Research tells us a lot about why people behave unethically. For example, there is evidence that people tend to be more dishonest later in the day, because they’re more fatigued, and when they’re anxious, because they’re more likely to look out for themselves. Many of these studies, however, only look at the unethical actions people take on behalf of themselves. But what about the many times when we act on behalf of others?
We act on behalf of others in many domains: business, politics, law, the social sector, and others. Managers seek resources for their employees, for example. Lawyers represent clients in negotiations.
My research suggests that people acting on behalf of others can be influenced by the values and perceived expectations of those they’re representing—specifically when it comes to acting ethically. My colleagues and I were especially interested to know how this might apply to women. Research has found that women perform worse in negotiations because they face backlash for acting assertively – but that one way around this backlash is by advocating for others.
We conducted four studies to examine whether people were more likely to lie when negotiating on behalf of others than for themselves. We recruited a total of 1,337 participants to engage in negotiations, and we found that gender played a role in how we negotiate for ourselves and others. Men were more likely than women to lie when they were negotiating for themselves, but not when negotiating for others. But the reverse was true for women – women were more likely than men to lie when they were negotiating on behalf of others.
In one study, we randomly assigned participants to act in a property negotiation as a buyer or as an agent representing the buyer. We told them that buyers wanted to build a commercial high-rise hotel on the property, but that the seller would reject their offer if they knew about this intent. We found that female participants assigned to the role of a buyer’s agent were more likely to lie than those assigned to be the buyer (64.4% vs. 44.4%) about their plans for the property in order to get the deal done. On the other hand, men showed no statistical difference in ethicality when acting for themselves or for others (60.6% vs. 72.2%).
When we asked why participants made the decisions they did, we saw that women were more likely to report feeling guilty about letting down those they were advocating for. They were more willing to engage in questionable behavior because they anticipated feeling more guilt and worried about disappointing others.
Even though our studies focused on women, other research has yielded similar general findings that people tend to act unethically when representing others, if they believe they’re okay with it or prefer it. One set of research studies showed that “utilitarian” individuals, or those who typically engage in conscious cost-benefit analyses when making decisions (e.g., “What do I or society have to gain or lose as a result of my choices and actions?”) are more likely to act unethically if they are acting on behalf of someone else who shares a similar utilitarian approach, verses when working for someone who is more “formalist” (or focused on upholding rules/principles).
Unethical behavior on others’ behalf can spread from minor misconduct here and there to more consequential actions if expectations and norms allow for it. If it’s acceptable to cut minor corners on a client deliverable to make sure a consulting team meets a deadline, for example, that could lead one to engage in more drastic misbehavior such as misrepresenting firm capabilities to ensure the consultancy secures a lucrative account. Research shows that this slippery slope isn’t uncommon.
So how can we combat the tendency to behave unethically when acting on someone else’s behalf? Our research suggests a few approaches:
Aim for intentionality: At the individual level, it’s important to be aware of your motivations when advocating for others. Does your desire to support others lead to a “win at all costs” mentality? Will you feel excessive guilt if you fail to represent them well? Asking yourself such questions in advocacy situations will make you more mindful of your values and intents, and likely keep you in more ethical lanes of behavior.
Ask for clarification: If you’re not sure how ethical those you’re representing are, seek clarification. There can be significant ambiguity in real-world advocacy situations, and that can lead to erroneous assumptions about someone’s ethics and expectations, which in turn may lead to unethical behavior on their behalf. Break through the ambiguity by asking for clarification on expectations related to ethicality (“It’s not about winning at all costs, right?”), while sharing your own expectations.
State your expectations: When you’re representing someone, you should also be upfront about where you are and aren’t willing to go. Similarly, when you’re in a group being represented, make your expectations around ethicality clear to those acting on your behalf (“We need to do this by the book”). Don’t leave space for erroneous assumptions. Moreover, look for signs that a representative may be more likely to act questionably. For example, if someone is expressing feeling guilty about letting you down, step in and assure them that they shouldn’t feel pressure to act untoward.
While the tendency to act unethically on behalf of others exists, the good news is that you can act to prevent such outcomes.
I’ve spent my career helping companies address their data and data quality opportunities. Overall, I rate progress as “slower than hoped.” While there are many contributing factors, one of the most important is the sheer lack of analytic talent, up and down the organization chart. In turn, this lack of talent makes it harder for companies to leverage their data, to take full advantage of their data scientists, and to get in front of data quality issues. Lack of talent breeds fear, exacerbating difficulties in adopting a data-driven culture. And so forth, in a vicious cycle.
Still, progress in the data space is inexorable and smart companies know they must address their talent gaps. It will take decades for the public education systems to churn out enough people with the needed skills — far too long for companies to wait. Fortunately managers, aided by a senior data scientist engaged for a few hours a week, can introduce five powerful “tools” that will help their existing teams start to use analytics more powerfully to solve important business problems. To be sure, these are not the only tools you’ll need — for example, I haven’t included A/B testing, understanding variation, or visualization here. Nor is my intent to make people experts. Rather, based on my experiences working with companies on their data strategy, these five concepts offer the biggest near-term bang for the buck.
