Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage

Introduction

The Promise of Evidence-Based Change

Evidence-based change is a mind-set and approach to making HR decisions. In evidence-based change, the principles covered in the first five chapters of this book are combined with a robust change-management process to ensure a sustainable competitive advantage for the organization.

The thinking behind evidence-based change was inspired in part by the evidence-based movement in medicine. That movement came about after medical researchers noticed that doctors, despite a vast amount of available medical research, were treating disease in idiosyncratic ways. They were using their own preferred treatments even when there was solid scientific evidence that other treatments were more effective. The evidence-based medicine movement encouraged doctors to determine, on the basis of the available evidence, which treatment for a particular disease was most effective and to apply that treatment. (This was hardly a radical notion, but human nature is such that people, even doctors, sometimes need a push to behave with scientific rationality.)

In matters of people management, too, decisions are often made without full reliance on evidence. In recruiting, for example, there is evidence that unstructured interviews are far less effective than more structured approaches in identifying the best candidates, and yet despite the evidence unstructured interviews are still the preferred method of many managers. Organizations with a strong and well-informed HR function often now take a more structured approach to interviewing—a victory for an evidence-based approach to HR. The HR profession can also claim to have taken a step forward and become more evidence-based in its use of scientifically designed employee surveys and scientifically designed goal setting in performance management.

These are the exceptions, however, not the norm. More often than not, decisions about HR and people management are made on the basis of the instincts or uninformed preferences of stakeholders. The future of the HR profession lies in improving its ability to make decisions based on evidence. That evidence may be from research studies, as was the case with structured interviews, but more commonly it is based on a clearly articulated logic informed by both qualitative and quantitative data. HR will never have the precision of engineering, but as we will see in this book, many organizations have gone a long way toward bringing a great deal of rigor to HR decision making.

Evidence-Based Change Principles in Action: Drawing the Right Conclusions

Imagine for a moment that you lead the human resources function of a major urban hospital, with more than five thousand employees. When organizational leaders’ annual performance scores come in, two profiles jump out at you:

Leader 1: Very high customer-satisfaction levels, low employee turnover, very high employee-engagement scores

Leader 2: Rock-bottom customer-satisfaction levels, high employee turnover, employee-engagement scores only slightly above average

Which of these two people would you expect to be the better leader? Whom do you think needs additional training? Who might even be facing termination if things don’t improve?

Let’s also assume that you are experiencing extraordinary pressure to play a significant role in boosting your hospital’s competitiveness in its region, both as an employer of choice and as a provider of patient-centric services, so that the community will come to your hospital instead of going to the one down the block. This means that you have to prune your people in such a way that employees as well as patients are happy. Again, whom would you keep? Whom would you let go?

The choice seems obvious and immediate. Leader 1 has happy customers and very low turnover. Employee engagement is high under this leader. This individual is stellar on all three performance metrics. Leader 2, by contrast, has low scores on employee engagement. Why are so many people leaving this leader’s unit? And if the customers aren’t happy either, what could possibly be the rationale for retaining this person? It would seem that this leader is meeting neither the goal of boosting the hospital’s competitiveness nor the goal of making the hospital an employer of choice.

So what will you do? Keep leader 1 and lose leader 2? In light of the performance indices alone, that would be the obvious decision. In light of the organizational objectives, however, it would most likely be the wrong decision.

Let’s look more closely at who these two people are, and at what they mean to your hospital and to the community overall.

Leader 1 is the head of a cardiovascular unit. His team is tightly knit, highly skilled, and collegial. And the hours are reasonable (8 am to 4:30 pm), allowing for a balanced life that syncs with the schedules of family members and friends—a rare experience for people who work in health care. The patients are profoundly satisfied with the results of this unit’s work because they can see immediate and obvious improvements in their quality of life. This leader is enjoying the trifecta of a successful performance record at the hospital—grateful customers, happy employees, and talent that stays.

