Marina Gorbis's Blog, page 1493

It’s a Good Thing You Waited Until Now to Found Your Start-up

Cloud computing and other tech developments have drastically cut the cost of founding a company, says Stanford University finance professor Ilya Strebulaev. The new technologies have reduced the expense of building high-tech start-ups by a factor of 10, a development that has opened up the start-up world to angel investors as never before. Angels, who tend to prefer small deals, are enjoying a growing competitive advantage over traditional venture-capital firms and now supply the overwhelming majority of high-growth start-ups with their first money (after the founders have tapped family and friends), Strebulaev says.

Boards with More Women Pay Less for Acquisitions

Companies that have more women on their boards of directors make fewer bids for mergers and acquisitions — and pay less for acquired companies. That’s the finding from a new paper ( Levi, Li, and Zhang, 2013 ) by Maurice Levi and Kai Li of the Sauder School of Business at the University of British Columbia and Feng Zhang at the David Eccles School of Business at the University of Utah.

Professor Li, defend your research. What makes M&A activity and boards ideally suited to a study of gender and leadership behavior?

Li: There are three main reasons. First, M&A is a very important corporate decision that can make or break a company. Many have been catastrophic — just think of the classic example, AOL and Time Warner. So it clearly called for further scrutiny: most companies barely break even on these deals, so why do they still go ahead with them? And are there any mitigating forces that reduce a company’s tendency to be acquisitive and thus better protect shareholder interests?

Second, M&As are different than regular organic growth in that completing such a deal is associated with much greater uncertainty. You’re buying an unknown entity, possibly also entering a new market. Past research, which we cite, has shown that men and women behave differently when faced with uncertainty in terms of how overconfident they are. Everyone is overconfident — we always think we are better than our true selves — and when men and women are dealing with knowns they tend to be fairly similar in that regard as well. But when they are looking at unknowns, or when feedback is delayed or uncertain instead of specific and immediate, women demonstrate less overconfidence than men. So the M&A setting is an ideal setting in terms of how it amplifies gender differences in responding to uncertainty with overconfidence.

The third reason is that while a board is not engaged in day-to-day operations like a CEO, it is very involved in M&A decision making and in the deal making process. It’s more likely that a single voice from a female director would be heard, and that we could be able to capture that.

Women make up such a small percentage of directors. Is that really enough of a sample size to be able to tell what’s going on?

Actually, this research has evolved over time — at the very beginning we were very ambitious and were just looking at the very top females in the corporate hierarchy, female CEOs. But they are just so few in number that we didn’t have enough to go on. But women make up about 10% of boards of directors. A typical board is about 10 people, so a majority of U.S. companies have at least one female director. So in that sense, compared with female CEOs, there are many more of them. And while that is still a small number, it’s growing because of increasing awareness and because there are so many female students in business education, both MBA programs and undergraduate programs, and they are climbing up the corporate hierarchy.

Most M&As fail, so from that perspective it sounds like fewer and smaller takeover bids is actually a good thing. But in part of the paper you conclude that there’s no evidence that women are “better monitors.” What do you mean by that?

That’s derived from other work [Chen, Harford, and Li, 2007] where we look at institutional investors and stock price reactions to deal announcements. If female directors are good monitors they would be monitoring how things proceed after the deal is announced. If you announce your takeover bid and your stock drops 20%, if you are a good monitor you might use the time between the announcement and the deal going through to revise the deal or cancel it. But we saw no difference in how men or women responded to negative market reactions to an M&A — once the board has made their decision, they generally just go ahead with it.

In the paper you write that each woman on a board reduces likelihood of an acquisition bid being made by 7.6% and reduces the bid premium of any takeover bid by 15.4%. At what point would that stabilize? For instance, if you had a board that was 100% female it’s tough to imagine they would never make any takeover bids.

A 100% female board is almost a counterfactual, while 100% male boards are a fact of life. So for a statistician that is really difficult to measure.

But we did see a nonlinear effect to having multiple women on a board; having two women produces a stronger effect than having a single female director. Having multiple women present, versus a lone voice, means that they can reinforce each other and also make it more likely that their views will be heard.

But so few boards have more than two women, we don’t know what would really happen after that, if the effect would keep compounding or if it would plateau.

Do female directors correlate with decreases in other risky activities?

In fact, we did find a similar result in R&D spending and capital expenditures — female board members also correlate negatively with those activities. That’s for the same reasons about overconfidence and uncertainty that effect M&As.