The first is learning to think like a data scientist. We don’t speak about this often enough, but it is really hard to acquire good data, analyze it properly, follow the clues those analyses offer, explore the implications, and present results in a fair, compelling way. This is the essence of data science. You can’t read about this in a book — you simply have to experience the work to appreciate it. To give your team some hands-on practice, charge them with selecting a topic of their own interest (such as “whether meetings start on time”) and then have them complete the exercise described in this article. The first step will lead to a picture similar to the one below, and the rest of the exercise involves exploring the implications of that picture.
How Often Do Meetings Start Late?
Give employees hands-on experience with data by asking them to collect and plot data on a familiar topic. Meeting start times are just one example.
Source: Thomas C. Redman
Charge that senior scientist you’ve engaged with helping people in completing the exercise, teaching them how to interpret some basic statistics, tables, and graphics, such as a time-series plot and Pareto chart. As they gain experience, encourage your team to apply what they’ve learned in their work everyday. Be sure to make time for people to show others what they’re learning, say by devoting fifteen minutes to the topic in each staff meeting. Most critically, lead by example — do this work yourself, present your results, and freely discuss the challenges you faced in doing the work.
As you and your team dive into data, you’ll certainly encounter quality issues, which is why pro-actively managing data quality is the next important skill to learn. Poor data is the norm — fouling operations, adding cost, and breeding mistrust in analytics. Fortunately, virtually everyone can make a positive impact here. The first step is to make a simple measurement using the Friday Afternoon Measurement method (the technique acquired this name because so many teams end up using it on Friday afternoon).
To do so, instruct your team members to assemble 10-15 critical data attributes for the last 100 units of work completed by their departments — essentially the last 100 data records. Then, they should work through each record, marking obvious errors. They should then count up the error-free records. The number, which can range from 0 to 100 represents the percent of data created correctly, their Data Quality (DQ) score. DQ can also be interpreted as the fraction of time the work is completed correctly, the first time. Most managers are surprised by the results — they expect to score in the high 90s, but DQ = 54 is the median score.
FAM can also point out which data attributes have the biggest error rates, suggesting where improvements can be made, using root cause analysis, described next. Charge each member of your team with making one such improvement.
Thus, look to the numbers to understand correlation and to the real-world phenomena to understand causation. Root cause analysis is a structured approach for getting to the real reasons things go wrong — the root causes. It is important because, too often, managers and teams often accept easy explanations and don’t dig deep enough. And problems remain. RCA can enable them to develop a clearer picture and take actions that are more likely to solve the problem.
To develop this skill with your team, start by discussing “how to explore cause and effect like a data scientist” with your staff. Then, the next time you find yourself tempted to accept someone’s intuitive reasoning as to why something went wrong, seize the opportunity to conduct a solid root cause analysis. There are many formal means to do so. “The five whys,” which forces you to make sure you’ve gotten to the root cause, and fishbone diagrams, which graphically represent multiple causes, are probably the best known. Have your data scientist pick one, and follow it! Over time, seek to make root cause analysis your standard for all important issues.
The fourth skill stems from the desire all managers have to “be in control.” My working definition of control is “the managerial act of comparing process to standards and acting on the difference.” But even the simplest process varies. How can one distinguish normal day-in, day-out variation from situations that are truly out of control? Fortunately, understanding and applying control charts provides a powerful way to do just that.
Control charts feature a plot of the data, the average, and two “control limits,” (an upper control limit and a lower control limit). Basic as they are, they reveal so much! For example, in the Figure below:
Since time period 9 falls outside the control limits, a manager can be certain this process is out of control. They should initiate a root cause analysis to figure out why.
There is an uptick in time period 4 that looks encouraging. But a manager should not get too excited — the uptick was more likely due to random variation and was not sustained.
It is clear enough that that this process only succeeds 60% of the time. If this is not good enough, the manager must make fundamental changes.
Engage your data scientist in helping you and your team try control charts on a few important processes. Learn as you go, understanding key terms, determining which control charts to use, and striving first to get processes under control — your confidence will grow, as will your ability to manage your team!
Finally, all managers and their teams should learn to understand and apply regression analysis. Regression provides a powerful means to explore the numerical relationships between variables. To help illustrate this, consider “umbrella sales.” There are dozens of factors that could increase sales (e.g., rain) or decrease sales (e.g., a competitor’s price cut). Regression provides a way to determine which variables are most important and their impact on sales. For example, an analysis may yield:
Monthly sales = 200 + 5*(days of rain) – 10*(competitor price cut in $) + error term
Absent other factors, monthly sales are about 200 units.
A day of rain is associated with the sale of five more umbrellas,
A competitor cutting its price by one dollar is associated with ten fewer umbrellas sold
The model is not perfect — hence the error term. For example, suppose you sold 250 umbrellas in a month when there were 15 rainy days and a competitor cut its price by $2. Based on the formula, one would expect umbrellas sales to be 200 + 5*15 – 10*2 = 255 units. So the error term in that case is 5 umbrellas.
