Following Trends: A Good Idea?

Professionals and managers are exposed to an avalanche of information about what is going on in their field of practice. Some of it is derived from discussions with others, some from practitioner journals and some from ideas presented at conferences. Continuous environmental scanning is a prudent strategy, particularly in the kind of dynamic environment that exists today. But data and information must be tested to see if it is relevant evidence that should impact decision-making. Having been a consultant, practitioner and teacher in the compensation management field for several decades I have seen a pattern that suggests many practitioners attempt to identify and follow trends. Trends are practices that a lot of others seem to be adopting. There have been periods where a number of “new” approaches seemed to have been granted the status of being the “new best shiny thing.” The publications are suddenly full of testimonials that broad-banding or competency-based pay will lessen or eliminate the difficulties associated with rewarding people.

When does something become a trend? When every account one sees in print extols the virtues of an approach and suggests adopting it does not present challenges it seems rational to follow the herd. The two practices just mentioned dominated the literature for a time, which was understandable since 100% of the adoptions were deemed to be successes (recent research on salary structures showed that a minuscule percentage of participating organizations use broad-banding today).

Yet upon reflection one has to ask who would write an article describing a failure they were responsible for? In a perfect world those who tried something that did not work would inform the profession that success was not guaranteed by communicating the outcome. Admittedly this may not be the best way to advance one’s standing in the field, but it does provide useful information. Learning what does not work or what only works in certain contexts can be every bit as valuable as learning what does work in certain contexts. This severe bias in the literature exists in the academic world as well. Journal editors are unlikely to accept papers about research studies in which the results did not support the hypothesis that was being tested, despite the fact that valuable intelligence could be gleaned from the study.

There is also a bias against publishing the 50th article concluding that a practice like “paying for performance” has a positive impact on motivation and performance. The “yes, I already knew that” reaction makes the information uninteresting and once a practice has experienced an adequate amount of supporting research further supporting documentation seems unwarranted. So Editors are always looking for something that seems new. But this distorts the probabilities that different practices will be successes or failures. Ten articles in the same year claiming massive success with broad-banding pay systems seems to elevate the practice to the status of “best new shiny thing.” But if thirty failures went undocumented this is not good intelligence. And certainly a sample consisting of ten successes is hardly compelling, given the number of organizations in operation at any time. People are subject to accepting samples that are too small to be statistically valid and this bias is the source of a bandwagon effect. The claim of newness is often exaggerated, to increase the appeal of a practice. I have uncovered at least two previous lives for the “new” broad-banding approach popularized a decade ago. Since few study history rigorously it is often possible to simulate newness by changing the name of something.

When a practitioner is determining whether a specific practice that was reported as being a success at a respected organization should be emulated there are often critical items of information missing, such as a detailed description of the context within which the practice was successful. Although there may be some information about the organization it is very rare for enough knowledge about the culture, the external environment and the internal realities to be available for someone to make a reasoned assessment about the similarity of the two contexts. The common assertion that “our organization is unique” is in fact almost always true… there are no two identical organizations. So why would a practitioner make a decision that was influenced by what happened at another organization (or ten organizations) that functioned in contexts that were at least some degree different? In addition to knowing about context similarity there must also be careful scrutiny of how the adopter defined and measured success. Did growth rate increase? Profits soar? Employee engagement increase? Unwanted turnover decline? And by how much? Once the success measures are calculated did the improvement warrant the resource investment in making changes? Research studies and benchmarking processes have been two of the most widely used tools for practitioners when they attempt to predict the probability of success when adopting a new strategy or program. But using them is fraught with peril if they are not done well.

The prevalent reporting of an increasing use of workforce analytics warrants the trend lablel. It seems obvious that relevant evidence will always be valuable when making decisions. Technology advances in AI and machine learning have created tools that enable organizations to use data on what has happened to improve the ability to predict what will happen. But lest adopters assume that analytical tools have great prediction power it would be prudent for them to acknowledge that the future may not be like the present or the past. If that is the case the data used may be inappropriate for deciding what will happen if a practice is adopted. There is a similar danger in using one’s past experience as a guide for going forward. Experienced people who may even be universally proclaimed to be experts may have a knowledge base that is less relevant today and in the future than it was when acquired and applied. And when a knowledge base is used to create an algorithm the quality of that tool will be impacted by the continued relevance of the knowledge used to create it. Since algorithms prescribe decisions based on rigidly prescribed logic they will not question “is this still going to work today?” Having just published a second edition of my first book I was startled at how much had changed in five years. Classics can be useful but generally for understanding fundamental principles, rather than discovering how a particular program would work today.

