant privacy options) are the norm in the corporate world now, with Microsoft and Google leading the way.

Third, it gives you a comprehensive record of what you’ve done. Maybe you get to the end of a blistering week and you’re not even sure what happened? It’s in your calendar. Or a performance review looms — what were the highs and lows of the last six months? It’s in your calendar. Or you’re keen to use an hour to plan the following week and need to know what’s on the horizon. It’s in your calendar. Just make sure you have your own personal (i.e. not exclusively employer-owned) version of this data, or someday it won’t be in your calendar.

Fourth, you will feel more in control. This is especially important because control (aka volition, autonomy, etc.) may be the biggest driver of happiness at work. Constant interruptions make us less happy and less productive. Timeboxing is the proper antidote to this. You decide what to do and when to do it, block out all distractions for that timeboxed period, and get it done. Repeat. Consistent control and demonstrable accomplishment is hugely satisfying, even addictive. This is not just about productivity (largely external), this is about intent (internal, visceral) and how we feel.

Fifth, you will be substantially more productive. Parkinson’s law flippantly states that work expands so as to fill the time available for its completion. Although it’s not really a law (it’s more of a wry observation), most of us would concede that there is some truth to it (especially as it pertains to meetings). A corollary of this observation in practice is that we often spend more time on a task than we should, influenced by the time that happens to be available (circumstantial) rather than how long the work should really take (objective). Disciplined timeboxing breaks us free of Parkinson’s law by imposing a sensible, finite time for a task and sticking to that. Although it’s hard to precisely quantify the benefits of any time management or productivity measures, this is clearly enormous. Just take a commonplace example: do you habitually take two hours (cumulatively, often drawn out over multiple sessions) to complete a task that really could have been done in a single, focused, time-boxed hour? If the answer is yes, then your personal productivity might be double what it is right now.

The benefits of calendarized timeboxing are many, varied, and highly impactful. The practice improves how we feel (control), how much we achieve as individuals (personal productivity), and how much we achieve in the teams we work in (enhanced collaboration). This may be the single most important skill or practice you can possibly develop as a modern professional, as it buys you so much time to accomplish anything else. It’s also straightforwardly applied and at no cost. Box some time to implement a version of this that works for you.

Why It’s So Hard to Sell New Products

Thomas Steenburgh, a marketing professor at the University of Virginia Darden School of Business, was inspired by his early career at Xerox to discover why firms with stellar sales and R&D departments still struggle to sell new innovations. The answer, he finds, is that too many companies expect shiny new products to sell themselves. Steenburgh explains how crafting new sales processes, incentives, and training can overcome the obstacles inherent in selling new products. He’s the coauthor, along with Michael Ahearne of the University of Houston’s Sales Excellence Institute, of the HBR article “How to Sell New Products.”

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Research: When Overconfidence Is an Asset, and When It’s a Liability

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What happens to people who are overconfident? Are they generally rewarded, promoted, and respected? Or do we distrust them and avoid collaborating with them? Our research suggests it may depend on how they express confidence.

One way people express confidence is verbally. We make specific, numeric expressions of confidence in our judgments, such as when making probabilistic forecasts (e.g., I’m 90% sure), or when estimating our performance relative to others (e.g., I’m in the top 10%). Much of the research on overconfidence looks at verbal expressions of overconfidence, because these can more clearly be compared to actual performance and outcomes.

But this is not the only, or even the most common, way that people express confidence. There are a number of nonverbal things we do, using body language and tone of voice, to appear confident. For example, people who feel confident tend to act dominant—speaking boldly and loudly, at a rapid pace, and starting the conversation with their own opinion. They may also nod their head for emphasis and generally have a larger presence in the room. They are seen as powerful, and others defer to them. These nonverbal expressions of confidence aren’t always perceived accurately, however, as things like culture and context can lead to different interpretations.

Both channels of communicating confidence, verbal and nonverbal, can be extremely effective at garnering positive attention and influence in groups. According to one hypothesis (the presumption of calibration hypothesis), we generally assume others have the self-knowledge to know how confident they should be, and we also assume they will truthfully communicate this confidence to us (the so-called truth bias), unless extenuating circumstances suggest otherwise. So whenever we encounter confidence, we tend to find it compelling, and we expect it to be justified.

