Talent Measurement Schools of Thought

Here is a puzzle: In our day-to-day life we do not treat people as inanimate objects—but we try to measure them as if they are! We treat the people in front of us as living, breathing, reacting entities, but few consider the complexity and reactivity of human nature when developing or managing with measures. Why the inconsistency?

Two Schools of Thought: The Taylor and Mayo Dichotomy

To solve this puzzle, you have to go back to school—graduate school. As a graduate student, you’ll probably learn one of two different approaches to talent measurement. One school of thought is focused on the technical aspect of measurement, and the other on the human aspect. The challenge for measurement professionals is to master both schools of thought. The two are rarely reconciled, however. Professionals generally have expertise primarily in one approach.

The technical or engineering school will teach you how to calculate reliability and validity, and introduce you to different measurement methods. This school of thought dates back to Frederick Taylor, one of the first manufacturing engineers. Frederick Taylor is considered the father of scientific management, which emphasizes task analysis, efficiency studies, time-and-motion studies, and using compensation schemes for motivation. 

The human relations school has a different point of view: Employees are complicated, and don’t work mechanistically. If your graduate program emphasizes human relations, you’re likely learn more about personality types or team functioning measures that will facilitate interactions between people at work. You’ll be introduced to validity and reliability, but you’ll be taught very little about the technology and theory of measures. The human relations school of thought dates back to Elton Mayo, a psychologist. 

The ghosts of Taylor and Mayo haunt today’s organizations. To this day, consultants, managers, and leaders adhere to one school or the other. Taylor adherents tend to advocate for measurement as a formal and rigid process. Mayo adherents focus more on group processes, interpersonal communication, and intrinsic motivation. 

Both Taylor and Mayo made essential contributions to the art of management and leadership. But it’s not an either/or choice. It often takes decades of experience to merge the two schools of thought into a practical working knowledge of measurement. Some never see the dichotomy and its implications.

I’m writing this blog post in the hope that we can accelerate the process of combining and ultimately uniting these two schools of measurement.

The Engineer: Frederick Winslow Taylor (1856 – 1915) “In the past the man has been first; in the future the system must be first.”

Taylor grew up affluent and gifted in the second half of the 19^th century, in an era of huge industrial change. He chose not to follow his father into the legal profession, although he was accepted into Harvard. Instead, he worked in industry, starting as a machinist and becoming a foreman, and went on to study engineering. 

As an engineer, he first improved manufacturing technology such as lathes and forging equipment. Early on, he noticed that these technical improvements demanded similar organizational innovations to be effective. As his ideas developed, he saw manufacturing as a larger system that could be improved by optimizing the various pieces to contribute to the larger system. Over the course of his career, he contributed his ideas to equipment (he had several important patents), business processes (such as accounting methods), and methods of managing employees.  

Taylor and Time-and-Motion Studies

As he looked at the larger manufacturing picture, Taylor was concerned that laborers were not working at full capacity. To fix this problem, he identified the optimum work-output level, and provided incentive pay for this level of output.  

Determining workers’ optimum output involved time-and-motion studies. Taylor divided the work into steps, each of which he timed separately. He then combined the time for each step into a total time for the job. By dividing the work day by the total job time, he arrived at an optimum production rate.

Workers were paid on a graduated scale. Low levels of output were paid very little, but as productivity approached the maximum, unit pay increased. Workers attaining the optimum production rate would be paid 60% more using Taylor’s methods. 

While he became infamous for his time-and-motion studies, it’s important to recognize that, for Taylor, these studies were part of a larger system of managing employees. Taylor used worker productivity as a talent measure. He studied measures of productivity to make decisions, organize work, set production expectations, motivate employees, and identify employees to retain. In the best cases, Taylor’s scientific management methods could reduce costs and increase productivity by 50% to 100%.

Human reaction to measures and management methods didn’t factor into Taylor’s thinking. He was convinced that employees only work for money. Labor problems were simply an engineering challenge to be managed. Taylor paid lip service to selecting and developing talent—he mostly set output targets. Workers who were able to keep up the pace self-selected and developed their capability.

Taylor’s blind spot—the human factor—can be seen in many contemporary organizational improvement interventions, such as re-engineering, which has a success rate as low as 30%. Human readiness and acceptance of change is often a barrier to re-engineering success.   

Taylor’s approach also was inconsistent. Sometimes it worked, sometimes it led to significant problems.

