The Industrial Age was primarily rules-based leading to procedures. The Information Age is primarily principles-based leading to creativity and innovation. So is HRM’s true role morphing into something different — HRC (Human Resources Coaching)? The nature/nurture question on skills/talent is also impacted in this calculus.
In the nature/nurture question, the nature part is the genetics (e.g. natural talent) providing the foundation (e.g. a 4-year-old piano prodigy). The nurture part is the epigenetics (biochemically speaking) or conditioning (psychologically speaking) that builds on that genetic foundation by external and internal means. Externally, we have training, education, experience, mentoring, coaching, etc. (e.g. the 4-year-old piano prodigy’s supportive parents and teachers). Internally, we have the person’s interest or motivational fuel (e.g. the 4-year-old piano prodigy loves playing the piano).
This combination (genetics and conditioning) is nothing new. What is new (from an HR standpoint) is in the modeling of this combination coupled with what would be the corresponding working role in a company. After all, HR stands for human resources. So this modeling has to be based on the principles underlying Information Age behavior because that is the context in which the human resource will work. And although data would be derivable from such a model, it cannot be boxed into a set of linear black and white data points. This factor alone may explain why reams of paperwork skillfully filled out by a successful job candidate does not really ensure the successful fit of a job to the hired person’s potential. And since the Millennials generation is more attuned than its older counterparts to the Information Age’s context, it is not surprising that it is the group most eager to address this disconnect. This is well exemplified in an Aon Hewitt study just released (http://www.aon.com/human-capital-consulting/thought-leadership/talent/inside-the-employee-mindset.jsp?utm_source=aoncomtalent&utm_medium=talentbanner&utm_campaign=employee-mindset).
Hiring in the Industrial Age was much simpler then as there were fewer factors in the equation. Nowadays it is often a much more complex process, not because HR wants it that way but because Industrial Age methods are often used for determining that Information Age context. Thus, the filtering (for the best candidates) needs to go nonlinear and more holistic. And we have a hint as to how we can approach this perceivably daunting task if we look at the key elements in that nature/nurture, genetics/conditioning combination.
The key is in our third word in the morphing of HRM to HRC, going from Human Resources Management to Human Resources Coaching. Managing is x directing y (the worker is told to…); coaching is x supporting the development of y (the worker is prepared to…). The goal here is quite simple really: Give a worker (y) enough reason to stay with a firm (x) and they will. And money isn’t always the answer or even part of the answer. As Dan Pink points out in his book, Drive, the carrot-and-stick approach doesn’t really work as a reward system. Even if absolute financial necessity is obliging a person to stay in their job, could it be said that their skills/talent are being optimally utilized, especially for creativity and innovation? Hardly. But with the key elements in our nature/nurture combination of genetics and conditioning, we can build a model against the backdrop of Information Age principles to increase the probability of a successful fit between work and worker.
For instance, let’s take the underlying principle of information flow. We have an interesting parallel here with fluid dynamics where information, like fluids, can not only move but can move with great force. And when bottlenecks or “dams” attempt to impede that flow, the degree of successful resistance is directly proportionate to the volume, nature, and velocity of both the information and the resistance to that information, again like with fluids and their resistant objects. To clarify, volume is the amount of the information and the resistance to it, nature is just that – the nature of the information and the resistance to it (their physical counterparts being the shape, viscosity, and other properties of a fluid and its resistant objects), and velocity is the speed at which the information is propagated and the speed at which resistant factors come against that information.
Now let’s take a subelement of our nature/nurture combination, say, the interest (or motivational fuel) of a knowledge worker. In the Industrial Age approach, matching buzz words (which would belong to that set of linear black and white data points above-mentioned) may yield a high score. This is great if you’re lining up dominoes. But people are not like a set of lined up dominoes. In contrast, if the interest of our featured knowledge worker is very keen for subject “A” even to the point of having been demonstrated at an early age, that factor would be a key element in our nature/nurture model. So in a job interview, asking a candidate what interested them as a child may have much more significance than if their resume had the right word in it (and wasn’t misspelled!). And what if the HR person wanted to fill a job opening that is primarily related to subject “B” while a job opening exists elsewhere (in the company) but was primarily related to subject “A”? If our knowledge worker really had little interest in subject “B” although well qualified for it, optimal utilization of their skills/talent would be, at best, questionable in that role. We may even venture to say that if the knowledge worker was not as qualified in subject “A” (as they would be in subject “B”), they would still be more productive (long-term) if given the subject “A” position.
Let’s now link the above two examples. We have a starting model of the nature/nature, genetics/conditioning combination for our knowledge worker, consisting of the worker’s keen interest in subject “A” which we’ve mapped to a job position (in subject “A”). Let’s now place that model in the context of the underlying principle of information flow, where the three main properties of information and the resistance to that information are volume, nature, and velocity. We can conduct a thought experiment as to how information flow would likely occur in that knowledge worker’s specific domain of work when we use an HRC (vs. HRM) approach by coaching (supporting the development of) our featured knowledge worker.
Let’s say a longstanding but resolvable problem continually arises in subject “A” and which has never been resolved, with all attempts to resolving it having failed. We have here resistance (the problem) against the associated information flow. The volume of resistance is high because the problem is very persistent, reoccurring many times despite efforts to resolve it. But our now newly hired knowledge worker will be eager to contribute to this area of interest (subject “A”). So information velocity will be high when flowing through our intrepid worker. The volume of information will also be high because the worker’s motivational fuel (interest) will no doubt drive the absorption of considerable amounts of information. The nature of the information worked with will be of high quality because the keen interest shown will act as a natural filter of relevant data. We can easily see that the likelihood of our knowledge worker coming up with a viable solution starts to look viable too.
If we now add to our model (of the nature/nurture, genetics/conditioning combination for our knowledge worker) the external aspect of conditioning, namely, the training, education, experience, mentoring, coaching, etc., we can again conduct a thought experiment on information flow. As our featured knowledge worker obtains training and/or education (in subject “A”), experience (with encountering the persistent problem), and mentoring/coaching from supportive, qualified human resources, the information flow (to solve the problem) would gather momentum and increase the chances that the problem will be resolved by our knowledge worker — and this without the Industrial Age’s “carrot and stick” or paperwork or metrics or what have you.
The results would speak for themselves when looking at the overall effect of the HRC (vs. HRM) approach, where Information Age work would be handled in a more natural way (e.g. in information flow). This approach would have a built-in proactive stance to it because it works with information dynamically (anticipating and preparing for the directional turns of information flow) instead of the reactive stance that Industrial Age work was conditioned for (assembly line breaks down -> fix the broken part -> resume assembly line).
Easier said than done, easier written than implemented, the challenges will be in real-world scenarios where, true to the nature of the Information Age, the application (of HRC) will appear messy at first. The volume, nature, and velocity of the resistance may be quite significant, especially among Industrial Age adherents. Also, this HRC approach may not be easily measurable or the results it produces may not come quickly. And there may even appear to be a “quiet before the storm” where the need for change may not be so evident and that all seems well until, of course, market forces hit hard, at which point it may be too late, or at best, change has to be undertaken more painfully.
Modeling for human behavior can be endlessly complex, but as exemplified above, one can start with a single key element in the nature/nurture, genetics/conditioning combination, mapping it to a specific aspect of a job position’s domain, then fitting that into the context of an underlying principle of the Information Age. Patiently monitoring the effect of this approach will allow for tweaking the modeling so that it is tailor-made for the company (your HR manuals may never be the same again!) as you add more and more elements to the model.