Thankfully the principles of evidence-based management are increasingly being used to improve decision quality. EBM prescribes the use of all relevant evidence to inform decisions. But meeting the relevance test is devilishly difficult. And the interpretation of the body of evidence can require a decision-maker to “grade” the quality of the different sources of evidence, especially when the sources seem to disagree. Making a good decision has not gotten any easier, despite the proliferation of data, information and new technology. All these sources can increase decision quality if properly interpreted and applied.

Jumping on the bandwagon when a “new” approach is dominating the literature without a rigorous process of examining the evidence can lead one down the foot worn path… which can be the wrong path for a particular organization at a particular point in time.


Aligning Talent Management Strategy With Organizational Strategy

An organization’s talent is potentially its most valuable asset. But this statement is only true if the workforce is made up of the right people, with the right knowledge/skills and the right motivation. In addition, it must be managed effectively. The talent management strategy provides a framework for creating the right talent pool. Staffing and developing the workforce and effectively and appropriately defining, measuring, managing and rewarding performance are the pre-requisites for success. And the profile of the optimal approach is one that will focus the right workforce on organizational objectives.

Figure 1 shows a model for deriving the human resource (talent) strategy from the organizational context and the business strategy. The context is a function of the vision/mission of the organization, the culture and the internal and external realities with which the organization must deal. Each organization must determine its context before examining alternative business strategies, since different strategies work or do not work depending on their fit to the context within which they must be executed. Then it can formulate a strategy for acquiring and managing the right talent pool.

The need to continuously evaluate the human resource strategy to be certain it is aligned with the context is particularly critical now. Technology and globalization are changing talent requirements. That type of change often demands new skills that may not be present in the current workforce. In some cases organizations have attempted to replace a significant part of their workforce with new employees better suited to the new talent requirements (e.g., newspapers replacing type setters with IT personnel when adopting automated front-end systems). Others have attempted to retrain existing employees, so they are competent to function in the new strategy.

However, there are significant costs associated with each of these approaches. Changing out the workforce has separation costs, hiring costs, training costs and potential litigation or even violence. Retraining the existing workforce can feel like repainting a commercial airliner while it continues scheduled runs, not to mention the significant training costs for the painters. Deciding which transitional approach to use should be based on a cost/benefit analysis and consider both the short and the long term.

Figure 1

Aligning Human Resource Strategy with Organizational Context

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Staffing and Development Strategy

By profiling the types of employees fitting the current talent strategy using competency models, an organization can use workforce planning to evaluate the adequacy of the current workforce and plan for future needs. The model in Figure 2 illustrates how to continuously evaluate the workforce in light of changes to the organization’s needs.

Evaluating workforce demographics can facilitate planning for impending retirements. The Water Research Foundation did a study that concluded that half of water utility critical skill people were eligible for retirement within five years. Failure to know that early and to act on it could create a crisis in a utility. And taking positive steps, such as improving job design, doing effective performance management and investing in career management programs can increase the capabilities of the existing workforce. That productivity increase can preclude the need to hire new talent. And making a business case for employee development can help to secure the resources to invest in the current workforce.

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By doing continuous environmental scanning and using scenario-based planning to plan for a range of possible futures an organization can continuously evaluate how well its talent fits its needs. As technology and globalization have provided new sources of talent each organization must decide whether the work that needs to be done can best be done by employees or if the use of contractors, consultants and freelancers can produce a better result. Both the global mobility of talent and the ability to have work done anywhere using technology have increased the potential supply. But attempting to integrate the work of outsiders and that being done by employees can be challenging. And it is often necessary to modify the value proposition to attract the best talent from the new sources.

Performance and Rewards Management Strategy

How effectively and appropriately an organization defines, measures, manages and rewards performance will have a significant impact on employee focus and motivation. If employees do not believe performance management is done poorly and/or their rewards are not equitable, competitive and appropriate that perception can reduce employee satisfaction and engagement, as well as increasing unwanted turnover.

If what is defined as performance changes over time the new criteria and standards must be clearly defined and understood by all parties. Effectively rewarding performance requires that rewards accrue to those who produce the results desired. For example, if a software provider wants to ensure its designers create products that are commercially successful, rather than just technologically superior, it might provide an incentive package that ties rewards to product acceptance by customers. Basing a portion of the awards on the product sales for the first one or two years provides a clear message that dazzling potential customers is not enough, they must be dazzled enough to buy the product.