Employee reactions to Taylor’s intervention often led to work actions and strikes.  Ultimately there was an congressional investigation. By the time of Taylor’s death at age 59, Congress had outlawed use of stopwatches and bonus payments in the federal government. Scientific management was increasingly discredited.

The Humanist: George Elton Mayo (1880 – 1949) “So long as … business methods take no account of human nature … expect strikes and sabotage to be the ordinary.”

Mayo grew up in a distinguished Australian family. He began his studies in medicine and ended up studying psychology, focusing on social interactions at work. His most famous research work can be found in the Hawthorne studies, which demonstrated that employees are largely influenced by social factors, and that they react to being observed.

Mayo’s most important work coincided with the Great Depression. He believed that the industrial revolution had shattered strong social relationships in the workplace, and he found that workers acted according to sentiments and emotion. He felt that if managers treated workers with respect and tried to meet their needs, then both workers and management would benefit.  

Mayo’s research indicated that belonging to a group is a more powerful motivator than money. In his management philosophy, he saw attitudes, proper supervision, and informal social relationships as the key to productivity.

Some consider Mayo’s work to be a reaction to Taylorism. But Mayo was also concerned with output and productivity. Unlike Taylor, however, he was interested in the social and psychological interventions that increased productivity. These interventions are indeed helpful, and understanding the human factor is critical.  

Thanks to Mayo’s work, we recognize that, in organizations, informal social structures matter as much as formal structures, such as the chain of command. For example, a likeable senior engineer who dislikes a new manager could undermine the manager’s authority by making jokes at his expense during every meeting. In effect, the engineer becomes more influential than the manager—outside the hierarchy of the organizational chart.

Today, many organizational interventions emphasize team-building, and are based on the recognition that organizational culture is important, and managers have ongoing relationships with employees. By acknowledging the importance of the informal structure of an organization, factors such as relationships, informal leadership, and influence can be aligned with organizational needs and direction.

Mayo’s insights were synthesized into a school of thought referred to as human relations. The human relations school continues strong to this day, often in the form of leadership development, team building, or change initiatives. 

The insight missed by Mayo is that measurement—even Taylor’s productivity measurements—are essentially a social process. Measurement is simply a method of communication—a way to make meaning between groups.

Since Mayo, many people have failed to make this essential connection: We can extend Mayo’s insight into the importance of informal (social) structures into an understanding of the importance of the informal (connotative or personal) meanings of measures. As I have discussed before, the informal meanings of measures matter as much as, if not more than, their formal meanings. Like social structures, these connotative meanings can be managed—but only when their existence and importance are acknowledged.

If you’re creating an organizational, and you follow Taylor, you may believe that compensation is the sole motivation for performance and advancement. If you follow Mayo, you may believe that love, fear, and other ineffable human factors are the primary motivators. 

In the same way, the designers of a formal measurement system may believe that their measures will motivate by providing people with a positive opportunity to make more money (the denotative meaning). Instead, the designers may find that the connotative meanings provoke reactions that ultimately trump their intentions—reactions from outright rejection to gaming the system.

It’s odd that many of our measurement systems haven’t progressed beyond Taylor’s way of thinking. We have many tools to address the informal meanings of measures—tools we can draw on from interpersonal communications theory, management practices, and organizational learning.

Another danger of following Mayo’s approach is that it often pays too much attention to the informal and emergent social structures of an organization. While these informal structures are powerful influences on individual performance, it is possible to merge formal and informal organizational structures into a shared structure. This is where measurement can be incredibly effective, if it is used as a means of communication: It can create shared meaning that bridges the organizational and personal definitions of performance, motivation, and reward.

Finding a Middle Ground

What’s most unfortunate about the Mayo vs. Taylor bifurcation is that they were both right: The difference between the two schools of thought is ideological, not practical. In practice, we use both approaches. We need both engineers and social scientists (psychologist and sociologists) to run organizations efficiently.

If we attend one graduate school, we may learn to develop measures that are technically good, but we’ll have trouble assessing the human reaction to measurement. If we attend another school, we may learn to facilitate social interaction and meaning, but we won’t be trained to motivate, direct, or improve performance through measurement and feedback. Personally, I attended a more technical school, but my life and work experiences have led me to appreciate a balanced approach.

What Taylor missed was the importance of social structures in motivation, and the human factor in reaction to measurement. What Mayo missed was that measurement in itself is a social process, and measures have informal (social) meanings that can be managed.

Today, 80 years after Mayo’s Hawthorne studies, we should be able to merge the two schools. There is a wealth of possibilities for applying Taylor’s ideas in measuring individual productivity. At the same time, we’ve vastly increased our understanding of human relations—there’s a huge industry that’s evolved out of Mayo’s original insights.

