Teams Blog

I always hesitate to write about “duh findings,” study results making so much sense, you wonder why the scientists bothered. But I know why they bother. Sometimes the expected answer proves incorrect. Also, though the information makes sense when you think about it, without the study you never would have thought about it. Those thoughts can lead to new insights.

Two Univ. of Maryland researchers asked a simple question, also the title of their study article: “When and Why Do Central Employees Speak Up?” As they point out, “When employees speak up openly on work-related matters, they aid in the early detection of problems and opportunities… and help their work groups respond successfully to unexpected situations…”

Logically, workers who are the most central to your work processes are the ones you want to hear from most. They are the most likely to see problems in the workflow or differences in the way teammates do the same work, for better or worse. Business professors Vijaya Venkataramani and Subrahmaniam Tangirala came up with the idea of surveying people of the same bank in India (see my study summary). This eliminated a lot of the factors that could complicate an experiment’s results. Everyone was in the same country within a distinct culture; subject to the same corporate policies, procedures and culture; in similar-sized units; doing basically the same work as the other groups. The professors asked about positive voice behaviors, defined in another study as “emphasizing expression of constructive challenge intended to improve rather than merely criticize. Voice is making innovative suggestions for change and recommending modifications to standard procedures even when others disagree.”

The survey asked group members whom they interacted with most (“central employees”) and other questions about other members. Central employees were found to speak up more only if they were considered influential, which was affected by whether they were considered good at their jobs (duh!). Even then, they spoke up only if they identified with the team, as shown by agreeing with statements like, “When I talk about my [work group], I usually say ‘we’ rather than ‘they.’” The professors suggest that managers who want employees’ feedback should improve their job skills and build team spirit. I agree.

But, I ask, do managers really want their people to speak up? I hear about plenty who claim to, but in reality discourage critical input. This led me back to the library book stacks to look up the study mentioned above. Respected teamwork researchers Linn Van Dyne and Jeffrey LePine at Michigan State Univ. wanted to know if people really draw a distinction between actions that are part of the job and those that go beyond the call of duty. You may again be thinking “duh,” but at the time (1998) scientists had not tested the assumption. Van Dyne and LePine (gotta love that rhyme) looked at “in-role” behaviors versus two kinds of “extra-role behaviors,” helping behavior and those voice behaviors. They used survey items about each worker such as:

• In-Role
  • “fulfills the responsibilities specified in his/her job description.”
  • “meets performance expectations.”
• Helping
  • “volunteers to do things for this work group.”
  • “attends functions that help this work group.”
  • “helps others in this group learn about the work.”
• Voice
  • “develops and makes recommendations concerning issues that affect this work group.”
  • “speaks up and encourages others in this group to get involved in issues that affect the group.”
  • “speaks up in this group with ideas for new projects or changes in procedures.”

The researchers surveyed 95 work groups at 21 employers plus each group’s supervisor. Each person rated themselves and four peers, and the supervisor rated every member. Van Dyne and LePine also did something too few researchers do, testing twice over six months. This makes it more likely the result you see at Time 2 (T2) was caused by the factor tested at Time 1 (T1). Otherwise, you only know there was a link between one description of the subject and another, not which caused the other.

In the Van Dyne and LePine study, people reported as helpful and speaking up at T1 (and T2) were rated more highly by everyone at T2. The effect was small, only adding 3% over in-role ratings. But if your manager is deciding between giving you a 3 or a 4 on your annual appraisal, that’s enough to make the difference. And it didn’t hurt ratings, I’m pleased to see.

I know from other studies that teams fostering open debate perform better than ones where no one speaks up, and people seen as helpers get more help from their co-workers. So if you are a team member, it is worth your while to take on extra job duties (making yourself more central) and non-job duties, including speaking up in the ways described above. Since self-ratings on extra-role behavior had no link to supervisor performance ratings, you might ask an honest someone on the team whether they think you help and speak up appropriately. If you’re a team manager, this research adds yet another reason to provide ongoing training to improve your teams’ job skills and reinforce team identity. The monthly “team-building activity” is not what I mean. This has to be a daily effort involving your every interaction with the team.