The use of outsiders brings into question whether the performance and rewards management strategies used for employees will be effective for those working under different arrangements. This is especially true when using freelancers (gig workers). Contractors and consulting firms are typically evaluated and rewarded using pre-determined contractual agreements. But freelancers may require individualized strategies, whether they are acquired through talent platforms or they deal directly with the organization. If they are satisfied with their treatment it will increase the chance that the best freelancers will view future engagements with the organization favorably.

Alignment Matters

The degree of alignment between the human resource (talent) management strategy and the organizational strategy will be a major factor in determining how effectively an organization manages its talent pool. Getting the right people on board is the first step. The second is to align their efforts and to focus it on meeting the objectives of the organization. This can be facilitated by effectively utilizing their talent and defining appropriate roles for them within a coherent structure. The third is to develop a performance model that defines and measures performance at the organizational, unit/group and individual levels using the appropriate criteria. The fourth is to reward performance equitably, competitively and appropriately, which will provide the motivation to extend one’s best effort and to focus that effort on what will contribute to organizational success. And the talent strategy must be continually assessed, considering the current context and the organizational strategy. Change may be challenging but rigidity can preclude success.

One On One Unstructured Interviews: A Flawed Selection Tool

When research informs us that a practice is not viable it would be expected that organizations would avoid relying on it. Yet one on one unstructured interviews continue to be the most widely used basis for selecting new hires from groups of candidates. Since research has shown that the practice has virtually no validity or reliability there must be something that prompts its continued use.
Being subject to numerous cognitive biases is a human condition. Kahneman & Tversky won a Nobel Prize for research that identified about 100 such biases, as well as developing an explanation of how we think. One of their useful insights differentiated between fast (System 1) and slow (System 2) thinking.

System 1 is activated automatically and is quick to find explanations for what is seen and for making snap decisions.

System 2 is calculative and is activated when System 1 is puzzled, senses contradictions or is exposed to evidence that is contradictory to held beliefs. In the book Blink Gladwell also pointed out our propensity to make snap decisions based on first impressions (a characteristic of System 1 being in charge). Some of the common biases also work against making good decisions:

  1. Overconfidence that one “knows” something (most common among experienced people);
  2. Tendency to filter evidence in a manner that results in being overly receptive to information that confirms existing beliefs or what one wishes were true;
  3. Over reliance on samples that are too small to be statistically sound;
  4. Over simplifying complex issues resulting in simplistic decisions;
  5. Susceptibility to physical characteristics being considered, eroding decision quality.

I was hired for my first job out of undergraduate school into a high potential program managed by a highly respected organization, based on a one on one unstructured interview. I was not well suited to the organization. To this day I am convinced that my success in being selected among many candidates with advanced degrees from more prestigious schools was due to the fact that the interviewer and I had both served as paratroopers in elite airborne divisions. How that discovery occurred I do not remember, but since the discussion was unstructured chance occurrences were made possible Since the role I was selected for did not require the combat skills I learned in the service the selection criteria I believe resulted in my hiring were inappropriate. The fact that my being hired subsequently resulted in excellent training in all aspects of business that I have used throughout my career makes it difficult for me to be so critical of a process that led to that selection. But my PhD training in Behavioral Science has made me a believer in sound research and has equipped me to override impulses and intuition when research findings so dictate.

Using flawed processes can also produce consequences for an organization that are more serious than just making low quality selection decisions. Another one of the biases we are subject to is preferring people who we like and who are like us. This can result in inappropriate decisions and result in statistically significant impact on protected classes, opening the door to employment bias litigation. And globalization has made talent more mobile, resulting in culturally diverse workforces and candidate samples. This diversity can create another problem. If one candidate has been socialized in a culture that encourages humility while another candidate has been taught to aggressively market one’s qualifications two equally qualified candidates can seem to be unequal when unstructured one on one interviews are used. Interviewers are of course influenced by how a candidate answers questions and represents him or herself and this may degrade the quality of a selection decision. Trompenaars prescribes 3Rs for dealing with cultural diversity:

  1. Recognize that cultural differences are present,
  2. Respect the right of people to hold different beliefs and values,
  3. Reconcile the issues those differences raise.