Did you find any differences in the kind of men who were more or less likely to move ahead with a risky M&A deal?

In a previous paper [Levi, Li, and Zhang, 2010] we did examine the role of testosterone in M&As. Testosterone increases your combativeness and dominance-seeking, and younger male CEOs with more of the hormone were more acquisitive than older CEOs, who didn’t have as much. If you’ve got more testosterone and it’s increasing your sense of dominance, then you want to buy companies and build a bigger empire.

Binge TV and the Art of Creating a Committed Customer

Netflix has turned my wife into a binge viewer. No jokes necessary about how she now enjoys more time in bed with her iPad than with her husband. This behavioral transformation has been fascinating to witness. She now explicitly seeks out shows—typically dramas—that can be watched three to four hours at a time. I would never have picked her for having this aesthetic sensibility or taste.

The company recently released a survey showing that—surprise!—61% of Americans described themselves as regular binge viewers. What’s more, 75% of those surveyed say that watching several episodes of their favorite shows in a row makes the program experience more enjoyable. Netflix also observed that its data demonstrated that roughly half of binge viewers polished off an entire season over the span of a single week and the bingeing majority committed to one show at a time rather than more promiscuous consumption.

The essential empirical insight here is that Netflix has effectively created a new genre of committed customer; one demonstrably underserved and underappreciated by broadcasters, cable TV, DVR, DVD and other media platforms. The binge viewing segment has played no small part in driving Netflix’s brand equity and market cap. Just as importantly, binge viewers are driving Netflix’s business model development, as well.

Netflix, more than any other company this past year, most brilliantly addresses the theme and answers the question posed by my Harvard Business Review Press’ ebook, Who Do You Want Your Customers to Become?  Netflix wants its customers to become binge viewers. The company ingeniously invests in content and creativity designed to make its customers more willing to spend more time enjoying their binges. Asked another way, is bingeing on entertainment to Netflix what searching for information is to Google?

The original series the company produces—such as “House of Cards” and “Orange is the New Black”— are crafted with binge viewing in mind. As surely as the rise of the syndicated rerun marketplace in the 1970s prompted television producers to create series and shows worth viewing more than three or four times, the bingeing phenomenon now asks the creative community to produce programming capable of being enjoyably consumed for hours at a time. That’s a non-trivial challenge but one promising all the riches, recognitions and rewards a successful pop culture breakthrough can provide.

Remember the $1 million prize Netflix offered to get people to improve its movies recommendation engine? You can bet the company’s savvier marketing leaders are looking to launch recommendation engines suggesting what programs would be best for hours of binge viewing. Nobody wants to binge on unappetizing programs.

If Netflix’s investments are designed to enhance the BQ —Binge Quotient—of its customers, what other innovations are likely? The company clearly has an economic stake in going beyond the individual binge to couples and families: to wit, the family that binges together, hinges together. Expect recommendation engines suggesting binge-worthy comedies for twenty-somethings and old marrieds.

How might parents safely and ethically train children to binge responsibly on “The Simpsons” and/or “The Muppets”? Bingeing counterparts to musical mix-tapes seem inevitable. Transforming Netflix’s customers into bingers or (at least) people willing to binge a few times a year seems like a remarkably strategic and strategically remarkable value proposition.

This Netflix transformation offers important insights to innovators worldwide: how are your organization’s products and services transforming how your customers spend their time and attention? How should you invest in enhancing that behavioral change? What is your company’s “binge”?

But Netflix better make sure it doesn’t become too successful at binge-worthy transformations. Just as McDonalds was criticized for the role its “supersizing” initiatives played in promoting possible obesity and overindulgence, we’ll see regulators worldwide look to set limits on how much bingeing is too much. As Mae West (you can find her on Netflix) once observed, “Too much of a good thing is wonderful.” But will Netflix’s regulators agree?

The Bias Undermining Your People Analytics

People analytics – the fast-growing practice which companies use to analyze large amounts of data to quantify employee performance – has the potential to revolutionize the workplace and vastly improve how all of us are rewarded for our efforts. But used the wrong way, people analytics can be just as blind and biased as human beings have always been.