One point the Maryland researchers missed, I think, is that making more workers “central” to the group’s efforts could encourage more positive voice behaviors. You can do this by providing more cross-training, so more people take on the critical roles as needed due to overload, absences, and people moving on. There are so many wins in that for you, them, and the company, I would run out of hyphens to describe the situation: “Win-win-win-win-…”

Action Item: Do some reading about how to build team identity, or contact me for suggestions.

Sources:

• Van Dyne, L., and J. LePine (1998), “Helping and Voice Extra-Role Behaviors: Evidence of Construct and Predictive Validity,” Academy of Management Journal 41(1):108.
• Venkataramani, V., and S. Tangirala (2010), “When and Why Do Central Employees Speak Up? An Examination of Mediating and Moderating Variables,” Journal of Applied Psychology 95(3):582.

Finally: 500 sources.

When I started TeamTrainers ten years ago, I wanted to make sure I was only telling my clients what really worked. I’d attended some trainings on team development and read some books in the six years I’d been doing it at my work. Few spoke of actions I was finding most effective, and some of the suggestions seem really dubious based on what I had learned of small group psychology. So I fell back on my early career as a journalist and “hit the stacks.” Every week for six months, I spent a day in the libraries at the Univ. of New Mexico. In those days, I was able to go through a half-dozen articles a week, focusing on scientific studies, and a book every couple of weeks. I think I had around 350 sources when I finished the first draft of my training method, The SuddenTeams™ Program. Mind you, not all of my sources are scientific. I have to create practical applications from the science, and examples from the real world help me teach them.

After moving to Seattle, for three years I published an e-newsletter, hitting the Univ. of Washington libraries a full day once a month. TeamResearch News has morphed into a collection of study summaries arranged by topic on my Web site. Along with other sources picked up over time, I was over 450 sources when I restarted the business here in Raleigh. Now I walk to the library at North Carolina State Univ. once a month, but usually only half a Saturday. (Fortunately, it’s easier to find studies on the Internet these days.) With the typical human penchant for nice, round numbers, I yearned to top the 500 mark. With the personality and performance article I wrote up last week, I did it. You can download the bibliography (PDF) and count them if you like.

Most people seem impressed when they hear about my research into “The Science of Teams™,” but I have run into skepticism. A meeting of potential referral partners in Seattle fell apart when one person took an anti-science stance. Speaking as a former reporter, I put a chunk of the blame on the media. When they report on studies without putting them into the bigger context; or make one study appear to contradict the next by not reporting the different methods; and hype books by people on the fringe of scientific thought as if those theories have been proven, the average reader is understandably confused.

But science learns the same way you do: through trial and error. Scientists do this in a very controlled way, however. They eliminate other factors that could have caused the result they saw, and try the same test again with some slight changes to see if that makes a difference, and they invite others to try it. They pore through other scientists’ work to get ideas and avoid others’ mistakes. And when they’re done with their trial, they have to submit the report to an anonymous team of colleagues who critique the article, questioning the scientist’s methods and conclusions (hence the term “peer-reviewed journal”) . Then the journal editors take a crack.

Tiny differences in how studies are put together can cause very different findings. Over time, however, a trend will develop until most of the scientists in a particular field of study agree on some basic truths. Sometimes new evidence causes a huge shift in thinking. But more often, especially in the behavior sciences, consensus develops in a slow, methodical way over many years, and proves able to predict results. They’ll still call it a “theory” though, as in “the theory of gravity.” And there are always “outliers,” exceptions to the rule.

But the media do not report all this. There have been countless “shifts” reported that from a scientific standpoint are relatively minor. Whether you eat a lot or a little salt, or go on a high-fat diet to shed some pounds quickly, is almost irrelevant. The basic truths of nutritional science have held accurate through countless studies over decades. You have a much better chance of being healthier than the average person  if over the long term you:

• eat a variety of food, including fresh fruits and vegetables.
• limit your fat intake, especially saturated fats.
• eat no more calories than you expend through exercise and daily activity.

The same is true in teamwork science. Sometimes the latest fad or buzzword flies in the face of science, with no studies supporting it. It’s just an idea somebody has. These eventually disappear, but not before wasting some teams’ time, money, and goodwill. Other popular team building solutions are like diets: they might have a short-term, temporary effect, but as soon as you go off the diet/activity, the bigger, underlying issues are still there—and the pounds or problems return.