Kahneman cites research that shows interviewers who have abundant sources of evidence that seems relevant do not generally do better than someone using a straight forward algorithm to make final decisions. For example, an algorithm for selecting new students for a university that relies on high school grade point average and a relevant aptitude test to select students usually outperforms interviewers with a wider variety of information. If the university is trying to predict if the person will make contributions as an alum knowing about family circumstances may be relevant. And if the school aspires to produce graduates who will be active in the community then high school activities may be relevant. But if the goal is to predict who will successfully complete their studies and do well that additional information is a contaminant that lessens decision quality. Suprisingly the act of predicting the future quality and price of a Boudreaux wine was done better by an algorithm using Spring rainfall, Summer rainfall and Summer temperatures than it was done by a panel of experts. Medical algorithms have diagnosed more reliably than trained specialists. All of these facts do not argue for a “machine replacing human” approach, but rather a blending of expert opinion and a structured decision process used consistently.

It has been shown that parole decisions by judges are impacted by how long it has been since their last meal and/or the cases that preceded the current one. This illustrates the fallibility of people across decisions. When left free to make decisions without common factors and factor weights the reliability of their decisions will suffer since physiological differences over time will produce inconsistent standards.

It is a “both – and” rather than an “either – or” approach that will produce the best decisions made by people. When an algorithm can be developed based on data, using AI and machine learning tools, its consistent use will produce more reliable decisions over time. But the validity of the decision is generally maximized by using both data and human judgment. Panel interviews with structured questions can help to moderate individual bias and to ensure everyone is asked to respond to the same questions posed in the same manner. When panel members have individually rated candidates they can then meet to compare their ratings and to discuss what led them to their conclusions.

Managers who will be responsible for the chosen candidate may believe they should be in control of a hiring decision. But they will often be subject to overconfidence bias and may resist the imposition of a structured process that gives other parties a say in the decision. Some Behavioral Science 101 training may convince them that utilizing a structured process will at the very least give them a trimmed down list of final candidates that contains only qualified people. They may still believe that they are the only one who can truly judge whether the manager and the person will work well together. But at the very least there should be a third party review of the rationale used by the manager during the final selection to minimize personal bias. And since the new hire will be an employee of the organization who just happens to work for a specific manager initially the organization should have a say about who joins its workforce.

Managing The Human – Technology Interface

The title of my third book, published in 2018. is “The Most Important Asset: Valuing Human Capital.” Although I believe what that title suggests it does not mean other assets are not critical. Financial, customer and technological capital are the vehicles through which people work to achieve organizational objectives. Of late there seems to be a belief that technology is the key to success, given the dramatic progress made in that arena. But when relative importance is debated there is often an “either – or” mindset that results in a competition between the types of capital. We are living in a “both – and” world where things are particles and waves, not one or the other. Perhaps the best way to relate human capital and technological capital is to conclude they are both necessary, but individually not sufficient, pre-requisites for success.

If the premise that both people and technology are critical is accepted it becomes apparent that the interface between them must be managed well. Either can undermine the effectiveness of the other. The concept of socio-technical systems came on the scene several decades ago. One of its principal prescriptions is that there needs to be a mindset that embraces the compatibility between the two types of capital. But because of the considerable rhetoric about robots and algorithms replacing people there is a danger that a people vs. technology war might break out. Even if technology does not replace someone it might significantly alter the knowledge and skill requirements people must possess and that can be threatening. People were selected into roles based on their qualifications to perform in those roles and if the work changes the qualifications change and the people must change. Mid and late career people may not be enthusiastic about “going back to school.” And they may feel betrayed if they think they were led to believe what they know and are able to do is all that is and all that will be required.

Recent research by the Workforce Institute at Kronos suggests employees often feel the technology they use at work is not as easy to adopt as the consumer applications they use, such as social media tools and games. If someone has learned to search for entertainment, do their banking and communicate with others seamlessly and with limited effort it is understandable that they would expect that work technology could be similarly effortless. Yet fewer than ¼ of those surveyed found that to be the case. These findings held true across the globe. In response to other questions asked over 1/3 felt that outdated processes and legacy technology made their jobs harder rather than easier. And younger workers believed this more strongly than those in mid to late career phases.

Despite dramatic advancements in technology the people who must use it are not happy with it. Since people have to create the technology there must be a disconnect between those that design it and those who use it. This can lead to a negative impact on employee engagement and satisfaction, which will produce a decline in performance level. Everyone has been frustrated by user manuals that are supposed to tell you how to put a product together or how to perform actions such as sending a text message. Successful consumer products generally are designed so that using them is intuitive. Yet much of the software used in workplaces seem to be the result of designers who did not think that important. If trying to use technology is frustrating, intimidating and more work than doing something manually there will be less adoption of new tools, even though they might increase productivity once a person figures them out.