Leader 2, by contrast, is the head of the hospital’s food service division. Patient-satisfaction scores are predictably low (hospital food is conventionally unpopular). The employee population includes a significant number of people who don’t speak English, the language in which the engagement survey is produced. Many of the moderately satisfied workers chose to ignore the survey, with the result that this leader’s employee-satisfaction scores are misleadingly low. The reason his department is experiencing such high turnover is not that his people are unhappy with their boss. In fact, he has created an extended family with his people, who hold him up as a beloved patriarch. The high turnover stems instead from the fact that he hires entry-level employees and teaches them essential work habits and life skills that equip them for better jobs, either within the hospital or out in the community, so many of them leave to take good jobs elsewhere or are promoted.

At first glance, it would be understandable if the dilemma represented by this scenario were seen simply as an example of what happens when HR and the departments it supports rely too much on any form of data, without taking the time to fully understand what that information means on a multilevel, organizational scale. Metrics run amok. But if senior leaders in HR don’t stop to fully consider the reality behind the performance scores for the heads of these two units, then senior HR leaders can easily make the mistake of trying to fix the wrong thing, imposing inappropriate values or objectives on one or another of these unit heads. They might even cause that person to become disengaged and, by extension, produce the same reaction in hundreds of potentially valuable workers. That could easily be the outcome if HR and the leaders it serves were to use performance data strictly in measure-and-response mode, which is the way data are too often used in HR organizations today.

So, evidence-based change is not simply creating data and acting on it. In this book, we invite you to consider how evidence-based change defines the next generation of HR within a much broader context that incorporates logic, strategic awareness, and savvy change management. The phrase evidence-based change refers to transforming organizations on the basis of well-grounded evidence and skillful influence and change management, rather than gut feelings, knee-jerk responses, or copying competitors’ practices. This approach extends the collection and analysis of data (such as performance) by using five important principles that elevate the data and their analysis to a new level, one that transforms organizations to create true strategic impact. When HR approaches its role with these five principles in mind, the collection and analysis of data produce insights and solutions that are far more nuanced and optimized to the organization’s context and systems, and thus more likely to create change that is more richly impactful and sustainable.

The Five Principles of Evidence-Based Change

Our example of the hospital is based on a true story and illustrates how HR leaders can ask better questions, find better answers, and run vastly more dynamic and business-relevant HR operations for their corporations. They can achieve these outcomes by using a disciplined approach to what might initially be perceived as merely thorny or intuitive talent-management issues. In other words, leading-edge HR functions are helping their organizations achieve greater success and create a more fulfilling work environment for their people by relying on five principles of evidence-based change:

1. Logic-driven analytics

2. Segmentation

3. Risk leverage

4. Integration and synergy

5. Optimization

Logic-Driven Analytics

To return to the hospital where you serve as HR leader, let’s say you think at first that you are being analytical, and therefore a smart partner to your senior leadership team, by handily serving up three measurements to gauge leadership acumen. Indeed, most leaders outside HR welcome, even demand, that HR issues be boiled down to a small set of consistent measures like these. But you quickly see that it is much too simplistic to expect one set of metrics to apply to all the leaders of this very complex organization, with its many constituencies and its wide variety of employee populations, ranging from entry-level food service workers to neurosurgeons. You understand that one set clearly does not fit all, and that there are nuances that, if observed, can lead you to much more robust conclusions and understandings of how your hospital can truly function at its optimum.

In fact, it is HR’s role not only to be aware of these vital nuances but also to approach HR’s systems, measures, and relationships with key constituents in such a way that the nuances are recognized, and organizational leaders are held accountable to a higher level of sophistication in analysis and decision making. This higher level is characterized by what we call logic-driven analytics.