One of the most well-established findings in social psychology is the “fundamental attribution error” which essentially describes how observers over-attribute their explanations for the causes of behavior to “the person” and under-attribute the causes of behavior to “the situation.” In careers and the workplace, this means that credit or blame for performance is likely to be assigned to an individual more based on his or her perceived character, personality, intentions or efforts rather than on the situation, context, opportunities or constraints within which that individual is working. This cognitive bias explains why salespeople who are lucky enough to be selling the right products to the right market at the right time get credit and get viewed as talented “A players,” while those who have the misfortune of selling the wrong products to the wrong market at the wrong time instead receive blame and become branded as mediocre “B” or “C” players. This bias also explains why a CFO may be perceived as cheap by disposition or why a team might attribute its internal conflicts to incompatible personalities instead of resulting from organizational incentives to compete rather than collaborate with one another.

In this evolving age of data, the latest manifestation of the fundamental attribution error arises in the rapidly growing field of people analytics. Organizations can now conduct large-scale analyses with all kinds of variables in order to try to predict which employees are likely to succeed, and which are not. Companies use variables such as college or graduate school grades, SAT or GMAT scores, years of working experience, and the results of cognitive ability or personality tests, to predict turnover rates, promotions, sales volume, or other performance outcomes. With the explosion of data that companies collect and compile about their employees, it is tempting to both categorize and classify employees who currently work at the organization and to simultaneously build profiles of ideal candidates, who will be likely to perform well, remain at the organization, get promoted, and be satisfied with their jobs.

But just as people are susceptible to making the fundamental attribution error, organizations risk making what might be called the fundamental analytic error. That is to say, in many instances, critical information is missing from human capital or people analytics: situational or contextual variables. An argument can be made that for the purposes of predicting, explaining, and improving variance in performance, situational variables might actually prove better than individual variables. Using the example of salespeople, more of the variation in sales volume may be attributable to product or territory than by which sales person happens to be selling a given product in a given location. What remains indisputable, however, is that the combination of individual and situational variables together will explain much more of the variance in performance or employee engagement than individual variables or situational variables could ever explain alone.

The fundamental analytic error tempts organizations for several reasons. It’s often much more politically expedient to blame individuals when things aren’t going well than to search for the underlying organizational causes of their difficulties. If the talents or efforts of individuals get credited or blamed for performance, then performance that doesn’t meet expectations does not raise tough questions about whether culture, product, strategy, incentives, or technologies might be improperly configured or misaligned with one another. In other words, poor performance can readily get attributed to employees rather than to their leaders, who are directly or indirectly responsible for creating the conditions that should enable people to succeed. If products are not selling, it may be very appealing to initiate an analytics project to look at salespeople’s attributes instead of getting customer feedback about the company’s products. If turnover is high among entry level employees, it could much more politically palatable to analyze the personality, style, education, experience level, and referral source of the employees who leave the organization, rather than to analyze the capabilities or managerial skills of their supervisors. And no amount or kind of human capital analytics is going to save an organization in denial about disruptive changes occurring in its industry or markets.

There is a better way. The most valuable human capital or people analytic initiatives get deployed in a scientific manner. Hypotheses, nested in some kind of conceptual framework, get formulated and tested and theories and hypotheses are all subject to falsification. So, if some associates in a law firm are performing well, while others perform poorly, it is reasonable to hypothesize that their law school grades, LSAT scores, and whether or not they clerked for a judge might help predict, and partially explain, their performance as attorneys. If the associates who have high grades and scores and clerked for a judge are high performers, and associates with low grades and scores who did not clerk are poor performers, a researcher could hypothesize that this correlation indicates causation: that intelligence and motivation are reflected in the lawyers’ resumes, and that higher intelligence and motivation in turn cause higher performance.

But before conclusions can be drawn, other explanations need to be considered and alternative analyses need to be conducted. The hypothetical law firm, for example, might look beyond human capital analysis of its associates and consider the impact of practice area, geographic location, and types of cases on associate retention and performance. A courageous investigator, willing to risk stirring up organizational politics, might even suggest that the law firm conduct analyses to learn whether associates who work for some partners outperform associates who work for others. These additional analyses might determine that variance in associate performance is a function of whether the partners they happen to be working for provide coaching, mentoring and support, and not a result of the associates’ grades, scores or personalities.

Human psychology and organizational politics are both biased towards attributing too much causality to people and their individual attributes and not enough causality to situations and organizational context. Human capital and people analytics, despite their big data-fueled power, can easily get misused in ways that serve only to justify existing organizational systems and to unfairly scapegoat individuals who are not performing well in no small measure because of the weaknesses and constraints of those systems. Only by taking a broader, more open, less biased and less political approach to conducting analyses about the factors that predict and explain performance can organizations hope to improve it over the long term.