Winston Churchill famously wrote, “democracy is the worst form of government except all the others that have been tried.” Scientists make mistakes, have egos, hang onto theories longer than they should, and otherwise show the same foibles as the rest of us. I rejected at least 100 studies for various reasons, including my belief that some were poor science. But scientists follow a process, the scientific method, and subject themselves to checks and balances the rest of us would find highly irritating, for a simple reason: they want the truth.

I’ll take that over some consultant pushing his or her latest Big Idea, or popular but unproven practices, any day. And today, I have 500 reasons supporting me.

Action Item: Test one of your beliefs about leading or being on teams. Check out the list at TeamResearch News for short study summaries on the topic, or contact me.

Researchers have been looking into manufacturing, supply chain, and project management for years, often making recommendations to managers. Maybe you have followed their advice on the job. But there’s a problem, according to professors Francesca Gino and Gary Pisano: “Most formal analytical models in operations management (OM) assume that the agents who participate in operating systems or processes—as decision makers, problem solvers, implementers, workers, or customers—are either fully rational or can be induced to behave rationally.” In other words, scientific theories about how to run a plant or project assume that people:

• identify and react only to relevant information;
• have the same preferences in every situation;
• consider all options before making a decision; and
• make those decisions without emotion.

Oh, yeah, sounds like every workplace I’ve been in.

Gino, of the Univ. of North Carolina-Chapel Hill but headed to Harvard Univ., and Pisano of Harvard, point out in a 2008 paper that people effects on a  system’s performance have permeated other fields of research, from economics and accounting to the law. But “a ‘behavioral perspective’ has largely been absent in the field of operations,” they write. Because of this, current OM models can’t really explain the difference between one firm’s performance and another’s. In turn, managers don’t find OM theories very useful. Especially relevant to Teams Blog is the authors’ point that “behavioral operations” researchers needs to look into how individual cognition and “social norms and systems affect operations.” Gino and Pisano say based on previous research (cited below) that this will lead to very different predictions about what will fix specific issues.

In an interview I asked Gino, an assistant professor of organizational behavior, why OM scientists resist research on the effect of human behavior. She said there is “skepticism from some people that maybe it is not dramatic or very significant…” She noted that her co-author’s interest was sparked by going into organizations and seeing what worked, which suggests that other academics have not done that. However, she said, “The researchers, the more they hear, the more they understand that it is important to study the psychology of people.”

People effects have explained results that defied scientific theories in other fields. In the OM world, this could explain “the tendency of projects to run late and over budget or the tendency of organizations to over commit their R&D resources,” the article says. Researchers have identified many biases and questionable rules-of-thumb that affect our decision-making. Gino and Pisano provide a somewhat depressing list of 19 shortcuts humans take in their decision-making that can mess up the results. Some include:

• “Information avoidance—People’s tendency to avoid information that might cause mental discomfort…”
• “Confirmation bias—People’s tendency to seek information consistent with their own views or hypotheses”
• “Law of small numbers—People’s tendency to consider small samples as representative of the (entire) populations from which they are drawn”
• “Sunk costs fallacy—People’s tendency to pay attention to information about costs that have already been incurred and that cannot be recovered… when making current decisions”
• “Conservatism—People’s failure to update their opinions or beliefs when they receive new information…”
• “Hindsight bias—People’s tendency to think of events that have occurred as more predictable than they in fact were before they took place”

Take another example the professors explore, the “anchoring and adjustment” bias. People often start their thinking from a particular point, sometimes without a good reason for it, and then stay too close to that point. In one study, software developers given a higher anchor to start with ended up with higher final estimates than when they were given lower or no “anchors,” the article says. Sales forecasts are often off because they start with the previous year’s sales instead of an unbiased analysis of this year’s market.

Of course, behavioral operations researchers and managers can’t erase human bias. However, Gino and Pisano write, “operating systems can be designed in such a way that systematic errors are eliminated, or at least their negative consequences reduced.”

I asked Gino what advice she would give, for instance, a chief operations officer whose IT planner tends to anchor too closely to industry averages. “First, you need to be aware of the bias, which is a very simple lesson, but it is hard to recognize,” she said. Have someone act as a devil’s advocate, she suggested, asking the planner to bring alternatives to the table and questions like, “How did you come up with this number?” I would add, based on something else she said, that you cannot push the person for a certain number and be surprised when it turns out wrong. In the IT scenario, don’t anchor yourself to industry averages if the planner offers good reasons not to.