When communicating with others employees must decide on whether to use the “efficient” digital approach or walk down the hall and have a person to person conversation. The latter approach is not feasible if the number of recipients is large or if they are scattered across the globe. Most people believe that communication is much less prone to misinterpretation when it is done face to face. But globalization and the increased use of outsiders to do work have both increased the probability that communication is going to be between parties who do not share history and who do not understand how others expect things to be done.

Organizations should consider the person – technology interface when deciding on what technology to adopt. Opting for the newest and shiniest object may be exciting and viewed as being progressive but if it fails to produce the desired results it is just disruption. And if people struggle with incorporating technology results are unlikely to meet expectations. If AI or machine learning algorithms replace human judgment there will be resistance from those who are likely to feel disrespected and diminished. But if the algorithms relieve drudgery by doing the well-defined repetitive work the reception is apt to be much warmer. If I have been lifting 100 pound objects all day and the organization reallocates that part of my job to a robot I am good with that… assuming I still have enough work to do to justify my employment.

Ergonomics (human factor engineering) has helped organizations design workplaces that make people more productive and help them avoid repetitive motion and vision problems. There should be designated experts responsible for doing the same oversight on workplace technology. When people and technology are compatible they both become more valuable.

Rewarding Performance With A Limited Budget

Economists are divided about the direction the current economic environment will take over the next few years. The last five years have been prosperous for many organizations, domestic and global. But compensation levels have not increased rapidly, despite very low unemployment rates. During recent years the rate at which pay levels have increased has been very low compared to historic averages, although pay rates have recovered somewhat from the economic downtown that started in 2007-8. According to the Salary Budget Survey published by the World at Work the low point for salary budgets was 2.2% in 2009, when the full force of the downturn that began in 2007 was felt. Since then the budgets have slowly increased to 3%, where they are projected to stay for 2019.

With unemployment being as low as it is in the U.S. it has surprised many that pay levels have not increased at a more rapid rate. Possible explanations have been plentiful but whether it is uncertainty on the part of organizations or some other factor the historically low rate of 3% does not provide the resources required to reward performance adequately… at least in the way most organizations have allocated budgets. Over the last 45 years salary budgets have on average exceeded the inflation rate by 1%, which offered organizations the latitude to keep raises larger than the cost of living. But during the 2005-11 period the gap between inflation and budgets was less than 0.5% (the exception was in 2009 when inflation was zero or negative). Since 2016 there has been a gap of less than 1% between budgets and inflation.

Given the current realities the question facing most compensation planners is how to optimally allocate the 3% budget. Since inflation was 2.4% in 2018 and is not expected to change much in 2019 organizations will struggle to increase real wages for their workforce by much. A second challenge has been moving employees through established salary ranges at a rate the convinces employees they are being fairly compensated. Since structures are typically anchored by tying the range midpoints to competitive averages prevailing in the labor markets an employee who has been in a job for at least five years and performed well will be likely to believe that (s)he is entitled to be paid at or near the midpoint. Making that happen given the current realities is a big challenge.

Optimal Allocation of Budgets

Most organizations using merit pay systems, the most common form of pay for performance, create guidelines to help managers allocate the budgets they are given. An example of a model for guiding increases is shown below. The assumptions are:

  1. a 3% budget,
  2. pay rates are evenly distributed across the pay range, and
  3. performance ratings are distributed as indicated in the table.

There are two principles underlying this approach:

  1. People who perform at higher levels should receive larger increases,
  2. Since increases are expressed as a % of current pay those in the lower part of the range should receive a higher % of their relatively low pay than those in the upper part of the range whose pay is significantly higher. If both parties were to receive the same $ increase the effect would be similar. If an organization has a weak performance management system this approach will not work well, since measuring performance accurately is not possible. And if an organization does not believe in differentiating based on performance their system will not result in pay actions that motivate performance.

Many public sector entities still use automatic, time-based step increases, which ties pay adjustments to longevity rather than performance. These systems do not provide motivation to perform well and they do not work well when economic conditions vary. The U.S. President claimed to have frozen pay for federal employees in the GS system for two years during the economic crisis, but in fact only froze the pay structure. Employees still got 5% step increases irrespective of their performance, which cost taxpayers more than competitive market conditions would have dictated. And when pay rates escalate more during good economic times the step rate system fails to reflect the realities prevailing in the market. Over the last decade a large number of public sector entities have converted their step rate structure to open ranges that enabled them to align more closely with competitive practice.