When You Criticize Someone, You Make It Harder for that Person to Change

“If everything worked out perfectly in your life, what would you be doing in ten years?”

Such a question opens us up to fresh possibilities, to reflect on what matters most to us, and even what deep values might guide us through life. This approach gives managers a tool for coaching their teams to get better results.

Contrast that mind-opening query with a conversation about what’s wrong with you, and what you need to do to fix yourself.  That line of thinking shuts us down, puts us on the defensive, and narrows our possibilities to rescue operations. Managers should keep this in mind, particularly during performance reviews.

That question about your perfect life in ten years comes from Richard Boyatzis, a professor at the Weatherhead School of Management at Case Western, and an old friend and colleague.  His recent research on the best approach to coaching has used brain imaging to analyze how coaching affects the brain differently when you focus on dreams instead of failings. These findings have great implications for how to best help someone – or yourself — improve.

As I quoted Boyatzis in my book Focus: The Hidden Driver of Excellence,  “Talking about your positive goals and dreams activates brain centers that open you up to new possibilities. But if you change the conversation to what you should do to fix yourself, it closes you down.”

Working with colleagues at Cleveland Clinic, Boyatzis put people through a positive, dreams-first interview or a negative, problems-focused one while their brains were scanned. The positive interview elicited activity in reward circuitry and areas for good memories and upbeat feelings – a brain signature of the open hopefulness we feel when embracing an inspiring vision. In contrast, the negative interview activated brain circuitry for anxiety, the same areas that activate when we feel sad and worried. In the latter state, the anxiety and defensiveness elicited make it more difficult to focus on the possibilities for improvement.

Of course a manager needs to help people face what’s not working. As Boyatzis put it, “You need the negative focus to survive, but a positive one to thrive. You need both, but in the right ratio.”

Barbara Frederickson, a psychologist at the University of North Carolina, finds that positive feelings enlarge the aperture of our attention to embrace a wider range of possibility and to motivate us to work toward a better future. She finds that people who do well in their private and work lives alike generally have a higher ratio of positive states to negative ones during their day.

Being in the positive mood range activates brain circuits that remind us of how good we will feel when we reach a goal, according to research by Richard Davidson at the University of Wisconsin. That’s the circuit that keeps us working away at the small steps we need to take toward a larger goal – whether finishing a major project or a change in our own behavior.

This brain circuitry — vital for working toward our goals — runs on dopamine, a feel-good brain chemical, along with endogenous opioids like endorphins, the “runner’s high” neurotransmitters. This chemical brew fuels drive and tags it with satisfying dollops of pleasure. That may be why maintaining a positive view pays off for performance, as Frederickson’s research has found: it energizes us, lets us focus better, be more flexible in our thinking, and connect effectively with the people around us.

Managers and coaches can keep this in mind. Boyatzis makes the case that understanding a person’s dreams can open a conversation about what it would take to fulfill those hopes. And that can lead to concrete learning goals. Often those goals are improving capacities like conscientiousness, listening, collaboration and the like – which can yield better performance.

Boyatzis tells of an executive MBA student, a manager who wanted to build better work relationships. The manager had an engineering background; when it came to getting a task done, “all he saw was the task,” says Boyatzis, “not the people he worked with to get it done.”

His learning curve involved tuning in to how other people felt. For a low-risk chance to practice this he took on coaching his son’s soccer team – and making the effort to notice how team members felt as he coached them. That became a habit he took back to work.

By starting with the positive goal he wanted to achieve – richer work relationships – rather than framing it as a personal flaw he wanted to overcome,  he made achieving his goal that much easier.

Bottom line: don’t focus on only on weaknesses, but on hopes and dreams. It’s what our brains are wired to do.

The Downside of the Fed’s Increasingly Complicated Expectations Game

Yesterday, the Federal Reserve announced that it’s kind of sort of about to start ever-so-timidly pulling back on the massive monetary stimulus it’s been pouring out since the financial crisis. Then, stock prices around the world jumped.

In the traditional view of monetary policy, this isn’t supposed to happen. Tightening by the Fed should make financial assets less valuable. And in fact the prices of the particular financial assets that the Fed announced that it will buy less of, Treasury bonds and mortgage-backed securities, did drop slightly on the news Wednesday. But stocks didn’t.