While other scientists are catching up to Gino, Pisano and other… okay, I can’t resist calling them “BO researchers*” at least once… take a look at the biases table in the article and maybe you’ll find your own answers. Or call me and I’ll show you how to account for people effects in your operation.

Sources:

• Bendoly, E. (06), K. Donohue, and K. Schultz (06), “Behavior in Operations Management: Assessing Recent Findings and Revisiting Old Assumptions,” Journal of Operations Management 24(6):737.
• Boudreau, J., W. Hopp, J. McClain, and L.J. Thomas (03), “On the Interface Between Operations and Human Resources Management,” Manufacturing & Service Operations Management 5(3):179.
• Gino, F., and G. Pisano (08), “Toward a Theory of Behavioral Operations,” Manufacturing & Service Operations Management 10(4):676.

*BO is American slang for a person’s smell or “body odor.”

A study I just posted to TeamResearch News about HR practices points out the way scientists think a little differently than most of us, and thus why I put a lot more stock in what they have to say than in most writers of business stories in popular publications.

This study was based on something called the Workplace and Employment Survey (WES), which is sponsored by the Canadian government and which people are legally required to respond to. Right off the bat, this makes the study different. The response rate of around 96% is four times better than the typical response rate to surveys of roughly 25%. Scientists without government sanctions supporting them go to great lengths to make sure results are not too badly skewed due to those low response rates, and thus are similar to what they would have gotten had they reached everybody.

Scientists are also careful about drawing conclusions from their work. A major mistake most people make when reading about studies—including most journalists and consultants—is to confuse correlation with causation. Simply put, just because two data are linked, that doesn’t mean one caused the other. In the HR study, for example, higher levels of training at a workplace were linked to higher levels of people quitting. Is this because a better-trained worker has more skills they can use to get a job elsewhere, as the scientists suggest based on other research? Probably. But it also could be that companies with higher “quit rates” have to provide more training because they have to hire more people to backfill those positions. A simple correlation does not show whether the training came first or the quitting came first, and the article’s authors say that. (Their study design provides some evidence, though.)

Scientists will point out where their data are lacking. In this study, the authors point out the WES data is not very detailed. It is possible that classroom training leads to higher quit rates, but on-the-job training leads to lower ones. You can’t draw a conclusion about all training from this gross figure (gross as in “general,” not as in “yucky”).

Scientists also are pretty quick, at least in publications, to point out where they were wrong. In part this is because they know in peer-reviewed journals, where other anonymous scientists critique the articles before they are published, if the authors don’t admit they were wrong, the reviewers will tell them. In this study, some of the researchers’ hypotheses turned out to be wrong, and they state that.

Finally, scientists are careful to limit their conclusions to what they actually investigated and found. For example, these authors point out the study was only about voluntary turnover, and there are likely to be compelling reasons for a company to offer training despite it harming this one metric. (If you doubt that, I refer you to the powerful evidence in the book The Fifth Discipline.)

Contrast all this to stories in popular business publications. They are not usually reviewed by other experts on the topic before publication. They assert positions without offering hard data to back it up. Their language is imprecise. I recently commented on another writer’s blog that the best “practices” a post claimed for teams were not “practices” at all, but descriptions of well-performing teams.

These stories also make claims they can’t support. A press release that got coverage from a national professional organization estimated financial losses due to workers who avoid conflict at work. But when I contacted the firm that put out the study, they admitted the sample was just anybody who responded to an online poll, and the demographics showed that the respondents in no way represent the common worker. Sixty-seven percent were female, for example. Two-thirds worked in companies of 750 or more and 71 percent had college degrees. Most people work for smaller firms, and only around 25% of Americans have degrees. Yet the release claimed, “New research reveals employees waste an average of $1,500 and an 8-hour workday for every crucial conversation they avoid.” No, it doesn’t. It says workers who use the Internet and happened to see an ad for the survey and are interested enough to respond gave that as the average answer, which probably would not turn out to be accurate if an outside observer actually measured the time.

In short: reader beware.