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Gary Hamel (London Business School) characterizes management as the technology of human accomplishment. It came into its own in the late 1800s and early 1900s, as the USA entered the new century. Among the traditional long-standing factors of production (land, labor, and capital) in the British tradition, management became the fourth, the one that helped make the other three productive.

Today, despite more than a century of management in organizations around the world, there is a growing dissatisfaction with management practice. It seems to have lost its way. The factors of production are at odds with each other, laying competing claims on the proverbial ‘pie.’ Management is gripped in an introspective search for meaning and validity.

In the 1900s, the traditional form of management (sometimes called Management 1.0) seemed rather sure of itself. It was about setting goals and objectives, then directing subordinate staff to achieve them. It was top-down, command and control, and authoritarian in nature. It helped train manual workers in productive ways to do their work. But now (in 2018), knowledge workers dominate in the workplace, and managers often know less than workers about how to get the work done. Today’s millennial workers want the freedom to do the work in their own way, in short, to be creative. They hunger for organizational goals that are more uplifting than profit or shareholder value.

Meanwhile, other forces are at the gates. There is a steady undertow pulling organizations toward the adoption of the latest data measurement and storage initiatives, with an implied promise that they offer a more ‘scientific’ approach to management. Louis Columbus, writing in Forbes, notes that a recent study found that ‘big data’ adoption rates reached 53% in 2017, up from 17% in 2015 (in the large companies referenced). Telecommunications and financial services companies have been the largest adopters so far, while data warehouse optimization has been the use case most cited, followed by customer/social analysis. The logic of big data is that large data sets can be mined to reveal patterns, associations, and trends, especially relating to human behaviors and their interactions.

But ‘big data’ is only one in a series of measurement initiatives that have entered the management toolbox over the years. Others have gone before, including the Balanced Scorecard, Six Sigma, Total Quality Management (TQM), Objectives and Key Results (OKRs), and Performance Management with Key Performance Indicators (KPIs). Each of these initiatives has its own built-in logic, but in general, they offer quantitative approaches surrounding so-called ‘scientific’ or evidence-based tools. But are they scientific or evidence-based? Do they offer validity in management?

In 1963, William Bruce Cameron noted that “not everything that can be counted counts, and not everything that counts can be counted.” I believe he was right, but too optimistic. Little that can be counted counts, and only that which counts should be counted. There is a clear need to fight the social undertow surrounding measurement initiatives, least organizations are dragged down.

When it comes to goals, a fundamental weakness of Management 1.0 has been that a management team can set their own goals and objectives, then declare success when they are achieved. It makes it easy to game the system. For instance, to say that the goal of an organization is to maximize shareholder value and then use financial engineering tricks to maximize the stock price is self-serving, especially when the bonus compensation of the management team is tied to related measures. It means that an organization may serve the incentives of the management team rather than the needs of customers or other key stakeholders.

For validity in management, it remains very important to thoughtfully select the goals, as well as what will be measured. For instance, if achieving organizational effectiveness was simply about measuring a set of key performance indicators (KPIs), then most organizations would be performing very well today. Unfortunately, not all indicators are created equal, and measurement alone does not verify causal linkages. Organizations often miss the point that before investing in a measurement initiative, strategies need to be well crafted, outcomes and associated indicators properly set, and one or more results chains identified and validated. Only then can a specific indicator be relied upon to provide a short-hand measure of effectiveness that is objectively valid.

This might be a good time to make a distinction between meaning and validity. Sociologists tell us that shared meaning in groups is socially constructed. We, along with our associates, family, and friends, jointly construct the meaning of our everyday existence. In an organization, shared beliefs emerge over time and become part of an organization’s culture, along with a sense of “how we do things around here.” Within a socially constructed view of the world, how can measurement approaches add validity? The short answer is that they probably can’t, except for social groups (largely made up of professionals) that attach shared meaning to quantitative methods.

In society more generally, we see examples of social groups that have been led to attach shared meaning to political views that are not in their best interest, through appeals to their emotions (e.g., political wedge issues such as immigration, abortion, civil rights, gay rights, etc.), or through appeals to symbols of their cultural identify (e.g., the flag, the military, religion, etc.). Because meaning is jointly constructed within a social group, it is hard for individuals to abandon a shared view unless their social group also abandons it.  To avoid being deceived by these media tactics, truth or validity must be confirmed by triangulation with independent sources that use different methods. If independent sources agree regarding the resulting conclusions, we can be relatively sure that we have arrived at the truth.

In summary, I want to leave you with four takeaways from today’s discussion:

1) measurement does not provide meaning by itself;

2) meaning is socially constructed through the interaction of group participants;

3) meaning is not the same as validity or truth, and

4) validity and truth are confirmed through triangulation with independent sources that use different methods.

A new philosophy of management that utilizes these principles is Management by Positive Organizational Effectiveness, which I have mentioned before. Under this approach, the goal of every organization is the same, that is, to be effective within its environment, and product and service teams are empowered to consider a key question every day, “How can we serve our environment well today?” Business, government, and nonprofits have the same challenge. It’s a probing question, and the answer may change over time. It’s a question that will be difficult to answer quickly in a bureaucratic, top-down, command-and-control management system. It is best handled by flexible, team-based management focused on the success of individual offerings to the environment by capable teams of knowledge workers. Of course, we are not suspending the principles of accounting, economics, or finance in making such decisions, but these are not necessarily constraints. The approach focuses on staying close to the customer, wandering around, and being passionate about serving.

For more information, grab a copy of my 2017 book, “Become Truly Great: Serve the common good through Management by Positive Organizational Effectiveness.”

Charles G. Chandler, Ph.D.

References:

Marsen, S. 2008. “The Role of Meaning in Human Thinking.” Journal of Evolution and Technology (17/1), pp. 45-58.

Chandler, C. G. 2017. Become Truly Great: Serve the Common Good through Management by Positive Organizational Effectiveness. Author Academy Elite: Powell, OH