When you view this reaction in terms of expectations, it makes a bit more sense. As economist Barry Eichengreen put it in the FT, the announcement of the start of the “taper” — the unwinding of the Fed’s bond-buying efforts — was widely expected. What was news in the announcement was that “future policy would remain loose for at least slightly longer than previously anticipated.”

Still, even Eichengreen thought the policy shift was too inconsequential to justify the market reaction. Maybe he’ll turn out to be right, and the post-announcement jump will soon be undone. I think there’s more afoot than that, though.

Long ago, a central bank’s job may have been as simple as taking away the punch bowl just as the party gets going, to paraphrase William McChesney Martin, the Fed’s chairman from 1951 to 1970. But in Martin’s day, the Federal Open Market Committee was able to make its monetary policy decisions in relative obscurity. The media paid little attention to its decisions and there was as yet no cottage industry of “Fed watchers” interpreting and forecasting its moves. Financial markets on the whole played a much smaller role in the economy.

Nowadays financial markets often drive the economy, and while this would seem to give the Fed more power, the amount of effort and attention now put into forecasting monetary policy and assessing its impact mean that exercising that power is far more complicated than it used to be.

Consider what happened in the mid-1990s, when the Fed raised short-term interest rates sharply in order to stamp out what it perceived as a possible overheating of the economy. “For the first time we got a so-called safe landing,” then-Fed-chairman Alan Greenspan recalled recently. “As soon as we let up, off it went, which suggests to me that what happened was that the equilibrium level of the Dow Jones Industrial Average [rose], because of our actions that failed to knock the economy down despite a three-percentage-point increase in the Federal Funds rate. [It] elevated the long-term asset value expectation.”

This is from a conversation I had with Greenspan at the end of November, which will be published here in a fuller version soon. “If that is true,” he went on,

and I raise it as a hypothesis because I don’t know how you could prove it one way or the other, it is saying that our very efforts made the situation worse. The notion that you could calibrate monetary policy to suppress a boom against the human nature bubbling up has no factual basis. The only place it exists is in the econometric models where the equations are so constructed that if you tighten monetary policy you smooth out the boom.

By adroitly managing a soft landing in the mid-1990s, then, the Fed conditioned markets to expect further such competence and economic stability, setting the stage for the great stock market bubble of the late 1990s. Greenspan again: “I think the evidence probably is conclusive, that a necessary and sufficient condition for a bubble is a prolonged period of economic stability, stable prices, and therefore low risk spreads, credit risk spreads.”

You could read this as trying to deflect blame for what transpired later: The crisis happened because we did such a great job! But I think the man is on to something. Expectations drive financial markets, they always have. What’s different is that the expectations-manufacturing industry has grown, a lot, and its impact on the economy has too. In the rational-expectations paradigm of a couple decades ago, this would be good news — better-informed markets would better anticipate what comes next. But reality is far noisier and more emotional than that, and market participants devote an awful lot of their brainpower to, in John Maynard Keynes’ words, “anticipating what average opinion expects the average opinion to be.”

From the perspective of Fed chairman Ben Bernanke and his soon-to-be-successor, Janet Yellen, the market’s initial reaction to the beginning of the taper is great news, for now. If markets come to expect that reductions in bond-buying by the Fed don’t result in meltdowns, the task of undoing the vast quantitative easing effort and getting Fed policy back to something approaching normal will be that much easier. But financial-market optimism and confidence have a tendency to eventually morph into exuberance and complacency. And it may be beyond the Fed’s power to control that cycle.

Entrepreneurs’ Brains are Wired Differently

Windows of entrepreneurial opportunity can open unexpectedly and briefly, typically under conditions of risk and uncertainty. Founder entrepreneurs must therefore be alert, tolerant of ambiguity and able to respond quickly when opportunity knocks. But does this mean that founders´ brains work differently, compared to other people, when detecting and choosing opportunities? Our research suggests that they do.

To learn more about the way founders think, we conducted a laboratory experiment that measured brain activity during a simple decision-making task. A group of 30 founder entrepreneurs, as well as 30 non-entrepreneurs, participated in a classic experiment called the Stroop Test. In this experiment, people are shown a series of images on which the names of colors are spelled in discordant colors. For example, the word red may be written in a blue color. Participants must then distinguish between the word itself and the color used to print it.