Finally, in resolving the polarity of these two approaches, we need to acknowledge that measurement is communication, and that communication is shared meaning. By starting with a simple point—that people always react to measurement, and that the reaction is unpredictable—we can take the denotative and connotative meanings of measures, the formal and informal structures in organizations, and the two schools of thought, and synthesize them into an elegant, effective approach to talent measurement.  

Comparison, Context, and Connotation: Turning Data into Insight

How can we turn inert data into dynamic insights? We turn measurement into data, data into information, information into meaning, and meaning into insight. Each of these four steps is critical. If you are building talent measurement systems or using talent measurement as part of your management responsibilities you should know the three key Cs.

Turning Data into Information: The Key of Comparison

Many organizations have generous amounts of data filling their databases, but few organizations make use of it. Data by itself is meaningless. And at this point, it’s clear that our processing skills haven’t kept pace with our ability to collect terabytes of it. 

Imagine looking at a column of numbers. What does it tell you? Nothing, I imagine.

Now imagine that you can look at the same column of numbers with other data points for comparison. You might compare each piece of data to:

• Time (each data point is part of series across time)
• Employees (each data point is one employee’s performance)
• A norm (one number represents typical performance, and another number represents actual performance)
• A goal (one number is the target, and the other number is the actual amount)
• An implication (one number is an employee’s performance, the other number is an incentive payment associated with it).

Data is useless unless it is compared to something. Trends matter. 

The need for comparison isn’t always obvious, however. Occasionally, I’m asked to analyze an employee survey and there’s no good way to compare the data. Often, data is collected without considering how it will be turned into information. you have to compare across time, subgroups or at least across different questions. To turn data into information, you need a comparator.

To understand a survey, comparator can be previous survey results, a goal, normative responses, or the results of measurement in similar organizations (the basis of benchmarking).   In a pinch, you can compare survey questions to each other, but this provides limited information.

Turning Information into Meaning: The Key of Context

Information, however, is also never useful in itself. To have meaning, it needs to  be placed in context and interpreted. It’s critical to ask why.

• Why does the pattern of numbers vary with time?
• Why do employees’ levels of performance vary? Are there differences in aptitude, skill, or motivation? Or are they working in different environments?
• Why is our data above or below the norm?
• Why are (or why aren’t) we achieving the goal?
• Why am I receiving a smaller bonus than other employees?

In many situations, meaning is elusive because it requires a broad understanding of context. If two employees have very different performance results, are they working in the same context? Does one employee have more difficult tasks—a more involved project, a larger territory, more complex machinery to run? Only when you’ve determined that the context is comparable can you infer that different levels of skill or motivation underlie the difference in results.

Management is drowning in information. As we collect more and more data, comparison becomes easy. But, putting information into context requires bridging different data sources, integration, and creative thinking. The strategic and operational environment matters. Important information is relevant, given the context. 

Turning Meaning into Insight: The Key of Connotation

Given the process of translating data into meaning, it’s easy to see why there is miscommunication. Two people looking at the same data can have completely different interpretations—and they may not even know it. 

This may seem paradoxical at first; after all, measures are precise way of communicating. We wouldn’t say a car “doesn’t need much gas.” Instead, we use mathematical precision to talk about miles per gallon. Imagine the reactions of shareholders and analysts if an executive talked about “a pretty good investment,” rather than discussing a percent return on equity.

Nevertheless, people with different perspectives or goals see measurement results very differently. As with other types of communication, measures have denotative and connotative  meaning. Making a distinction between the two types of meaning is the key to understanding measures.

To illustrate the difference, let’s look at the term re-engineering. In literal (denotative) terms, re-engineering is a way to understand business processes and optimize them. In subjective (connotative) terms, there are implications of re-engineering—mass layoffs. I once used re-engineering as an example in a speech that I was giving in a company and received a strange and hostile response.  It turns out that the there was a history of using the term euphemistically!  That speech never recovered—the mere use of the term destroyed any trust between the audience and me.  

In measurement, the denotative meaning is often defined mathematically. It is the connotative meanings, however, that often matter to employees. In other words, the implications of the measures are more important than the measures themselves.  

It’s important to remember that the implications of measures are personal. How employees interpret measures, and how they react to performance appraisals, are influenced by their upbringing, their personalities, their motivations, and their worldviews. 