An example of using logic-driven analytics would be recognizing why survey scores that would suggest poor performance in the cardiovascular world might actually indicate excellent leadership of a team of food service workers in their first jobs. The logic rests on the fact that part of the organization’s objective in the food service area is to develop relatively early-stage talent so that those employees can be ready for deployment throughout the hospital, having been trained in a work ethic specifically suited to the organization. Thus, success requires hiring those who may be less emotionally connected to the work, those who are more likely to be promoted or move on to better jobs in other organizations, and those who must deal with the reality of hospital food quality. This is very different from the cardiovascular unit, which draws a population already deeply qualified and committed to the work, stable in their career, and creating life-saving changes in patient lives. The appropriate goal for human capital standards, investments, and developmental approaches in the food service division is to yield a cadre of well-trained employees who can then move within or outside the organization, whereas the appropriate goal for the cardiovascular unit is to take seasoned professionals and create a world-class team.

Segmentation

Logic-driven analytics often produces insights like this, showing that one unit or employee group is very different from another. This leads to the principle of segmentation. In the hospital example the entire analysis rested on recognizing food services and cardiology as distinct segments that may need to be treated differently. It is a straightforward idea, but one that conflicts with a long-held HR tradition of resisting any attempt to give one group special treatment. Yet applying different standards about what counts as a good engagement score for a cardiology leader and a food services leader doesn’t impinge on fairness. Indeed, using logic-based analysis to identify and understand differences can actually increase fairness.

While segmentation is one of the most common tools used in evidence-based change, it is possible to go overboard. HR must understand where segmentation is vital to the organization and where it is less necessary. A standard set of leadership measures may be adequate for 80 percent of the organization’s leaders but disastrous for the other 20 percent. Therefore, the next generation of HR must acknowledge these differences, communicate them, and then motivate actions that reflect them as well as the consequent need to divide the workforce into segments for the purpose of evaluation.

Returning once again to our hospital example, let’s say that in order to get the most out of your organization’s developmental and leadership investments in its food service unit, you start thinking of this function as a sort of farm team for the rest of your hospital. Once you have identified the vital importance of distinguishing between the food service segment and the cardiovascular segment, you see different ways to invest in each of them. The entire employment proposition and life cycle will be different for each segment, not only because the employee populations have distinct needs and backgrounds but also because the two units serve such different roles in the talent pipeline. Making those investments properly is the principle of optimization, discussed later, but optimization almost always requires segmentation, which involves understanding differences and their importance.

Risk Leverage

Next-generation HR is not simply about reducing the risk of turnover or low performance. It is also about the practice of risk leverage. In other words, it is about knowing when—and when not—to take risks.

For example, from a simplistic perspective, the high level of turnover in food service would be defined as a higher risk in the area of talent, and it would be a reason for holding the leader accountable for reducing that risk. But evidence-based change requires that risk in the food service unit be seen as different from risk in the cardiovascular unit. Yes, turnover in the food service unit is a risk, but it is far less significant than the risk of promoting employees who will fail later on because the food service unit didn’t sufficiently develop their basic employment habits or vet them for future employability. The latter risk affects how well the unit produces employees able to grow within the organization, serving its many needs for decades. Some of those people may initially present themselves as diamonds in the rough, and so if the leader of your food service unit is pressed too hard to reduce the risk of turnover, that leader may not groom individuals for future positions, for fear employees will leave for better jobs elsewhere. The right level of risk requires taking some risk of employees leaving for other employers, in order to get the value of the grooming that occurs for future jobs at the hospital.

By concentrating fundamental developmental tasks in a function such as food service, HR also manages another aspect of workforce risk—the risk of error or noncompliant behavior. Food safety is a critical concern in the hospital, of course, but a hospital is not a five-star restaurant. So, some food quality variation is an acceptable risk, when it gives the organization a place where those with rudimentary employment skills can be employed and groomed. By concentrating elementary developmental phases in food service and setting standards in food service that are different from those of the cardiovascular unit, HR optimizes risk.

Integration and Synergy

Recognizing the differences in needs and risk among vital workforce segments is a start, but it is also important that things work well together. This is the principle of integration and synergy. This can happen at many levels. It includes how the individual HR practices work together, but it also includes how the HR processes in different units work together across the organization. When the principle of integration and synergy is in play, combinations add up to more than the sum of their parts: 1 + 1 = 3.