We measured brain activity over multiple cycles of the task, each cycle lasting one second. The brain activity of founder entrepreneurs was significantly different, compared to the non-entrepreneurs. In the initial stage of brain activation, when people first recognize the problem, founders were quicker to respond and were less inhibited. They quickly absorbed and embraced the problem, despite its ambiguity, In contrast, the non-entrepreneurs were slower during this initial phase. They tried to resolve more of the ambiguity before continuing.

In the later stages of problem processing, also within a split second, the founder entrepreneurs were different again. They dedicated more brain resources—in terms of information processing and speed—to resolve residual ambiguities. In other words, the entrepreneurs thought more intensely about the problem after they had already embraced it. In contrast, the non-entrepreneurs used less brain resources during this latter phase, presumably because they had resolved more ambiguity during the initial stage of the process.

These results are surprising and novel from the perspective of neuroscience. But does split-second decision-making in a lab say anything about how entrepreneurs really think? Evidence suggests it does. Quick responses matter. Imagine a potential founder who is scanning for opportunities — maybe there are early signs of new customer needs in the mobile e-commerce market. If she or he is not ready to embrace and explore this ambiguous problem, the opportunity will go undetected. Or consider a founder who is confronted with an urgent problem that must be tackled immediately. There is no time to resolve ambiguity and uncertainty. In fact, it may not be possible to do so. Instead, the founder must embrace the problem and move forward. In practice, we observe this kind of behavior often. Entrepreneurs frequently dive into a challenge without fully analyzing it. Deeper understanding evolves over time as they experiment and discover more about the market and customer, as in the Lean Startup approach to venture creation: embrace the problem, discover the customer, experiment and prototype, be ready to pivot and if necessary fail fast. We believe we are seeing this process sped up. And for the first time, we show that the brain is fundamentally involved.

Another study confirmed our thinking. This time we interviewed founder entrepreneurs about their decision-making processes. Founders were clearly more inclined to quickly grasp an opportunity, using a set of simple tests: did the opportunity fit their core strategy; did they already know the market; could they trust the other parties involved; did they have a good gut feeling; and finally, was the worst case scenario not too bad. A negative answer to any question could be a reason to stop. In this fashion, the entrepreneurs used simple criteria to rapidly embrace or reject opportunities. As one entrepreneur explained: “I think often I’ll make a tentative decision on gut, and I could do that almost immediately. I might within two minutes have the information that I’ll think, yes, this is worthwhile. Then I’ll go and hunt around for information that will help me to decide if that’s the right decision.” Just as in the first experiment, this entrepreneur responded quickly to the problem and was happy to delay the resolution of ambiguity and uncertainty until later stages of decision-making.

When combined, our studies add new evidence to support the view that founders think differently in decision-making, especially about problems and opportunities. Similar to the Lean Startup approach, founders embrace ambiguous problems more quickly, using simple rules to move forward, then dedicate more effort to resolve ambiguity and uncertainty during later stages of decision-making. In these respects, it appears that founders´ brains are wired differently. We expect this difference will be explained by a combination of factors: a genetic component, early development and learning, and adult experience in problem resolution and decision-making. The brain is not hard wired, it is richly complex. For us, this is good news. Change and variety are part of being human, and so we shouldn’t be surprised that entrepreneurs’ brains are a little bit different.

The Management Myths Hurting Your Business

Freek Vermeulen of London Business School explains how best practices become bad practices. He is the author of Business Exposed: The Naked Truth About What Really Goes on in the World of Business.

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Women Will Tolerate Sexually Explicit Ads — at the Right Price

Kathleen D. Vohs, the Land O’Lakes Professor of Excellence in Marketing at the University of Minnesota’s Carlson School of Management, and her colleagues set up a study in which men and women viewed advertisements for wristwatches. One of the watches was priced low, at $10; the other ran for $1250. The subjects viewed each watch against both a simple mountain backdrop and a sexually explicit scene.

So do women ever think sex sells? Professor Vohs, defend your research.

When men and women view sex-based ads featuring a cheap watch versus an expensive one, their reactions differ. Men’s reactions don’t vary much, regardless of how much the watch costs. Women, in contrast, strongly dislike the sexual ad when it’s selling a very cheap watch, but they tolerate it when it’s selling a watch that’s expensive.

Is this surprising?