Although the merit pay guideline model is widely used there remains the issue of how well the system motivates people to perform well. In the model shown here the increase for an outstanding performer is twice that of someone who meets standards, assuming their pay rates are in the same zone of the range. The relative size of the increases would seem to send the message that performance is valued and rewarded. But the absolute size of the increases is likely to be the focus of employee scrutiny. Is a 5% increase (which is only 2.5% greater than the increase for someone meeting expectations) adequate for someone who is in the top 10% and whose current pay rate is at or near market average levels? When that employee asks why the reward for “leaving it all on the field” was not greater one of the responses could be that the budget is small and that employees who meet standards should receive some reward, even though it is only half of theirs on a relative basis. If that outstanding performer has read a good book on compensation management they might respond by saying someone who is at or near market and who just meets standards may not need an increase.

Finding the optimal allocation approach is important if employees are to view the distribution of budgets as equitable, competitive and appropriate. But there is no one “best” answer for an organization. There are other ways to approach allocation. One is to introduce performance-based cash awards. If pay rates are reasonably in line with competitive levels further base pay increases increase fixed costs, since pay adjustments are career annuities in most cases. By allocating 1% of the 3% budget to performance-based cash awards the approach shown below can provide a greater reward for performance. It also reduces the rate at which payroll cost is compounded.

This approach increases the impact of performance ratings on awards and for that reason it should not be considered if the performance management system is not well designed and accepted as sound by the parties at interest. Hooking up higher current appliances to old wiring may not be wise and differentiating this dramatically based on performance ratings should be considered only when those ratings are trusted.

A final option when there is considerable uncertainty about the organization’s short-term economic outlook is to replace base pay increases with cash awards, at least in the short-run. This avoids increasing fixed costs and receiving the award in one lump sum may be more attractive to employees than working the whole year to get the full amount.

Organization culture will certainly impact decisions about how to allocate budgets. So will the nature of an organization’s revenue stream. If revenue is highly variable spending the budget for base pay adjustments increases fixed costs and that can cause a misalignment between revenue and costs… not a good long-term strategy. If revenues fall dramatically and management decides payroll cost must be frozen or even reduced another set of issues arises. Payroll reductions can be accomplished by reducing pay rates or reducing staff size. I was asked by the HR Director of a city how they should reduce payroll by 1.5% without terminating employees during a crisis period. The person was astute and realized that given the costs associated with terminations would probably outweigh the savings in the short run (see Responsible Restructuring by Wayne Cascio for an excellent treatment of these issues). We discussed ways of reducing pay rates, the last option on the table, and decided they could cut the rates of the highest paid people by 3%, those in the middle by 1.5% and not to cut the pay of those closest to subsistence level. But the complexities of executing that strategy were daunting and another option surfaced. Employees were contributing only 5% to the cost of health care benefits, when the competitive range was more like 20-25% of the cost, so increasing the contribution rate could produce the savings needed. This was an approach that had the double benefit of avoiding pay cuts and also adjusting the employee contribution share to a more reasonable level. Pay cuts can be viewed as a breach of an emotional, if not legal, contract and the prospect of cutting pay can spur ingenuity by compensation practitioners.

The good news would be that everyone is in the same boat, struggling with allocating small budgets optimally. But that is of course not the case. Some organizations are in a position to budget more for awards, whether they be in the form of base pay increases, cash awards or even equity. The playing field is not level. Each organization needs to compete for talent in a manner that fits their realities. Some may choose to differentiate across the workforce, rewarding people in critical occupations that are in short supply more generously. Although that can raise equity issues economic realities may mandate it. To reiterate my favorite principle: what works if what fits… a specific organization at a specific time.

What Is This Thing Called Culture?