In the hospital, for instance, the food service unit must actually work in synergy with talent needs in the rest of the hospital. If food service is merely regarded as a closed system designed to achieve an appropriate standard of food service performance, then typical practice will be to hire to those standards, reward employees for food service outcomes, and weed out workers solely on the basis of job performance. Synergy, however, suggests that the food service unit serves a larger purpose—its employees are regarded as a farm team for other positions elsewhere in the hospital. Therefore, the leader of the food service unit, much more than the leader of the cardiovascular unit, must combine HR practices to create not only great performance but also great potential. At the level of HR practices, synergy means the combination of (1) a performance incentive that rewards patient-centric service both within and outside the core job and (2) a hiring standard that looks for a strong service orientation, not just food-preparation ability, and offers an opportunity to produce food service workers with the passion to excel elsewhere in the organization. Combining training in such topics as food service and food safety/handling skills with training in teamwork produces better-trained food service workers as well as workers who have been vetted and prepared for future talent demands throughout the hospital.

Optimization

Optimization means making the right investments, investing more where it will make a big difference, and having the courage to make smaller investments where the difference is less. It means knowing the right balance between standardizing and customizing. The insights from principles like segmentation, risk leverage, integration and synergy, and logic-driven analytics often lead to the uncomfortable conclusion that getting it right means doing things differently in different places.

In the hospital, turnover in the food service unit is regretted, but probably not as significantly as turnover among cardiovascular professionals. The hospital might optimize by investing in retention reduction strategies like high pay and tenure-based benefits less in the food service unit than in the cardiovascular unit. On the contrary, the hospital might invest more in mentoring for food service workers than in its cardiovascular unit. An investment in mentoring better fits the goal of taking people with raw potential and turning them into valuable long-term employees. Optimization is all about redirecting investments away from areas with low impact to areas of higher value based on the evidence.

Optimization questions some assumptions like hire the best person for each role. In the hospital, high selection standards are absolutely appropriate in the cardiovascular unit, where you don’t want people learning basic work skills and habits on the job; you want them ready from day one. But if you apply the same selection standard to the food service unit, you not only will delay fulfilling your immediate needs for staff but also will rob yourself of the opportunity to recruit candidates that may be rough, where you can serve the role of preparing them for the hospital’s more general talent pipeline.

Optimization is all about acting on the evidence even when it means redirecting investments from well-liked but low-impact programs toward higher-impact programs, and doing so even when it means treating different groups differently.

Mental Models for HR Decisions

An evidence-based approach to HR serves a dual purpose—it helps HR leaders make the right decisions about people, and it helps those HR leaders engage other organizational stakeholders in a decision process that collaboratively reaches the right decisions.

In the absence of an evidence-based approach, however, the organization’s non-HR managers typically apply certain familiar mental models to HR situations. To understand motivation, for example, managers draw their insights not from psychology, the science of human behavior, but instead from the more familiar framework of economics. Economics-based models make the assumption that humans are perfectly rational and driven entirely by incentives. Although such assumptions are erroneous, most economists feel that these assumptions work well enough for economic purposes. But organizational managers steeped in economic thinking may apply such models too directly. For example, research shows that social justice and group loyalty may constitute very strong motivations for employees, but there is evidence of managers assuming that employees are motivated purely by self-interest and are likely to try to take every advantage.

Accounting provides another set of mental models in which organizational managers are steeped. Accounting models often position people as a cost. Therefore, organizational managers steeped in accounting models are driven to cut headcounts and to reduce expenses for compensation and training. In a manufacturing environment, of course, managers see machinery as an element of production; they don’t suggest replacing high-quality machines with inferior equipment just to save money. Managers in nonmanufacturing environments need frameworks that will help them think about investments in human resources with the same rigor that is brought to bear on manufacturing-sector decisions about investments in machinery.

HR could develop its own mental models as the foundation for an evidence-based approach, but there is good reason to look to outside models, especially models with which organizational managers are already comfortable. One such model is that of consumer behavior. Managers know that consumers are influenced by trust in brands, by loyalty, by social factors, and