We were working from a theory that woman have a vested interest in seeing sex portrayed in a certain manner, including advertising, because sex is much costlier for them from a biological perspective and socio-cultural perspective. In society, women can get into a whole lot of trouble if they behave sexually in a manner that’s not going to lead them to good long-term consequences. Biologically, they could have to carry a child. Socio-culturally, they could become stigmatized. There are a whole host of things in between.

We posited that, because of this, women are choosy about the way that they want sex to be portrayed, and they want it to align with their basic core values about the when and under what conditions sex should take place.

So we wanted to find a good way to communicate the idea that sex should be rare, valuable, and special, from an advertising perspective. What a better way to do that than by creating an implicit association between sex and a product that’s rare, valuable, and special?

What types of sexual imagery are you testing? What are your parameters for deciding that’s considered sexual or not?

In our study, we pilot tested a scene that’s highly sexual. It’s a woman who’s naked and pressed up against a wall, and then a man, also naked, pressing into her. His body is between her thighs. Her breast is showing, but he’s covering her other breast and her pubic region, and you see his buttocks. And her face shows an unmistakably erotic expression.

We pre-tested it so that men and women both think it’s super sexy, but nobody thinks that he’s dominating her. Nobody thinks she’s dominating him. And nobody really reports feeling like someone is being objectified. It’s just sexy.

But it can be difficult to talk about sex — or at least talk about it openly and honestly. How do you get people to talk about whether using sex in advertising is appealing or persuasive so know that they’re being authentic?

There’s actually some really interesting research on this from my colleagues Jaideep Sengupta and Darren Dahl. They basically figured out that, uniformly, women and men just say, hey, sexually-charged advertising doesn’t work on me; it’s disgusting and degrading. But that’s not actually what we think happens in the real world.

So people are lying to themselves? How can you tell?

When people view ads, they mostly do it in this really quick, heuristic way. They’re walking to the subway, and on the side of the wall is a very sexy scene. Or they’re flipping through a magazine or doing something online. For the most part, people are not devoting a ton of time to pondering how they really feel about ads when they actually see them.

And so we used a methodology that researchers use when they want to know people’s spontaneous gut reactions. We had participants view the advertisements while rehearsing a really lengthy number in their head. This kind of dual-task situation takes up people’s conscious cognitive capacity and allows their spontaneous reactions to emerge.

So we’re having them flip through several ads while rehearsing the lengthy number. And the sexual one is one of them. Then we ask the participants to report the number they were thinking of. So now they have their cognitive faculties back. Then they answer a bunch of questions: In particular, what about this one about the watches? People report their thoughts and feelings about the watch ads and that is how we gauged their reactions.

How do you determine what an “expensive” product is versus a “cheap” one? Where’s the line? 

We didn’t determine the price in any systematic way, but rather wanted to choose prices that were believable but clearly indicated cheap vs. expensive.

What if sex doesn’t have anything to do with your results? What if women just like expensive things more than cheap things?

We thought of that, we tested that by using a mountain scene. If we had just used sexual scenes, and some advertised high-priced products and some low-priced products, and women liked the former, you’d be like, so what? Maybe women just like expensive things.

But what you see is that when a mountainscape advertises an expensive watch versus an inexpensive watch, women’s reactions are essentially the same. They’re not sensitive to the price of the watch. That’s key.

In large part due to the diamond industry’s marketing efforts to make diamonds seem rare , jewelry ads have long used romantic and sexual imagery to pitch their products.

By using sexual imagery for a similar product — a watch — could it just be that women are more familiar with sex in this context? And that they would react differently if you showed, say, an expensive crystal bowl or a Lexus?

We would never say that our results apply to everything. That’s not tenable. But again, you’d be surprised at the range of products that use sex to sell.

For example, there’s this ad for Scrabble that features a naked woman. She’s all oiled up, and the Scrabble game is on her crotch. I’m not kidding. Now, whether that’s effective, who knows?

I would say that companies trying to attract male consumers seem to act as if the product category doesn’t seem to matter. For female consumers, I would bet it does. For example, I think you want the product to be something that women find appealing at some level. An expensive vacuum cleaner might hark back too much to the ‘50s, for instance.

The ads you used were based on heterosexual couple. But what about people who aren’t heterosexual?

The model we used in this study applies to heterosexual sexual relations only, but we’d really like to investigate that further.

So in the end, does your research mean that women are using consumerism — what they will or will not be inclined to buy — as a way to control how sex should be viewed?