A recent post on LinkedIn discussed the reasons Publix was cited as the best place to work among very large companies. Having had dialogue with HR executives from Publix I sense a considerable amount of pride they feel when they tell people where they work. And since the award was the result of random polling of employees, rather than a carefully crafted mission or culture statement that was intended to make Publix look good it has credence. The organization is privately held… by employees. So that may be a reason for their positive reactions. Owners tend to want to maximize the positive PR, since shoppers will prefer stores where they are treated well by people who seem to be happy to be there. But what role does this thing called culture actually play in eliciting the behavior it wants?
Culture is like the water fish swim in. It has a major impact on the lives of the denizens even though they are not aware of it. If you ask a fish why the water is so blue you would probably get the response “the what?” So, if the setting in which employees work does impact their attitudes (and research indicates it certainly does) how do you define and evaluate an organizational culture? Having co-authored the book “Rewarding Performance Globally” with Fons Trompenaars, leading cross-cultural researcher and guru, I became a student of the relationship between organizational culture and workforce culture. I teach a Global Workforce Management course for DePaul U. in their MSHR and MBA programs and focus on the issues created when someone with a specific cultural orientation enters an organization with its own cultural orientation.

If the organization has a culture that results in rewards being tied to individual performance, but an employee has been socialized in a culture that is collectivist, there is a difference of perspective that may impact that person’s acceptance of the prevailing culture. It will likely impact whether they choose to stay with the organization and perform well. People will be inclined to behave in a manner that seems to be encouraged by the organization’s reward system. Investment bankers almost brought the economy down with instruments that they created and were richly rewarded for. Regrettably for the banks what the asked for (by rewarding it) turned out in many cases to be disasters. Wells Fargo employees exhibited fraudulent behavior because the pressure to meet new account goals was so intense. “What you measure and reward you most surely will get more of” is a principle management should consider when eliciting patterns of behavior. No one knows how the employees felt when they took these actions, but one might surmise those with strong moral compasses probably were not happy with their actions.

So how does an organization define the type of culture it believes will be optimal? Even more importantly how does one define culture? After sitting in many client meetings discussing culture I believe defining it is one of the most daunting challenges management faces. Numerous shelves dedicated to culture exist in most bookstores and libraries. The advice varies from encouraging behavior that emulates Genghis Khan to emulating Jesus. And how an organization creates the culture it desires is not clear. One of the findings that comes out of the extensive research is that management must exhibit the behaviors it wants employees to exhibit. If a multinational pays commission on foreign business that was obtained by bribing a government official it sends the message that business results take priority over lawful behavior. If managers get results by treating their employees like serfs that also sends a signal. And if the first step taken by an organization facing an economic downturn is to downsize the workforce it sends the message that employees are its most disposable, not its most valuable, asset. No matter what mission statements, policy statements or cultural definitions say they must be made real by behaving consistently with what they prescribe.

Perhaps Publix could not define its culture specifically if asked. But if the example set by management is consistent with how it wishes to conduct business and the employees can accept the direction provided it may not be all that important. And the fact that the #1 ranking was based on a random sample of employee opinions there must be a very high level of acceptance. Other organizations may rank being the most profitable or being the organization with the most rapid escalation in stock price above being seen as a great place to work. That is their choice. But increases in stock price or profits are most often the result of the workforce being competent and committed to the success of the organization.

My first job out of undergraduate school was with Johnson & Johnson. As a part of the training for those in the executive development program the J&J Credo was explicated… so deeply that I wondered what the payback could be from the big investment in understanding it. When the Tylenol crisis happened and everyone reacted immediately in a manner consistent with what the Credo stood for it became clear to me. That was the organization’s way of defining and creating the culture it wanted. Since it is not possible to prescribe the behavior desired for every possible occurrence the Credo served as a nudge in the right direction. The one-page Credo does not look like a detailed description of the desired culture. But it seemed to produce the desired effect when it counted.

Did You Hear The One About The Data Scientist And The HR Professional?

The technological advances in data analysis are presenting new opportunities for HR professionals to understand what causes what and to project what might happen in the future based on what has happened and is happening. But taking advantage of what technology offers is sometimes easier said than done. Most HR practitioners lack PhDs in quantitative methods. There is a limit to the number of courses in any curriculum leading to a BA, and even an MS, in a field related to HR and there is a lot to learn about the principles underlying sound workforce management, so it is unlikely HR practitioners are going to know enough about data science to take full advantage of what technology offers. Conversely, data scientists tend to focus their formal education as well, with limited exposure to behavioral science topics. So there generally is a limited amount of overlap in the knowledge possessed by HR professionals and data scientists.