For example, if a salesperson is focused primarily on money as an indicator of success, he may consider performance measurement only in terms of the size of his bonus. It’s likely this will lead to misunderstandings with a company executive, who is looking at the measures to answer different questions: Does the salesperson need more training? Is the product “good.” Did the customers have good experiences?

In another common example, it may not be possible for an employee to receive measurement-based feedback constructively for any number of reasons. If the employee is perfectionistic or competitive, she may only be able to receive the feedback as criticism. Another employee may be so consumed with feeling miserable about failing to meet last year’s goal that he can’t engage in an authentic conversation about the future.

In my experience, many employees will try to avoid measurement because they distrust management, or are afraid of being targeted in a blame-oriented culture. This example of connotation is probably all too familiar to the readers of this blog.

Of course, personal perceptions and assumptions can be influenced. To build meaning, and ultimately insights, organizations must spend time decoding both the denotative and connotative meaning of measures. Unless both the denotations and connotations are addressed, there is little chance of communicating with the measures successfully, or gaining insight from the data.

Decoding measures is a dialogue. As a consultant, I need to discuss both the objective and subjective implications of measures with my clients. Managers need to do the same with their employees. It is only through dialogue that the measures’ contrasts and contexts, the meanings and implications, can be understood.

This is the path from data to shared insights. Of course, the path is littered with suggestions from many sources. But the salient point remains: To reach shared insights from measurement, organizations need to confirm that there is shared meaning. We can think about this as a four-step process:

There is no data without measurement, no information without data in comparison, no meaning without an understanding of information in context, and no insight without communicating shared meanings.

Is your organization taking steps to make sure that insights are gained from measurement?

In the next posts I’ll talk about motivation, organizational learning, and accountability. In the meantime, I welcome your thoughts.

Charley Morrow

Human Performance Measures: Start of a Series

I’ve been working with people measures for more than 25 years. Nearly every day, I see strong reactions to these common leadership tools.  Some embrace measurement as a tool for positive change, and others are nervous. Some question the measures, and others hide behind the authority of the data. 

These reactions to measurement and data fascinate me. They also hold the key to getting results from measurement systems. 

When measurement systems work well, people develop understanding, gain insight, become motivated, and set new directions. Just as often, however, measures simply do not work. In these cases, people ignore the measures or build elaborate defenses to dodge, manipulate, or diminish the data.

Over the next few months I’ll be writing about how systems and people respond to measures of human performance and how organizations can get beyond negative reactions. This is a topic I’ve been researching for years, and it may be my strongest and most nuanced area of understanding. 

I started my career focused on measurement systems. I took enough graduate courses in statistics and methodology to work as a psychometrician, and my dissertation combined the disciplines of psychology and economics. 

As I matured and worked in the real world of organizations, I started to see that the value of measurement can be found less in precision and mathematical finesse than in communication and learning. The most elegant performance management system is useless unless it is genuinely called on to help people communicate, learn, and adapt. 

In other words, measures need to be applied to produce data; data needs to be reviewed and interpreted to be useful; and useful information needs to be considered in context if people are to learn and improve. 

I can say with confidence that measures and data alone will not change organizations or behavior. There are too many psychological, organizational, and social factors that can prevent measures from translating into learning and improvement.

As a society, we spend huge sums of money on human performance measurement—and we start measurement early. All of us are familiar with the U.S. public education system, which now tests every student in the third through eight grade annually. In a number of states, databases are being developed to link these test scores to school, teachers, and student demographic information. 

When we graduate from the public education system, we find that most large organizations rely on annual employee appraisal systems. A manager can spend a few months each year rating employees, summarizing the information, and providing feedback. 

Despite the intensity of the data-gathering, improvement is not obvious. Many are dissatisfied with the measurement systems.  As a result, these measurement systems are often re-imagined and implemented with great hope and promise, only to fail. I don’t think much of this activity and investment. Don’t misunderstand: I’m a fan of measurement, because it’s critical to precise feedback and growth. But I’m an advocate for thoughtful investment in measurement. I’ve seen its transformative power. 

The public education system is still experimenting with measurement systems, and will be for years to come. Some corporations rethink their annual appraisal systems regularly. 

Technological and social trends suggest that performance measurement will only increase. Some argue that this investment is inappropriate. Addressing the merits of this societal investment isn’t my purpose here. My purpose is to make sure that individuals, organizations, and society get more value from the investments that are made.

I have workable tools and tips to make sure all of this data yields some return. Paradoxically, I won’t spend much time writing about measures. As I’ve said, it’s not as much about the measures as how they are used. I hope you will find the posts in the following weeks useful.