That women want to alter how sexual images should be used and understood in the marketplace makes sense. But as long as marketers get the response they are looking for out of some segment (e.g., men), then women’s reactions to sex-based ads won’t have much influence on the way that sex is portrayed.

So what should advertisers change in light of your findings?

Our research suggests that what’s currently happening from a marketing perspective isn’t as effective as it could be. In general, advertisers simply skew their sex-based ads then towards their target market — men — who generally appreciate it.

We’re trying to say to marketers: You are missing out on a huge segment you could be appealing to. Women will tolerate sex-based ads. I want to be clear: we don’t find that women in general like sex-based ads outright. In the best of conditions, women’s reactions to the sex-based ad are equivalent to a neutral (nonsex) ad. But marketers can set up the right associations to up the odds that women won’t reject a sex-based ad outright.

If your product is appealing to women, or if you want to cut through the clutter, marketers should think about ways that to leverage sex-based ads in a way that women would find appealing.

Never Say Goodbye to a Great Employee

So-called “boomerang” employees — those who leave and then return — will become an increasingly valuable source of talent over the years ahead. In what’s perhaps the most frequently discussed example, some women chose to off ramp for several years sometime in their career, and many are eager for opportunities to return. Older workers may present boomerang possibilities as well.  Sixty percent of workers age 60-plus say they will look for a new job after they retire – possibly back in your organization.

But it would be a mistake to only focus on these two groups; there are also those who left initially due to personal issues, other job opportunities, or even a round of layoffs.

Former employees, of course, offer many advantages: they are familiar with your operations and culture, know many of your current employees and clients, and may require little or no training to start making contributions.  Often they are cheaper to hire, particularly if former managers have maintained contact while the employee is away.

So how do you make it so these boomerang employees actually want to return to your company?

The biggest challenge to leveraging boomerang talent for most organizations is the nature of the “out” process itself.  For most of us, departures, whether initiated by the employee or the company, are negative events.  They are weighed down with feelings of guilt and failure, often on both sides. This negativity occurs because conventional “outs” are shaped by the expectations we convey about the relationship from the beginning — that we want unconditional loyalty and that it will be rewarded (perhaps, “wink, wink”) with a steady career and comfortable retirement.  When these expectations are not borne out, due to either parties’ initiative, bad feelings are the inevitable result.

Setting the stage for positive “outs” and creating the possibility of happy returns requires redefining the relationship from the very beginning — setting different expectations during the hiring process.  Today more than 25 percent of the working population goes through career transitions every year and half of all hourly workers leave new jobs within the first 120 days, according to research conducted by Talya N. Bauer of SHRM Foundation; clearly the “employee for life” model has run its course.

Rather than implying that you expect indefinite tenure and unconditional loyalty, ask for the employee’s full discretionary effort for the time they will be here.  And rather than signaling that you will provide opportunities for life (something few hires actually trust anyway), make it clear that you are offering interesting and challenging work, coupled with fair arrangements, while it is available.

Reducing the implied promise of long-term protection and care sets the expectation that departures will naturally occur when that interesting and challenging work comes to an end. It conveys the expectation that departures can be mutually positive and facilitates multiple employment stints (off-ramps on-ramps, boomerangs, and retiree returns) as the company’s work load warrants.  This philosophy focuses on matching relevant skills and capabilities in the moment and recognizes, where appropriate, the legitimacy of concurrent employment arrangements.

Creating an environment that leverages the power of positive “outs” is greatly enhanced by forward thinking work arrangements that are designed to let people connect and reconnect with your organization in a variety of ways.  For example:

Flexible time: Flexible shifts, compressed workweeks, and individualized work schedules.
Reduced time: Part-time options, job sharing, self-scheduling, leave-of-absence programs, and cyclic or project-based work.
Flexible place: Mobile work and telecommuting.
Tasks, not time: Requirements to put in only as much time as it actually takes to get the work done, removing restrictions around a prescribed time or place.
Decelerating roles: Career path options that go ‘down’ (to lower levels of responsibility).

In addition to setting the right tone at the beginning, structure the exit process to facilitate re-entry and build a flexible network of talent possibilities. Invite them to join your network, build your own flexible talent pool, and create a residual knowledge bank. Regardless of whether their departure is voluntary or involuntary, it’s never wise to say goodbye to a good employee.

Talent and the New World of Hiring

An HBR Insight Center




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