Sometimes though, we find out information that suggests someone was actually overconfident and makes us second-guess our initial view. Maybe they said they were extremely sure, or their body language exuded confidence, and it turned out they were wrong. Here, the research has been mixed about the consequences of overconfidence.

Some research has reported that being overconfident while participating in a group activity did not damage the person’s reputation. Individuals who had acted confident about their task performance, but were later revealed to be worse at the task than they had claimed, did not suffer a severe drop in their social status in the group relative to someone who had been well-calibrated. The researchers concluded that “the status benefits of overconfidence outweighed any possible status costs”.

But other research, using vignettes or videos of people, found that eyewitnesses and job applicants who verbally stated their confidence level to evaluators did take a large reputational hit once additional information suggested they had been overconfident. For these individuals, it seems they could have saved their credibility by being more modest.

In a series of studies recently published in the Journal of Personality and Social Psychology, we teamed up with other researchers to investigate why some of us had previously observed that overconfidence could be a liability, while some of us had found it was not. We noticed a pattern in the existing research: that the confidence expressions in studies on in-person groups were primarily nonverbal; whereas in studies with vignettes or videos, they were primarily verbal. So we tested whether the way confidence was expressed was what determined the consequences of being overconfident.

In our first study, we asked 444 online participants (mTurkers) in the U.S. to choose between two candidates to collaborate with on a task–one who was confident and one who was cautious. Participants overwhelmingly selected the confident candidate, regardless of whether confidence was described using verbal statements from the candidates, or was inferred from how the candidates carried themselves on recorded video. Then participants received performance information that could help them detect overconfidence; they found that, despite their confidence, all candidates were equally mediocre at a pre-screening version of the task.

After this revelation, now the channel by which confidence was communicated made a difference. If the candidate had expressed confidence verbally, the candidate suffered a big blow to reputation and lost the advantage; fewer people selected them as collaborators compared to the cautious candidate. However, if the candidate had expressed confidence nonverbally, this candidate kept the advantage.

We repeated this study with male and female candidates, and with two different types of tasks (an emotional IQ test and guessing strangers’ ages from photographs). We observed the same pattern of results. Confidence was always beneficial to a candidate initially, but if the candidate’s performance did not live up to expectations, then the channel of communication became a deciding factor in the candidates’ desirability as a collaborator.

In a follow-up study, we replicated this finding with 256 undergraduate participants, some of whom got to meet the candidates in person, ask them questions, and observe their nonverbal behavior, before selecting a collaborator; while others in the verbal behavior condition read the same verbal statements as in the first study. We saw even stronger results.

Why might the channel of communication have such an important role in whether overconfidence is a social liability? One feature of nonverbal behavior is that it is not so clearly tied to a specific, falsifiable claim as are verbal expressions. We explored whether this was what mattered in follow-up studies.

In one study, we asked 462 mTurk participants to select a collaborator, as the participants had done in our first studies. After the candidates’ less-than-stellar performance was revealed, we explained that the candidates had each overtly denied being overconfident about their task ability. Our participants found the denial much more plausible when the candidate had expressed confidence nonverbally rather than verbally. This was a strong indicator that plausible deniability could be behind the advantage for the candidates expressing overconfidence nonverbally.

In another follow-up study, we again found evidence to suggest that channel of communication played a key role because of plausible deniability. This time we used a judge-advisor paradigm, in which we asked 302 undergraduate participants, many of whom were majoring in business, to act as managers evaluating advisors. The advisors expressed confidence or cautiousness about their decisions on a recorded video, with varying levels of plausible deniability. For example, in one condition, the advisors expressed confidence, or lack thereof, with the video sound on mute to showcase their nonverbal behavior (e.g., head nodding for emphasis).

The results replicated our previous studies, in that confidence, no matter how it was expressed, was beneficial until it became clear that performance fell short. Then, overconfidence cost the advisors. But those who expressed confidence nonverbally  (with a higher level of plausible deniability) did not lose all of their initial advantage.

These studies point to one reason why some people are penalized for being overconfident while others are not. It’s harder to hold people accountable for overestimating their abilities or knowledge when they express that confidence in nonverbal ways. It’s important to recognize these cues that demonstrate confidence – standing tall, speaking loudly, and dominating conversation – to avoid  giving those who are overconfident undue influence.  You can also hold colleagues accountable by asking them to be specific in their confidence assessments and by verifying their accuracy over time.