This chapter is from the book
This chapter is from the book
This chapter is from the book
Today's HR Measurement Approaches6
Table 1-1 shows four key categories and examples of today's HR measurements. The last two columns of Table 1-1 describe the primary appeal of each category of measures, and the "tough questions" that reveal potential limitations or assumptions of each method.
Table 1-1. HR Measurement Alternatives
|
Measurement Approach |
Example Measures |
Primary Appeal |
Tough Questions |
|
Efficiency of HRM operations |
Cost per hire, time to fill, training costs. Ratio of HR staff to total employees. |
Explicit cost-value calculations. Logic of cost savings is easy to relate to accounting. Standardization makes benchmarking comparisons easier. |
Wouldn't outsourcing cut costs even more? Do these cost savings come at the price of workforce value? Why should our costs be the same as the industry's? |
|
HR activity and "best-practice" indexes |
Human capital benchmarks. Human capital index. |
HR practices are associated with familiar financial outcomes. Data from many organizations lends credibility. Suggests there may be practices or combinations that generally raise profits, sales, etc. ... |
What is the logic connecting these activities with such huge financial effects? Will the practices that worked in other organizations necessarily work in ours? Does having these practices mean they are implemented well? |
|
HR dashboard or HR scorecard |
How the organization or HR function meets goals of "customers, financial markets, operational excellence, and learning." |
Vast array of HR measures can be categorized. The "balanced scorecard" concept is known to business leaders. Software allows users to customize analysis. |
Can this scorecard prove a connection between people and strategic outcomes? Which numbers and drill-downs are most critical to our success? |
|
Causal chain |
Models link employee attitudes to service behavior to customer responses to profit. |
Useful logic linking employee variables to financial outcomes. Valuable for organizing and analyzing diverse data elements. |
Is this the best path from talent to profits? How do our HR practices work together? What logic can we use to find more connections like this? |
|
Source: John W. Boudreau and Peter M. Ramstad, "Strategic HRM Measurement in the 21st Century: From Justifying HR to Strategic Talent Leadership." In HRM in the 21st Century, Marshall Goldsmith, Robert P. Gandossy, & Marc S. Efron (eds.), 79–90. New York: John Wiley, 2003. |
|||
HRM Operations...Measuring Efficiency
The first row of Table 1-1 describes measures focused on "efficiency" (see also Figure 1-1). These measures are usually expressed in terms of "input-output" ratios, such as the time to fill vacancies, turnover rates, turnover costs, and compensation budgets compared to total expenses.7 These approaches are compelling because they connect HR processes to accounting outcomes (dollars), and because they can show that HR operations achieve visible cost reductions, particularly when compared to other organizations. They are frequently a significant motivator for HR outsourcing. Many applications of Six Sigma to HR tend to focus on such measures to detect opportunities to improve costs or speed. One of the major limitations of these types of measures, however, is that they are not really HR measures at all—instead, they are efficiency ratios that can be used to monitor overhead costs in nearly any staff function. As a result, efficiency-focused systems can omit the value of talent. Fixa ting on cost reduction alone can lead to the rejection of more expensive decision options that are the better value. Efficiency-based measures alone, no matter how "financially" compelling, cannot reflect the value of talent. Finally, they focus almost exclusively on the HR function, and not on the decisions made elsewhere within the organization.
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Measuring Effectiveness...Demonstrating the Effects of HR Practices
The next row of Table 1-1, "HR activity and 'best-practice' indexes," directly measures the association between the reported existence of HR activities, such as merit pay, teams, valid selection, training, and so on, and changes in financial outcomes, such as profits and shareholder-value creation.8 Some results show strikingly strong associations between certain HR activities and financial outcomes, which has been used to justify investments in those activities. However, most existing research cannot prove that investing in HR activities causes superior financial outcomes.9 Another limitation of such measures is that they use one description of HR practices to represent an entire organization, when in reality HR practices vary significantly across divisions, geographic locations, and so forth. This may partly explain why managers in the same organization might inconsistently report the frequency of use of human resource management (HRM) activities.10 Also, such systems typically only measure the existence of HRM activities or practices, but not their effects. Even when an actual relationship exists, simply duplicating others' best practices may fail to differentiate the organization's competitive position. The best the organization can hope to achieve is to become a perfect copy of someone else.