Like all analyses, the more variables, the more complex the analysis, so start by focusing on one independent (e.g., explanatory) variable. In parallel, read “A Refresher in Regression Analysis,” which uses umbrella sales as an example to explain the terms and underlying concepts. Charge your data scientist with helping your team do the work, and making sure team members don’t get bogged down in details. Only then should you move onto two, three, or more variables and more complex regression models.
These five tools are powerful, even elegant, in their own ways. They provide far greater capabilities than the steps described here, which aim only to get you started. You’re certain to take some false steps along the way, but press on. Work with your data scientist to learn even more. As your team grows more confident in using analytics, the business benefits you gain will more than justify the effort.
How much can we expect business to lead on sustainability? What should be a company’s biggest priority: serving its shareholders, providing jobs, or addressing the health of our planet?
Often, these goals are at odds. So we’re bringing together, in a special forum live-streamed from the MIT campus, two leading voices in the sustainability debate. MIT’s Yossi Sheffi and sustainability expert and author Andrew Winston will debate and discuss the role of for-profit businesses in supporting — and investing in — sustainability goals.
Be a part of the conversation, and submit your questions below to Yossi Sheffi and Andrew Winston. Select questions will be answered during the Q&A session.
Hear the speakers tackle:
How much companies really can control their emissions, even when they want to
Whether consumers are willing to pay to support sustainability
The extent to which companies can compel their suppliers to meet sustainability standards
Whether there’s really a one-to-one trade-off on jobs versus sustainability
About 80% of U.S. and European CEOs surveyed by McKinsey say they worry about ensuring that their companies have the right skills mix to thrive in the age of AI and automation. Those leaders come from a variety of industries, and they’re smart to be thinking about talent at a strategic level.
Given the complexities of implementing the new technologies, companies will, of course, need people who can design the right algorithms and interpret the data. But they’ll also need so-called “softer” skills to do the work that machines aren’t capable of doing. Our research suggests that demand for social and emotional skills will grow by about one-quarter by 2030, and we also see a clear shift toward higher cognitive skills, including creativity and complex information processing.
An HR tool kit for shaping a workforce already exists: Companies can retrain people, redeploy them to make the best use of skills available, contract work out, hire new people, and release those who do not meet the organization’s needs. But the external labor market can do only so much to address the anticipated shifts in demand, the pace of which will accelerate over the next decade, according to our research. If the volume of companies seeking to hire for the necessary technical and soft skills rises too rapidly, full-time salaries and contractor rates will skyrocket, and most organizations will be unable to compete with global platforms and tech giants for the talent they seek.
Redeployment may help companies play to workers’ strengths, but skills gaps will inevitably remain. So in the AI era, companies should double down on retraining the people they have, with an emphasis on lifelong learning and adaptability.
Though reskilling (teaching employees new or qualitatively different skills) and upskilling (raising existing skill levels) have become hot topics, there seems to be more talk than large-scale action in these areas. A key choice is whether to use in-house training resources and programs tailored to the company or to partner with an educational institution to provide external learning opportunities for employees. AT&T has chosen the latter course: The company has developed a broad set of partnerships with 32 universities and multiple online education platforms to enable employees to earn the credentials needed for new digital roles.
Other companies, including the German software giant SAP and Walmart, have opted for in-house training programs. Walmart has set up more than 100 “academies” in the United States that provide classroom and hands-on training for front-line supervisors and managers. SAP has constructed a series of “learning journeys” for thousands of employees at its Digital Business Services division that feature boot camps, shadowing senior colleagues, peer coaching, and digital learning.
Both approaches make workforce learning a priority. Partnering with external educational providers may be easier for most companies, for the same reason that most organizations can’t afford to compete with deep-pocketed tech giants to hire experienced tech talent. A starting point in either case is to take stock of the skills that are already present in the workforce and then map those skills to the skills companies are projected to need in the future.
Taking inventory is also a first step on the way toward making HR more strategic, as many companies will need to do in the next few years. With strategy likely to be highly dependent on the availability of talent in an AI-fueled future, HR will need to play a larger role in long-term planning, with the chief human resources officer evolving into as central a figure as the CFO.
HR departments will also have to undergo profound changes in the way they work. That means developing an internal market for talent as well as a marketplace for lifelong training experiments — and supporting employees’ learning by analyzing skill building by career path, for instance, and focusing on closer human-machine “interwork.” The long-term goal is to embed a new flexibility and adaptability in the workforce, accompanied by a new adaptability within the HR function.
Ensuring that the right skills are in place at the right time is shaping up as one of the biggest corporate challenges of our time. Given the winner-takes-most dynamic that we are already seeing, first with digital transformation more broadly defined and now in more pronounced fashion with AI and automation, no company can afford to underestimate the coming skill shifts and how those may affect their prospects.
Society is aging and healthcare costs keep rising. By digitizing the system, health services can be provided at lower cost and higher quality. A new study reveals the areas for and extent of potential improvements.
Advanced analysis of genomic data integrated with electronic medical records and other data sets, combined with effective reimbursement strategies and full data-regulatory compliance, will distinguish winning diagnostics companies.