- Making HR Measurement Strategic
- How a Decision Science Influences HR Measurement
- Connecting Measures and Organization Effectiveness
- The "LAMP" Framework
- Today's HR Measurement Approaches
- Conclusion
- Software to Accompany Chapters 311
This chapter is from the book
This chapter is from the book
This chapter is from the book
How a Decision Science Influences HR Measurement
When HR measures are carefully aligned with powerful, logical frameworks, human capital measurement systems not only track the effectiveness of talent policies and practices, they actually teach the logical connections, because organization leaders use the measurement systems to make decisions. This is precisely what occurs in other business disciplines. For example, the power of a consistent, rigorous logic, combined with measures, is what makes financial tools such as economic value added (EVA) and net present value (NPV) so useful. They elegantly combine both numbers and logic, and help business leaders become better at making decisions about financial resources.
Business leaders and employees routinely are expected to understand the logic that explains how decisions about money and customers connect to organization success. Even those outside the finance profession understand principles of cash flow and return on investment. Even those outside the marketing profession understand principles of market segmentation and product life cycle. In the same way, human capital measurement systems can enhance how well users understand the logic that connects organization success to decisions about their own talent, and the talent of those whom they lead or work with.
The greatest opportunity is in improving those decisions that are made outside of the HR function. Just as with decisions about financial and customer resources, talent decisions reside with executives, managers, supervisors, and employees who are making decisions that impact talent, including their own talent, as well as those they are responsible for or interact with. Even in core HR processes, such as succession planning, performance management, staffing, and leadership development, improvements in effectiveness rely much more on improving the competency and engagement of non-HR leaders than on anything that HR typically controls directly.
Why use the term science? Because the most successful professions rely on decision systems that follow scientific principles and that have a strong capacity to incorporate new scientific knowledge quickly into practical applications. Disciplines such as finance, marketing, and operations provide leaders with frameworks that show how those resources affect strategic success, and the frameworks themselves reflect findings from universities, research centers, and scholarly journals. Their decision models and their measurement systems are compatible with the scholarly science that supports them. Yet, with talent and human resources, the frameworks used by leaders in organizations often bear distressingly little similarity to the scholarly research in human resources and human behavior at work. For examples, see the work of Sara Rynes and colleagues.1
For measures to support a true decision science, they must do more than just incorporate facts and numbers. More specifically, a decision science for talent draws upon and informs scientific study related to people in organizations. There is a vast array of research about human behavior at work, labor markets, and how organizations can better compete with and for talent and how it is organized. Disciplines such as psychology, economics, sociology, organization theory, game theory, and even operations management and human physiology all contain potent research frameworks and findings based on the scientific method. A scientific approach reveals how decisions and decision-based measures can bring the insights of these fields to bear on the practical issues confronting organization leaders and employees. You will learn how to use these research findings as you master the HR measurement techniques described in this book.
Boudreau and Ramstad noted five important elements in a mature decision science: a logical decision framework; management systems integration; shared mental models; a focus on optimization; and data, measurement, and analysis. In this book, we focus on two of these: logical decision frameworks and the data, analysis, and measures supporting them. So, let's define what we mean by a decision framework and how measures integrate with it.
Decision Frameworks
A decision framework provides the logical connections between decisions about a resource (for example, financial capital, customers, or talent) and the strategic success of the organization. This is true in HR, as we show in subsequent chapters that describe such connections in various domains of HR. It is also true in other more familiar decision sciences such as finance and marketing. It is instructive to compare HR to these other disciplines. Figure 1-1 shows how a decision framework for talent and HR, which Boudreau and Ramstad called "talentship," has a parallel structure to decision frameworks for finance and marketing.
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Reprinted by permission of Harvard Business School Press, from Beyond HR: The New Science of Human Capital by John Boudreau and Peter M. Ramstad. Boston, MA, 2007, pp. 31. Copyright © 2007 by the Harvard Business School Publishing Corporation. All rights reserved.
Figure 1-1 Finance, Marketing, and Talentship Decision Frameworks.
Finance is a decision science for the resource of money, marketing is the decision science for the resource of customers, and talentship is the decision science for the resource of talent. In all three decision sciences, the elements combine to show how one factor interacts with others to produce value.
To illustrate the logic of such a framework, consider marketing as an example. Investments in marketing produce a product, promotion, price, and placement mix, which creates responses in certain customer segments, which in turn creates changes in the lifetime pro fits from those customers. Similarly, with regard to talent decisions, efficiency describes the connection between investments in people and the talent-related programs and practices they produce (such as cost per training hour). Effectiveness describes the connection between the programs/practices and the changes in the talent quality or organizational characteristics (such as whether trainees increase their skill or their interactions with others in the organization). Impact describes the connection between the changes in talent/organization elements and the strategic success of the organization (such as whether increased skill actually enhances the organizational processes or initiatives that are most vital to strategic success). The chapters in this book show how to measure not just HR efficiency, but also elements of effectiveness and impact. In addition, each chapter provides a logical framework for the measures, to enhance decision making and organizational change.
Data, Measurement, and Analysis
In a well-developed decision science, the measures and data are deployed through management systems, they are used by leaders who understand the principles, and they are supported by professionals who add insight and expertise. In stark contrast, HR data, information, and measurement face a paradox today. There is increasing sophistication in technology, data availability, and the capacity to report and disseminate HR information, but investments in HR data systems, scorecards, and integrated enterprise resource systems fail to create the strategic insights needed to drive organizational effectiveness. HR measures exist mostly in areas where the accounting systems require information to control labor costs or to monitor functional activity. Efficiency gets a lot of attention, but effectiveness and impact are often unmeasured. In short, many organizations are "hitting a wall" in HR measurement.