These limitations can be seen by an analogy to advertising. It is quite likely that studies would show an association between financial performance and the presence of television-advertising activity, perhaps even that advertising activity rises before financial outcomes rise. This would suggest that among organizations that compete where advertising matters, advertising decisions relate to financial outcomes. Would it also mean that every organization should advertise on television? Obviously not.
Thus, these approaches shed some valuable light on the important question of whether HR activities relate to financial outcomes, and they have made important contributions to HRM research. However, even their strongest advocates agree that they do not measure the connections that explain why HRM practices might associate with financial outcomes, and they do not reflect other key elements of strategic success. They leave unanswered whether and how groups of employees significantly affect key processes and outcomes.
HR Scorecards
The third row of Table 1-1 describes HR "scorecards" or "dashboards," inspired by Kaplan and Norton,11 who proposed adding measures of "customer" (such as customer satisfaction, market share, and so on), "internal processes" (such as cycle time, quality, and cost), and "learning and growth" (systems, organization procedures, and people that contribute to competitive advantage) to traditional financial measures. HR scorecards include measures aligned and arranged into each of the four perspectives.12 Such approaches tie HR measures to a compelling business concept and, in principle, can articulate links between HR measures and strategic or financial outcomes.
Today's scorecards or "dashboards," built on data warehouses, allow users to "drill down" using a potentially huge array of variables customized to unique personal preferences. For example, HR training costs conceivably can be broken down by location, course, and diversity category, and then linked to attitudes, performance, and turnover. Although impressive, in the hands of the unsophisticated, such approaches risk creating information overload, or even worse, a false certainty about the connection between talent and strategic success. As Walker and MacDonald observed in describing the GTE/Verizon scorecard, "The measures taken in isolation can be misleading." They describe one GTE/Verizon call center where, "when HR reviewed the call center results from the HR Scorecard...the HR metrics showed a very low cost per hire, a very quick cycle time to fill jobs, and an average employee separation rate ... the staffing metrics showed a high efficiency and cost control." However, the call center accomplished this by "changing talent pools and reducing the investments in selection methods [that] kept costs low while bringing in applicants who were ready to start quickly but were harder to train and keep ...a bad tradeoff." GTE/Verizon was fortunate to have HR analysts who discovered this flaw in logic, but the example shows that even the best scorecards and drill-down technology alone do not necessarily provide the logical framework users need to make the best talent decisions.
HR scorecards are also often limited by relegating HR to measuring only the "learning and growth" category, or by applying the four categories only to the HR function, calculating HR-function "financials" (for example, HR program budgets), "customers" (for example, HR client-satisfaction surveys), "operational efficiency" (for example, the yield rates of recruitment sources), and "learning and growth" (for example, the qualifications of HR professionals). Both lead to measurement systems with weak (if any) links to organizational outcomes.
When we work with scorecard designers, they note that the majority of scorecards measure only HR operations and activities, the elements of efficiency and effectiveness in Figure 1-1. Scorecards admirably draw attention to impact, but the actual link between logic and measurement is often superficial, such as linking the organizational goal of "speed to customers" with the HR scorecard measure "faster time to fill," or linking the strategic goal of "global integration" with the HR scorecard measure of "number of cross-region assignments completed." Still, the scorecard-design principle of connectedness has promise, as we shall see in Chapters 3 through 11.
Causal Chains
The bottom row of Table 1-1 describes causal-chain analysis, which focuses on measuring the specific links between HRM programs or individual characteristics and business processes or outcomes. Recall our earlier example, where Sears, a large U.S. retailer, used data to connect the attitudes of store associates, their on-the-job behaviors, the responses of store customers, and the revenue performance of the stores. This measurement approach offers tangible data and frameworks that actually measure the intervening links between human capacity (in this case, store-associate attitudes reflecting their commitment or motivation) and business outcomes (such as store revenues). In terms of Table 1-1, causal-chain analysis comes closest to mapping all the linking elements.
The drawback is that all causal chains simplify reality. At the same time, they are so compelling that they might motivate oversimplification. Finding that employee attitudes predict customer responses, organizations may invest heavily to maximize employee attitudes. At some point, other factors (such as employee knowledge of products) become more important. Continuing to raise attitudes can actually be suboptimal, even if it produces small additional changes in business outcomes. It's important to have a logical framework that can reveal the new paths as they emerge.