One of the requirements for people from different disciplines to be able to work together is that they share language and knowledge. If one party speaks Chinese and the other English, there are tools for easy language translation available today. It is much more difficult to translate the technical language data scientists speak into a form that HR professionals speak. This has been an issue for a very long time when academics do research and practitioners attempt to understand and apply research findings. The scientific method is rigid in its requirements and compromising on those just to make research results accessible to practitioners is a breach of the rules (but there is no rule against trying to translate findings at least partially). One of the potential solutions to the lack of common ground is to have people with some knowledge of both fields do a translation of what research has found in the pop literature, where practitioners tend to get their information. But the translation often results in authors “cherry picking” research findings that support the point an author is trying to make, consciously or not. In some cases, the translator has a surface level of competence in interpreting research, and this can result in attempting to generalize findings beyond where they would apply.

A popular book attempted to support the claim that extrinsic rewards diminish the ability to experience intrinsic rewards. “Evidence” included a controlled lab study that found that people threw tennis balls at targets longer if they did not receive the very small rewards on offer. Even though the study seemed to meet the “internal validity” requirements for a valid study (it was well designed to determine what would happen under those specific conditions), it attempted to apply the lab results to employees working for many years, often doing things viewed as undesirable, to support themselves. The “external validity” requirements for a study to be generalized to a different context were definitely not met. To make matters worse numerous field studies done in contexts similar to real world contexts (thereby meeting the external validity test) have refuted the lab study findings. The bottom line is that whoever uses research needs to have the ability to determine if it applies to the situation being evaluated. And not knowing that research needs to meet both validity tests can mislead someone attempting to apply research findings where they do not apply. The growing adoption of evidence-based management in decision-making is a positive trend. But the evidence used must be sound and must apply to the matter at hand. Workforce analytics can be of enormous value to an HR practitioner. But there are two requirements. First, the analysis must be done in a manner consistent with the principles of the scientific method. Second, lab tests must meet the external validity test just discussed if their results are to be applied to the field.

One of the most common applications of workforce analytics is the attempt to predict who might be prone to voluntarily leaving the organization. Top performers in critical occupations will always be in short supply and will be both expensive and difficult to replace. So being aware of issues that may lead to turnover before they reach a critical level can enable the practitioner to adopt preventative strategies. When someone resigns they have often left (mentally) already and recovery strategies are much more difficult to pull off than preventative measures. But how does an HR practitioner get inside someone’s head and discover if they are edging towards the exit? By using data and finding causal relationships between factors leading to termination steps can be taken to focus on what might be most effective to address. Employee satisfaction certainly will influence whether someone is receptive to a call from a recruiter or an internet posting of an opportunity. So will employee engagement. And using technology enables organizations to get a reading on where their employees are on the satisfaction and engagement scales. Some models for predicting potential turnover also include characteristics of who have left in the past, either in the organization being studied or in other similar organizations (i.e., length of service, age, etc.). The use of big data, machine learning, and AI can be used to create prediction models.

Data scientists are apt to believe these models are strong predictors of what will happen. They create algorithms and apply them. But two potential limitations are often ignored:

  1. Data is from the past and the present and if the future is going to be different the predictive power may be diminished
  2. Data scientists are working with numbers, without consideration of human factors that may be better predictors.

If an employee is subjected to real or perceived mistreatment by a manager on Monday, a notice of departure may be tendered that week, even though that employee had not possessed the “prone to leave” factors, at least until that event. There are other signs of trouble brewing that may have been visible to an HR professional but not included in the prediction model. Behavior, events in an employee’s personal life, feedback from peers, subordinates, and managers… all of these indicators of an employee’s mindset may be recognizable by the HR professional. So in order for predictions to be as good as they can be it is prudent to augment the data analytics with the human stuff, either by building factors into the predictive model or by an interpretation of quantitative results using qualitative measures.

Recently I was asked whether a client organization should assign a data scientist to HR. I am in favor of collaboration between people from the two disciplines and believe that credible evidence can come in the form of “hard” evidence (numbers) and “soft” evidence (professional knowledge and judgment). But co-locating people with different perspectives does not necessarily result in the pooling of knowledge and consideration of both points of view. So should HR professionals add “speaks analytics” to their competency model? Yes, to some degree. Should data scientists add “respects the impact of human behavior” to their competency model? Yes, to some degree. Who has to walk the furthest out on the bridge between them to achieve mutual understanding will depend on the issues being dealt with and the parties involved. As a faculty member for DePaul U.’s MSHR program, I try to ensure students understand the need to respect both the quantitative and qualitative perspectives Although I do not know I hope the faculty developing data scientists are equally respectful of the “other way.” If nothing else, balanced approaches lessen the fear of being replaced by robots.